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Contributions project released public project’s open source license.","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"filing-issues","dir":"","previous_headings":"","what":"Filing Issues","title":"Contributing to {rtables}","text":"Issues used establish prioritized timeline track development progress within package. new feature feel enhance experience package users, please open Feature Request issue. notice bug existing code, please file Bug Fix issue description bug reprex (reproducible example). types issues (questions, typos ’ve noticed, improvements documentation, etc.) can filed well. Click file new issue, see list current issues. 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Whether opening issue pull request, detailed description, easier package maintainers help ! make code changes package, please follow following process.","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Creating Pull Requests","what":"Pull Request Process","title":"Contributing to {rtables}","text":"{rtables} package part NEST project utilizes staged.dependencies ensure simplify development process track upstream downstream package dependencies. highly recommend installing using package developing within {rtables}.","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"id_1-create-a-branch","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process","what":"1. Create a branch","title":"Contributing to {rtables}","text":"order work new pull request, please first create branch main upon can work commit changes. comply staged.dependencies standards, {rtables} uses following branch naming convention: issue#_description_of_issue@target_merge_branch example, 443_refactor_splits@main. cases, target merge branch base (main) branch. cases, change {rtables} may first require upstream changes {formatters} package. Suppose branch 100_update_fmts@main {formatters} containing required upstream changes. branch created {rtables} named follows example: 443_refactor_splits@100_update_fmts@main. ensures correct branches checked running tests, etc. details staged.dependencies branch naming conventions, click .","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"id_2-code","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process","what":"2. Code","title":"Contributing to {rtables}","text":"Work within {rtables} package apply code changes. Avoid combining issues single branch - ideally, branch associated single issue prefixed issue number. information basics {rtables} package, please read package vignettes, available . advanced development work within {rtables}, consider reading {rtables} Developer Guide. Developer Guide can accessed {rtables} site navigation bar, listed convenience: Developer Guide: Split Machinery Developer Guide: Tabulation Developer Guide: Debugging {rtables} Beyond Developer Guide: Sparse Notes {rtables} Internals","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"Code style","title":"Contributing to {rtables}","text":"{rtables} package follows tidyverse style guide please adhere guidelines submitted code. making changes file within package, can apply package styler automatically check lint running following two lines code within file:","code":"styler:::style_active_file() lintr:::addin_lint()"},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"documentation","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"Documentation","title":"Contributing to {rtables}","text":"Package documentation uses roxygen2. contribution requires updates documentation, ensure roxygen comments updated within source code file. updating roxygen documentation, run devtools::document() update accompanying .Rd files (update files hand!).","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"tests","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"Tests","title":"Contributing to {rtables}","text":"ensure high code coverage, create tests using testthat package. cases, changes package code necessitate addition one tests ensure added features working expected existing features broken.","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"news","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"NEWS","title":"Contributing to {rtables}","text":"making updates package, please add descriptive entry NEWS file reflects changes. See tidyverse style guide guidelines creating NEWS entry.","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"id_3-make-a-pull-request","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process","what":"3. Make a Pull Request","title":"Contributing to {rtables}","text":"previous two steps complete, can create pull request. Indicate description issue addressed pull request, utilize labels help reviewers identify category changes contained within pull request. pull request created, series checks automatically triggered, including R CMD check, tests/code coverage, auto-documentation, . checks must passing order eventually merge pull request, changes may required order resolve status checks. pull requests must also reviewed approved least one package maintainers can merged. review automatically requested several {rtables} maintainers upon creating pull request. maintainer reviews pull request, please try address comments short order - {rtables} package updated regular basis leaving pull request open long likely result merge conflicts create work developer.","code":""},{"path":"https://insightsengineering.github.io/rtables/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to {rtables}","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://insightsengineering.github.io/rtables/ISSUE_TEMPLATE.html","id":null,"dir":"","previous_headings":"","what":"Reporting an Issue with rtables","title":"Reporting an Issue with rtables","text":"Please briefly describe problem , relevant, output expect. Please also provide output utils::sessionInfo() devtools::session_info() end post. possible, please include minimal, reproducible example. rtables team much likely resolve issue able reproduce locally. Please delete preamble read . brief description problem","code":"library(rtables) # your reproducible example here"},{"path":"https://insightsengineering.github.io/rtables/articles/advanced_usage.html","id":"note","dir":"Articles","previous_headings":"","what":"NOTE","title":"{rtables} Advanced Usage","text":"vignette currently development. code prose appears version vignette main branch repository work/correct, likely final form. Initialization","code":"library(rtables)"},{"path":"https://insightsengineering.github.io/rtables/articles/advanced_usage.html","id":"control-splitting-with-provided-function-limited-customization","dir":"Articles","previous_headings":"","what":"Control splitting with provided function (limited customization)","title":"{rtables} Advanced Usage","text":"rtables provides array functions control splitting logic without creating entirely new split functions. default split_*_by facets data based categorical variable. continuous variables, split_*_by_cutfun can leveraged create categories corresponding faceting, break points dependent data. Alternatively, split_*_by_cuts can used breakpoints predefined split_*_by_quartiles data faceted quantile.","code":"d1 <- subset(ex_adsl, AGE < 25) d1$AGE <- as.factor(d1$AGE) lyt1 <- basic_table() %>% split_cols_by(\"AGE\") %>% analyze(\"SEX\") build_table(lyt1, d1) ## 20 21 23 24 ## ———————————————————————————————————— ## F 0 2 4 5 ## M 1 1 2 3 ## U 0 0 0 0 ## UNDIFFERENTIATED 0 0 0 0 sd_cutfun <- function(x) { cutpoints <- c( min(x), mean(x) - sd(x), mean(x) + sd(x), max(x) ) names(cutpoints) <- c(\"\", \"Low\", \"Medium\", \"High\") cutpoints } lyt1 <- basic_table() %>% split_cols_by_cutfun(\"AGE\", cutfun = sd_cutfun) %>% analyze(\"SEX\") build_table(lyt1, ex_adsl) ## Low Medium High ## —————————————————————————————————————— ## F 36 165 21 ## M 21 115 30 ## U 1 8 0 ## UNDIFFERENTIATED 0 1 2 lyt1 <- basic_table() %>% split_cols_by_cuts( \"AGE\", cuts = c(0, 30, 60, 100), cutlabels = c(\"0-30 y.o.\", \"30-60 y.o.\", \"60-100 y.o.\") ) %>% analyze(\"SEX\") build_table(lyt1, ex_adsl) ## 0-30 y.o. 30-60 y.o. 60-100 y.o. ## ——————————————————————————————————————————————————————— ## F 71 150 1 ## M 48 116 2 ## U 2 7 0 ## UNDIFFERENTIATED 1 2 0"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/advanced_usage.html","id":"adding-an-overall-column-only-when-the-split-would-already-define-2-facets","dir":"Articles","previous_headings":"Custom Split Functions","what":"Adding an Overall Column Only When The Split Would Already Define 2+ Facets","title":"{rtables} Advanced Usage","text":"custom split functions can anything, including conditionally applying one existing custom split functions. define function constructor accepts variable name want check, return custom split function behavior want using functions provided rtables cases: gives us desired behavior one column corner case: standard multi-column case: Notice use add_overall_level function constructor, immediately call constructed function --one-columns case.","code":"picky_splitter <- function(var) { function(df, spl, vals, labels, trim) { orig_vals <- vals if (is.null(vals)) { vec <- df[[var]] vals <- if (is.factor(vec)) levels(vec) else unique(vec) } if (length(vals) == 1) { do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim) } else { add_overall_level( \"Overall\", label = \"All Obs\", first = FALSE )(df = df, spl = spl, vals = orig_vals, trim = trim) } } } d1 <- subset(ex_adsl, ARM == \"A: Drug X\") d1$ARM <- factor(d1$ARM) lyt1 <- basic_table() %>% split_cols_by(\"ARM\", split_fun = picky_splitter(\"ARM\")) %>% analyze(\"AGE\") build_table(lyt1, d1) ## A: Drug X ## ———————————————— ## Mean 33.77 build_table(lyt1, ex_adsl) ## A: Drug X B: Placebo C: Combination All Obs ## ———————————————————————————————————————————————————————— ## Mean 33.77 35.43 35.43 34.88"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/advanced_usage.html","id":"what-is--spl_context","dir":"Articles","previous_headings":"Leveraging .spl_context","what":"What Is .spl_context?","title":"{rtables} Advanced Usage","text":".spl_context (see ?spl_context) mechanism rtables tabulation machinery gives custom split, analysis content (row-group summary) functions information overarching facet-structure splits cells generate reside . particular .spl_context ensures functions know (thus computations based ) following types information:","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/advanced_usage.html","id":"different-formats-for-different-values-within-a-row-split","dir":"Articles","previous_headings":"Leveraging .spl_context","what":"Different Formats For Different Values Within A Row-Split","title":"{rtables} Advanced Usage","text":"","code":"dta_test <- data.frame( USUBJID = rep(1:6, each = 3), PARAMCD = rep(\"lab\", 6 * 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)), CHG = c(1:9, rep(NA, 9)) ) my_afun <- function(x, .spl_context) { n <- sum(!is.na(x)) meanval <- mean(x, na.rm = TRUE) sdval <- sd(x, na.rm = TRUE) ## get the split value of the most recent parent ## (row) split above this analyze val <- .spl_context[nrow(.spl_context), \"value\"] ## do a silly thing to decide the different format precisiosn ## your real logic would go here valnum <- min(2L, as.integer(gsub(\"[^[:digit:]]*\", \"\", val))) fstringpt <- paste0(\"xx.\", strrep(\"x\", valnum)) fmt_mnsd <- sprintf(\"%s (%s)\", fstringpt, fstringpt) in_rows( n = n, \"Mean, SD\" = c(meanval, sdval), .formats = c(n = \"xx\", \"Mean, SD\" = fmt_mnsd) ) } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% analyze_colvars(my_afun) build_table(lyt, dta_test) ## A B C ## AVAL CHG AVAL CHG AVAL CHG ## ——————————————————————————————————————————————————————————————————————————— ## V1 ## n 2 2 1 1 0 0 ## Mean, SD 7.5 (2.1) 2.5 (2.1) 3.0 (NA) 7.0 (NA) NA NA ## V2 ## n 2 2 1 1 0 0 ## Mean, SD 6.50 (2.12) 3.50 (2.12) 2.00 (NA) 8.00 (NA) NA NA ## V3 ## n 2 2 1 1 0 0 ## Mean, SD 5.50 (2.12) 4.50 (2.12) 1.00 (NA) 9.00 (NA) NA NA"},{"path":"https://insightsengineering.github.io/rtables/articles/advanced_usage.html","id":"simulating-baseline-comparison-in-row-space","dir":"Articles","previous_headings":"Leveraging .spl_context","what":"Simulating ‘Baseline Comparison’ In Row Space","title":"{rtables} Advanced Usage","text":"can simulate formal modeling reference row(s) using extra_args machinery","code":"my_afun <- function(x, .var, .spl_context) { n <- sum(!is.na(x)) meanval <- mean(x, na.rm = TRUE) sdval <- sd(x, na.rm = TRUE) ## get the split value of the most recent parent ## (row) split above this analyze val <- .spl_context[nrow(.spl_context), \"value\"] ## we show it if its not a CHG within V1 show_it <- val != \"V1\" || .var != \"CHG\" ## do a silly thing to decide the different format precisiosn ## your real logic would go here valnum <- min(2L, as.integer(gsub(\"[^[:digit:]]*\", \"\", val))) fstringpt <- paste0(\"xx.\", strrep(\"x\", valnum)) fmt_mnsd <- if (show_it) sprintf(\"%s (%s)\", fstringpt, fstringpt) else \"xx\" in_rows( n = if (show_it) n, ## NULL otherwise \"Mean, SD\" = if (show_it) c(meanval, sdval), ## NULL otherwise .formats = c(n = \"xx\", \"Mean, SD\" = fmt_mnsd) ) } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% analyze_colvars(my_afun) build_table(lyt, dta_test) ## A B C ## AVAL CHG AVAL CHG AVAL CHG ## ——————————————————————————————————————————————————————————————————————————— ## V1 ## n 2 1 0 ## Mean, SD 7.5 (2.1) 3.0 (NA) NA ## V2 ## n 2 2 1 1 0 0 ## Mean, SD 6.50 (2.12) 3.50 (2.12) 2.00 (NA) 8.00 (NA) NA NA ## V3 ## n 2 2 1 1 0 0 ## Mean, SD 5.50 (2.12) 4.50 (2.12) 1.00 (NA) 9.00 (NA) NA NA my_afun <- function(x, .var, ref_rowgroup, .spl_context) { n <- sum(!is.na(x)) meanval <- mean(x, na.rm = TRUE) sdval <- sd(x, na.rm = TRUE) ## get the split value of the most recent parent ## (row) split above this analyze val <- .spl_context[nrow(.spl_context), \"value\"] ## we show it if its not a CHG within V1 show_it <- val != ref_rowgroup || .var != \"CHG\" fmt_mnsd <- if (show_it) \"xx.x (xx.x)\" else \"xx\" in_rows( n = if (show_it) n, ## NULL otherwise \"Mean, SD\" = if (show_it) c(meanval, sdval), ## NULL otherwise .formats = c(n = \"xx\", \"Mean, SD\" = fmt_mnsd) ) } lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% analyze_colvars(my_afun, extra_args = list(ref_rowgroup = \"V1\")) build_table(lyt2, dta_test) ## A B C ## AVAL CHG AVAL CHG AVAL CHG ## ————————————————————————————————————————————————————————————————————— ## V1 ## n 2 1 0 ## Mean, SD 7.5 (2.1) 3.0 (NA) NA ## V2 ## n 2 2 1 1 0 0 ## Mean, SD 6.5 (2.1) 3.5 (2.1) 2.0 (NA) 8.0 (NA) NA NA ## V3 ## n 2 2 1 1 0 0 ## Mean, SD 5.5 (2.1) 4.5 (2.1) 1.0 (NA) 9.0 (NA) NA NA"},{"path":"https://insightsengineering.github.io/rtables/articles/baseline.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Comparing Against Baselines or Control","text":"Often data one column considered reference/baseline/comparison group compared data columns. example, lets calculate average age: difference average AGE placebo arm arms: Note column order changed reference group displayed first column. cases want cells blank reference column, (e.g., “B: Placebo”) use non_ref_rcell() instead rcell(), pass .in_ref_col second argument: can see arguments available afun manual analyze().","code":"library(rtables) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 34.91 33.02 34.57 lyt2 <- basic_table() %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% analyze(\"AGE\", afun = function(x, .ref_group) { in_rows( \"Difference of Averages\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\") ) }) tbl2 <- build_table(lyt2, DM) tbl2 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————— # Difference of Averages 1.89 0.00 1.55 lyt3 <- basic_table() %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { in_rows( \"Difference of Averages\" = non_ref_rcell(mean(x) - mean(.ref_group), is_ref = .in_ref_col, format = \"xx.xx\") ) } ) tbl3 <- build_table(lyt3, DM) tbl3 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————— # Difference of Averages 1.89 1.55 lyt4 <- basic_table() %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { in_rows( \"Difference of Averages\" = non_ref_rcell(mean(x) - mean(.ref_group), is_ref = .in_ref_col, format = \"xx.xx\"), \"another row\" = non_ref_rcell(\"aaa\", .in_ref_col) ) } ) tbl4 <- build_table(lyt4, DM) tbl4 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————— # Difference of Averages 1.89 1.55 # another row aaa aaa"},{"path":"https://insightsengineering.github.io/rtables/articles/baseline.html","id":"row-splitting","dir":"Articles","previous_headings":"","what":"Row Splitting","title":"Comparing Against Baselines or Control","text":"adding row-splitting reference data may represented column without row splitting. example: data assigned .ref_full full data reference column whereas data assigned .ref_group respects subsetting defined row-splitting hence subset argument x df afun.","code":"lyt5 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"AGE\", afun = function(x, .ref_group, .ref_full, .in_ref_col) { in_rows( \"is reference (.in_ref_col)\" = rcell(.in_ref_col), \"ref cell N (.ref_group)\" = rcell(length(.ref_group)), \"ref column N (.ref_full)\" = rcell(length(.ref_full)) ) }) tbl5 <- build_table(lyt5, subset(DM, SEX %in% c(\"M\", \"F\"))) tbl5 # A: Drug X B: Placebo C: Combination # (N=121) (N=106) (N=129) # —————————————————————————————————————————————————————————————————————— # F # is reference (.in_ref_col) FALSE TRUE FALSE # ref cell N (.ref_group) 56 56 56 # ref column N (.ref_full) 106 106 106 # M # is reference (.in_ref_col) FALSE TRUE FALSE # ref cell N (.ref_group) 50 50 50 # ref column N (.ref_full) 106 106 106"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Example Clinical Trials Tables","text":"vignette create demographic table adverse event table response table time--event analysis table using rtables layout facility. , demonstrate layout based tabulation framework can specify structure relations commonly found analyzing clinical trials data. Note data created using random number generators. ex_* data currently attached rtables package provided formatters package created using publicly available random.cdisc.data R package. packages used vignette :","code":"library(rtables) library(tibble) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"demographic-table","dir":"Articles","previous_headings":"","what":"Demographic Table","title":"Example Clinical Trials Tables","text":"Demographic tables summarize variables content different population subsets (encoded columns). One feature analyze() introduced previous vignette analysis function afun can specify multiple rows in_rows() function: Multiple variables can analyzed one analyze() call: Hence, afun can process different data vector types (.e. variables selected data) fairly close standard demographic table. function either creates count table number summary argument x factor numeric, respectively: Note use rcell wrap results order add formatting instructions rtables. can use s_summary outside context tabulation: can now create commonly used variant demographic table: Note analyze() can also called multiple times sequence: leads table identical summary_tbl: clinical trials analyses number patients per column often referred N (rather overall population outside clinical trials commonly referred N). Column Ns added setting show_colcounts argument basic_table() TRUE:","code":"ADSL <- ex_adsl # Example ADSL dataset lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx.xx - xx.xx\") ) }) tbl <- build_table(lyt, ADSL) tbl # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = c(\"AGE\", \"BMRKR1\"), afun = function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx.xx - xx.xx\") ) }) tbl2 <- build_table(lyt2, ADSL) tbl2 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————— # AGE # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR1 # Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) # Range 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 s_summary <- function(x) { if (is.numeric(x)) { in_rows( \"n\" = rcell(sum(!is.na(x)), format = \"xx\"), \"Mean (sd)\" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = \"xx.xx (xx.xx)\"), \"IQR\" = rcell(IQR(x, na.rm = TRUE), format = \"xx.xx\"), \"min - max\" = rcell(range(x, na.rm = TRUE), format = \"xx.xx - xx.xx\") ) } else if (is.factor(x)) { vs <- as.list(table(x)) do.call(in_rows, lapply(vs, rcell, format = \"xx\")) } else { stop(\"type not supported\") } } s_summary(ADSL$AGE) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod row_label # 1 n 400 0 n # 2 Mean (sd) 34.88 (7.44) 0 Mean (sd) # 3 IQR 10.00 0 IQR # 4 min - max 20.00 - 69.00 0 min - max s_summary(ADSL$SEX) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod row_label # 1 F 222 0 F # 2 M 166 0 M # 3 U 9 0 U # 4 UNDIFFERENTIATED 3 0 UNDIFFERENTIATED summary_lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze(c(\"AGE\", \"SEX\"), afun = s_summary) summary_tbl <- build_table(summary_lyt, ADSL) summary_tbl # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 summary_lyt2 <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze(\"AGE\", s_summary) %>% analyze(\"SEX\", s_summary) summary_tbl2 <- build_table(summary_lyt2, ADSL) summary_tbl2 # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 identical(summary_tbl, summary_tbl2) # [1] TRUE summary_lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARMCD\") %>% analyze(c(\"AGE\", \"SEX\"), s_summary) summary_tbl3 <- build_table(summary_lyt3, ADSL) summary_tbl3 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"variations-on-the-demographic-table","dir":"Articles","previous_headings":"Demographic Table","what":"Variations on the Demographic Table","title":"Example Clinical Trials Tables","text":"now show couple variations demographic table developed . variations structure analysis, hence don’t require modification s_summary function. start standard table analyzing variables AGE BMRKR2 variables: Assume like analysis carried per gender encoded row space: now subset ADSL include males females analysis order reduce number rows table: Note UNDIFFERENTIATED U levels still show table. tabulation respects factor levels level order, exactly split table function . empty levels dropped rtables needs know splitting time via split_fun argument split_rows_by(). number predefined functions. example drop_split_levels() required drop empty levels splitting time. Splitting big topic eventually addressed specific package vignette. table labels M F descriptive. can add full labels follows: next table variation stratify gender AGE analysis. nested argument set FALSE analyze() call: split rows groups (Male Female ) one might want summarize groups: usually showing count column percentages. especially important missing data. example, create table add missing data AGE variable: easy see many females males arm n represents number non-missing data elements variables. Groups within rows defined splitting can summarized summarize_row_groups(), example: couple things note : Group summaries produce “content” rows. Visually, ’s impossible distinguish data rows content rows. difference justified (’s important design decision) paginate tables content rows default repeated group gets divided via pagination. Conceptually content rows summarize patient population analyzed hence often count & group percentages (default behavior summarize_row_groups()). can recreate default behavior (count percentage) defining cfun illustrative purposes results table : Note cfun, like afun (used analyze()), can operate either variables, passed via x argument, data.frames tibbles, passed via df argument (afun can optionally request df ). Unlike afun, cfun must accept labelstr second argument gives default group label (factor level splitting) hence modified:","code":"lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl <- build_table(lyt, ADSL) tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 50 45 40 # MEDIUM 37 56 42 # HIGH 47 33 50 lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl <- build_table(lyt, ADSL) tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 # U # AGE # n 3 2 4 # Mean (sd) 31.67 (3.21) 31.00 (5.66) 35.25 (3.10) # IQR 3.00 4.00 3.25 # min - max 28.00 - 34.00 27.00 - 35.00 31.00 - 38.00 # BMRKR2 # LOW 2 1 1 # MEDIUM 1 0 2 # HIGH 0 1 1 # UNDIFFERENTIATED # AGE # n 1 0 2 # Mean (sd) 28.00 (NA) NA 45.00 (1.41) # IQR 0.00 NA 1.00 # min - max 28.00 - 28.00 Inf - -Inf 44.00 - 46.00 # BMRKR2 # LOW 1 0 2 # MEDIUM 0 0 0 # HIGH 0 0 0 ADSL_M_F <- filter(ADSL, SEX %in% c(\"M\", \"F\")) lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl2 <- build_table(lyt2, ADSL_M_F) tbl2 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ————————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 # U # AGE # n 0 0 0 # Mean (sd) NA NA NA # IQR NA NA NA # min - max Inf - -Inf Inf - -Inf Inf - -Inf # BMRKR2 # LOW 0 0 0 # MEDIUM 0 0 0 # HIGH 0 0 0 # UNDIFFERENTIATED # AGE # n 0 0 0 # Mean (sd) NA NA NA # IQR NA NA NA # min - max Inf - -Inf Inf - -Inf Inf - -Inf # BMRKR2 # LOW 0 0 0 # MEDIUM 0 0 0 # HIGH 0 0 0 lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, child_labels = \"visible\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl3 <- build_table(lyt3, ADSL_M_F) tbl3 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # —————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 ADSL_M_F_l <- ADSL_M_F %>% mutate(lbl_sex = case_when( SEX == \"M\" ~ \"Male\", SEX == \"F\" ~ \"Female\", SEX == \"U\" ~ \"Unknown\", SEX == \"UNDIFFERENTIATED\" ~ \"Undifferentiated\" )) lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"visible\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl4 <- build_table(lyt4, ADSL_M_F_l) tbl4 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # —————————————————————————————————————————————————————————————— # Female # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # Male # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 lyt5 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"visible\") %>% analyze(\"AGE\", s_summary, show_labels = \"visible\") %>% analyze(\"BMRKR2\", s_summary, nested = FALSE, show_labels = \"visible\") tbl5 <- build_table(lyt5, ADSL_M_F_l) tbl5 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # —————————————————————————————————————————————————————————————— # Female # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # Male # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 47 44 37 # MEDIUM 36 56 40 # HIGH 47 32 49 insert_NAs <- function(x) { x[sample(c(TRUE, FALSE), length(x), TRUE, prob = c(0.2, 0.8))] <- NA x } set.seed(1) ADSL_NA <- ADSL_M_F_l %>% mutate(AGE = insert_NAs(AGE)) lyt6 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by( \"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"visible\" ) %>% analyze(\"AGE\", s_summary) %>% analyze(\"BMRKR2\", s_summary, nested = FALSE, show_labels = \"visible\") tbl6 <- build_table(lyt6, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl6 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ———————————————————————————————————————————————————————————— # Female # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BMRKR2 # LOW 47 44 37 # MEDIUM 36 56 40 # HIGH 47 32 49 lyt7 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% analyze(\"AGE\", s_summary) %>% analyze(\"BMRKR2\", afun = s_summary, nested = FALSE, show_labels = \"visible\") tbl7 <- build_table(lyt7, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl7 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ———————————————————————————————————————————————————————————— # Female 79 (60.8%) 77 (58.3%) 66 (52.4%) # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male 51 (39.2%) 55 (41.7%) 60 (47.6%) # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BMRKR2 # LOW 47 44 37 # MEDIUM 36 56 40 # HIGH 47 32 49 lyt8 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels) %>% summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) { in_rows( rcell(nrow(df) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\"), .labels = labelstr ) }) %>% analyze(\"AGE\", s_summary) %>% analyze(\"BEP01FL\", afun = s_summary, nested = FALSE, show_labels = \"visible\") tbl8 <- build_table(lyt8, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl8 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ———————————————————————————————————————————————————————————— # Female 79 (60.77%) 77 (58.33%) 66 (52.38%) # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male 51 (39.23%) 55 (41.67%) 60 (47.62%) # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BEP01FL # Y 67 63 65 # N 63 69 61 lyt9 <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"hidden\") %>% summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) { in_rows( rcell(nrow(df) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\"), .labels = paste0(labelstr, \": count (perc.)\") ) }) %>% analyze(\"AGE\", s_summary) %>% analyze(\"BEP01FL\", s_summary, nested = FALSE, show_labels = \"visible\") tbl9 <- build_table(lyt9, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl9 # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————————————————— # Female: count (perc.) 79 (60.77%) 77 (58.33%) 66 (52.38%) # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male: count (perc.) 51 (39.23%) 55 (41.67%) 60 (47.62%) # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BEP01FL # Y 67 63 65 # N 63 69 61"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"using-layouts","dir":"Articles","previous_headings":"Demographic Table","what":"Using Layouts","title":"Example Clinical Trials Tables","text":"Layouts couple advantages tabulating tables directly: .e. separate analyses description actual data referencing variable names happens via strings (non-standard evaluation (NSE) needed, though arguably either feature shortcoming) layouts can reused example demonstrates reusability layouts: can now build table ADSL patients older 18:","code":"adsl_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"SEX\"), afun = s_summary) adsl_lyt # A Pre-data Table Layout # # Column-Split Structure: # ARM (lvls) # # Row-Split Structure: # AGE:SEX (** multivar analysis **) adsl_tbl <- build_table(adsl_lyt, ADSL) adsl_tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ——————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 adsl_f_tbl <- build_table(lyt, ADSL %>% filter(AGE > 18)) # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf adsl_f_tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 # U # AGE # n 3 2 4 # Mean (sd) 31.67 (3.21) 31.00 (5.66) 35.25 (3.10) # IQR 3.00 4.00 3.25 # min - max 28.00 - 34.00 27.00 - 35.00 31.00 - 38.00 # BMRKR2 # LOW 2 1 1 # MEDIUM 1 0 2 # HIGH 0 1 1 # UNDIFFERENTIATED # AGE # n 1 0 2 # Mean (sd) 28.00 (NA) NA 45.00 (1.41) # IQR 0.00 NA 1.00 # min - max 28.00 - 28.00 Inf - -Inf 44.00 - 46.00 # BMRKR2 # LOW 1 0 2 # MEDIUM 0 0 0 # HIGH 0 0 0"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"adverse-events","dir":"Articles","previous_headings":"","what":"Adverse Events","title":"Example Clinical Trials Tables","text":"number different adverse event tables. now present two tables show adverse events ID grade ID. time won’t use ADAE dataset random.cdisc.data rather generate dataset fly (see Adrian’s 2016 Phuse paper):","code":"set.seed(1) lookup <- tribble( ~AEDECOD, ~AEBODSYS, ~AETOXGR, \"HEADACHE\", \"NERVOUS SYSTEM DISORDERS\", \"5\", \"BACK PAIN\", \"MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS\", \"2\", \"GINGIVAL BLEEDING\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"HYPOTENSION\", \"VASCULAR DISORDERS\", \"3\", \"FAECES SOFT\", \"GASTROINTESTINAL DISORDERS\", \"2\", \"ABDOMINAL DISCOMFORT\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"DIARRHEA\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"ABDOMINAL FULLNESS DUE TO GAS\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"NAUSEA (INTERMITTENT)\", \"GASTROINTESTINAL DISORDERS\", \"2\", \"WEAKNESS\", \"MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS\", \"3\", \"ORTHOSTATIC HYPOTENSION\", \"VASCULAR DISORDERS\", \"4\" ) normalize <- function(x) x / sum(x) weightsA <- normalize(c(0.1, dlnorm(seq(0, 5, length.out = 25), meanlog = 3))) weightsB <- normalize(c(0.2, dlnorm(seq(0, 5, length.out = 25)))) N_pop <- 300 ADSL2 <- data.frame( USUBJID = seq(1, N_pop, by = 1), ARM = sample(c(\"ARM A\", \"ARM B\"), N_pop, TRUE), SEX = sample(c(\"F\", \"M\"), N_pop, TRUE), AGE = 20 + rbinom(N_pop, size = 40, prob = 0.7) ) l.adae <- mapply( ADSL2$USUBJID, ADSL2$ARM, ADSL2$SEX, ADSL2$AGE, FUN = function(id, arm, sex, age) { n_ae <- sample(0:25, 1, prob = if (arm == \"ARM A\") weightsA else weightsB) i <- sample(seq_len(nrow(lookup)), size = n_ae, replace = TRUE, prob = c(6, rep(1, 10)) / 16) lookup[i, ] %>% mutate( AESEQ = seq_len(n()), USUBJID = id, ARM = arm, SEX = sex, AGE = age ) }, SIMPLIFY = FALSE ) ADAE2 <- do.call(rbind, l.adae) ADAE2 <- ADAE2 %>% mutate( ARM = factor(ARM, levels = c(\"ARM A\", \"ARM B\")), AEDECOD = as.factor(AEDECOD), AEBODSYS = as.factor(AEBODSYS), AETOXGR = factor(AETOXGR, levels = as.character(1:5)) ) %>% select(USUBJID, ARM, AGE, SEX, AESEQ, AEDECOD, AEBODSYS, AETOXGR) ADAE2 # # A tibble: 3,118 × 8 # USUBJID ARM AGE SEX AESEQ AEDECOD AEBODSYS AETOXGR # # 1 1 ARM A 45 F 1 NAUSEA (INTERMITTENT) GASTROINTESTIN… 2 # 2 1 ARM A 45 F 2 HEADACHE NERVOUS SYSTEM… 5 # 3 1 ARM A 45 F 3 HEADACHE NERVOUS SYSTEM… 5 # 4 1 ARM A 45 F 4 HEADACHE NERVOUS SYSTEM… 5 # 5 1 ARM A 45 F 5 HEADACHE NERVOUS SYSTEM… 5 # 6 1 ARM A 45 F 6 HEADACHE NERVOUS SYSTEM… 5 # 7 1 ARM A 45 F 7 HEADACHE NERVOUS SYSTEM… 5 # 8 1 ARM A 45 F 8 HEADACHE NERVOUS SYSTEM… 5 # 9 1 ARM A 45 F 9 HEADACHE NERVOUS SYSTEM… 5 # 10 1 ARM A 45 F 10 FAECES SOFT GASTROINTESTIN… 2 # # ℹ 3,108 more rows"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"adverse-events-by-id","dir":"Articles","previous_headings":"Adverse Events","what":"Adverse Events By ID","title":"Example Clinical Trials Tables","text":"start defining events summary function: , population 5 patients one patient 2 AEs one patient 1 AE three patients AEs get following summary: .N_col argument special keyword argument build_table() passes population size respective column. list keyword arguments functions passed afun analyze(), refer documentation ?analyze. now use s_events_patients summary function tabulation: Note column Ns wrong default set number rows per group (.e. number AEs per arm ). also affects percentages. table interested number patients per column/arm usually taken ADSL (var ADSL2 ). rtables handles allowing us override column counts computed. can specify alt_counts_df build_table(). , rtables calculates column counts applying column faceting alt_counts_df primary data tabulation: Alternatively, desired column counts already calculated, can specified directly via col_counts argument build_table(), though specifying alt_counts_df preferred mechanism. next calculate information per system organ class: now add count table AEDECOD AEBODSYS. default analyze() behavior factor create count table per level (using rtab_inner): indent_mod argument enables relative indenting changes tree structure table result desired indentation default. table far however usual adverse event table counts total number events number subjects one events particular term. get correct table need write custom analysis function: desired AE table : Note missing overall summary first two rows. can added initial analyze() call. Finally, wanted prune 0 count rows can trim_rows() function: Pruning larger topic separate rtables package vignette.","code":"s_events_patients <- function(x, labelstr, .N_col) { in_rows( \"Total number of patients with at least one event\" = rcell(length(unique(x)) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\"), \"Total number of events\" = rcell(length(x), format = \"xx\") ) } s_events_patients(x = c(\"id 1\", \"id 1\", \"id 2\"), .N_col = 5) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 Total number of patients with at least one event 2 (40.00%) 0 # 2 Total number of events 3 0 # row_label # 1 Total number of patients with at least one event # 2 Total number of events adae_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"USUBJID\", s_events_patients) adae_tbl <- build_table(adae_lyt, ADAE2) adae_tbl # ARM A ARM B # (N=2060) (N=1058) # ————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (5.53%) 150 (14.18%) # Total number of events 2060 1058 adae_adsl_tbl <- build_table(adae_lyt, ADAE2, alt_counts_df = ADSL2) adae_adsl_tbl # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 adae_soc_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"USUBJID\", s_events_patients) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", nested = FALSE) %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) adae_soc_tbl <- build_table(adae_soc_lyt, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl # ARM A ARM B # (N=146) (N=154) # ———————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 adae_soc_lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1) %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) %>% analyze(\"AEDECOD\", indent_mod = -1) adae_soc_tbl2 <- build_table(adae_soc_lyt2, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl2 # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 113 65 # ABDOMINAL FULLNESS DUE TO GAS 119 65 # BACK PAIN 0 0 # DIARRHEA 107 53 # FAECES SOFT 122 58 # GINGIVAL BLEEDING 147 71 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 152 62 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 135 75 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 138 67 # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 787 420 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 104 58 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 136 64 # WEAKNESS 0 0 table_count_once_per_id <- function(df, termvar = \"AEDECOD\", idvar = \"USUBJID\") { x <- df[[termvar]] id <- df[[idvar]] counts <- table(x[!duplicated(id)]) in_rows( .list = as.vector(counts), .labels = names(counts) ) } table_count_once_per_id(ADAE2) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 ABDOMINAL DISCOMFORT 23 0 # 2 ABDOMINAL FULLNESS DUE TO GAS 21 0 # 3 BACK PAIN 20 0 # 4 DIARRHEA 7 0 # 5 FAECES SOFT 11 0 # 6 GINGIVAL BLEEDING 15 0 # 7 HEADACHE 100 0 # 8 HYPOTENSION 16 0 # 9 NAUSEA (INTERMITTENT) 21 0 # 10 ORTHOSTATIC HYPOTENSION 14 0 # 11 WEAKNESS 16 0 # row_label # 1 ABDOMINAL DISCOMFORT # 2 ABDOMINAL FULLNESS DUE TO GAS # 3 BACK PAIN # 4 DIARRHEA # 5 FAECES SOFT # 6 GINGIVAL BLEEDING # 7 HEADACHE # 8 HYPOTENSION # 9 NAUSEA (INTERMITTENT) # 10 ORTHOSTATIC HYPOTENSION # 11 WEAKNESS adae_soc_lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1) %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) %>% analyze(\"AEDECOD\", afun = table_count_once_per_id, show_labels = \"hidden\", indent_mod = -1) adae_soc_tbl3 <- build_table(adae_soc_lyt3, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl3 # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 24 28 # ABDOMINAL FULLNESS DUE TO GAS 18 26 # BACK PAIN 0 0 # DIARRHEA 17 17 # FAECES SOFT 17 14 # GINGIVAL BLEEDING 18 25 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 20 20 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 58 45 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 40 36 # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 113 133 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 44 31 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 49 44 # WEAKNESS 0 0 adae_soc_lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"USUBJID\", afun = s_events_patients) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1, section_div = \"\") %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) %>% analyze(\"AEDECOD\", table_count_once_per_id, show_labels = \"hidden\", indent_mod = -1) adae_soc_tbl4 <- build_table(adae_soc_lyt4, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl4 # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 24 28 # ABDOMINAL FULLNESS DUE TO GAS 18 26 # BACK PAIN 0 0 # DIARRHEA 17 17 # FAECES SOFT 17 14 # GINGIVAL BLEEDING 18 25 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 20 20 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 58 45 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 40 36 # # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 113 133 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 44 31 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 49 44 # WEAKNESS 0 0 trim_rows(adae_soc_tbl4) # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 24 28 # ABDOMINAL FULLNESS DUE TO GAS 18 26 # DIARRHEA 17 17 # FAECES SOFT 17 14 # GINGIVAL BLEEDING 18 25 # NAUSEA (INTERMITTENT) 20 20 # # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # BACK PAIN 58 45 # WEAKNESS 40 36 # # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # HEADACHE 113 133 # # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # HYPOTENSION 44 31 # ORTHOSTATIC HYPOTENSION 49 44"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"adverse-events-by-id-and-by-grade","dir":"Articles","previous_headings":"Adverse Events","what":"Adverse Events By ID and By Grade","title":"Example Clinical Trials Tables","text":"adverse events table ID grade shows many patients least one adverse event per grade different subsets data (e.g. defined system organ class). table show zero count grades. Note add “overall” groups custom split function. layouting concepts needed create table already introduced far:","code":"table_count_grade_once_per_id <- function(df, labelstr = \"\", gradevar = \"AETOXGR\", idvar = \"USUBJID\", grade_levels = NULL) { id <- df[[idvar]] grade <- df[[gradevar]] if (!is.null(grade_levels)) { stopifnot(all(grade %in% grade_levels)) grade <- factor(grade, levels = grade_levels) } id_sel <- !duplicated(id) in_rows( \"--Any Grade--\" = sum(id_sel), .list = as.list(table(grade[id_sel])) ) } table_count_grade_once_per_id(ex_adae, grade_levels = 1:5) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod row_label # 1 --Any Grade-- 365 0 --Any Grade-- # 2 1 131 0 1 # 3 2 70 0 2 # 4 3 74 0 3 # 5 4 25 0 4 # 6 5 65 0 5 adae_grade_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze( \"AETOXGR\", afun = table_count_grade_once_per_id, extra_args = list(grade_levels = 1:5), var_labels = \"- Any adverse events -\", show_labels = \"visible\" ) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1) %>% summarize_row_groups(cfun = table_count_grade_once_per_id, format = \"xx\", indent_mod = 1) %>% split_rows_by(\"AEDECOD\", child_labels = \"visible\", indent_mod = -2) %>% analyze( \"AETOXGR\", afun = table_count_grade_once_per_id, extra_args = list(grade_levels = 1:5), show_labels = \"hidden\" ) adae_grade_tbl <- build_table(adae_grade_lyt, ADAE2, alt_counts_df = ADSL2) adae_grade_tbl # ARM A ARM B # (N=146) (N=154) # ————————————————————————————————————————————————————————————————————— # - Any adverse events - # --Any Grade-- 114 150 # 1 32 34 # 2 22 30 # 3 11 21 # 4 8 6 # 5 41 59 # GASTROINTESTINAL DISORDERS # --Any Grade-- 114 130 # 1 77 96 # 2 37 34 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL DISCOMFORT # --Any Grade-- 68 49 # 1 68 49 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 73 51 # 1 73 51 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 68 40 # 1 68 40 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 76 44 # 1 0 0 # 2 76 44 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 80 52 # 1 80 52 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 83 50 # 1 0 0 # 2 83 50 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # WEAKNESS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # --Any Grade-- 98 81 # 1 0 0 # 2 58 45 # 3 40 36 # 4 0 0 # 5 0 0 # ABDOMINAL DISCOMFORT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 79 62 # 1 0 0 # 2 79 62 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # WEAKNESS # --Any Grade-- 73 43 # 1 0 0 # 2 0 0 # 3 73 43 # 4 0 0 # 5 0 0 # NERVOUS SYSTEM DISORDERS # --Any Grade-- 113 133 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 113 133 # ABDOMINAL DISCOMFORT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 113 133 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 113 133 # HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # WEAKNESS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # VASCULAR DISORDERS # --Any Grade-- 93 75 # 1 0 0 # 2 0 0 # 3 44 31 # 4 49 44 # 5 0 0 # ABDOMINAL DISCOMFORT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HYPOTENSION # --Any Grade-- 66 43 # 1 0 0 # 2 0 0 # 3 66 43 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 70 54 # 1 0 0 # 2 0 0 # 3 0 0 # 4 70 54 # 5 0 0 # WEAKNESS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"response-table","dir":"Articles","previous_headings":"","what":"Response Table","title":"Example Clinical Trials Tables","text":"response table create composed 3 parts: Binary response table Unstratified analysis comparison vs. control group Multinomial response table Let’s start first part fairly simple derive: Note set ref_group argument split_cols_by() current table effect use cell data responder non-responder counts. ref_group argument needed part 2 3 table. now look implementation part 2: unstratified analysis comparison vs. control group. Let’s start analysis function: Hence can now add next vignette table: Next add part 3: multinomial response table. , adding row-split response level, thing binary response table . can now create final response table three parts: case wanted rename levels AVALC remove CI NE follows: Note table missing rows gaps make readable. row spacing feature rtables roadmap implemented future.","code":"ADRS_BESRSPI <- ex_adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate( rsp = factor(AVALC %in% c(\"CR\", \"PR\"), levels = c(TRUE, FALSE), labels = c(\"Responders\", \"Non-Responders\")), is_rsp = (rsp == \"Responders\") ) s_proportion <- function(x, .N_col) { in_rows( .list = lapply( as.list(table(x)), function(xi) rcell(xi * c(1, 1 / .N_col), format = \"xx.xx (xx.xx%)\") ) ) } rsp_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") rsp_tbl <- build_table(rsp_lyt, ADRS_BESRSPI) rsp_tbl # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # ——————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) s_unstrat_resp <- function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { return(in_rows( \"Difference in Response Rates (%)\" = rcell(numeric(0)), \"95% CI (Wald, with correction)\" = rcell(numeric(0)), \"p-value (Chi-Squared Test)\" = rcell(numeric(0)), \"Odds Ratio (95% CI)\" = rcell(numeric(0)) )) } fit <- stats::prop.test( x = c(sum(x), sum(.ref_group)), n = c(length(x), length(.ref_group)), correct = FALSE ) fit_glm <- stats::glm( formula = rsp ~ group, data = data.frame( rsp = c(.ref_group, x), group = factor(rep(c(\"ref\", \"x\"), times = c(length(.ref_group), length(x))), levels = c(\"ref\", \"x\")) ), family = binomial(link = \"logit\") ) in_rows( \"Difference in Response Rates (%)\" = non_ref_rcell( (mean(x) - mean(.ref_group)) * 100, .in_ref_col, format = \"xx.xx\" ), \"95% CI (Wald, with correction)\" = non_ref_rcell( fit$conf.int * 100, .in_ref_col, format = \"(xx.xx, xx.xx)\" ), \"p-value (Chi-Squared Test)\" = non_ref_rcell( fit$p.value, .in_ref_col, format = \"x.xxxx | (<0.0001)\" ), \"Odds Ratio (95% CI)\" = non_ref_rcell( c( exp(stats::coef(fit_glm)[-1]), exp(stats::confint.default(fit_glm, level = .95)[-1, , drop = FALSE]) ), .in_ref_col, format = \"xx.xx (xx.xx - xx.xx)\" ) ) } s_unstrat_resp( x = ADRS_BESRSPI %>% filter(ARM == \"A: Drug X\") %>% pull(is_rsp), .ref_group = ADRS_BESRSPI %>% filter(ARM == \"B: Placebo\") %>% pull(is_rsp), .in_ref_col = FALSE ) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 Difference in Response Rates (%) 17.91 0 # 2 95% CI (Wald, with correction) (7.93, 27.89) 0 # 3 p-value (Chi-Squared Test) 0.0006 0 # 4 Odds Ratio (95% CI) 2.79 (1.53 - 5.06) 0 # row_label # 1 Difference in Response Rates (%) # 2 95% CI (Wald, with correction) # 3 p-value (Chi-Squared Test) # 4 Odds Ratio (95% CI) rsp_lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") %>% analyze( \"is_rsp\", s_unstrat_resp, show_labels = \"visible\", var_labels = \"Unstratified Response Analysis\" ) rsp_tbl2 <- build_table(rsp_lyt2, ADRS_BESRSPI) rsp_tbl2 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) # Unstratified Response Analysis # Difference in Response Rates (%) -17.91 5.83 # 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61) # p-value (Chi-Squared Test) 0.0006 0.1436 # Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75) s_prop <- function(df, .N_col) { in_rows( \"95% CI (Wald, with correction)\" = rcell(binom.test(nrow(df), .N_col)$conf.int * 100, format = \"(xx.xx, xx.xx)\") ) } s_prop( df = ADRS_BESRSPI %>% filter(ARM == \"A: Drug X\", AVALC == \"CR\"), .N_col = sum(ADRS_BESRSPI$ARM == \"A: Drug X\") ) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 95% CI (Wald, with correction) (49.38, 66.67) 0 # row_label # 1 95% CI (Wald, with correction) rsp_lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") %>% analyze( \"is_rsp\", s_unstrat_resp, show_labels = \"visible\", var_labels = \"Unstratified Response Analysis\" ) %>% split_rows_by( var = \"AVALC\", split_fun = reorder_split_levels(neworder = c(\"CR\", \"PR\", \"SD\", \"NON CR/PD\", \"PD\", \"NE\"), drlevels = TRUE), nested = FALSE ) %>% summarize_row_groups() %>% analyze(\"AVALC\", afun = s_prop) rsp_tbl3 <- build_table(rsp_lyt3, ADRS_BESRSPI) rsp_tbl3 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) # Unstratified Response Analysis # Difference in Response Rates (%) -17.91 5.83 # 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61) # p-value (Chi-Squared Test) 0.0006 0.1436 # Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75) # CR 78 (58.2%) 55 (41.0%) 97 (73.5%) # 95% CI (Wald, with correction) (49.38, 66.67) (32.63, 49.87) (65.10, 80.79) # PR 36 (26.9%) 35 (26.1%) 23 (17.4%) # 95% CI (Wald, with correction) (19.58, 35.20) (18.92, 34.41) (11.38, 24.99) # SD 20 (14.9%) 44 (32.8%) 12 (9.1%) # 95% CI (Wald, with correction) (9.36, 22.11) (24.97, 41.47) (4.79, 15.34) rsp_label <- function(x) { rsp_full_label <- c( CR = \"Complete Response (CR)\", PR = \"Partial Response (PR)\", SD = \"Stable Disease (SD)\", `NON CR/PD` = \"Non-CR or Non-PD (NON CR/PD)\", PD = \"Progressive Disease (PD)\", NE = \"Not Evaluable (NE)\", Missing = \"Missing\", `NE/Missing` = \"Missing or unevaluable\" ) stopifnot(all(x %in% names(rsp_full_label))) rsp_full_label[x] } rsp_lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") %>% analyze( \"is_rsp\", s_unstrat_resp, show_labels = \"visible\", var_labels = \"Unstratified Response Analysis\" ) %>% split_rows_by( var = \"AVALC\", split_fun = keep_split_levels(c(\"CR\", \"PR\", \"SD\", \"PD\"), reorder = TRUE), nested = FALSE ) %>% summarize_row_groups(cfun = function(df, labelstr, .N_col) { in_rows(nrow(df) * c(1, 1 / .N_col), .formats = \"xx (xx.xx%)\", .labels = rsp_label(labelstr)) }) %>% analyze(\"AVALC\", afun = s_prop) %>% analyze(\"AVALC\", afun = function(x, .N_col) { in_rows(rcell(sum(x == \"NE\") * c(1, 1 / .N_col), format = \"xx.xx (xx.xx%)\"), .labels = rsp_label(\"NE\")) }, nested = FALSE) rsp_tbl4 <- build_table(rsp_lyt4, ADRS_BESRSPI) rsp_tbl4 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) # Unstratified Response Analysis # Difference in Response Rates (%) -17.91 5.83 # 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61) # p-value (Chi-Squared Test) 0.0006 0.1436 # Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75) # Complete Response (CR) 78 (58.21%) 55 (41.04%) 97 (73.48%) # 95% CI (Wald, with correction) (49.38, 66.67) (32.63, 49.87) (65.10, 80.79) # Partial Response (PR) 36 (26.87%) 35 (26.12%) 23 (17.42%) # 95% CI (Wald, with correction) (19.58, 35.20) (18.92, 34.41) (11.38, 24.99) # Stable Disease (SD) 20 (14.93%) 44 (32.84%) 12 (9.09%) # 95% CI (Wald, with correction) (9.36, 22.11) (24.97, 41.47) (4.79, 15.34) # Progressive Disease (PD) 0 (0.00%) 0 (0.00%) 0 (0.00%) # 95% CI (Wald, with correction) (0.00, 2.72) (0.00, 2.72) (0.00, 2.76) # Not Evaluable (NE) 0.00 (0.00%) 0.00 (0.00%) 0.00 (0.00%)"},{"path":"https://insightsengineering.github.io/rtables/articles/clinical_trials.html","id":"time-to-event-analysis-table","dir":"Articles","previous_headings":"","what":"Time to Event Analysis Table","title":"Example Clinical Trials Tables","text":"time event analysis table constructed consists four parts: Overall subject counts Censored subjects summary Cox proportional-hazards analysis Time--event analysis table constructed sequential use analyze() function, four custom analysis functions corresponding four parts listed . addition table includes referential footnotes relevant table contents. table faceted column-wise arm. First start loading necessary packages preparing data used construction table. adtte dataset used preparing models adtte2 dataset handles missing values “Censor Date Description” column used produce final table. add censoring data example purposes. Next create basic analysis function, a_count_subjs prints overall unique subject counts percentages within data. analysis function created generate counts censored subjects level factor variable dataset. case cnsr_counter function applied CNSDTDSC variable contains censor date description censored subject. function generates counts fractions unique subjects corresponding factor level, excluding missing values (uncensored patients). Cox proportional-hazards (Cox P-H) analysis generated next third custom analysis function, a_cph. Prior creating analysis function, Cox P-H model fit data using coxph() Surv() functions survival package. model used input a_cph analysis function returns hazard ratios, 95% confidence intervals, p-values comparing reference group - case leftmost column. fourth final analysis function, a_tte, generates time first adverse event table three rows corresponding Median, 95% Confidence Interval, Min Max respectively. First survival table constructed summary table survival model using survfit() Surv() functions survival package. table given input a_tte produces table time first adverse event consisting previously mentioned summary statistics. Additionally, a_tte function creates referential footnote within table indicate censoring occurred data. Now able use four analysis functions build time event analysis table. set show_colcounts argument basic_table() TRUE first print total subject counts column. Next use split_cols_by() split table three columns corresponding three different levels ARM, specify first arm, \": Drug X\" act reference group compared - reference group used Cox P-H analysis. call analyze() sequentially using four custom analysis functions argument afun specifying additional arguments necessary. use build_table() construct rtable using adtte2 dataset. Finally, annotate table using fnotes_at_path() function specify product-limit estimates used calculate statistics listed “Time first adverse event” heading within table. referential footnote created earlier time--event analysis function (a_tte) also displayed.","code":"library(survival) adtte <- ex_adaette %>% dplyr::filter(PARAMCD == \"AETTE2\", SAFFL == \"Y\") # Add censoring to data for example adtte[adtte$AVAL > 1.0, ] <- adtte[adtte$AVAL > 1.0, ] %>% mutate(AVAL = 1.0, CNSR = 1) adtte2 <- adtte %>% mutate(CNSDTDSC = ifelse(CNSDTDSC == \"\", \"__none__\", CNSDTDSC)) a_count_subjs <- function(x, .N_col) { in_rows( \"Subjects with Adverse Events n (%)\" = rcell(length(unique(x)) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\") ) } cnsr_counter <- function(df, .var, .N_col) { x <- df[!duplicated(df$USUBJID), .var] x <- x[x != \"__none__\"] lapply(table(x), function(xi) rcell(xi * c(1, 1 / .N_col), format = \"xx (xx.xx%)\")) } cph <- coxph(Surv(AVAL, CNSR == 0) ~ ACTARM + STRATA1, ties = \"exact\", data = adtte) a_cph <- function(df, .var, .in_ref_col, .ref_full, full_cox_fit) { if (.in_ref_col) { ret <- replicate(3, list(rcell(NULL))) } else { curtrt <- df[[.var]][1] coefs <- coef(full_cox_fit) sel_pos <- grep(curtrt, names(coefs), fixed = TRUE) hrval <- exp(coefs[sel_pos]) sdf <- survdiff(Surv(AVAL, CNSR == 0) ~ ACTARM + STRATA1, data = rbind(df, .ref_full)) pval <- (1 - pchisq(sdf$chisq, length(sdf$n) - 1)) / 2 ci_val <- exp(unlist(confint(full_cox_fit)[sel_pos, ])) ret <- list( rcell(hrval, format = \"xx.x\"), rcell(ci_val, format = \"(xx.x, xx.x)\"), rcell(pval, format = \"x.xxxx | (<0.0001)\") ) } in_rows( .list = ret, .names = c(\"Hazard ratio\", \"95% confidence interval\", \"p-value (one-sided stratified log rank)\") ) } surv_tbl <- as.data.frame( summary(survfit(Surv(AVAL, CNSR == 0) ~ ACTARM, data = adtte, conf.type = \"log-log\"))$table ) %>% dplyr::mutate( ACTARM = factor(gsub(\"ACTARM=\", \"\", row.names(.)), levels = levels(adtte$ACTARM)), ind = FALSE ) a_tte <- function(df, .var, kp_table) { ind <- grep(df[[.var]][1], row.names(kp_table), fixed = TRUE) minmax <- range(df[[\"AVAL\"]]) mm_val_str <- format_value(minmax, format = \"xx.x, xx.x\") rowfn <- list() if (all(df$CNSR[df$AVAL == minmax[2]])) { mm_val_str <- paste0(mm_val_str, \"*\") rowfn <- \"* indicates censoring\" } in_rows( Median = kp_table[ind, \"median\", drop = TRUE], \"95% confidence interval\" = unlist(kp_table[ind, c(\"0.95LCL\", \"0.95UCL\")]), \"Min Max\" = mm_val_str, .formats = c(\"xx.xx\", \"xx.xx - xx.xx\", \"xx\"), .row_footnotes = list(NULL, NULL, rowfn) ) } lyt <- basic_table(show_colcounts = TRUE) %>% ## Column faceting split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %>% ## Overall count analyze(\"USUBJID\", a_count_subjs, show_labels = \"hidden\") %>% ## Censored subjects summary analyze(\"CNSDTDSC\", cnsr_counter, var_labels = \"Censored Subjects\", show_labels = \"visible\") %>% ## Cox P-H analysis analyze(\"ARM\", a_cph, extra_args = list(full_cox_fit = cph), show_labels = \"hidden\") %>% ## Time-to-event analysis analyze( \"ARM\", a_tte, var_labels = \"Time to first adverse event\", show_labels = \"visible\", extra_args = list(kp_table = surv_tbl), table_names = \"kapmeier\" ) tbl_tte <- build_table(lyt, adtte2) fnotes_at_path( tbl_tte, c(\"ma_USUBJID_CNSDTDSC_ARM_kapmeier\", \"kapmeier\") ) <- \"Product-limit (Kaplan-Meier) estimates.\" tbl_tte # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————————————————————————————————————— # Subjects with Adverse Events n (%) 134 (100.00%) 134 (100.00%) 132 (100.00%) # Censored Subjects # Clinical Cut Off 6 (4.48%) 3 (2.24%) 14 (10.61%) # Completion or Discontinuation 9 (6.72%) 5 (3.73%) 9 (6.82%) # End of AE Reporting Period 14 (10.45%) 7 (5.22%) 14 (10.61%) # Preferred Term 11 (8.21%) 5 (3.73%) 13 (9.85%) # Hazard ratio 0.7 1.0 # 95% confidence interval (0.5, 0.9) (0.8, 1.4) # p-value (one-sided stratified log rank) 0.1070 0.4880 # Time to first adverse event {1} # Median 0.23 0.39 0.29 # 95% confidence interval 0.18 - 0.33 0.29 - 0.49 0.22 - 0.35 # Min Max {2} 0.0, 1.0* 0.0, 1.0* 0.0, 1.0* # ———————————————————————————————————————————————————————————————————————————————————————— # # {1} - Product-limit (Kaplan-Meier) estimates. # {2} - * indicates censoring # ————————————————————————————————————————————————————————————————————————————————————————"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"customizing-appearance","dir":"Articles","previous_headings":"","what":"Customizing Appearance","title":"Customizing Appearance","text":"vignette, describe various ways can modify customize appearance rtables. Loading package:","code":"library(rtables) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"rows-and-cell-values-alignments","dir":"Articles","previous_headings":"Customizing Appearance","what":"Rows and cell values alignments","title":"Customizing Appearance","text":"possible align content assigning \"left\", \"center\" (default), \"right\" .aligns align arguments in_rows() rcell(), respectively. also possible use decimal, dec_right, dec_left decimal alignments. first takes numerical values aligns decimal character . every value column align = \"decimal\". Also numberic without decimal values aligned according imaginary . specified . dec_left dec_right behave similarly, difference column present empty spaces left right, pushes values towards left right taking one value decimal characters, right, non-decimal values left. details, please read related documentation page help(\"decimal_align\"). Please consider using ?in_rows ?rcell clarifications two arguments, use formatters::list_valid_aligns() see available alignment options. following show two simplified examples use align .aligns, respectively. concepts can well applied clinical table shown following, complex, example.","code":"# In rcell we use align. lyt <- basic_table() %>% analyze(\"AGE\", function(x) { in_rows( left = rcell(\"l\", align = \"left\"), right = rcell(\"r\", align = \"right\"), center = rcell(\"c\", align = \"center\") ) }) tbl <- build_table(lyt, DM) tbl # all obs # ———————————————— # left l # right r # center c # In in_rows, we use .aligns. This can either set the general value or the # single values (see NB). lyt2 <- basic_table() %>% analyze(\"AGE\", function(x) { in_rows( left = rcell(\"l\"), right = rcell(\"r\"), center = rcell(\"c\"), .aligns = c(\"right\") ) # NB: .aligns = c(\"right\", \"left\", \"center\") }) tbl2 <- build_table(lyt2, DM) tbl2 # all obs # ———————————————— # left l # right r # center c lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"STRATA1\"), function(x) { if (is.numeric(x)) { in_rows( \"mean\" = rcell(mean(x)), \"sd\" = rcell(sd(x)), .formats = c(\"xx.x\"), .aligns = \"left\" ) } else if (is.factor(x)) { rcell(length(unique(x)), align = \"right\") } else { stop(\"Unsupported type\") } }, show_labels = \"visible\", na_str = \"NE\") tbl3 <- build_table(lyt3, ex_adsl) tbl3 # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # AGE # mean 32.8 34.1 35.2 # sd 6.1 7.1 7.4 # STRATA1 # STRATA1 3 3 3 # M # AGE # mean 35.6 37.4 35.4 # sd 7.1 8.7 8.2 # STRATA1 # STRATA1 3 3 3 # U # AGE # mean 31.7 31.0 35.2 # sd 3.2 5.7 3.1 # STRATA1 # STRATA1 3 2 3 # UNDIFFERENTIATED # AGE # mean 28.0 NE 45.0 # sd NE NE 1.4 # STRATA1 # STRATA1 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"top-left-materials","dir":"Articles","previous_headings":"Customizing Appearance","what":"Top-left Materials","title":"Customizing Appearance","text":"sequence strings printed area column header display first row label can modified pre-processing using label position argument row splits split_rows_by, append_topleft function, post-processing using top_left() function. Note: Indenting automatically added label_pos = \"topleft\". Within layout initializer: Specify label position using split_rows function. Notice position STRATA1 SEX. Post-processing using top_left() function:","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\") %>% append_topleft(\"New top_left material here\") build_table(lyt, DM) # New top_left material here A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # A # Mean 32.53 32.30 35.76 # B # Mean 35.46 32.42 34.39 # C # Mean 36.34 34.45 33.54 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\", label_pos = \"topleft\") %>% split_rows_by(\"SEX\", label_pos = \"topleft\") %>% analyze(\"AGE\") build_table(lyt, DM) # STRATA1 # SEX A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————— # A # F # Mean 30.91 32.91 35.95 # M # Mean 35.07 31.09 35.60 # U # Mean NA NA NA # UNDIFFERENTIATED # Mean NA NA NA # B # F # Mean 34.85 32.88 34.42 # M # Mean 36.64 32.09 34.37 # U # Mean NA NA NA # UNDIFFERENTIATED # Mean NA NA NA # C # F # Mean 35.19 36.00 34.32 # M # Mean 37.39 32.81 32.83 # U # Mean NA NA NA # UNDIFFERENTIATED # Mean NA NA NA lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"STRATA1\"), function(x) { if (is.numeric(x)) { in_rows( \"mean\" = rcell(mean(x)), \"sd\" = rcell(sd(x)), .formats = c(\"xx.x\"), .aligns = \"left\" ) } else if (is.factor(x)) { rcell(length(unique(x)), align = \"right\") } else { stop(\"Unsupported type\") } }, show_labels = \"visible\", na_str = \"NE\") %>% build_table(ex_adsl) # Adding top-left material top_left(lyt) <- \"New top-left material here\" lyt # New top-left material here A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # F # AGE # mean 32.8 34.1 35.2 # sd 6.1 7.1 7.4 # STRATA1 # STRATA1 3 3 3 # M # AGE # mean 35.6 37.4 35.4 # sd 7.1 8.7 8.2 # STRATA1 # STRATA1 3 3 3 # U # AGE # mean 31.7 31.0 35.2 # sd 3.2 5.7 3.1 # STRATA1 # STRATA1 3 2 3 # UNDIFFERENTIATED # AGE # mean 28.0 NE 45.0 # sd NE NE 1.4 # STRATA1 # STRATA1 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"table-inset","dir":"Articles","previous_headings":"Customizing Appearance","what":"Table Inset","title":"Customizing Appearance","text":"Table title, table body, referential footnotes main footers can inset left alignment titles provenance footer materials. can modified within layout initializer basic_table() using inset argument post-processing table_inset(). Using layout initializer: Using post-processing function: Without inset - inset 5 characters - example table produced clinical data. Compare inset table main footer two tables. Without inset - inset - Notice, inset apply title materials (main title, subtitles, page titles), provenance footer materials. Inset settings applied top-left materials, referential footnotes main footer materials horizontal dividers.","code":"lyt <- basic_table(inset = 5) %>% analyze(\"AGE\") build_table(lyt, DM) # all obs # —————————————— # Mean 34.22 lyt <- basic_table() %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl # all obs # —————————————— # Mean 34.22 table_inset(tbl) <- 5 tbl # all obs # —————————————— # Mean 34.22 analysisfun <- function(x, ...) { in_rows( row1 = 5, row2 = c(1, 2), .row_footnotes = list(row1 = \"row 1 rfn\"), .cell_footnotes = list(row2 = \"row 2 cfn\") ) } lyt <- basic_table( title = \"Title says Whaaaat\", subtitles = \"Oh, ok.\", main_footer = \"ha HA! Footer!\", prov_footer = \"provenaaaaance\" ) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = analysisfun) result <- build_table(lyt, ex_adsl) result # Title says Whaaaat # Oh, ok. # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # row1 {1} 5 5 5 # row2 1, 2 {2} 1, 2 {2} 1, 2 {2} # —————————————————————————————————————————————————— # # {1} - row 1 rfn # {2} - row 2 cfn # —————————————————————————————————————————————————— # # ha HA! Footer! # # provenaaaaance table_inset(result) <- 5 result # Title says Whaaaat # Oh, ok. # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # row1 {1} 5 5 5 # row2 1, 2 {2} 1, 2 {2} 1, 2 {2} # —————————————————————————————————————————————————— # # {1} - row 1 rfn # {2} - row 2 cfn # —————————————————————————————————————————————————— # # ha HA! Footer! # # provenaaaaance"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"horizontal-separation","dir":"Articles","previous_headings":"Customizing Appearance","what":"Horizontal Separation","title":"Customizing Appearance","text":"character value can specified modify horizontal separation column headers table. Horizontal separation applies : separating title + subtitles column labels + top left materials, column labels + top left material row labels + cells, row labels + cells footer content, Referential footnotes main + provenance content something sides divider. , replace default line “=”.","code":"tbl <- basic_table() %>% split_cols_by(\"Species\") %>% add_colcounts() %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), function(x) { in_rows( mean_sd = c(mean(x), sd(x)), var = var(x), min_max = range(x), .formats = c(\"xx.xx (xx.xx)\", \"xx.xxx\", \"xx.x - xx.x\"), .labels = c(\"Mean (sd)\", \"Variance\", \"Min - Max\") ) }) %>% build_table(iris, hsep = \"=\") tbl # setosa versicolor virginica # (N=50) (N=50) (N=50) # ====================================================== # Sepal.Length # Mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) # Variance 0.124 0.266 0.404 # Min - Max 4.3 - 5.8 4.9 - 7.0 4.9 - 7.9 # Petal.Width # Mean (sd) 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) # Variance 0.011 0.039 0.075 # Min - Max 0.1 - 0.6 1.0 - 1.8 1.4 - 2.5"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"section-dividers","dir":"Articles","previous_headings":"Customizing Appearance","what":"Section Dividers","title":"Customizing Appearance","text":"character value can specified section divider succeed every group defined split instruction. Note, trailing divider end table never printed. , “+” repeated used section divider. Section dividers can set ” ” create blank line. Separation characters can specified different row splits. However, one printed “pile ” next .","code":"lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(head(names(iris), -1), afun = function(x) { list( \"mean / sd\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = rcell(diff(range(x)), format = \"xx.xx\") ) }, section_div = \"+\") build_table(lyt, iris) # setosa versicolor virginica # —————————————————————————————————————————————————————— # Sepal.Length # mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 1.50 2.10 3.00 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Sepal.Width # mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 2.10 1.40 1.60 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Petal.Length # mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 0.90 2.10 2.40 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Petal.Width # mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 0.50 0.80 1.10 lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(head(names(iris), -1), afun = function(x) { list( \"mean / sd\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = rcell(diff(range(x)), format = \"xx.xx\") ) }, section_div = \" \") build_table(lyt, iris) # setosa versicolor virginica # —————————————————————————————————————————————————————— # Sepal.Length # mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) # # range 1.50 2.10 3.00 # # Sepal.Width # mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32) # # range 2.10 1.40 1.60 # # Petal.Length # mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55) # # range 0.90 2.10 2.40 # # Petal.Width # mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) # # range 0.50 0.80 1.10 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", section_div = \"=\") %>% split_rows_by(\"STRATA1\", section_div = \"~\") %>% analyze(\"AGE\", mean, var_labels = \"Age\", format = \"xx.xx\") build_table(lyt, DM) # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————————————————————————————————————— # ASIAN # A # mean 32.19 33.90 36.81 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean 34.12 31.62 34.73 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean 36.21 33.00 32.39 # =================================================================================== # BLACK OR AFRICAN AMERICAN # A # mean 31.50 28.57 33.62 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean 35.60 30.83 33.67 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean 35.50 34.18 35.00 # =================================================================================== # WHITE # A # mean 37.67 31.33 33.17 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean 39.86 39.00 34.75 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean 39.75 44.67 36.75 # =================================================================================== # AMERICAN INDIAN OR ALASKA NATIVE # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # MULTIPLE # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # OTHER # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # UNKNOWN # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"indent-modifier","dir":"Articles","previous_headings":"Customizing Appearance","what":"Indent Modifier","title":"Customizing Appearance","text":"Tables default indenting level splitting. custom indent value can supplied indent_mod argument within split function modify default. Compare indenting tables : Default Indent - Modified indent -","code":"basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # U # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # UNDIFFERENTIATED # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # —————————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", indent_mod = 3) %>% split_rows_by(\"STRATA1\", indent_mod = 5) %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # U # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # UNDIFFERENTIATED # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # —————————————————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"variable-label-visibility","dir":"Articles","previous_headings":"Customizing Appearance","what":"Variable Label Visibility","title":"Customizing Appearance","text":"split instructions, visibility label variable split can modified visible, hidden topleft show_labels argument, label_pos argument, child_labels argument applicable. Note: name levels contained variable. analyze calls, indicates variable visible multiple variables analyzed level nesting. Visibility labels groups generated split can also modified using child_label argument split call. child_label argument can force labels visible addition content rows hide move content rows. Notice placement “AGE” label example: set default, label AGE repeated since one variable analyzed level nesting. Override setting show_labels argument “visible”. example using label_pos argument modifying label visibility: Label order mirror order split_rows_by calls. labels subgroups hidden, label_pos argument set hidden. “SEX” label position hidden - “SEX” label position top-left materials -","code":"lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, child_labels = \"visible\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, show_labels = \"default\") build_table(lyt, DM) # A: Drug X B: Placebo C: Combination # (N=121) (N=106) (N=129) # ————————————————————————————————————————————————————————————————— # F # A # mean 30.9090909090909 32.9090909090909 35.95 # B # mean 34.8518518518519 32.8823529411765 34.4210526315789 # C # mean 35.1904761904762 36 34.3181818181818 # M # A # mean 35.0714285714286 31.0909090909091 35.6 # B # mean 36.6428571428571 32.0869565217391 34.3684210526316 # C # mean 37.3913043478261 32.8125 32.8333333333333 lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, child_labels = \"hidden\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, show_labels = \"visible\") build_table(lyt2, DM) # A: Drug X B: Placebo C: Combination # (N=121) (N=106) (N=129) # ————————————————————————————————————————————————————————————————— # A # AGE # mean 30.9090909090909 32.9090909090909 35.95 # B # AGE # mean 34.8518518518519 32.8823529411765 34.4210526315789 # C # AGE # mean 35.1904761904762 36 34.3181818181818 # A # AGE # mean 35.0714285714286 31.0909090909091 35.6 # B # AGE # mean 36.6428571428571 32.0869565217391 34.3684210526316 # C # AGE # mean 37.3913043478261 32.8125 32.8333333333333 basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, label_pos = \"visible\") %>% split_rows_by(\"STRATA1\", label_pos = \"hidden\") %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # ———————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————— # SEX # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # ———————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, label_pos = \"topleft\") %>% split_rows_by(\"STRATA1\", label_pos = \"hidden\") %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————— # SEX A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"cell-label-and-annotation-wrapping","dir":"Articles","previous_headings":"Customizing Appearance","what":"Cell, Label, and Annotation Wrapping","title":"Customizing Appearance","text":"rtable can rendered customized width setting custom rendering widths cell contents, row labels, titles/footers. demonstrated using sample data table . section aim render table reduced width since table wide contents several cells, labels, titles/footers. following sections use toString() function render table string form. resulting string representation ready printed written plain text file, use strsplit() function combination matrix() function preview rendered wrapped table matrix form within vignette.","code":"trimmed_data <- ex_adsl %>% filter(SEX %in% c(\"M\", \"F\")) %>% filter(RACE %in% levels(RACE)[1:2]) levels(trimmed_data$ARM)[1] <- \"Incredibly long column name to be wrapped\" levels(trimmed_data$ARM)[2] <- \"This_column_name_should_be_split_somewhere\" wide_tbl <- basic_table( title = \"Title that is too long and also needs to be wrapped to a smaller width\", subtitles = \"Subtitle that is also long and also needs to be wrapped to a smaller width\", main_footer = \"Footnote that is wider than expected for this table.\", prov_footer = \"Provenance footer material that is also wider than expected for this table.\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze( c(\"AGE\", \"EOSDY\"), na_str = \"Very long cell contents to_be_wrapped_and_splitted\", inclNAs = TRUE ) %>% build_table(trimmed_data) wide_tbl # Title that is too long and also needs to be wrapped to a smaller width # Subtitle that is also long and also needs to be wrapped to a smaller width # # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # Incredibly long column name to be wrapped This_column_name_should_be_split_somewhere C: Combination # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN # AGE # Mean 32.50 36.68 36.99 # EOSDY # Mean Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted # BLACK OR AFRICAN AMERICAN # AGE # Mean 34.27 34.93 33.71 # EOSDY # Mean Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # # Footnote that is wider than expected for this table. # # Provenance footer material that is also wider than expected for this table."},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"cell-label-wrapping","dir":"Articles","previous_headings":"Customizing Appearance > Cell, Label, and Annotation Wrapping","what":"Cell & Label Wrapping","title":"Customizing Appearance","text":"width rendered table can customized wrapping column widths. done setting custom width values via widths argument toString() function. length vector passed widths argument must equal total number columns table, including row labels column, value vector corresponding maximum width (characters) allowed column, left right. Similarly, wrapping can applied exporting table via one four export_as_* functions implementing pagination via paginate_table() function rtables package. cases, rendered column widths set using colwidths argument takes input format widths argument toString(). example, wide_tbl four columns (1 row label column 3 content columns) set widths use rendered table. set width row label column 10 characters widths 3 content columns 8 characters. words longer specified width broken continued following line. default 3 spaces separating columns rendered table can customized via col_gap argument toString() width customization desired. resulting output can see table correctly rendered using wrapping total width 43 characters, titles footers remain wider rendered table.","code":"result_wrap_cells <- toString(wide_tbl, widths = c(10, 8, 8, 8)) matrix_wrap_cells <- matrix(strsplit(result_wrap_cells, \"\\n\")[[1]], ncol = 1) matrix_wrap_cells # [,1] # [1,] \"Title that is too long and also needs to be wrapped to a smaller width\" # [2,] \"Subtitle that is also long and also needs to be wrapped to a smaller width\" # [3,] \"\" # [4,] \"———————————————————————————————————————————\" # [5,] \" Incredib This_col \" # [6,] \" ly long umn_name \" # [7,] \" column _should_ \" # [8,] \" name be_split \" # [9,] \" to be _somewhe C: Combi\" # [10,] \" wrapped re nation \" # [11,] \"———————————————————————————————————————————\" # [12,] \"ASIAN \" # [13,] \" AGE \" # [14,] \" Mean 32.50 36.68 36.99 \" # [15,] \" EOSDY \" # [16,] \" Mean Very Very Very \" # [17,] \" long long long \" # [18,] \" cell cell cell \" # [19,] \" contents contents contents\" # [20,] \" to_be_wr to_be_wr to_be_wr\" # [21,] \" apped_an apped_an apped_an\" # [22,] \" d_splitt d_splitt d_splitt\" # [23,] \" ed ed ed \" # [24,] \"BLACK OR \" # [25,] \"AFRICAN \" # [26,] \"AMERICAN \" # [27,] \" AGE \" # [28,] \" Mean 34.27 34.93 33.71 \" # [29,] \" EOSDY \" # [30,] \" Mean Very Very Very \" # [31,] \" long long long \" # [32,] \" cell cell cell \" # [33,] \" contents contents contents\" # [34,] \" to_be_wr to_be_wr to_be_wr\" # [35,] \" apped_an apped_an apped_an\" # [36,] \" d_splitt d_splitt d_splitt\" # [37,] \" ed ed ed \" # [38,] \"———————————————————————————————————————————\" # [39,] \"\" # [40,] \"Footnote that is wider than expected for this table.\" # [41,] \"\" # [42,] \"Provenance footer material that is also wider than expected for this table.\""},{"path":"https://insightsengineering.github.io/rtables/articles/custom_appearance.html","id":"title-footer-wrapping","dir":"Articles","previous_headings":"Customizing Appearance > Cell, Label, and Annotation Wrapping","what":"Title & Footer Wrapping","title":"Customizing Appearance","text":"addition wrapping column widths, titles footers can wrapped setting tf_wrap = TRUE toString() setting max_width argument toString() maximum width (characters) allowed titles/footers. four export_as_* functions paginate_table() can also wrap titles/footers setting two arguments. following code, set max_width = 43 rendered table annotations maximum width 43 characters.","code":"result_wrap_cells_tf <- toString( wide_tbl, widths = c(10, 8, 8, 8), tf_wrap = TRUE, max_width = 43 ) matrix_wrap_cells_tf <- matrix(strsplit(result_wrap_cells_tf, \"\\n\")[[1]], ncol = 1) matrix_wrap_cells_tf # [,1] # [1,] \"Title that is too long and also needs to be\" # [2,] \"wrapped to a smaller width\" # [3,] \"Subtitle that is also long and also needs\" # [4,] \"to be wrapped to a smaller width\" # [5,] \"\" # [6,] \"———————————————————————————————————————————\" # [7,] \" Incredib This_col \" # [8,] \" ly long umn_name \" # [9,] \" column _should_ \" # [10,] \" name be_split \" # [11,] \" to be _somewhe C: Combi\" # [12,] \" wrapped re nation \" # [13,] \"———————————————————————————————————————————\" # [14,] \"ASIAN \" # [15,] \" AGE \" # [16,] \" Mean 32.50 36.68 36.99 \" # [17,] \" EOSDY \" # [18,] \" Mean Very Very Very \" # [19,] \" long long long \" # [20,] \" cell cell cell \" # [21,] \" contents contents contents\" # [22,] \" to_be_wr to_be_wr to_be_wr\" # [23,] \" apped_an apped_an apped_an\" # [24,] \" d_splitt d_splitt d_splitt\" # [25,] \" ed ed ed \" # [26,] \"BLACK OR \" # [27,] \"AFRICAN \" # [28,] \"AMERICAN \" # [29,] \" AGE \" # [30,] \" Mean 34.27 34.93 33.71 \" # [31,] \" EOSDY \" # [32,] \" Mean Very Very Very \" # [33,] \" long long long \" # [34,] \" cell cell cell \" # [35,] \" contents contents contents\" # [36,] \" to_be_wr to_be_wr to_be_wr\" # [37,] \" apped_an apped_an apped_an\" # [38,] \" d_splitt d_splitt d_splitt\" # [39,] \" ed ed ed \" # [40,] \"———————————————————————————————————————————\" # [41,] \"\" # [42,] \"Footnote that is wider than expected for\" # [43,] \"this table.\" # [44,] \"\" # [45,] \"Provenance footer material that is also\" # [46,] \"wider than expected for this table.\""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"debugging","dir":"Articles > Dev-guide","previous_headings":"","what":"Debugging","title":"Debugging in {rtables} and Beyond","text":"short non-comprehensive guide debugging rtables. Regardless, considered valid personal use discretion.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"coding-in-practice","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Coding in Practice","title":"Debugging in {rtables} and Beyond","text":"easy read find problems clever impossible debug","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"some-definitions","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Some Definitions","title":"Debugging in {rtables} and Beyond","text":"Coding Error - Code intended -> Bug punch card Unexpected Input - Defensive programming FAIL FAST FAIL LOUD (FFFL) -> useful time consuming Bug Dependency -> never use dependencies can!","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"considerations-about-fffl","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Considerations About FFFL","title":"Debugging in {rtables} and Beyond","text":"Errors close possible source. example, bad inputs found early. worst possible example software silently giving incorrect results. Common things can catch early missing values, column length == 0, length > 1.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"general-suggestions","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"General Suggestions","title":"Debugging in {rtables} and Beyond","text":"Robust code base attempt possibly problematic operations. Read Error Messages debugcall can add signature (formals) trace powerful can add reaction tracer good precise find happens options(error = recover) one best tools debug core tool developing allows step point function call sequence. dump.frames debugger: saves file object call debugger step recover.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"warn-global-option","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"warn Global Option","title":"Debugging in {rtables} and Beyond","text":"<0 ignored 0 top level function call 1 immediately occur >=2 throws errors <<- recover debugger gives global environment","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"lo-fi-debugging","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"lo-fi debugging","title":"Debugging in {rtables} and Beyond","text":"PRINT / CAT always low level debugging can used. helpful server jobs maybe terminal console output available browser() can used. example, can print position state function certain point untill find break point. comment blocks -> work pipes (can use identity() step nothing break pipes) browser() bombing","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"regression-tests","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Regression Tests","title":"Debugging in {rtables} and Beyond","text":"Almost every bug become regression test.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"debugging-with-pipes","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Debugging with Pipes","title":"Debugging in {rtables} and Beyond","text":"Pipes better write code horrible debug T pipe %T>% print midway debug_pipe() -> like T pipe going browser()","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"shiny-debugging","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Shiny Debugging","title":"Debugging in {rtables} and Beyond","text":"difficult due reactivity.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"general-suggestion","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"General Suggestion","title":"Debugging in {rtables} and Beyond","text":"CLEVER CODE - , CLEVER ALSO SUBJECTIVE CHANGE TIME.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_debug_rtables.html","id":"debugging-in-rtables","dir":"Articles > Dev-guide","previous_headings":"","what":"Debugging in rtables","title":"Debugging in {rtables} and Beyond","text":"invite smart developer use provided examples way get “interactive” dynamic view internal algorithms routinely executed constructing tables rtables. achieved using browser() debugonce() internal exported functions (rtables::: rtables::), see moment. invite continuously autonomously explore multiple S3 S4 objects constitute complexity power rtables. , use following functions: methods(generic_function): function lists methods available generic function. Specifically S4 generic functions, showMethods(generic_function) gives detailed information method (e.g. inheritance). class(object): function returns class object. class one built-classes R, can use information search documentation examples. help(class) may informative call documentation specific class. Similarly, ? operator bring documentation page different S4 methods. S3 methods necessary postfix class name dot (e.g. ?summary.lm). getClass(class): describes type class compact way, slots , relationships may classes may inherit inherited . getClass(object) can see values slots object assigned. possible use str(object, max.level = 2) see less formal compact descriptions slots, may problematic one objects class slots. Hence, maximum number levels always limited 2 3 (max.level = 2). Similarly, attributes() can used retrieve information, need remember storing important variables way encouraged. Information regarding type class can retrieved mode() indirectly summary() .S4(). *getAnywhere(function) useful get source code internal functions specific generics. works well S3 methods, display relevant namespace methods found. Similarly, getMethod(S4_generic, S4_class) can retrieve source code class-specific S4 methods. eval(debugcall(generic_function(obj))): useful way browse S4 method, specifically defined object, without manually insert browser() code. also possible similarly R > 3.4.0 debug*() calls can triggering signature (class) specified. modern simplified wrappers tracing function trace().","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_notes.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Sparse Notes on {rtables} Internals","text":"collection notes divided issues working document end dev vignette one day.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_notes.html","id":"section_div-notes","dir":"Articles > Dev-guide","previous_headings":"","what":"section_div notes","title":"Sparse Notes on {rtables} Internals","text":"Everything layout built split objects, reside 00_tabletrees.R. section_div defined internally split object child_section_div assigned NA_character default. needs split objects need separator divisor. Object-wise, virtual class Split contains section_div following subclasses. tagged “X” constructor allows section_div assigned value different NA_character, “NX” otherwise. can updated related layout functions. important, covered tests analyze split_rows_by. Now relevant understand information saved table object built build_table. need see present assigned. Let’s go back 00tabletree.Rand look trailing_section_div. classes definitions goes, notice search trailing_section_div present virtual classes TableRow VTableTree. following class hierarchy makes `trailing_section_div: Always check constructors finding classes. case example, DataRow ContentRow share constructor, need add identical getter setters two classes virtual class TableRow. Different story LabelRow needs handle differently. Now, understand two feature, lets see structure table built section dividers: , show trailing_section_div methods TableRow virtual object, LabelRow, VTableTree. three make whole section_div structure VTableTree present TableTree ElementaryTable two main table objects. NA_character_ section_div printed split divisions. LabelRow TableRow different assignment allows row-wise modification separators. special case ContentRow, represented content_table(obj) one-line ElementaryTable, label row turned . Please take moment check following setter: only_sep_sections parameter used change separators (splits) data rows. happening forcefully set TRUE, automatically activated section_div(tbl) <- char_v character vector length < nrow(tbl). Notice exception ContentRow activated switcher is_content_table. content rows visible label row. see main table structure change two blocks depending only_sep_sections. TRUE VTableTree modified leading split section separators modified. Also consider looking section_div getter tests test-accessors.R insights structure. Also understand exactly bound output, please check result make_row_df() column trailing_sep. Indeed, alternative iterative method used make_row_df retrieve information separators table row. trailing separator definition, added header_section_div function parameter basic_table, possibly add empty line header (e.g. header_section_div(tbl) = \" \"). trailing separator, separator added header. close circle, please check trailing_sep header_section_div propagated printed/used formatters::toString.","code":"library(rtables) ## Loading required package: formatters ## Loading required package: magrittr ## ## Attaching package: 'rtables' ## The following object is masked from 'package:utils': ## ## str getClass(\"Split\") ## Virtual Class \"Split\" [package \"rtables\"] ## ## Slots: ## ## Name: payload name split_label ## Class: ANY character character ## ## Name: split_format split_na_str split_label_position ## Class: FormatSpec character character ## ## Name: content_fun content_format content_na_str ## Class: listOrNULL FormatSpec character ## ## Name: content_var label_children extra_args ## Class: character logical list ## ## Name: indent_modifier content_indent_modifier content_extra_args ## Class: integer integer list ## ## Name: page_title_prefix child_section_div ## Class: character character ## ## Known Subclasses: ## Class \"CustomizableSplit\", directly ## Class \"AllSplit\", directly ## Class \"VarStaticCutSplit\", directly ## Class \"VarDynCutSplit\", directly ## Class \"VAnalyzeSplit\", directly ## Class \"CompoundSplit\", directly ## Class \"VarLevelSplit\", by class \"CustomizableSplit\", distance 2 ## Class \"MultiVarSplit\", by class \"CustomizableSplit\", distance 2 ## Class \"RootSplit\", by class \"AllSplit\", distance 2 ## Class \"ManualSplit\", by class \"AllSplit\", distance 2 ## Class \"CumulativeCutSplit\", by class \"VarStaticCutSplit\", distance 2 ## Class \"AnalyzeVarSplit\", by class \"VAnalyzeSplit\", distance 2 ## Class \"AnalyzeColVarSplit\", by class \"VAnalyzeSplit\", distance 2 ## Class \"AnalyzeMultiVars\", by class \"CompoundSplit\", distance 2 ## Class \"VarLevWBaselineSplit\", by class \"VarLevelSplit\", distance 3 # Known Subclasses: #? Class \"CustomizableSplit\", directly # vclass used for grouping different split types (I guess) # Class \"AllSplit\", directly # NX # Class \"VarStaticCutSplit\", directly # X via make_static_cut_split # Class \"VarDynCutSplit\", directly # X # Class \"VAnalyzeSplit\", directly # X #? Class \"CompoundSplit\", directly # Used only for AnalyzeMultiVars (maybe not needed?) # Class \"VarLevelSplit\", by class \"CustomizableSplit\", distance 2 # X # Class \"MultiVarSplit\", by class \"CustomizableSplit\", distance 2 # X # Class \"RootSplit\", by class \"AllSplit\", distance 2 # NX # Class \"ManualSplit\", by class \"AllSplit\", distance 2 # X # Class \"CumulativeCutSplit\", by class \"VarStaticCutSplit\", distance 2 # X via make_static_cut_split # Class \"AnalyzeVarSplit\", by class \"VAnalyzeSplit\", distance 2 # Virtual # Class \"AnalyzeColVarSplit\", by class \"VAnalyzeSplit\", distance 2 # X # Class \"AnalyzeMultiVars\", by class \"CompoundSplit\", distance 2 # X # Class \"VarLevWBaselineSplit\", by class \"VarLevelSplit\", distance 3 # NX getClass(\"TableRow\") ## Virtual Class \"TableRow\" [package \"rtables\"] ## ## Slots: ## ## Name: leaf_value var_analyzed label ## Class: ANY character character ## ## Name: row_footnotes trailing_section_div level ## Class: list character integer ## ## Name: name col_info format ## Class: character InstantiatedColumnInfo FormatSpec ## ## Name: na_str indent_modifier table_inset ## Class: character integer integer ## ## Extends: ## Class \"VLeaf\", directly ## Class \"VTableNodeInfo\", directly ## Class \"VNodeInfo\", by class \"VLeaf\", distance 2 ## ## Known Subclasses: \"DataRow\", \"ContentRow\", \"LabelRow\" # Extends: # Class \"VLeaf\", directly # Class \"VTableNodeInfo\", directly # Class \"VNodeInfo\", by class \"VLeaf\", distance 2 # # Known Subclasses: \"DataRow\", \"ContentRow\", \"LabelRow\" getClass(\"VTableTree\") ## Virtual Class \"VTableTree\" [package \"rtables\"] ## ## Slots: ## ## Name: children rowspans labelrow ## Class: list data.frame LabelRow ## ## Name: page_titles horizontal_sep header_section_div ## Class: character character character ## ## Name: trailing_section_div col_info format ## Class: character InstantiatedColumnInfo FormatSpec ## ## Name: na_str indent_modifier table_inset ## Class: character integer integer ## ## Name: level name main_title ## Class: integer character character ## ## Name: subtitles main_footer provenance_footer ## Class: character character character ## ## Extends: ## Class \"VTableNodeInfo\", directly ## Class \"VTree\", directly ## Class \"VTitleFooter\", directly ## Class \"VNodeInfo\", by class \"VTableNodeInfo\", distance 2 ## ## Known Subclasses: \"ElementaryTable\", \"TableTree\" # Extends: # Class \"VTableNodeInfo\", directly # Class \"VTree\", directly # Class \"VTitleFooter\", directly # Class \"VNodeInfo\", by class \"VTableNodeInfo\", distance 2 # # Known Subclasses: \"ElementaryTable\", \"TableTree\" lyt <- basic_table() %>% split_rows_by(\"ARM\", section_div = \"+\") %>% split_rows_by(\"STRATA1\", section_div = \"\") %>% analyze(\"AGE\", afun = function(x) list(\"Mean\" = mean(x), \"Standard deviation\" = sd(x)), format = list(\"Mean\" = \"xx.\", \"Standard deviation\" = \"xx.\"), section_div = \"~\") tbl <- build_table(lyt, DM) print(tbl) ## all obs ## ———————————————————————————————— ## A: Drug X ## A ## Mean 33 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ## B ## Mean 35 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ## C ## Mean 36 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 9 ## ++++++++++++++++++++++++++++++++ ## B: Placebo ## A ## Mean 32 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 ## ## B ## Mean 32 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 ## ## C ## Mean 34 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ++++++++++++++++++++++++++++++++ ## C: Combination ## A ## Mean 36 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ## B ## Mean 34 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 ## ## C ## Mean 34 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 print(class(tbl)) # TableTree ## [1] \"TableTree\" ## attr(,\"package\") ## [1] \"rtables\" # methods(\"trailing_section_div\") # to see this please do devtools::load_all() # [1] trailing_section_div,LabelRow-method # trailing_section_div,TableRow-method # trailing_section_div,VTableTree-method setMethod(\"section_div<-\", \"VTableTree\", function(obj, value, only_sep_sections = FALSE) { char_v <- as.character(value) tree_depths <- unname(vapply(collect_leaves(obj), tt_level, numeric(1))) max_tree_depth <- max(tree_depths) stopifnot(is.logical(only_sep_sections)) .check_char_vector_for_section_div(char_v, max_tree_depth, nrow(obj)) # Automatic establishment of intent if (length(char_v) < nrow(obj)) { only_sep_sections <- TRUE } # Case where only separators or splits need to change externally if (only_sep_sections && length(char_v) < nrow(obj)) { if (length(char_v) == 1) { char_v <- rep(char_v, max_tree_depth - 1) # -1 is the data row } # Case where char_v is longer than the max depth char_v <- char_v[seq_len(min(max_tree_depth, length(char_v)))] # Filling up with NAs the rest of the tree depth section div chr vector missing_char_v_len <- max_tree_depth - length(char_v) char_v <- c(char_v, rep(NA_character_, missing_char_v_len)) # char_v <- unlist( # lapply(tree_depths, function(tree_depth_i) char_v[seq_len(tree_depth_i)]), # use.names = FALSE # ) } # Retrieving if it is a contentRow (no need for labelrow to be visible in this case) content_row_tbl <- content_table(obj) is_content_table <- isS4(content_row_tbl) && nrow(content_row_tbl) > 0 # Main table structure change if (labelrow_visible(obj) || is_content_table) { if (only_sep_sections) { # Only tables are modified trailing_section_div(tt_labelrow(obj)) <- NA_character_ trailing_section_div(obj) <- char_v[1] section_div(tree_children(obj), only_sep_sections = only_sep_sections) <- char_v[-1] } else { # All leaves are modified trailing_section_div(tt_labelrow(obj)) <- char_v[1] trailing_section_div(obj) <- NA_character_ section_div(tree_children(obj), only_sep_sections = only_sep_sections) <- char_v[-1] } } else { section_div(tree_children(obj), only_sep_sections = only_sep_sections) <- char_v } obj })"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Split Machinery","text":"article intended use developers contain low-level explanations topics covered. user-friendly vignettes, please see Articles page rtables website. code prose appears version article main branch repository may reflect specific state things can less recent. guide describes important pieces split machinery unlikely change. Regardless, invite reader keep mind current repository code may drifted following material document, always best practice read code directly main. Please keep mind rtables still active development, seen efforts multiple contributors across different years. Therefore, may legacy mechanisms ongoing transformations look different future. working document may subjected deprecation updates, keep xxx comments indicate placeholders warnings -’s need work.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"introduction","dir":"Articles > Dev-guide","previous_headings":"","what":"Introduction","title":"Split Machinery","text":"scope article understanding rtables creates facets splitting incoming data hierarchical groups go root node singular rcells. latter level, also called leaf-level, contains final partition subjected analysis functions. details user perspective can found Split Functions vignette function documentation like ?split_rows_by ?split_funcs. following article describe split machinery works row domain. information split machinery works column domain covered separate article.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"process-and-methods","dir":"Articles > Dev-guide","previous_headings":"","what":"Process and Methods","title":"Split Machinery","text":"Beforehand, encourage reader familiarize Debugging {rtables} article rtables Developers Guide. document generally valid R programming, tailored study understand complex packages rely heavily S3 S4 object programming like rtables. , explore study split machinery growing amount complexity, following relevant functions methods throughout execution. going basic complex discussing important special cases, hope able give good understanding split machinery works. practice, majority split engine resides source file R/split_funs.R, occasional incursion R/make_split_fun.R custom split function creation rarer references general tabulation files.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"do_split","dir":"Articles > Dev-guide","previous_headings":"","what":"do_split","title":"Split Machinery","text":"split machinery fundamental rtables relevant functions like do_split executed even split requested. following example shows can enter do_split start understanding class hierarchy main split engine. following code, copied do_split function code allow reader go general structure enhanced comments sections. section code reflects roughly one section article. see input parameters used. important parameters spl df - split objects input data.frame, respectively.","code":"library(rtables) # debugonce(rtables:::do_split) # Uncomment me to enter the function!!! basic_table() %>% build_table(DM) ## all obs ## —————————— # rtables 0.6.2 ### NB This is called at EACH level of recursive splitting do_split <- function(spl, df, vals = NULL, labels = NULL, trim = FALSE, spl_context) { # - CHECKS - # ## This will error if, e.g., df does not have columns ## required by spl, or generally any time the split (spl) ## can not be applied to df check_validsplit(spl, df) # - SPLIT FUNCTION - # ## In special cases, we need to partition data (split) ## in a very specific way, e.g. depending on the data or ## external values. These can be achieved by using a custom ## split function. ## note the <- here!!! if (!is.null(splfun <- split_fun(spl))) { ## Currently split functions take df, vals, labels and ## return list(values = ..., datasplit = ..., labels = ...), ## with an optional additional 'extras' element if (func_takes(splfun, \".spl_context\")) { ret <- tryCatch( splfun(df, spl, vals, labels, trim = trim, .spl_context = spl_context ), error = function(e) e ) ## rawvalues(spl_context)) } else { ret <- tryCatch(splfun(df, spl, vals, labels, trim = trim), error = function(e) e ) } if (is(ret, \"error\")) { stop( \"Error applying custom split function: \", ret$message, \"\\n\\tsplit: \", class(spl), \" (\", payloadmsg(spl), \")\\n\", \"\\toccured at path: \", spl_context_to_disp_path(spl_context), \"\\n\" ) } } else { # - .apply_split_inner - # ## This is called when no split function is provided. Please note that this function ## will also probably be called when the split function is provided, as long as the ## main splitting method is not willingly modified by the split function. ret <- .apply_split_inner(df = df, spl = spl, vals = vals, labels = labels, trim = trim) } # - EXTRA - # ## this adds .ref_full and .in_ref_col if (is(spl, \"VarLevWBaselineSplit\")) { ret <- .add_ref_extras(spl, df, ret) } # - FIXUPVALS - # ## This: ## - guarantees that ret$values contains SplitValue objects ## - removes the extras element since its redundant after the above ## - ensures datasplit and values lists are named according to labels ## - ensures labels are character not factor ret <- .fixupvals(ret) # - RETURN - # ret }"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"checks-and-classes","dir":"Articles > Dev-guide","previous_headings":"do_split","what":"Checks and Classes","title":"Split Machinery","text":"start looking first function called do_split. give us good overview split defined. function , course, check function (check_validsplit) used verify split valid data. following describe split-class hierarchy step--step, invite reader explore well. Let’s first search package check_validsplit. find defined generic R/split_funs.R, applied following “split” classes: VarLevelSplit, MultiVarSplit, VAnalyzeSplit, CompoundSplit, Split. Another way find information, useful spread complicated objects, using showMethods(check_validsplit). virtual class VAnalyzeSplit (convention virtual classes start “V”) defines main parent analysis split discuss detail related vignette vignette() (xxx). , can see analyze() calls actually mimic split objects create different results specific final split (node). Now, notice check_validsplit also called another location, main R/tt_dotabulation.R source file. something related making “analyze” rows mainly checks VAnalyzeSplit. See Tabulation article details. discuss classes appear examples. See class hierarchy Table Hierarchy article. moment, see class(spl) (main do_split function) dealing AllSplit object. calling showMethods(check_validsplit) produce following: means listed classes dedicated definition check_validsplit may largely differ others. class AllSplit function definition inherited Split class. Therefore, understand AllSplit parent class Split. one first definitions virtual class package one include “V” prefix. classes defined along constructors R/00tabletrees.R. Reading AllSplit structured can useful understanding split objects expected work. Please see comments following: can also print information calling getClass(\"AllSplit\") general slot definition, calling getClass(spl). Note first call give also lot information class hierarchy. information regarding class hierarchy, please refer relevant article . discuss majority slots end document. Now, let’s see can find values described constructor within object. , show compact representation given str. multiple hierarchical slots contain objects , calling str much less informative maximum level nesting set (e.g. max.level = 2). Details slots become necessary future examples, deal time. Now, gave hint complex class hierarchy makes rtables, explore autonomously. Let’s go forward do_split. case, AllSplit inherited Split, sure called function following (read comment!):","code":"# rtables 0.6.2 Function: check_validsplit (package rtables) spl=\"AllSplit\" (inherited from: spl=\"Split\") spl=\"CompoundSplit\" spl=\"MultiVarSplit\" spl=\"Split\" spl=\"VAnalyzeSplit\" spl=\"VarLevelSplit\" # rtables 0.6.2 setClass(\"AllSplit\", contains = \"Split\") AllSplit <- function(split_label = \"\", cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = NULL, extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), ...) { if (is.null(split_name)) { # If the split has no name if (nzchar(split_label)) { # (std is \"\") split_name <- split_label } else { split_name <- \"all obs\" # No label, a standard split with all # observations is assigned. } } new(\"AllSplit\", split_label = split_label, content_fun = cfun, content_format = cformat, content_na_str = cna_str, split_format = split_format, split_na_str = split_na_str, name = split_name, label_children = FALSE, extra_args = extra_args, indent_modifier = as.integer(indent_mod), content_indent_modifier = as.integer(cindent_mod), content_var = cvar, split_label_position = \"hidden\", content_extra_args = cextra_args, page_title_prefix = NA_character_, child_section_div = NA_character_ ) } # rtables 0.6.2 Browse[2]> str(spl, max.level = 2) Formal class 'AllSplit' [package \"rtables\"] with 17 slots ..@ payload : NULL ..@ name : chr \"all obs\" ..@ split_label : chr \"\" ..@ split_format : NULL ..@ split_na_str : chr NA ..@ split_label_position : chr \"hidden\" ..@ content_fun : NULL ..@ content_format : NULL ..@ content_na_str : chr NA ..@ content_var : chr \"\" ..@ label_children : logi FALSE ..@ extra_args : list() ..@ indent_modifier : int 0 ..@ content_indent_modifier: int 0 ..@ content_extra_args : list() ..@ page_title_prefix : chr NA ..@ child_section_div : chr NA # rtables 0.6.2 ## Default does nothing, add methods as they become required setMethod( \"check_validsplit\", \"Split\", function(spl, df) invisible(NULL) )"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"split-functions-and--apply_split_inner","dir":"Articles > Dev-guide","previous_headings":"do_split","what":"Split Functions and .apply_split_inner","title":"Split Machinery","text":"diving custom split functions, need take moment analyze .apply_split_inner works. function routinely called whether split function. Let’s see case entering debugonce(.apply_split_inner). course, still currently browsing within do_split debug mode first example. print comment function following: reading .apply_split_inner, see fundamental functions - defined strictly internal use (convention start “.”) - generics depend kind split input. R/split_funs.R kind groups generic definitions beginning file. functions main dispatchers majority split machinery. clear example shows using S4 logic enables better clarity flexibility programming, allowing easy extension program. compactness also show showMethods result generic. Now, know .applysplit_extras function called first. specify vals therefore NULL. S4 generic function can seen showMethod(.applysplit_extras), definition can seen following: .applysplit_extras, simply extracts extra arguments split objects assigns relative split values. function covered detail later section. still split values available, function exit empty split. Otherwise, data divided different splits data subsets (facets) .applysplit_datapart. current example, resulting list comprises whole input dataset (getMethod(\".applysplit_datapart\", \"AllSplit\") list evident: function (spl, df, vals) list(df)). Next, split labels checked. present, split values (vals) used .applysplit_partlabels, transformed .character(vals) applied Split object. Otherwise, inserted labels checked names split values. Lastly, split values ordered according spl_child_order. case, concerns general AllSplit, sorting happen, .e. dependent simply number split values (seq_along(vals)).","code":"# rtables 0.6.2 .apply_split_inner <- function(spl, df, vals = NULL, labels = NULL, trim = FALSE) { # - INPUTS - # # In this case .applysplit_rawvals will attempt to find the split values if vals is NULL. # Please notice that there may be a non-mutually exclusive set or subset of elements that # will constitute the split. # - SPLIT VALS - # ## Try to calculate values first - most of the time we can if (is.null(vals)) { vals <- .applysplit_rawvals(spl, df) } # - EXTRA PARAMETERS - # # This call extracts extra parameters from the split, according to the split values extr <- .applysplit_extras(spl, df, vals) # If there are no values to do the split upon, we return an empty final split if (is.null(vals)) { return(list( values = list(), datasplit = list(), labels = list(), extras = list() )) } # - DATA SUBSETTING - # dpart <- .applysplit_datapart(spl, df, vals) # - LABEL RETRIEVAL - # if (is.null(labels)) { labels <- .applysplit_partlabels(spl, df, vals, labels) } else { stopifnot(names(labels) == names(vals)) } # - TRIM - # ## Get rid of columns that would not have any observations, ## but only if there were any rows to start with - if not ## we're in a manually constructed table column tree if (trim) { hasdata <- sapply(dpart, function(x) nrow(x) > 0) if (nrow(df) > 0 && length(dpart) > sum(hasdata)) { # some empties dpart <- dpart[hasdata] vals <- vals[hasdata] extr <- extr[hasdata] labels <- labels[hasdata] } } # - ORDER RESULTS - # # Finds relevant order depending on spl_child_order() if (is.null(spl_child_order(spl)) || is(spl, \"AllSplit\")) { vord <- seq_along(vals) } else { vord <- match( spl_child_order(spl), vals ) vord <- vord[!is.na(vord)] } ## FIXME: should be an S4 object, not a list ret <- list( values = vals[vord], datasplit = dpart[vord], labels = labels[vord], extras = extr[vord] ) ret } # rtables 0.6.2 # Retrieves the values that will constitute the splits (facets), not necessarily a unique list. # They could come from the data cuts for example -> it can be anything that produces a set of strings. setGeneric( \".applysplit_rawvals\", function(spl, df) standardGeneric(\".applysplit_rawvals\") ) # Browse[2]> showMethods(.applysplit_rawvals) # Function: .applysplit_rawvals (package rtables) # spl=\"AllSplit\" # spl=\"ManualSplit\" # spl=\"MultiVarSplit\" # spl=\"VAnalyzeSplit\" # spl=\"VarLevelSplit\" # spl=\"VarStaticCutSplit\" # Nothing here is inherited from the virtual class Split!!! # Contains the subset of the data (default, but these can overlap and can also NOT be mutually exclusive). setGeneric( \".applysplit_datapart\", function(spl, df, vals) standardGeneric(\".applysplit_datapart\") ) # Same as .applysplit_rawvals # Extract the extra parameter for the split setGeneric( \".applysplit_extras\", function(spl, df, vals) standardGeneric(\".applysplit_extras\") ) # Browse[2]> showMethods(.applysplit_extras) # Function: .applysplit_extras (package rtables) # spl=\"AllSplit\" # (inherited from: spl=\"Split\") # spl=\"Split\" # This means there is only a function for the virtual class Split. # So all splits behave the same!!! # Split label retrieval and assignment if visible. setGeneric( \".applysplit_partlabels\", function(spl, df, vals, labels) standardGeneric(\".applysplit_partlabels\") ) # Browse[2]> showMethods(.applysplit_partlabels) # Function: .applysplit_partlabels (package rtables) # spl=\"AllSplit\" # (inherited from: spl=\"Split\") # spl=\"MultiVarSplit\" # spl=\"Split\" # spl=\"VarLevelSplit\" setGeneric( \"check_validsplit\", # our friend function(spl, df) standardGeneric(\"check_validsplit\") ) # Note: check_validsplit is an internal function but may one day be exported. # This is why it does not have the \".\" prefix. setGeneric( \".applysplit_ref_vals\", function(spl, df, vals) standardGeneric(\".applysplit_ref_vals\") ) # Browse[2]> showMethods(.applysplit_ref_vals) # Function: .applysplit_ref_vals (package rtables) # spl=\"Split\" # spl=\"VarLevWBaselineSplit\" # rtables 0.6.2 Browse[3]> getMethod(\".applysplit_rawvals\", \"AllSplit\") Method Definition: function (spl, df) obj_name(spl) Signatures: spl target \"AllSplit\" defined \"AllSplit\" # What is obj_name -> slot in spl Browse[3]> obj_name(spl) [1] \"all obs\" # coming from Browse[3]> getMethod(\"obj_name\", \"Split\") Method Definition: function (obj) obj@name ##### Slot that we could see from str(spl, max.level = 2) Signatures: obj target \"Split\" defined \"Split\""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"a-simple-split","dir":"Articles > Dev-guide","previous_headings":"","what":"A Simple Split","title":"Split Machinery","text":"following, demonstrate row splits work using features already described. add two splits see behavior do_split changes. Note add analyze call split behave , giving empty table observations. default, calling analyze variable calculate mean data subset generated splits. want go beyond first call do_split design applied observations, purpose generating root split contains data splits (indeed AllSplit). achieve use debug(rtables:::do_split) instead debugonce(rtables:::do_split) need step splits. Alternatively, possible use powerful trace function enter cases input specific class. , following can used: trace(\"do_split\", quote((!(spl, \"AllSplit\")) browser()), = asNamespace(\"rtables\")). Note specify namespace . Multiple tracer elements can added expression(E1, E2), c(quote(E1), quote(E2)). Specific steps can specified parameter. Remember call untrace(\"do_split\", quote((!(spl, \"AllSplit\")) browser()), = asNamespace(\"rtables\")) finished remove trace. continuing, want check formal class spl. , can directly infer class different now (VarLevelSplit) understand split label hidden (split_label_position slot). Moreover, see specific value order specific split values. VarLevelSplit also seems three slots AllSplit. precisely? Remember always check constructor class definition R/00tabletrees.R exploratory tools suffice. Now, check_validsplit(spl, df) use different method (getMethod(\"check_validsplit\", \"VarLevelSplit\")). uses internal utility function .checkvarsok check vars, .e. payload, actually present names(df). next relevant function .apply_split_inner, exactly changes using debugonce(.apply_split_inner). course, function called directly custom split function provided. Since parameter vals specified (NULL), split values retrieved df using split payload select specific columns (varvec <- df[[spl_payload(spl)]]). Whenever split values specified retrieved selected column unique values (character) levels (factor). Next, .applysplit_datapart creates named list facets data subsets. case, result actually mutually exclusive partition data. specify split values column content retrieved via unique (case character vector) levels (case factors). .applysplit_partlabels bit less linear take account possibility specified labels payload. Instead looking function source code getMethod(\".applysplit_partlabels\", \"VarLevelSplit\"), can enter S4 generic function debugging mode follows: case, final labels vals explicitly assigned. order retrieved split object (spl_child_order(spl)) matched current split values. returned list processed . continue next call do_split, procedure followed second ARM split. applied partition created first split. main df now constituted subset (facet) total data, determined first split. repeated iteratively many data splits requested. concluding iteration, take moment discuss detail .fixupvals(partinfo) works. generic function source code can easily accessed. suggest running debugonce(.fixupvals) understand practice. fundamental aspects .fixupvals(partinfo) follows: Ensures labels character factor. Ensures splits data list values named according labels. Guarantees ret$values contains SplitValue objects. Removes list element extra since now included SplitValue. Note function can occasionally called return object (named list now). course, first call checks applied.","code":"# rtables 0.6.2 library(rtables) library(dplyr) # This filter is added to avoid having too many calls to do_split DM_tmp <- DM %>% filter(ARM %in% names(table(DM$ARM)[1:2])) %>% # limit to two filter(SEX %in% c(\"M\", \"F\")) %>% # limit to two mutate(SEX = factor(SEX), ARM = factor(ARM)) # to drop unused levels # debug(rtables:::do_split) lyt <- basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(\"BMRKR1\") # analyze() is needed for the table to have non-label rows lyt %>% build_table(DM_tmp) ## all obs ## ———————————————————— ## A: Drug X ## F ## Mean 6.06 ## M ## Mean 5.42 ## B: Placebo ## F ## Mean 6.24 ## M ## Mean 5.97 # undebug(rtables:::do_split) # rtables 0.6.2 Browse[2]> str(spl, max.level = 2) Formal class 'VarLevelSplit' [package \"rtables\"] with 20 slots ..@ value_label_var : chr \"ARM\" ..@ value_order : chr [1:2] \"A: Drug X\" \"B: Placebo\" ..@ split_fun : NULL ..@ payload : chr \"ARM\" ..@ name : chr \"ARM\" ..@ split_label : chr \"ARM\" ..@ split_format : NULL ..@ split_na_str : chr NA ..@ split_label_position : chr \"hidden\" ..@ content_fun : NULL ..@ content_format : NULL ..@ content_na_str : chr NA ..@ content_var : chr \"\" ..@ label_children : logi NA ..@ extra_args : list() ..@ indent_modifier : int 0 ..@ content_indent_modifier: int 0 ..@ content_extra_args : list() ..@ page_title_prefix : chr NA ..@ child_section_div : chr NA # rtables 0.6.2 slots_as <- getSlots(\"AllSplit\") # inherits virtual class Split and is general class for all splits # getClass(\"CustomizableSplit\") # -> Extends: \"Split\", Known Subclasses: Class \"VarLevelSplit\", directly slots_cs <- getSlots(\"CustomizableSplit\") # Adds split function slots_vls <- getSlots(\"VarLevelSplit\") slots_cs[!(names(slots_cs) %in% names(slots_as))] # split_fun # \"functionOrNULL\" slots_vls[!(names(slots_vls) %in% names(slots_cs))] # value_label_var value_order # \"character\" \"ANY\" # rtables 0.6.2 eval(debugcall(.applysplit_partlabels(spl, df, vals, labels))) # We leave to the smart developer to see how the labels are assigned # Remember to undebugcall() similarly! # rtables 0.6.2 # Can find the following core function: # vals <- make_splvalue_vec(vals, extr, labels = labels) # ---> Main list of SplitValue objects: iterative call of # new(\"SplitValue\", value = val, extra = extr, label = label) # Structure of ret before calling .fixupvals Browse[2]> str(ret, max.level = 2) List of 4 $ values : chr [1:2] \"A: Drug X\" \"B: Placebo\" $ datasplit:List of 2 ..$ A: Drug X : tibble [121 × 8] (S3: tbl_df/tbl/data.frame) ..$ B: Placebo: tibble [106 × 8] (S3: tbl_df/tbl/data.frame) $ labels : Named chr [1:2] \"A: Drug X\" \"B: Placebo\" ..- attr(*, \"names\")= chr [1:2] \"A: Drug X\" \"B: Placebo\" $ extras :List of 2 ..$ : list() ..$ : list() # Structure of ret after the function call Browse[2]> str(.fixupvals(ret), max.level = 2) List of 3 $ values :List of 2 ..$ A: Drug X :Formal class 'SplitValue' [package \"rtables\"] with 3 slots ..$ B: Placebo:Formal class 'SplitValue' [package \"rtables\"] with 3 slots $ datasplit:List of 2 ..$ A: Drug X : tibble [121 × 8] (S3: tbl_df/tbl/data.frame) ..$ B: Placebo: tibble [106 × 8] (S3: tbl_df/tbl/data.frame) $ labels : Named chr [1:2] \"A: Drug X\" \"B: Placebo\" ..- attr(*, \"names\")= chr [1:2] \"A: Drug X\" \"B: Placebo\" # The SplitValue object is fundamental Browse[2]> str(ret$values) List of 2 $ A: Drug X :Formal class 'SplitValue' [package \"rtables\"] with 3 slots .. ..@ extra: list() .. ..@ value: chr \"A: Drug X\" .. ..@ label: chr \"A: Drug X\" $ B: Placebo:Formal class 'SplitValue' [package \"rtables\"] with 3 slots .. ..@ extra: list() .. ..@ value: chr \"B: Placebo\" .. ..@ label: chr \"B: Placebo\""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"pre-made-split-functions","dir":"Articles > Dev-guide","previous_headings":"A Simple Split","what":"Pre-Made Split Functions","title":"Split Machinery","text":"start examining split function already defined rtables. scope filtering specific values follows: root split, enter split based SEX. specified split function, can retrieve split function using splfun <- split_fun(spl) enter -else statement two possible cases: whether split context . cases, error catching framework used give informative errors case failure. Later see depth works. invite reader always keep eye spl_context, fundamental sophisticated splits, e.g. cases split depends mainly preceding splits values. split function called, please take moment look drop_split_levels defined. see function fundamentally wrapper .apply_split_inner drops empty factor levels, therefore avoiding empty splits. many pre-made split functions included rtables. list functions can found Split Functions vignette, via ?split_funcs. leave developer look split functions work, particular trim_levels_to_map may interest.","code":"library(rtables) # debug(rtables:::do_split) # uncomment to see into the main split function basic_table() %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"BMRKR1\") %>% build_table(DM) ## all obs ## ———————————————— ## F ## Mean 6.04 ## M ## Mean 5.64 # undebug(rtables:::do_split) # This produces the same output as before (when filters were used) # rtables 0.6.2 > drop_split_levels function(df, spl, vals = NULL, labels = NULL, trim = FALSE) { # Retrieve split column var <- spl_payload(spl) df2 <- df ## This call is exactly the one we used when filtering to get rid of empty levels df2[[var]] <- factor(df[[var]]) ## Our main function! .apply_split_inner(spl, df2, vals = vals, labels = labels, trim = trim ) }"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"creating-custom-split-functions","dir":"Articles > Dev-guide","previous_headings":"A Simple Split","what":"Creating Custom Split Functions","title":"Split Machinery","text":"Now create custom split function. Firstly, see system manages error messages. general understanding custom split functions created, please read Custom Split Functions section Advanced Usage vignette see ?custom_split_funs. following code use browser() enter custom split functions. invite reader activate options(error = recover) investigate cases encounter error. Note can revert default behavior restarting R session, caching default option value, using callr retrieve default follows: default_opts <- callr::r(function(){options()}); options(error = default_opts$error). commented debugging lines allow inspect error. Alternatively, using recover option allow possibility select frame number, .e. trace level, enter. Selecting last frame number (10 case) allow see value ret rtables:::do_split causes error informative error message follows created. previous split function fails exploratory_split_fun given arguments accepts. simple way avoid add ... function call. Now let’s construct interesting split function (error): Now take moment dwell machinery included rtables create custom split functions. , please read relevant documentation ?make_split_fun. pre-made split functions included rtables written make_split_fun stable constructor functions previously used. invite reader take look make_split_fun.R. majority functions understandable knowledge gained guide far. important note core split function specified, commonly case, make_split_fun calls do_base_split directly, minimal wrapper well-known do_split. drop_facet_levels, example, pre-processing function core simply removes empty factor levels split “column”, thus avoiding showing empty lines. also possible provide list functions, can seen examples ?make_split_fun. Note pre- post-processing requires list input support possibility combining multiple functions. contrast, core splitting function must single function call expected stacked features. rarely needs modified majority included split functions work pre- post-processing. Included post-processing functions interesting interact split object, e.g. reordering facets adding overall facet (add_overall_facet). attentive reader noticed core function relies do_split many post-processing functions rely make_split_result, best way get correct split return structure. Note modifying core split works row space moment.","code":"# rtables 0.6.2 # Table call with only the function changing simple_table <- function(DM, f) { lyt <- basic_table() %>% split_rows_by(\"ARM\", split_fun = f) %>% analyze(\"BMRKR1\") lyt %>% build_table(DM) } # First round will fail because there are unused arguments exploratory_split_fun <- function(df, spl) NULL # debug(rtables:::do_split) err_msg <- tryCatch(simple_table(DM, exploratory_split_fun), error = function(e) e) # undebug(rtables:::do_split) message(err_msg$message) ## Error applying custom split function: unused arguments (vals, labels, trim = trim) ## split: VarLevelSplit (ARM) ## occured at path: root # rtables 0.6.2 # Debugging level 10: tt_dotabulation.R#627: do_split(spl, df, spl_context = spl_context) # Original call and final error > simple_table(DM, exploratory_split_fun) Error in do_split(spl, df, spl_context = spl_context) : Error applying custom split function: unused arguments (vals, labels, trim = trim) # This is main error split: VarLevelSplit (ARM) # Split reference occured at path: root # Path level (where it occurred) # rtables 0.6.2 f_brakes_if <- function(split_col = NULL, error = FALSE) { function(df, spl, ...) { # order matters! more than naming # browser() # To check how it works if (is.null(split_col)) { # Retrieves the default split_col <- spl_variable(spl) # Internal accessor to split obj } my_payload <- split_col # Changing split column value vals <- levels(df[[my_payload]]) # Extracting values to split datasplit <- lapply(seq_along(vals), function(i) { df[df[[my_payload]] == vals[[i]], ] }) names(datasplit) <- as.character(vals) # Error if (isTRUE(error)) { # browser() # If you need to check how it works mystery_error_values <- sapply(datasplit, function(x) mean(x$BMRKR1)) if (any(mystery_error_values > 6)) { stop( \"It should not be more than 6! Should it be? Found in split values: \", names(datasplit)[which(mystery_error_values > 6)] ) } } # Handy function to return a split result!! make_split_result(vals, datasplit, vals) } } simple_table(DM, f_brakes_if()) # works! ## all obs ## ———————————————————————— ## A: Drug X ## Mean 5.79 ## B: Placebo ## Mean 6.11 ## C: Combination ## Mean 5.69 simple_table(DM, f_brakes_if(split_col = \"STRATA1\")) # works! ## all obs ## ———————————————— ## A ## Mean 5.95 ## B ## Mean 5.90 ## C ## Mean 5.71 # simple_table(DM, f_brakes_if(error = TRUE)) # does not work, but returns an informative message # Error in do_split(spl, df, spl_context = spl_context) : # Error applying custom split function: It should not be more than 6! Should it be? Found in split values: B: Placebo # split: VarLevelSplit (ARM) # occurred at path: root"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"spl_context---adding-context-to-our-splits","dir":"Articles > Dev-guide","previous_headings":"A Simple Split > Creating Custom Split Functions","what":".spl_context - Adding Context to Our Splits","title":"Split Machinery","text":"best way understand split context , use , read Leveraging .spl_context section Advanced Usage vignette, use browser() within split function see structured. .spl_context needed rewriting core functions, propose wrapper do_base_split , handy redirection standard do_split without split function part (.e. wrapper .apply_split_inner, real core splitting machinery). curiosity, set trim = TRUE . trimming works mixed table (values 0s content), trim 0s. rarely case, encourage using replacement functions trim_levels_to_group trim_levels_to_map trimming. Nowadays, even impossible set differently trim = FALSE. (write issue informative error list xxx). can see split column variable (split, first column) level splitting procedure. value current split value dealt . next column, let’s see number rows data frames: sapply(.spl_context$full_parent_df, nrow) # [1] 356 121 36 36. Indeed, root level contains full input data frame, levels subgroups full data according split value. all_cols_n shows exactly numbers just described. obs current filter applied columns. Applying root data (row subgroup data) reveals current column-wise facet (row-wise row split). also possible use information make complex splits column space using full data frame value splits select interested values. something change simplify within rtables need becomes apparent.","code":"# rtables 0.6.2 browsing_f <- function(df, spl, .spl_context, ...) { # browser() # do_base_split(df, spl, ...) # order matters!! This would fail if done do_base_split(spl = spl, df = df, vals = NULL, labels = NULL, trim = TRUE) } fnc_tmp <- function(innervar) { # Exploring trim_levels_in_facets (check its form) function(ret, ...) { # browser() for (var in innervar) { # of course AGE is not here, so nothing is dropped!! ret$datasplit <- lapply(ret$datasplit, function(df) { df[[var]] <- factor(df[[var]]) df }) } ret } } basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"STRATA1\") %>% split_rows_by_cuts(\"AGE\", cuts = c(0, 50, 100), cutlabels = c(\"young\", \"old\") ) %>% split_rows_by(\"SEX\", split_fun = make_split_fun( pre = list(drop_facet_levels), # This is dropping the SEX levels (AGE is upper level) core_split = browsing_f, post = list(fnc_tmp(\"AGE\")) # To drop these we should use a split_fun in the above level )) %>% summarize_row_groups() %>% build_table(DM) # The following is the .spl_contest printout: Browse[1]> .spl_context split value full_parent_df all_cols_n all obs 1 root root c(\"S1\", .... 356 TRUE, TR.... 2 ARM A: Drug X c(\"S6\", .... 121 TRUE, TR.... 3 STRATA1 A c(\"S14\",.... 36 TRUE, TR.... 4 AGE young c(\"S14\",.... 36 TRUE, TR.... # NOTE: make_split_fun(pre = list(drop_facet_levels)) and drop_split_levels # do the same thing in this case"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"extra-arguments-extra_args","dir":"Articles > Dev-guide","previous_headings":"A Simple Split","what":"Extra Arguments: extra_args","title":"Split Machinery","text":"functionality well-known used setting analysis functions (somewhat complicated example can found Example Complex Analysis Function vignette), show can also apply splits. demonstrated, seem like impossible cases considered vestigial deprecated.","code":"# rtables 0.6.2 # Let's use the tracer!! my_tracer <- quote(if (length(spl@extra_args) > 0) browser()) trace( what = \"do_split\", tracer = my_tracer, where = asNamespace(\"rtables\") ) custom_mean_var <- function(var) { function(df, labelstr, na.rm = FALSE, ...) { # browser() mean(df[[var]], na.rm = na.rm) } } DM_ageNA <- DM DM_ageNA$AGE[1] <- NA basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% summarize_row_groups( cfun = custom_mean_var(\"AGE\"), extra_args = list(na.rm = TRUE), format = \"xx.x\", label_fstr = \"label %s\" ) %>% # content_extra_args, c_extra_args are different slots!! (xxx) split_rows_by(\"STRATA1\", split_fun = keep_split_levels(\"A\")) %>% analyze(\"AGE\") %>% # check with the extra_args (xxx) build_table(DM_ageNA) # You can pass extra_args down to other splits. It is possible this will not not # work. Should it? That is why extra_args lives only in splits (xxx) check if it works # as is. Difficult to find an use case for this. Maybe it could work for the ref_group # info. That does not work with nesting already (fairly sure that it will break stuff). # Does it make sense to have more than one ref_group at any point of the analysis? No docs, # send a warning if users try to nest things with ref_group (that is passed around via # extra_args) # As we can see that was not possible. What if we now force it a bit? my_split_fun <- function(df, spl, .spl_context, ...) { spl@extra_args <- list(na.rm = TRUE) # does not work because do_split is not changing the object # the split does not do anything with it drop_split_levels(df, spl) } # does not work basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = my_split_fun) %>% analyze(\"AGE\", inclNAs = TRUE, afun = mean) %>% # include_NAs is set FALSE build_table(DM_ageNA) # extra_args is in available in cols but not in rows, because different columns # may need it for different col space. Row-wise it seems not necessary. # The only thing that works is adding it to analyze (xxx) check if it is worth adding # We invite the developer now to test all the test files of this package with the tracer on # therefore -> extra_args is not currently used in splits (xxx could be wrong) # could be not being hooked up untrace(what = \"do_split\", where = asNamespace(\"rtables\")) # Let's try with the other variables identically my_tracer <- quote(if (!is.null(vals) || !is.null(labels) || isTRUE(trim)) { print(\"A LOT TO SAY\") message(\"CANT BLOCK US ALL\") stop(\"NOW FOR SURE\") browser() }) trace( what = \"do_split\", tracer = my_tracer, where = asNamespace(\"rtables\") ) # Run tests by copying the above in setup-fakedata.R (then devtools::test()) untrace( what = \"do_split\", where = asNamespace(\"rtables\") )"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_split_machinery.html","id":"multivarsplit-compoundsplit-examples","dir":"Articles > Dev-guide","previous_headings":"","what":"MultiVarSplit & CompoundSplit Examples","title":"Split Machinery","text":"final part article still construction, hence non-specific mentions list. xxx CompoundSplit generates facets one variable (e.g. cumulative distributions) MultiVarSplit uses different variables split. See AnalyzeMultiVars, inherits CompoundSplit details analyzes facets multiple times. MultiVarColSplit works analyze_colvars, scope article. .set_kids_sect_sep adds things children (can set split). First, want see MultiVarSplit class behaves example case taken ?split_rows_by_multivar. print output, notice two groups (one called “SEX” “STRATA1”) identical along columns. subgroup actually created. interesting way personalize splits help custom split functions split context, widely different subgroups table. invite reader try understand split_rows_by_multivar can row splits (see xxx comment previous code), split_cols_by_multivar . known bug moment, work towards fix . Known issues often linked source code GitHub issue number (e.g. #690). Lastly, briefly show example split cut function replace solve empty age groups problem . propose simplified situation: row split cases (*_cuts *_cutfun), empty levels dropped. expected can avoided using dedicated split function. Intentionally looking future split possible order determine element present . moment possible add spl_fun dedicated split functions like split_rows_by_cuts. Note previous table used summarize_row_groups, analyze calls. rendered table nicely, standard method use summarize_row_groups intended decorate row groups, .e. rows labels. Internally, rows called content rows analysis functions summarize_row_groups called cfun instead afun. Indeed, tabulation machinery also presents two differently described Tabulation Row Structure section Tabulation vignette. can try construct split function cuts manually make_split_fun: Alternatively, choose prune rows prune_table! add pre-proc z-scoring","code":"# rtables 0.6.2 my_tracer <- quote(if (is(spl, \"MultiVarSplit\")) browser()) trace( what = \"do_split\", tracer = my_tracer, where = asNamespace(\"rtables\") ) # We want also to take a look at the following: debugonce(rtables:::.apply_split_inner) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by_multivar(c(\"BMRKR1\", \"BMRKR1\"), varlabels = c(\"SD\", \"MEAN\") ) %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(\"PAK\") ) %>% # xxx for #690 #691 summarize_row_groups() %>% analyze(c(\"AGE\", \"SEX\")) build_table(lyt, DM) # xxx check empty space on top -> check if it is a bug, file it untrace( what = \"do_split\", where = asNamespace(\"rtables\") ) # rtables 0.6.2 cutfun <- function(x) { # browser() cutpoints <- c(0, 50, 100) names(cutpoints) <- c(\"\", \"Younger\", \"Older\") cutpoints } tbl <- basic_table(show_colcounts = TRUE) %>% split_rows_by(\"ARM\", split_fun = drop_and_remove_levels(c(\"B: Placebo\", \"C: Combination\"))) %>% split_rows_by(\"STRATA1\") %>% split_rows_by_cutfun(\"AGE\", cutfun = cutfun) %>% # split_rows_by_cuts(\"AGE\", cuts = c(0, 50, 100), # cutlabels = c(\"young\", \"old\")) %>% # Works the same split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% # This is degenerate!!! build_table(DM) tbl ## all obs ## (N=356) ## ————————————————————————— ## A: Drug X ## A ## AGE ## Younger ## F 22 (6.2%) ## M 14 (3.9%) ## Older ## B ## AGE ## Younger ## F 26 (7.3%) ## M 14 (3.9%) ## Older ## F 1 (0.3%) ## C ## AGE ## Younger ## F 19 (5.3%) ## M 21 (5.9%) ## Older ## F 2 (0.6%) ## M 2 (0.6%) my_count_afun <- function(x, .N_col, .spl_context, ...) { # browser() out <- list(c(length(x), length(x) / .N_col)) names(out) <- .spl_context$value[nrow(.spl_context)] # workaround (xxx #689) in_rows( .list = out, .formats = c(\"xx (xx.x%)\") ) } # ?make_split_fun # To check for docs/examples # Core split cuts_core <- function(spl, df, vals, labels, .spl_context) { # browser() # file an issue xxx # variables that are split on are converted to factor during the original clean-up # cut split are not doing it but it is an exception. xxx # young_v <- as.numeric(df[[\"AGE\"]]) < 50 # current solution: young_v <- as.numeric(as.character(df[[\"AGE\"]])) < 50 make_split_result(c(\"young\", \"old\"), datasplit = list(df[young_v, ], df[!young_v, ]), labels = c(\"Younger\", \"Older\") ) } drop_empties <- function(splret, spl, fulldf, ...) { # browser() nrows_data_split <- vapply(splret$datasplit, nrow, numeric(1)) to_keep <- nrows_data_split > 0 make_split_result( splret$values[to_keep], splret$datasplit[to_keep], splret$labels[to_keep] ) } gen_split <- make_split_fun( core_split = cuts_core, post = list(drop_empties) ) tbl <- basic_table(show_colcounts = TRUE) %>% split_rows_by(\"ARM\", split_fun = keep_split_levels(c(\"A: Drug X\"))) %>% split_rows_by(\"STRATA1\") %>% split_rows_by(\"AGE\", split_fun = gen_split) %>% analyze(\"SEX\") %>% # It is the last step!! No need of BMRKR1 right? # split_rows_by(\"SEX\", split_fun = drop_split_levels, # child_labels = \"hidden\") %>% # close issue #689. would it work for # analyze_colvars? probably (xxx) # analyze(\"BMRKR1\", afun = my_count_afun) %>% # This is NOT degenerate!!! BMRKR1 is only placeholder build_table(DM) tbl # rtables 0.6.2 tbl <- basic_table(show_colcounts = TRUE) %>% split_rows_by(\"ARM\", split_fun = keep_split_levels(c(\"A: Drug X\"))) %>% split_rows_by(\"STRATA1\") %>% split_rows_by_cuts( \"AGE\", cuts = c(0, 50, 100), cutlabels = c(\"young\", \"old\") ) %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% # This is degenerate!!! # we keep it until #689 build_table(DM) tbl ## all obs ## (N=356) ## ————————————————————— ## A: Drug X ## A ## young ## F 22 (6.2%) ## M 14 (3.9%) ## old ## B ## young ## F 26 (7.3%) ## M 14 (3.9%) ## old ## F 1 (0.3%) ## C ## young ## F 19 (5.3%) ## M 21 (5.9%) ## old ## F 2 (0.6%) ## M 2 (0.6%) # Trying with pruning prune_table(tbl) # (xxx) what is going on here? it is degenerate so it has no real leaves ## NULL # It is degenerate -> what to do? # The same mechanism is applied in the case of NULL leaves, they are rolled up in the # table tree"},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Table Hierarchy","text":"article intended use developers contain low-level explanations topics covered. user-friendly vignettes, please see Articles page rtables website. code prose appears version article main branch repository may reflect specific state things can less recent. guide describes important aspects table hierarchy unlikely change. Regardless, invite reader keep mind current repository code may drifted following material document, always best practice read code directly main. Please keep mind rtables still active development, seen efforts multiple contributors across different years. Therefore, may legacy mechanisms ongoing transformations look different future.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"introduction","dir":"Articles > Dev-guide","previous_headings":"","what":"Introduction","title":"Table Hierarchy","text":"scope vignette understand structure rtable objects, class hierarchy exploration tree structures S4 objects. Exploring table structure enables better understanding rtables concepts split machinery, tabulation, pagination export. details user’s perspective table structure can found relevant vignettes. isS4 getclass - class structure","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"process-and-methods","dir":"Articles > Dev-guide","previous_headings":"","what":"Process and Methods","title":"Table Hierarchy","text":"invite developers use provided examples interactively explore rtables hierarchy. helpful command ‘getClass’ list slots associated class, addition related classes relative distances.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"table-representation","dir":"Articles > Dev-guide","previous_headings":"","what":"Table Representation","title":"Table Hierarchy","text":"“PredataAxisLayout” class used define data subset instructions tabulation. 2 subclasses (one axis): PredataColLayout, PredataRowLayout","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"content-summary-row-groups","dir":"Articles > Dev-guide","previous_headings":"","what":"Content (summary row groups)","title":"Table Hierarchy","text":"Splits core functionality rtables tabulation calculations often required subsets data.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"split-machinery","dir":"Articles > Dev-guide","previous_headings":"","what":"Split Machinery","title":"Table Hierarchy","text":"“TreePos” class contains split information list splits, split label values, subsets data generated split. AllSplit RootSplit MultiVarSplit VarStaticCutSplit CumulativeCutSplit VarDynCutSplit CompoundSplit VarLevWBaselineSplit highest level table hierarchy belong “TableTree”. code identifies slots associated class. S4 object, slots can accessed using “@” (similar use “$” list objects). ’ll notice classes fall “Extends”. classes contained relationship TableTree object “virtual” classes. avoid repetition slots carrying data (set slots example) multiple classes may need, rtables extensively uses virtual classes. virtual class instantiated, purpose classes inherit information .","code":"library(rtables) getClass(\"TreePos\") ## Class \"TreePos\" [package \"rtables\"] ## ## Slots: ## ## Name: splits s_values sval_labels subset ## Class: list list character SubsetDef getClass(\"TableTree\") ## Class \"TableTree\" [package \"rtables\"] ## ## Slots: ## ## Name: content page_title_prefix children ## Class: ElementaryTable character list ## ## Name: rowspans labelrow page_titles ## Class: data.frame LabelRow character ## ## Name: horizontal_sep header_section_div trailing_section_div ## Class: character character character ## ## Name: col_info format na_str ## Class: InstantiatedColumnInfo FormatSpec character ## ## Name: indent_modifier table_inset level ## Class: integer integer integer ## ## Name: name main_title subtitles ## Class: character character character ## ## Name: main_footer provenance_footer ## Class: character character ## ## Extends: ## Class \"VTableTree\", directly ## Class \"VTableNodeInfo\", by class \"VTableTree\", distance 2 ## Class \"VTree\", by class \"VTableTree\", distance 2 ## Class \"VTitleFooter\", by class \"VTableTree\", distance 2 ## Class \"VNodeInfo\", by class \"VTableTree\", distance 3 lyt <- basic_table(title = \"big title\") %>% split_rows_by(\"SEX\", page_by = TRUE) %>% analyze(\"AGE\") tt <- build_table(lyt, DM) # Though we don't recommend using str for studying rtable objects, # we do find it useful in this instance to visualize the parent/child relationships. str(tt, max.level=2) ## Formal class 'TableTree' [package \"rtables\"] with 20 slots ## ..@ content :Formal class 'ElementaryTable' [package \"rtables\"] with 19 slots ## ..@ page_title_prefix : chr \"SEX\" ## ..@ children :List of 4 ## ..@ rowspans :'data.frame': 0 obs. of 0 variables ## ..@ labelrow :Formal class 'LabelRow' [package \"rtables\"] with 13 slots ## ..@ page_titles : chr(0) ## ..@ horizontal_sep : chr \"—\" ## ..@ header_section_div : chr NA ## ..@ trailing_section_div: chr NA ## ..@ col_info :Formal class 'InstantiatedColumnInfo' [package \"rtables\"] with 9 slots ## ..@ format : NULL ## ..@ na_str : chr NA ## ..@ indent_modifier : int 0 ## ..@ table_inset : int 0 ## ..@ level : int 1 ## ..@ name : chr \"SEX\" ## ..@ main_title : chr \"big title\" ## ..@ subtitles : chr(0) ## ..@ main_footer : chr(0) ## ..@ provenance_footer : chr(0) ## Warning: str provides a low level, implementation-detail-specific description ## of the TableTree object structure. See table_structure(.) for a summary of ## table struture intended for end users."},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"tree-paths","dir":"Articles > Dev-guide","previous_headings":"","what":"Tree Paths","title":"Table Hierarchy","text":"Root Leaves, vectors vectors Tables tree, nodes tree can summaries associated . Tables trees nested structure. also benefit keeping repeating necessary information trying paginate table. Children ElementaryTables row objects. TableTree can children either row objects table objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_table_hierarchy.html","id":"todo","dir":"Articles > Dev-guide","previous_headings":"Tree Paths","what":"TODO:","title":"Table Hierarchy","text":"Create Tree Diagram showing class hierarchy.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_tabulation.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Tabulation","text":"article intended use developers contain low-level explanations topics covered. user-friendly vignettes, please see Articles page rtables website. code prose appears version article main branch repository may reflect specific state things can less recent. guide describes important aspects tabulation process unlikely change. Regardless, invite reader keep mind current repository code may drifted following material document, always best practice read code directly main. Please keep mind rtables still active development, seen efforts multiple contributors across different years. Therefore, may legacy mechanisms ongoing transformations look different future. working document may subjected deprecation updates, keep xxx comments indicate placeholders warnings -’s need work.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_tabulation.html","id":"introduction","dir":"Articles > Dev-guide","previous_headings":"","what":"Introduction","title":"Tabulation","text":"Tabulation rtables process takes pre-defined layout applies data. layout object, splits analyzes, can applied different data produce valid tables. process happens principally within tt_dotabulation.R file user-facing function build_table resides . occasionally use functions methods present files, like colby_construction.R make_subset_expr.R. assume reader already familiar documentation build_table. suggest reading Split Machinery article prior one, instrumental understanding layout object, essentially built splits, tabulated data supplied.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/dev-guide/dg_tabulation.html","id":"tabulation","dir":"Articles > Dev-guide","previous_headings":"","what":"Tabulation","title":"Tabulation","text":"enter build_table using debugonce see works. Now let’s look within build_table call. initial check layout pre-data table layout, checks column layout defined (clayout accessor), .e. column split. case, obs column added automatically observations. , couple defensive programming calls checks transformations finally data. can divided two categories: mainly concern layout, defined generics, concern data, instead function dependent layout class. Indeed, layout structured can divided clayout rlayout (column row layout). first one used create cinfo, general object container column splits information. second one contains obligatory data split, .e. root split (accessible root_spl), row splits’ vectors iterative splits row space. following, consider initial checks defensive programming. Along various checks defensive programming, find PreDataAxisLayout virtual class row column layouts inherit . Virtual classes handy group classes need share things like labels functions need applicable relative classes. See information rtables class hierarchy dedicated article . Now, continue build_table. checks, notice TreePos() constructor object retains representation tree position along split values labels. mainly used create_colinfo, enter now debugonce(create_colinfo). function creates object represents column splits everything else may related columns. particular, column counts calculated function. parameter inputs follows: create_colinfo make_subset_expr.R. , see topleft present build_table, override one lyt. Entering create_colinfo, see following calls: Next function determination column counts. Currently, happens leaf level, can certainly calculated independently levels (open issue rtables, .e. print levels’ totals). Precedence column counts may documented (xxx todo). main use case analyzing participation-level dataset, multiple records per subject, like retain total numbers subjects per column, often taken subject-level dataset, use column counts. Originally, counts able added vector, often case users like possibility use alt_counts_df. cinfo object (InstantiatedColumnInfo) created information. continue inside build_table, see .make_ctab used make root split. general procedure generates initial root split content row. ctab applied content row, row contains label. ?summarize_row_groups, know rtables defines label rows, .e. content rows. .make_ctab similar function actual creates table rows, .make_tablerows. Note function uses parent_cfun .make_caller retrieve content function inserted levels. split structural handling table object row-creation engine, divided .make_tablerows call. search package, find function called twice, .make_ctab .make_analyzed_tab. two final elements table construction: creation rows. Going back build_table, see row layout actually list split vectors. fundamental line, kids <- lapply(seq_along(rlyt), function() {, allows us appreciate . Going forward see recursive_applysplit applied split vector. may worthwhile check vector looks like test case. last print informative. can see layout construction object built 2 VarLevelSplits rows one final AnalyzeMultiVars, leaf analysis split final level rows. second split vector following AnalyzeVarSplit. xxx get multiple split vectors, need escape nesting nest = FALSE adding split_rows_by call analyze call. Continuing recursive_applysplit, made two main calls: one .make_ctab makes content row calculates counts specified, .make_split_kids. eventually contains recursive_applysplit applied split vector built Splits analyze splits. generic handy switch different downstream processes. case (rlyt[[1]]) call method getMethod(\".make_split_kids\", \"Split\") twice getting analysis split. , (xxx) multi-variable split applies .make_split_kids elements, turn calling main getMethod(\".make_split_kids\", \"VAnalyzeSplit\") turn go .make_analyzed_tab. interesting edge cases different split cases, like split_by_multivars one splits reference group. internal code , called baseline. follow variable across function layers, see split (do_split) happens (getMethod(\".make_split_kids\", \"Split\")) second split reference group. done make available row calculate, example, differences reference group. Now move towards .make_tablerows, analysis functions become key place applied analyzed. First, external tryCatch used cache errors higher level, differentiate two major blocks. function parameters quite intuitive, exception spl_context. fundamental parameter keeps information splits can visible analysis functions. look value, see carried updated everywhere split happens, except columns. Column-related information added last, gen_onerv, lowest level one result value produced. .make_tablerows go gen_rowvalues, aside row referential footers handling. gen_rowvalues unpacks cinfo object crosses arriving row split information generate rows. particular, rawvals <- mapply(gen_onerv, maps columns generate list values corresponding table row. Looking final gen_onerv see (!(val, \"RowsVerticalSection\")) function in_rows called. invite reader explore building blocks in_rows , .make_tablerows constructs data row (DataRow) content row (ContentRow) depending whether called .make_ctab .make_analyzed_tab. .make_tablerows either makes content table “analysis table”. gen_rowvalues generates list stacks (RowsVerticalSection, one rows potentially!) column. add: conceptual part -> calculating things column putting side side slicing rows putting together -> rtables row dominant.","code":"# rtables 0.6.2 library(rtables) debugonce(build_table) # A very simple layout lyt <- basic_table() %>% split_rows_by(\"STRATA1\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_cols_by(\"ARM\") %>% analyze(\"BMRKR1\") # lyt must be a PreDataTableLayouts object is(lyt, \"PreDataTableLayouts\") lyt %>% build_table(DM) ## do checks and defensive programming now that we have the data lyt <- fix_dyncuts(lyt, df) # Create the splits that depends on data lyt <- set_def_child_ord(lyt, df) # With the data I set the same order for all splits lyt <- fix_analyze_vis(lyt) # Checks if the analyze last split should be visible # If there is only one you will not get the variable name, otherwise you get it if you # have multivar. Default is NA. You can do it now only because you are sure to # have the whole layout. df <- fix_split_vars(lyt, df, char_ok = is.null(col_counts)) # checks if split vars are present lyt[] # preserve names - warning if names longer, repeats the name value if only one lyt@.Data # might not preserve the names # it works only when it is another class that inherits from lists # We suggest doing extensive testing about these behaviors in order to do choose the appropriate one cinfo <- create_colinfo( lyt, # Main layout with col split info df, # df used for splits and col counts if no alt_counts_df is present rtpos, # TreePos (does not change out of this function) counts = col_counts, # If we want to overwrite the calculations with df/alt_counts_df alt_counts_df = alt_counts_df, # alternative data for col counts total = col_total, # calculated from build_table inputs (nrow of df or alt_counts_df) topleft # topleft information added into build_table ) clayout <- clayout(lyt) # Extracts column split and info if (is.null(topleft)) { topleft <- top_left(lyt) # If top_left is not present in build_table, it is taken from lyt } ctree <- coltree(clayout, df = df, rtpos = rtpos) # Main constructor of LayoutColTree # The above is referenced as generic and principally represented as # setMethod(\"coltree\", \"PreDataColLayout\", (located in `tree_accessor.R`). # This is a call that restructures information from clayout, df, and rtpos # to get a more compact column tree layout. Part of this design is related # to past implementations. cexprs <- make_col_subsets(ctree, df) # extracts expressions in a compact fashion. # WARNING: removing NAs at this step is automatic. This should # be coupled with a warning for NAs in the split (xxx) colextras <- col_extra_args(ctree) # retrieves extra_args from the tree. It may not be used # rtables 0.6.2 # A very simple layout lyt <- basic_table() %>% split_rows_by(\"STRATA1\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_cols_by(\"ARM\") %>% analyze(\"BMRKR1\") rlyt <- rtables:::rlayout(lyt) str(rlyt, max.level = 2) Formal class 'PreDataRowLayout' [package \"rtables\"] with 2 slots ..@ .Data :List of 2 # rlyt is a rtables object (PreDataRowLayout) that is also a list! ..@ root_split:Formal class 'RootSplit' [package \"rtables\"] with 17 slots # another object! # If you do summarize_row_groups before anything you act on the root split. We need this to # have a place for the content that is valid for the whole table. str(rtables:::root_spl(rlyt), max.level = 2) # it is still a split str(rlyt[[1]], max.level = 3) # still a rtables object (SplitVector) that is a list Formal class 'SplitVector' [package \"rtables\"] with 1 slot ..@ .Data:List of 3 .. ..$ :Formal class 'VarLevelSplit' [package \"rtables\"] with 20 slots .. ..$ :Formal class 'VarLevelSplit' [package \"rtables\"] with 20 slots .. ..$ :Formal class 'AnalyzeMultiVars' [package \"rtables\"] with 17 slots # rtables 0.6.2 str(rlyt[[2]], max.level = 5) Formal class 'SplitVector' [package \"rtables\"] with 1 slot ..@ .Data:List of 1 .. ..$ :Formal class 'AnalyzeVarSplit' [package \"rtables\"] with 21 slots .. .. .. ..@ analysis_fun :function (x, ...) .. .. .. .. ..- attr(*, \"srcref\")= 'srcref' int [1:8] 1723 5 1732 5 5 5 4198 4207 .. .. .. .. .. ..- attr(*, \"srcfile\")=Classes 'srcfilealias', 'srcfile' .. .. .. ..@ default_rowlabel : chr \"Var3 Counts\" .. .. .. ..@ include_NAs : logi FALSE .. .. .. ..@ var_label_position : chr \"default\" .. .. .. ..@ payload : chr \"VAR3\" .. .. .. ..@ name : chr \"VAR3\" .. .. .. ..@ split_label : chr \"Var3 Counts\" .. .. .. ..@ split_format : NULL .. .. .. ..@ split_na_str : chr NA .. .. .. ..@ split_label_position : chr(0) .. .. .. ..@ content_fun : NULL .. .. .. ..@ content_format : NULL .. .. .. ..@ content_na_str : chr(0) .. .. .. ..@ content_var : chr \"\" .. .. .. ..@ label_children : logi FALSE .. .. .. ..@ extra_args : list() .. .. .. ..@ indent_modifier : int 0 .. .. .. ..@ content_indent_modifier: int 0 .. .. .. ..@ content_extra_args : list() .. .. .. ..@ page_title_prefix : chr NA .. .. .. ..@ child_section_div : chr NA"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"vignette demonstrate complex analysis function can constructed order build highly-customized tables rtables. example detail steps creating analysis function calculate basic univariable Cox regression summary table analyze treatment effect ARM variable covariate/interaction effects survival analysis. Cox regression analysis function customization options capability fitting multivariable Cox regression models, see summarize_coxreg() function tern package, builds upon concepts used construction example. packages used vignette :","code":"library(rtables) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"data-pre-processing","dir":"Articles","previous_headings":"","what":"Data Pre-Processing","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"First, prepare data used generate table example. use example ADTTE (Time--Event Analysis) dataset ex_adtte formatters package, contains treatment variable ARM, several variables can chosen covariates, censor variable CNSR derive event variable EVENT required model. purpose example, use age (AGE) race (RACE) covariates. prepare data needed observe desired effects summary table. PARAMCD filtered records overall survival (OS) included, filter mutate include levels interest covariates. ARM variable mutated indicate \"B: Placebo\" used reference level treatment variable, EVENT variable derived CNSR.","code":"adtte <- ex_adtte anl <- adtte %>% dplyr::filter(PARAMCD == \"OS\") %>% dplyr::filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% dplyr::filter(RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\")) %>% dplyr::mutate(RACE = droplevels(RACE)) %>% dplyr::mutate(ARM = droplevels(stats::relevel(ARM, \"B: Placebo\"))) %>% dplyr::mutate(EVENT = 1 - CNSR)"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"tidy-method-for-summary-coxph-objects-tidy-summary-coxph","dir":"Articles","previous_headings":"Creating Helper Functions: Cox Regression Model Calculations","what":"tidy Method for summary.coxph Objects: tidy.summary.coxph","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"method allows tidy function broom package operate summary.coxph output, extracting values interest analysis returning tidied tibble::tibble() object.","code":"tidy.summary.coxph <- function(x, ...) { is(x, \"summary.coxph\") pval <- x$coefficients confint <- x$conf.int levels <- rownames(pval) pval <- tibble::as_tibble(pval) confint <- tibble::as_tibble(confint) ret <- cbind(pval[, grepl(\"Pr\", names(pval))], confint) ret$level <- levels ret$n <- x[[\"n\"]] ret }"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"function-to-estimate-interaction-effects-h_coxreg_inter_effect","dir":"Articles","previous_headings":"Creating Helper Functions: Cox Regression Model Calculations","what":"Function to Estimate Interaction Effects: h_coxreg_inter_effect","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"h_coxreg_inter_effect helper function used within following helper function, h_coxreg_extract_interaction, estimate interaction effects given model given covariate. function calculates desired statistics given model returns data.frame label information row well statistics n, hr (hazard ratio), lcl (CI lower bound), ucl (CI upper bound), pval (effect p-value), pval_inter (interaction p-value). numeric covariate selected, median value used sole “level” interaction effect calculated. non-numeric covariates, interaction effect calculated level covariate, result returned separate row.","code":"h_coxreg_inter_effect <- function(x, effect, covar, mod, label, control, data) { if (is.numeric(x)) { betas <- stats::coef(mod) attrs <- attr(stats::terms(mod), \"term.labels\") term_indices <- grep(pattern = effect, x = attrs[!grepl(\"strata\\\\(\", attrs)]) betas <- betas[term_indices] betas_var <- diag(stats::vcov(mod))[term_indices] betas_cov <- stats::vcov(mod)[term_indices[1], term_indices[2]] xval <- stats::median(x) effect_index <- !grepl(covar, names(betas)) coef_hat <- betas[effect_index] + xval * betas[!effect_index] coef_se <- sqrt(betas_var[effect_index] + xval^2 * betas_var[!effect_index] + 2 * xval * betas_cov) q_norm <- stats::qnorm((1 + control$conf_level) / 2) } else { var_lvl <- paste0(effect, levels(data[[effect]])[-1]) # [-1]: reference level giv_lvl <- paste0(covar, levels(data[[covar]])) design_mat <- expand.grid(effect = var_lvl, covar = giv_lvl) design_mat <- design_mat[order(design_mat$effect, design_mat$covar), ] design_mat <- within(data = design_mat, expr = { inter <- paste0(effect, \":\", covar) rev_inter <- paste0(covar, \":\", effect) }) split_by_variable <- design_mat$effect interaction_names <- paste(design_mat$effect, design_mat$covar, sep = \"/\") mmat <- stats::model.matrix(mod)[1, ] mmat[!mmat == 0] <- 0 design_mat <- apply(X = design_mat, MARGIN = 1, FUN = function(x) { mmat[names(mmat) %in% x[-which(names(x) == \"covar\")]] <- 1 mmat }) colnames(design_mat) <- interaction_names coef <- stats::coef(mod) vcov <- stats::vcov(mod) betas <- as.matrix(coef) coef_hat <- t(design_mat) %*% betas dimnames(coef_hat)[2] <- \"coef\" coef_se <- apply(design_mat, 2, function(x) { vcov_el <- as.logical(x) y <- vcov[vcov_el, vcov_el] y <- sum(y) y <- sqrt(y) y }) q_norm <- stats::qnorm((1 + control$conf_level) / 2) y <- cbind(coef_hat, `se(coef)` = coef_se) y <- apply(y, 1, function(x) { x[\"hr\"] <- exp(x[\"coef\"]) x[\"lcl\"] <- exp(x[\"coef\"] - q_norm * x[\"se(coef)\"]) x[\"ucl\"] <- exp(x[\"coef\"] + q_norm * x[\"se(coef)\"]) x }) y <- t(y) y <- by(y, split_by_variable, identity) y <- lapply(y, as.matrix) attr(y, \"details\") <- paste0( \"Estimations of \", effect, \" hazard ratio given the level of \", covar, \" compared to \", effect, \" level \", levels(data[[effect]])[1], \".\" ) xval <- levels(data[[covar]]) } data.frame( effect = \"Covariate:\", term = rep(covar, length(xval)), term_label = as.character(paste0(\" \", xval)), level = as.character(xval), n = NA, hr = if (is.numeric(x)) exp(coef_hat) else y[[1]][, \"hr\"], lcl = if (is.numeric(x)) exp(coef_hat - q_norm * coef_se) else y[[1]][, \"lcl\"], ucl = if (is.numeric(x)) exp(coef_hat + q_norm * coef_se) else y[[1]][, \"ucl\"], pval = NA, pval_inter = NA, stringsAsFactors = FALSE ) }"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"function-to-extract-effect-information-h_coxreg_extract_interaction","dir":"Articles","previous_headings":"Creating Helper Functions: Cox Regression Model Calculations","what":"Function to Extract Effect Information: h_coxreg_extract_interaction","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"Using previous two helper functions, h_coxreg_extract_interaction uses ANOVA extract information given model given covariate. function extract different information depending whether effect interest treatment/main effect interaction effect, returns data.frame label information row (corresponding effect) well statistics n, hr, lcl, ucl, pval, pval_inter (interaction effects ). helper function used directly within analysis function analyze Cox regression model extract relevant information processed displayed within output table.","code":"h_coxreg_extract_interaction <- function(effect, covar, mod, data) { control <- list(pval_method = \"wald\", ties = \"exact\", conf_level = 0.95, interaction = FALSE) test_statistic <- c(wald = \"Wald\", likelihood = \"LR\")[control$pval_method] mod_aov <- withCallingHandlers( expr = car::Anova(mod, test.statistic = test_statistic, type = \"III\"), message = function(m) invokeRestart(\"muffleMessage\") ) msum <- if (!any(attr(stats::terms(mod), \"order\") == 2)) summary(mod, conf.int = control$conf_level) else mod_aov sum_anova <- broom::tidy(msum) if (!any(attr(stats::terms(mod), \"order\") == 2)) { effect_aov <- mod_aov[effect, , drop = TRUE] pval <- effect_aov[[grep(pattern = \"Pr\", x = names(effect_aov)), drop = TRUE]] sum_main <- sum_anova[grepl(effect, sum_anova$level), ] term_label <- if (effect == covar) { paste0(levels(data[[covar]])[2], \" vs control (\", levels(data[[covar]])[1], \")\") } else { unname(formatters::var_labels(data, fill = TRUE)[[covar]]) } y <- data.frame( effect = ifelse(covar == effect, \"Treatment:\", \"Covariate:\"), term = covar, term_label = term_label, level = levels(data[[effect]])[2], n = mod[[\"n\"]], hr = unname(sum_main[\"exp(coef)\"]), lcl = unname(sum_main[grep(\"lower\", names(sum_main))]), ucl = unname(sum_main[grep(\"upper\", names(sum_main))]), pval = pval, stringsAsFactors = FALSE ) y$pval_inter <- NA y } else { pval <- sum_anova[sum_anova$term == effect, ][[\"p.value\"]] ## Test the interaction effect pval_inter <- sum_anova[grep(\":\", sum_anova$term), ][[\"p.value\"]] covar_test <- data.frame( effect = \"Covariate:\", term = covar, term_label = unname(formatters::var_labels(data, fill = TRUE)[[covar]]), level = \"\", n = mod$n, hr = NA, lcl = NA, ucl = NA, pval = pval, pval_inter = pval_inter, stringsAsFactors = FALSE ) ## Estimate the interaction y <- h_coxreg_inter_effect( data[[covar]], covar = covar, effect = effect, mod = mod, label = unname(formatters::var_labels(data, fill = TRUE)[[covar]]), control = control, data = data ) rbind(covar_test, y) } }"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"creating-a-helper-function-cached_model","dir":"Articles","previous_headings":"","what":"Creating a Helper Function: cached_model","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"Next, create helper function, cached_model, used within analysis function cache return fitted Cox regression model current covariate. df argument directly inherited df argument passed analysis function, contains full dataset analyzed. cov argument covariate analyzed depending current row context. treatment effect currently analyzed, value empty string. cache_env parameter environment object used store model current covariate, also passed analysis function. course, function can also run outside analysis function still cache return Cox regression model. Using arguments, cached_model function first checks model given covariate cov already stored caching environment cache_env. , model retrieved returned cached_model. , model must constructed. done first constructing model formula, model_form, starting treatment effect (ARM) adding covariate effect one currently analyzed. Cox regression model fit using df model formula, model returned stored caching environment object cache_env[[cov]].","code":"cached_model <- function(df, cov, cache_env) { ## Check if a model already exists for ## `cov` in the caching environment if (!is.null(cache_env[[cov]])) { ## If model already exists, retrieve it from cache_env model <- cache_env[[cov]] } else { ## Build model formula model_form <- paste0(\"survival::Surv(AVAL, EVENT) ~ ARM\") if (length(cov) > 0) { model_form <- paste(c(model_form, cov), collapse = \" * \") } else { cov <- \"ARM\" } ## Calculate Cox regression model model <- survival::coxph( formula = stats::as.formula(model_form), data = df, ties = \"exact\" ) ## Store model in the caching environment cache_env[[cov]] <- model } model }"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"creating-the-analysis-function-a_cox_summary","dir":"Articles","previous_headings":"","what":"Creating the Analysis Function: a_cox_summary","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"data prepared helper function created, can proceed construct analysis function a_cox_summary, used populate rows table. order used generate data rows (interaction effects) content rows (main effects), must create function can used afun analyze cfun summarize_row_groups. Therefore, function must accept labelstr parameter. arguments analysis function follows: df - data.frame full dataset required fit Cox regression model. labelstr - string label variable analyzed current row/column split context. .spl_context - data.frame containing value column used analysis function determine name variable/covariate current split. details information stored .spl_context see ?analyze. stat format - strings indicate statistic column currently format applied print statistic. cache_env - environment object can used store cached models can prevent repeatedly fitting model. Instead, model generated per covariate reused. argument passed directly cached_model helper function defined previously. cov_main - logical value indicating whether current row summarizing covariate main effects. analysis function works within given row/column split context using current covariate (cov) cached_model function obtain desired Cox regression model. model, h_coxreg_extract_interaction function able extract information/statistics relevant analysis store data.frame. rows data.frame interest current row/column split context extracted statistic printed current column retrieved rows. Finally, formatted cells statistic returned VerticalRowsSection object. detail see commented function code , purpose line within a_cox_summary described.","code":"a_cox_summary <- function(df, labelstr = \"\", .spl_context, stat, format, cache_env, cov_main = FALSE) { ## Get current covariate (variable used in latest row split) cov <- tail(.spl_context$value, 1) ## If currently analyzing treatment effect (ARM) replace empty ## value of cov with \"ARM\" so the correct model row is analyzed if (length(cov) == 0) cov <- \"ARM\" ## Use cached_model to get the fitted Cox regression ## model for the current covariate model <- cached_model(df = df, cov = cov, cache_env = cache_env) ## Extract levels of cov to be used as row labels for interaction effects. ## If cov is numeric, the median value of cov is used as a row label instead cov_lvls <- if (is.factor(df[[cov]])) levels(df[[cov]]) else as.character(median(df[[cov]])) ## Use function to calculate and extract information relevant to cov from the model cov_rows <- h_coxreg_extract_interaction(effect = \"ARM\", covar = cov, mod = model, data = df) ## Effect p-value is only printed for treatment effect row if (!cov == \"ARM\") cov_rows[, \"pval\"] <- NA_real_ ## Extract rows containing statistics for cov from model information if (!cov_main) { ## Extract rows for main effect cov_rows <- cov_rows[cov_rows$level %in% cov_lvls, ] } else { ## Extract all non-main effect rows cov_rows <- cov_rows[nchar(cov_rows$level) == 0, ] } ## Extract value(s) of statistic for current column and variable/levels stat_vals <- as.list(apply(cov_rows[stat], 1, function(x) x, simplify = FALSE)) ## Assign labels: covariate name for main effect (content) rows, ARM comparison description ## for treatment effect (content) row, cov_lvls for interaction effect (data) rows nms <- if (cov_main) labelstr else if (cov == \"ARM\") cov_rows$term_label else cov_lvls ## Return formatted/labelled row in_rows( .list = stat_vals, .names = nms, .labels = nms, .formats = setNames(rep(format, length(nms)), nms), .format_na_strs = setNames(rep(\"\", length(nms)), nms) ) }"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"selecting-parameters","dir":"Articles","previous_headings":"","what":"Selecting Parameters","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"able customize Cox regression summary using analysis function selecting covariates (labels), statistics (labels), statistic formats use generating output table. also initialize new environment object used analysis function caching environment store models . purpose example, choose 5 possible statistics include table: n, hazard ratio, confidence interval, effect p-value, interaction p-value.","code":"my_covs <- c(\"AGE\", \"RACE\") ## Covariates my_cov_labs <- c(\"Age\", \"Race\") ## Covariate labels my_stats <- list(\"n\", \"hr\", c(\"lcl\", \"ucl\"), \"pval\", \"pval_inter\") ## Statistics my_stat_labs <- c(\"n\", \"Hazard Ratio\", \"95% CI\", \"p-value\\n(effect)\", \"p-value\\n(interaction)\") ## Statistic labels my_formats <- c( n = \"xx\", hr = \"xx.xx\", lcl = \"(xx.xx, xx.xx)\", pval = \"xx.xxxx\", pval_inter = \"xx.xxxx\" ## Statistic formats ) my_env <- new.env() ny_cache_env <- replicate(length(my_stats), list(my_env)) ## Caching environment"},{"path":"https://insightsengineering.github.io/rtables/articles/example_analysis_coxreg.html","id":"constructing-the-table","dir":"Articles","previous_headings":"","what":"Constructing the Table","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"Finally, table layout can constructed used build desired table. first split basic_table using split_cols_by_multivar ensure statistic exists column. , choose variable (case STUDYID) shares value every row, use split variable every column full dataset used compute model every column. use extra_args argument list element’s element positions correspond children (columns generated ) split. arguments inherited following layout elements operating within split, use elements argument inputs. elaborate , three elements extra_args: stat, format, cache_env - arguments a_cox_summary length equal number columns (defined ). use analysis function following column split, depending current column context, corresponding element three list elements inherited extra_args used input. example, analyze_colvars called a_cox_summary afun performing calculations column 1, my_stats[1] (\"n\") given argument stat, my_formats[1] (\"xx\") argument format, my_cache_env[1] (my_env) cache_env. useful table since want column print values different statistic apply corresponding format. Next, can use summarize_row_groups generate content row treatment effect. first instance extra_args column split inherited used argument input cfun. generating treatment effect row, want add rows covariates. use split_rows_by_multivar split rows covariate apply appropriate labels. Following row split, use summarize_row_groups a_cox_summary cfun generate one content row covariate main effect. contents extra_args column split inherited input. specify cov_main = TRUE extra_args argument main effects rather interactions considered. Since split, instance extra_args inherited following layout elements. cov_main singular value, cov_main = TRUE used within every column context. last part table covariate interaction effects. use analyze_colvars a_cox_summary afun, inherit extra_args column split. Using rtables “analyze” function generates data rows, one row corresponding covariate level (median value, numeric covariates), nested content row (main effect) covariate. Using pre-processed anl dataset, can now build output final Cox regression summary table.","code":"lyt <- basic_table() %>% ## Column split: one column for each statistic split_cols_by_multivar( vars = rep(\"STUDYID\", length(my_stats)), varlabels = my_stat_labs, extra_args = list( stat = my_stats, format = my_formats, cache_env = ny_cache_env ) ) %>% ## Create content row for treatment effect summarize_row_groups(cfun = a_cox_summary) %>% ## Row split: one content row for each covariate split_rows_by_multivar( vars = my_covs, varlabels = my_cov_labs, split_label = \"Covariate:\", indent_mod = -1 ## Align split label left ) %>% ## Create content rows for covariate main effects summarize_row_groups( cfun = a_cox_summary, extra_args = list(cov_main = TRUE) ) %>% ## Create data rows for covariate interaction effects analyze_colvars(afun = a_cox_summary) cox_tbl <- build_table(lyt, anl) cox_tbl ## p-value p-value ## n Hazard Ratio 95% CI (effect) (interaction) ## ———————————————————————————————————————————————————————————————————————————————————————————————— ## A: Drug X vs control (B: Placebo) 247 0.97 (0.71, 1.32) 0.8243 ## Covariate: ## Age 247 0.7832 ## 34 0.92 (0.68, 1.26) ## Race 247 0.7441 ## ASIAN 1.03 (0.68, 1.57) ## BLACK OR AFRICAN AMERICAN 0.78 (0.41, 1.49) ## WHITE 1.06 (0.55, 2.04)"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Exploratory Analysis","text":"vignette, like introduce qtable() can used easily create cross tabulations exploratory data analysis. qtable() extension table() base R can much beyond creating two-way contingency tables. function simple use interface internally builds layouts using rtables framework.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting Started","title":"Exploratory Analysis","text":"Load packages used vignette: Let’s start seeing table() can : can easily recreate cross-tables qtable() specifying data.frame variable(s) tabulate. col_vars row_vars arguments control split data across columns rows respectively. Aside display style, main difference qtable() add (N=xx) table header default. can removed show_colcounts. variables used row column facets empty strings (““). non empty values required labels generating table. code generate error.","code":"library(rtables) library(dplyr) table(ex_adsl$ARM) # # A: Drug X B: Placebo C: Combination # 134 134 132 table(ex_adsl$SEX, ex_adsl$ARM) # # A: Drug X B: Placebo C: Combination # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 qtable(ex_adsl, col_vars = \"ARM\") # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ——————————————————————————————————————————————— # count 134 134 132 qtable(ex_adsl, col_vars = \"ARM\", row_vars = \"SEX\") # A: Drug X B: Placebo C: Combination # count (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————— # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 qtable(ex_adsl, \"ARM\", show_colcounts = FALSE) # count all obs # ———————————————————————— # A: Drug X 134 # B: Placebo 134 # C: Combination 132 tmp_adsl <- ex_adsl tmp_adsl$new <- rep_len(c(\"\", \"A\", \"B\"), nrow(tmp_adsl)) qtable(tmp_adsl, row_vars = \"new\")"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"nested-tables","dir":"Articles","previous_headings":"","what":"Nested Tables","title":"Exploratory Analysis","text":"Providing one variable name row column structure qtable() create nested table. Arbitrary nesting supported dimension. Note default, unobserved factor levels within facet included table. can modified drop_levels. code adds row 0s STRATA1 level “B” nested SEX level “UNDIFFERENTIATED”. contrast, table() return nested table. Rather produces list contingency tables two variables used inputs. help stats::ftable() nested structure can achieved two steps.","code":"qtable(ex_adsl, row_vars = c(\"SEX\", \"STRATA1\"), col_vars = c(\"ARM\", \"STRATA2\")) # A: Drug X B: Placebo C: Combination # S1 S2 S1 S2 S1 S2 # count (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) # ———————————————————————————————————————————————————————————————————————— # F # A 12 9 11 13 7 11 # B 14 11 12 15 9 12 # C 17 16 13 13 14 13 # M # A 5 11 10 9 6 14 # B 13 8 7 10 9 12 # C 8 6 13 6 8 11 # U # A 1 0 1 0 1 0 # B 1 0 0 1 0 1 # C 1 0 0 0 1 1 # UNDIFFERENTIATED # A 0 0 0 0 0 1 # C 1 0 0 0 1 0 qtable( ex_adsl, row_vars = c(\"SEX\", \"STRATA1\"), col_vars = c(\"ARM\", \"STRATA2\"), drop_levels = FALSE ) # A: Drug X B: Placebo C: Combination # S1 S2 S1 S2 S1 S2 # count (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) # ———————————————————————————————————————————————————————————————————————— # F # A 12 9 11 13 7 11 # B 14 11 12 15 9 12 # C 17 16 13 13 14 13 # M # A 5 11 10 9 6 14 # B 13 8 7 10 9 12 # C 8 6 13 6 8 11 # U # A 1 0 1 0 1 0 # B 1 0 0 1 0 1 # C 1 0 0 0 1 1 # UNDIFFERENTIATED # A 0 0 0 0 0 1 # B 0 0 0 0 0 0 # C 1 0 0 0 1 0 table(ex_adsl$SEX, ex_adsl$STRATA1, ex_adsl$ARM, ex_adsl$STRATA2) # , , = A: Drug X, = S1 # # # A B C # F 12 14 17 # M 5 13 8 # U 1 1 1 # UNDIFFERENTIATED 0 0 1 # # , , = B: Placebo, = S1 # # # A B C # F 11 12 13 # M 10 7 13 # U 1 0 0 # UNDIFFERENTIATED 0 0 0 # # , , = C: Combination, = S1 # # # A B C # F 7 9 14 # M 6 9 8 # U 1 0 1 # UNDIFFERENTIATED 0 0 1 # # , , = A: Drug X, = S2 # # # A B C # F 9 11 16 # M 11 8 6 # U 0 0 0 # UNDIFFERENTIATED 0 0 0 # # , , = B: Placebo, = S2 # # # A B C # F 13 15 13 # M 9 10 6 # U 0 1 0 # UNDIFFERENTIATED 0 0 0 # # , , = C: Combination, = S2 # # # A B C # F 11 12 13 # M 14 12 11 # U 0 1 1 # UNDIFFERENTIATED 1 0 0 t1 <- ftable(ex_adsl[, c(\"SEX\", \"STRATA1\", \"ARM\", \"STRATA2\")]) ftable(t1, row.vars = c(\"SEX\", \"STRATA1\")) # ARM A: Drug X B: Placebo C: Combination # STRATA2 S1 S2 S1 S2 S1 S2 # SEX STRATA1 # F A 12 9 11 13 7 11 # B 14 11 12 15 9 12 # C 17 16 13 13 14 13 # M A 5 11 10 9 6 14 # B 13 8 7 10 9 12 # C 8 6 13 6 8 11 # U A 1 0 1 0 1 0 # B 1 0 0 1 0 1 # C 1 0 0 0 1 1 # UNDIFFERENTIATED A 0 0 0 0 0 1 # B 0 0 0 0 0 0 # C 1 0 0 0 1 0"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"na-values","dir":"Articles","previous_headings":"","what":"NA Values","title":"Exploratory Analysis","text":"far examples seen, used counts summarize data table cell default analysis used qtable(). Internally, single analysis variable specified avar used generate counts table. default analysis variable first variable data. case ex_adsl “STUDYID”. Let’s see happens introduce NA values analysis variable: resulting table showing 0’s across cells values analysis variable NA. Keep behavior mind quick exploratory analysis using default counts aggregate function qtable. suit purpose, can either pre-process data recode NA values use another analysis function. see latter done Custom Aggregation section. addition, row column variables NA levels explicitly labelled . done, columns /rows reflect full data. Explicitly labeling NA levels column facet adds column table:","code":"tmp_adsl <- ex_adsl tmp_adsl[[1]] <- NA_character_ qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"SEX\") # F M U UNDIFFERENTIATED # count (N=222) (N=166) (N=9) (N=3) # ————————————————————————————————————————————————————————————— # A: Drug X 0 0 0 0 # B: Placebo 0 0 0 0 # C: Combination 0 0 0 0 # Recode NA values tmp_adsl[[1]] <- addNA(tmp_adsl[[1]]) qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"SEX\") # F M U UNDIFFERENTIATED # count (N=222) (N=166) (N=9) (N=3) # ————————————————————————————————————————————————————————————— # A: Drug X 79 51 3 1 # B: Placebo 77 55 2 0 # C: Combination 66 60 4 2 tmp_adsl$new1 <- factor(NA_character_, levels = c(\"X\", \"Y\", \"Z\")) qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"new1\") # X Y Z # count (N=0) (N=0) (N=0) # —————————————————————————————————————— # A: Drug X 0 0 0 # B: Placebo 0 0 0 # C: Combination 0 0 0 tmp_adsl$new2 <- addNA(tmp_adsl$new1) levels(tmp_adsl$new2)[4] <- \"\" # NA needs to be a recognizible string qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"new2\") # X Y Z # count (N=0) (N=0) (N=0) (N=400) # ———————————————————————————————————————————————— # A: Drug X 0 0 0 134 # B: Placebo 0 0 0 134 # C: Combination 0 0 0 132"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"custom-aggregation","dir":"Articles","previous_headings":"","what":"Custom Aggregation","title":"Exploratory Analysis","text":"powerful feature qtable() user can define type function used summarize data facet. can specify type analysis summary using afun argument: Note analysis variable AGE analysis function name included top right header table. analysis function returns vector 2 3 elements, result displayed multi-valued single cells. want use analysis function 3 summary elements, can use list. case, values displayed table multiple stacked cells within facet. list elements named, names used row labels. advanced formatting can controlled in_rows(). See function documentation details.","code":"qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = mean) # A: Drug X B: Placebo C: Combination # AGE - mean (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————— # S1 34.10 36.46 35.70 # S2 33.38 34.40 35.24 qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = range) # A: Drug X B: Placebo C: Combination # AGE - range (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————— # S1 23.0 / 48.0 24.0 / 62.0 20.0 / 69.0 # S2 21.0 / 50.0 21.0 / 58.0 23.0 / 64.0 fivenum2 <- function(x) { setNames(as.list(fivenum(x)), c(\"min\", \"Q1\", \"MED\", \"Q3\", \"max\")) } qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = fivenum2) # A: Drug X B: Placebo C: Combination # AGE - fivenum2 (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————— # S1 # min 23.00 24.00 20.00 # Q1 28.00 30.00 30.50 # MED 34.00 36.00 35.00 # Q3 39.00 40.50 40.00 # max 48.00 62.00 69.00 # S2 # min 21.00 21.00 23.00 # Q1 29.00 29.50 30.00 # MED 32.00 32.00 34.50 # Q3 38.00 39.50 38.00 # max 50.00 58.00 64.00 meansd_range <- function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx - xx\") ) } qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = meansd_range) # A: Drug X B: Placebo C: Combination # AGE - meansd_range (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————————————— # S1 # Mean (sd) 34.10 (6.71) 36.46 (7.72) 35.70 (8.22) # Range 23 - 48 24 - 62 20 - 69 # S2 # Mean (sd) 33.38 (6.40) 34.40 (7.99) 35.24 (7.39) # Range 21 - 50 21 - 58 23 - 64"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"marginal-summaries","dir":"Articles","previous_headings":"","what":"Marginal Summaries","title":"Exploratory Analysis","text":"Another feature qtable() ability quickly add marginal summary rows summarize_groups argument. summary add table count non-NA records analysis variable level nesting. example, compare two tables: second table, marginal summary rows level two row facet variables: STRATA1 STRATA2. number 18 second row gives count observations part ARM level “: Drug X”, STRATA1 level “”, STRATA2 level “S1”. percent calculated cell count divided column count given table header. can see mean AGE 31.61 subgroup based 18 subjects correspond 13.4% subjects arm “: Drug X”. See ?summarize_row_groups add marginal summary rows using core rtables framework.","code":"qtable( ex_adsl, row_vars = c(\"STRATA1\", \"STRATA2\"), col_vars = \"ARM\", avar = \"AGE\", afun = mean ) # A: Drug X B: Placebo C: Combination # AGE - mean (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————— # A # S1 31.61 36.68 34.00 # S2 34.40 33.55 34.35 # B # S1 34.57 37.68 35.83 # S2 32.79 34.77 36.68 # C # S1 35.26 35.38 36.58 # S2 32.95 34.89 34.72 qtable( ex_adsl, row_vars = c(\"STRATA1\", \"STRATA2\"), col_vars = \"ARM\", summarize_groups = TRUE, avar = \"AGE\", afun = mean ) # A: Drug X B: Placebo C: Combination # AGE - mean (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————— # A 38 (28.4%) 44 (32.8%) 40 (30.3%) # S1 18 (13.4%) 22 (16.4%) 14 (10.6%) # AGE - mean 31.61 36.68 34.00 # S2 20 (14.9%) 22 (16.4%) 26 (19.7%) # AGE - mean 34.40 33.55 34.35 # B 47 (35.1%) 45 (33.6%) 43 (32.6%) # S1 28 (20.9%) 19 (14.2%) 18 (13.6%) # AGE - mean 34.57 37.68 35.83 # S2 19 (14.2%) 26 (19.4%) 25 (18.9%) # AGE - mean 32.79 34.77 36.68 # C 49 (36.6%) 45 (33.6%) 49 (37.1%) # S1 27 (20.1%) 26 (19.4%) 24 (18.2%) # AGE - mean 35.26 35.38 36.58 # S2 22 (16.4%) 19 (14.2%) 25 (18.9%) # AGE - mean 32.95 34.89 34.72"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"table-decorations","dir":"Articles","previous_headings":"","what":"Table Decorations","title":"Exploratory Analysis","text":"Tables generated qtable() can include annotations titles, subtitles footnotes like :","code":"qtable( ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", title = \"Strata 2 Summary\", subtitle = paste0(\"STUDY \", ex_adsl$STUDYID[1]), main_footer = paste0(\"Date: \", as.character(Sys.Date())) ) # Strata 2 Summary # STUDY AB12345 # # ——————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # count (N=134) (N=134) (N=132) # ——————————————————————————————————————————————— # S1 73 67 56 # S2 61 67 76 # ——————————————————————————————————————————————— # # Date: 2024-02-22"},{"path":"https://insightsengineering.github.io/rtables/articles/exploratory_analysis.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Exploratory Analysis","text":"learned vignette: qtable() can replace extend uses table() stats::ftable() qtable() useful exploratory data analysis intended use qtable() exploratory data analysis, limited functionality building complex tables. details get started core rtables layout functionality see introduction vignette.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"formats-precedence","dir":"Articles","previous_headings":"","what":"Formats Precedence","title":"Format Precedence and NA Handling","text":"Users rtables package can specify format numbers reporting tables printed. Formatting functionality provided formatters R package. See formatters::list_valid_format_labels() list available formats. format can specified user different places. may happen , single table layout, format specified one place. case, final format applied depends format precedence rules defined rtables. vignette, describe basic rules rtables format precedence. examples shown vignette utilize example ADSL dataset, demographic table summarizes variables content different population subsets (encoded columns). Note ex_* data currently attached rtables package provided formatters package created using publicly available random.cdisc.data R package.","code":"library(rtables) ADSL <- ex_adsl"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"format-precedence-and-inheritance-rules","dir":"Articles","previous_headings":"Formats Precedence","what":"Format Precedence and Inheritance Rules","title":"Format Precedence and NA Handling","text":"format numbers printed can specified user different places. context precedence, important level split hierarchy formats specified . general, two levels: cell level -called parent table level. concept cell parent table results way rtables package stores resulting tables. models resulting tables hierarchical, tree-like objects cells (leaves) containing multiple values. Particularly noteworthy context fact actual table splitting occurs row-dominant way (even column splitting present layout). rtables provides user-end function table_structure() prints structure given table object. simple illustration, consider following example: table, 4 sub-tables SEX table. : F, M, U, UNDIFFERENTIATED. sub-tables one sub-table AGE. example, first AGE sub-table, parent table F. concept hierarchical, tree-like representations resulting tables translates directly format precedence inheritance rules. general principle, format finally applied cell one specific, , one closest cell given path tree. Hence, precedence-inheritance chain looks like following: chain, outermost parent_table least specific place specify format, cell specific one. cases format specified user one place, one specific applied cell. specific format selected user split, default format applied. default format \"xx\" yields formatting .character() function. following sections vignette, illustrate format precedence rules examples.","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = mean) adsl_analyzed <- build_table(lyt, ADSL) adsl_analyzed # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————————————————— # F # mean 32.7594936708861 34.1168831168831 35.1969696969697 # M # mean 35.5686274509804 37.4363636363636 35.3833333333333 # U # mean 31.6666666666667 31 35.25 # UNDIFFERENTIATED # mean 28 NA 45 table_structure(adsl_analyzed) # [TableTree] SEX # [TableTree] F # [ElementaryTable] AGE (1 x 3) # [TableTree] M # [ElementaryTable] AGE (1 x 3) # [TableTree] U # [ElementaryTable] AGE (1 x 3) # [TableTree] UNDIFFERENTIATED # [ElementaryTable] AGE (1 x 3) parent_table -> parent_table -> ... -> parent_table -> cell"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"standard-format","dir":"Articles","previous_headings":"Formats Precedence","what":"Standard Format","title":"Format Precedence and NA Handling","text":"simple layout explicitly set format output analysis function. case, default format applied.","code":"lyt0 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = mean) build_table(lyt0, ADSL) # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————— # mean 33.7686567164179 35.4328358208955 35.4318181818182"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"cell-format","dir":"Articles","previous_headings":"Formats Precedence","what":"Cell Format","title":"Format Precedence and NA Handling","text":"format cell can explicitly specified via rcell() in_rows() functions. former essentially collection data objects latter collection rcell() objects. previously mentioned, specific place format can specified user. format specified places time, one specified via in_rows() takes highest precedence. Technically, case, format defined rcell() simply overwritten one defined in_rows(). format specified in_rows() applied cells rows (overriding previously specified cell-specific values), indicates precedence rules described still place.","code":"lyt1 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\") }) build_table(lyt1, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 lyt1a <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x)), .formats = \"xx.xx\" ) }) build_table(lyt1a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xxx\"), .formats = \"xx.xx\" ) }) build_table(lyt2, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"parent-table-format-and-inheritance","dir":"Articles","previous_headings":"Formats Precedence","what":"Parent Table Format and Inheritance","title":"Format Precedence and NA Handling","text":"addition cell level, format can specified parent table level. format set user cell, specific format cell one defined innermost parent table split (). cell format also specified cell, parent table format ignored cell since cell format specific therefore takes precedence. following, slightly complicated, example, can observe partial inheritance. , SD cells inherit parent table’s format Mean cells .","code":"lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", mean, format = \"xx.x\") build_table(lyt3, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # mean 33.8 35.4 35.4 lyt4 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze( vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\") }, format = \"xx.x\" ) build_table(lyt4, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 lyt4a <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x)), \"SD\" = rcell(sd(x)), .formats = \"xx.xx\" ) }, format = \"xx.x\" ) build_table(lyt4a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 # SD 6.55 7.90 7.72 lyt5 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xx\"), \"SD\" = rcell(sd(x)) ) }, format = \"xx.x\" ) build_table(lyt5, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 # SD 6.6 7.9 7.7"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"na-handling","dir":"Articles","previous_headings":"","what":"NA Handling","title":"Format Precedence and NA Handling","text":"Consider following layout resulting table created: output cell corresponding UNDIFFERENTIATED level SEX B: Placebo level ARM displayed NA. occurs non-NA values facet used compute mean. rtables allows user specify string display cell values NA. Similar formats numbers, user can specify string replace NA parameter format_na_str .format_na_str. can specified cell parent table level. NA string precedence inheritance rules number format precedence, described previous section vignette. illustrate examples.","code":"lyt6 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = mean, format = \"xx.xx\") build_table(lyt6, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # mean 32.76 34.12 35.20 # M # mean 35.57 37.44 35.38 # U # mean 31.67 31.00 35.25 # UNDIFFERENTIATED # mean 28.00 NA 45.00"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"replacing-na-values-at-the-cell-level","dir":"Articles","previous_headings":"NA Handling","what":"Replacing NA Values at the Cell Level","title":"Format Precedence and NA Handling","text":"cell level, possible replace NA values custom string means format_na_str parameter rcell() .format_na_str parameter in_rows(). NA string specified places time, one specified in_rows() takes precedence. Technically, case NA replacement string defined rcell() simply overwritten one defined in_rows(). NA string specified in_rows() applied cells, rows (overriding previously specified cell specific values), means precedence rules described still place.","code":"lyt7 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\", format_na_str = \"\") }) build_table(lyt7, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00 lyt7a <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xx\"), .format_na_strs = \"\" ) }) build_table(lyt7a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00 lyt8 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xx\", format_na_str = \"\"), .format_na_strs = \"\" ) }) build_table(lyt8, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00"},{"path":"https://insightsengineering.github.io/rtables/articles/format_precedence.html","id":"parent-table-replacement-of-na-values-and-inheritance-principles","dir":"Articles","previous_headings":"NA Handling","what":"Parent Table Replacement of NA Values and Inheritance Principles","title":"Format Precedence and NA Handling","text":"addition cell level, string replacement NA values can specified parent table level. replacement string specified user cell, specific NA string cell one defined innermost parent table split (). NA value replacement string also specified cell level, one set parent table level ignored cell cell level format specific therefore takes precedence. following, slightly complicated example, can observe partial inheritance NA strings. , SD cells inherit parent table’s NA string, Mean cells .","code":"lyt9 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", mean, format = \"xx.xx\", na_str = \"not available\") build_table(lyt9, ADSL) # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————— # F # mean 32.76 34.12 35.20 # M # mean 35.57 37.44 35.38 # U # mean 31.67 31.00 35.25 # UNDIFFERENTIATED # mean 28.00 not available 45.00 lyt10 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze( vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\", format_na_str = \"\") }, na_str = \"not available\" ) build_table(lyt10, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00 lyt10a <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x)), \"SD\" = rcell(sd(x)), .formats = \"xx.xx\", .format_na_strs = \"\" ) }, na_str = \"not available\" ) build_table(lyt10a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # SD 6.09 7.06 7.43 # M # Mean 35.57 37.44 35.38 # SD 7.08 8.69 8.24 # U # Mean 31.67 31.00 35.25 # SD 3.21 5.66 3.10 # UNDIFFERENTIATED # Mean 28.00 45.00 # SD 1.41 lyt11 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format_na_str = \"\"), \"SD\" = rcell(sd(x)) ) }, format = \"xx.xx\", na_str = \"not available\" ) build_table(lyt11, ADSL) # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # SD 6.09 7.06 7.43 # M # Mean 35.57 37.44 35.38 # SD 7.08 8.69 8.24 # U # Mean 31.67 31.00 35.25 # SD 3.21 5.66 3.10 # UNDIFFERENTIATED # Mean 28.00 45.00 # SD not available not available 1.41"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to {rtables}","text":"rtables package provides framework create, tabulate, output tables R. design requirements rtables origin studying tables commonly used report analyses clinical trials; however, careful keep rtables general purpose toolkit. vignette, give short introduction rtables tabulating table. content vignette based following two resources: rtables useR 2020 presentation Gabriel Becker rtables - Framework Creating Complex Structured Reporting Tables Via Multi-Level Faceted Computations. packages used vignette rtables dplyr:","code":"library(rtables) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Introduction to {rtables}","text":"build table using rtables two components required: layout constructed using rtables functions, data.frame unaggregated data. two elements combined build table object. Table objects contain information content structure table, well instructions information processed construct table. obtaining table object, formatted table can printed ASCII format, exported variety formats (.txt, .pdf, .docx, etc.).","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Introduction to {rtables}","text":"data used vignette made using random number generators. data content relatively simple: one row per imaginary person one column per measurement: study arm, country origin, gender, handedness, age, weight. Note use factor variables level order represented row column order tabulate information df .","code":"n <- 400 set.seed(1) df <- tibble( arm = factor(sample(c(\"Arm A\", \"Arm B\"), n, replace = TRUE), levels = c(\"Arm A\", \"Arm B\")), country = factor(sample(c(\"CAN\", \"USA\"), n, replace = TRUE, prob = c(.55, .45)), levels = c(\"CAN\", \"USA\")), gender = factor(sample(c(\"Female\", \"Male\"), n, replace = TRUE), levels = c(\"Female\", \"Male\")), handed = factor(sample(c(\"Left\", \"Right\"), n, prob = c(.6, .4), replace = TRUE), levels = c(\"Left\", \"Right\")), age = rchisq(n, 30) + 10 ) %>% mutate( weight = 35 * rnorm(n, sd = .5) + ifelse(gender == \"Female\", 140, 180) ) head(df) # # A tibble: 6 × 6 # arm country gender handed age weight # # 1 Arm A USA Female Left 31.3 139. # 2 Arm B CAN Female Right 50.5 116. # 3 Arm A USA Male Right 32.4 186. # 4 Arm A USA Male Right 34.6 169. # 5 Arm B USA Female Right 43.0 160. # 6 Arm A USA Female Right 43.2 126."},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"building-a-table","dir":"Articles","previous_headings":"","what":"Building a Table","title":"Introduction to {rtables}","text":"aim vignette build following table step step:","code":"# Arm A Arm B # Female Male Female Male # (N=96) (N=105) (N=92) (N=107) # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.87 40.43 40.33 37.68 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.64 40.19 40.16 40.65 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.36 39.68 39.21 40.07 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.94 39.80 38.53 39.02"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"quick-start","dir":"Articles","previous_headings":"","what":"Quick Start","title":"Introduction to {rtables}","text":"table can achieved via qtable() function. new tabulation rtables layout framework, can use convenience wrapper create many types two-way frequency tables. purpose qtable enable quick exploratory data analysis. See exploratory_analysis vignette details. code recreate table : qtable function arguments can see many key concepts underlying rtables layout framework. user needs define: variables used facets row /column space? variable used summary analysis? function used summary? table include marginal summaries? labels needed clarify table content? sections look translating questions set features part rtables layout framework. Now let’s take look building example table layout.","code":"qtable(df, row_vars = c(\"country\", \"handed\"), col_vars = c(\"arm\", \"gender\"), avar = \"age\", afun = mean, summarize_groups = TRUE, row_labels = \"mean\" ) # Arm A Arm B # Female Male Female Male # age - mean (N=96) (N=105) (N=92) (N=107) # —————————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.87 40.43 40.33 37.68 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.64 40.19 40.16 40.65 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.36 39.68 39.21 40.07 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.94 39.80 38.53 39.02"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"layout-instructions","dir":"Articles","previous_headings":"","what":"Layout Instructions","title":"Introduction to {rtables}","text":"rtables basic table defined 0 rows one column representing data. Analyzing variable one way adding row: code first described table assigned description variable lyt. built table using actual data build_table(). description table called table layout. basic_table() start every table layout contains information one column representing data. analyze() instruction adds layout age variable analyzed mean() analysis function result rounded 1 decimal place. Hence, layout “pre-data”, , ’s description build table get data. can look layout isolated: general layouting instructions summarized : basic_table() layout representing table zero rows one column row space: split_rows_by(), split_rows_by_multivar(), split_rows_by_cuts(), split_rows_by_cutfun(), split_rows_by_quartiles() column space: split_cols_by(), split_cols_by_multivar(), split_cols_by_cuts(), split_cols_by_cutfun(), split_cols_by_quartiles() Summarizing Groups: summarize_row_groups() Analyzing Variables: analyze(), analyze_colvars() Using functions, possible create wide variety tables show document.","code":"lyt <- basic_table() %>% analyze(\"age\", mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # all obs # —————————————— # mean 39.4 lyt # A Pre-data Table Layout # # Column-Split Structure: # () # # Row-Split Structure: # age (** analysis **)"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"adding-column-structure","dir":"Articles","previous_headings":"","what":"Adding Column Structure","title":"Introduction to {rtables}","text":"now add structure columns adding column split based factor variable arm: resulting table one column per factor level arm. data represented first column df[df$arm == \"ARM \", ]. Hence, split_cols_by() partitions data among columns default. Column splitting can done recursive/nested manner adding sequential split_cols_by() layout instruction. ’s also possible add non-nested split. splitting arm gender: first column represents data df df$arm == \"\" & df$gender == \"Female\" second column data df df$arm == \"\" & df$gender == \"Male\", .","code":"lyt <- basic_table() %>% split_cols_by(\"arm\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # ———————————————————— # mean 39.5 39.4 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————— # mean 38.8 40.1 39.6 39.2"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"adding-row-structure","dir":"Articles","previous_headings":"","what":"Adding Row Structure","title":"Introduction to {rtables}","text":"far, created layouts analysis column splitting instructions, .e. analyze() split_cols_by(), respectively. resulted table multiple columns one data row. add row structure stratifying mean analysis country (.e. adding split row space): table data used derive first data cell (average age female Canadians Arm ) df$country == \"CAN\" & df$arm == \"Arm \" & df$gender == \"Female\". cell value can also calculated manually: Row structure can also used group table titled groups pages rendering. via ‘page splits’, declared via page_by = TRUE within call split_rows_by: go detail page-splits control page-group specific titles Title footer vignette. Note print render table without pagination, page_by splits currently rendered normal row splits. may change future releases.","code":"lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # —————————————————————————————————————— # CAN # mean 38.2 40.3 40.3 38.9 # USA # mean 39.2 39.7 38.9 39.6 mean(df$age[df$country == \"CAN\" & df$arm == \"Arm A\" & df$gender == \"Female\"]) # [1] 38.22447 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\", page_by = TRUE) %>% split_rows_by(\"handed\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) cat(export_as_txt(tbl, page_type = \"letter\", page_break = \"\\n\\n~~~~~~ Page Break ~~~~~~\\n\\n\")) # # country: CAN # # ———————————————————————————————————————— # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————— # Left # mean 38.9 40.4 40.3 37.7 # Right # mean 36.6 40.2 40.2 40.6 # # # ~~~~~~ Page Break ~~~~~~ # # # country: USA # # ———————————————————————————————————————— # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————— # Left # mean 40.4 39.7 39.2 40.1 # Right # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"adding-group-information","dir":"Articles","previous_headings":"","what":"Adding Group Information","title":"Introduction to {rtables}","text":"adding row splits, get default label rows split level, example CAN USA table . Besides column space subsetting, now subsetted data cell. often useful defining row splitting display information row group. rtables referred content information, .e. mean() row 2 descendant CAN (visible via indenting, though table underlying tree structure importance vignette). order add content information turn CAN label row content row, summarize_row_groups() function required. default, count (nrows()) percentage data relative column associated data calculated: relative percentage average age female Canadians calculated follows: group percentages per row split sum 1 column. can split row space dividing country handedness: Next, add count percentage summary handedness within country:","code":"lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # —————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # mean 38.2 40.3 40.3 38.9 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # mean 39.2 39.7 38.9 39.6 df_cell <- subset(df, df$country == \"CAN\" & df$arm == \"Arm A\" & df$gender == \"Female\") df_col_1 <- subset(df, df$arm == \"Arm A\" & df$gender == \"Female\") c(count = nrow(df_cell), percentage = nrow(df_cell) / nrow(df_col_1)) # count percentage # 45.00000 0.46875 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left # mean 38.9 40.4 40.3 37.7 # Right # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left # mean 40.4 39.7 39.2 40.1 # Right # mean 36.9 39.8 38.5 39.0 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.9 40.4 40.3 37.7 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.4 39.7 39.2 40.1 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"comparing-with-other-tabulation-frameworks","dir":"Articles","previous_headings":"","what":"Comparing with Other Tabulation Frameworks","title":"Introduction to {rtables}","text":"number table frameworks available R, including: gt xtable tableone tables number reasons choose rtables (yet another tables R package): Output tables ASCII text files. Table rendering (ASCII, HTML, etc.) separate data model. Hence, one always access non-rounded/non-formatted numbers. Pagination horizontal vertical directions meet health authority submission requirements. Cell, row, column, table reference system. Titles, footers, referential footnotes. Path based access cell content useful automated content generation. depth comparisons various tabulation frameworks can found Overview table R packages chapter Tables Clinical Trials R book compiled R Consortium Tables Working Group.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/introduction.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to {rtables}","text":"vignette learned: Every cell associated subset data - means much tabulation splitting/subsetting data. Tables can described pre-data using layouts. Tables form visualization data. vignettes rtables package provide detailed information rtables package. recommend continue tabulation_dplyr vignette compares information derived table vignette using dplyr.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/introspecting_tables.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introspecting Tables","text":"First, let’s set simple table.","code":"lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\") %>% split_cols_by(\"STRATA2\") %>% split_rows_by(\"STRATA1\") %>% add_overall_col(\"All\") %>% summarize_row_groups() %>% analyze(\"AGE\", afun = max, format = \"xx.x\") tbl <- build_table(lyt, ex_adsl) tbl # ARM A ARM B ARM C # S1 S2 S1 S2 S1 S2 All # (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) (N=400) # ————————————————————————————————————————————————————————————————————————————————————————————————— # A 18 (24.7%) 20 (32.8%) 22 (32.8%) 22 (32.8%) 14 (25.0%) 26 (34.2%) 122 (30.5%) # max 40.0 46.0 62.0 50.0 47.0 45.0 62.0 # B 28 (38.4%) 19 (31.1%) 19 (28.4%) 26 (38.8%) 18 (32.1%) 25 (32.9%) 135 (33.8%) # max 48.0 47.0 58.0 58.0 46.0 64.0 64.0 # C 27 (37.0%) 22 (36.1%) 26 (38.8%) 19 (28.4%) 24 (42.9%) 25 (32.9%) 143 (35.8%) # max 48.0 50.0 48.0 51.0 69.0 50.0 69.0"},{"path":"https://insightsengineering.github.io/rtables/articles/introspecting_tables.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting Started","title":"Introspecting Tables","text":"can get basic table dimensions, number rows, number columns following code:","code":"dim(tbl) # [1] 6 7 nrow(tbl) # [1] 6 ncol(tbl) # [1] 7"},{"path":"https://insightsengineering.github.io/rtables/articles/introspecting_tables.html","id":"detailed-table-structure","dir":"Articles","previous_headings":"","what":"Detailed Table Structure","title":"Introspecting Tables","text":"table_structure() function prints summary table’s row structure one two levels detail. default, summarizes structure subtable level. detail argument set \"row\", however, provides detailed row-level summary acts useful alternative might normally use str() function interrogate compound nested lists. make_row_df() make_col_df() functions create data.frame variety information table’s structure. useful introspection purposes label, name, abs_rownumber, path node_class columns (remainder information returned data.frame used pagination) also wrapper function, row_paths() available make_row_df display row path structure: default make_row_df() summarizes visible rows, setting visible_only FALSE gives us structural summary table full hierarchy subtables, including represented directly visible rows: make_col_df() similarly accepts visible_only, though meaning slightly different, indicating whether leaf columns summarized (defaults TRUE) whether higher level groups columns - analogous subtables row space - summarized well. Similarly, wrapper function col_paths() available, displays column structure: row_paths_summary() col_paths_summary() functions wrap respective make_*_df functions, printing name, node_class, path information (row case), label path information (column case), indented illustrate table structure:","code":"table_structure(tbl) # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 7] # [ElementaryTable] AGE (1 x 7) # [TableTree] B [cont: 1 x 7] # [ElementaryTable] AGE (1 x 7) # [TableTree] C [cont: 1 x 7] # [ElementaryTable] AGE (1 x 7) table_structure(tbl, detail = \"row\") # TableTree: [STRATA1] (STRATA1) # labelrow: [STRATA1] (STRATA1) - # children: # TableTree: [A] (A) # labelrow: [A] (A) - # content: # ElementaryTable: [A@content] () # labelrow: [] () - # children: # ContentRow: [A] (A) # children: # ElementaryTable: [AGE] (AGE) # labelrow: [AGE] (AGE) - # children: # DataRow: [max] (max) # TableTree: [B] (B) # labelrow: [B] (B) - # content: # ElementaryTable: [B@content] () # labelrow: [] () - # children: # ContentRow: [B] (B) # children: # ElementaryTable: [AGE] (AGE) # labelrow: [AGE] (AGE) - # children: # DataRow: [max] (max) # TableTree: [C] (C) # labelrow: [C] (C) - # content: # ElementaryTable: [C@content] () # labelrow: [] () - # children: # ContentRow: [C] (C) # children: # ElementaryTable: [AGE] (AGE) # labelrow: [AGE] (AGE) - # children: # DataRow: [max] (max) make_row_df(tbl)[, c(\"label\", \"name\", \"abs_rownumber\", \"path\", \"node_class\")] # label name abs_rownumber path node_class # 1 A A 1 STRATA1,.... ContentRow # 2 max max 2 STRATA1,.... DataRow # 3 B B 3 STRATA1,.... ContentRow # 4 max max 4 STRATA1,.... DataRow # 5 C C 5 STRATA1,.... ContentRow # 6 max max 6 STRATA1,.... DataRow row_paths(tbl) # [[1]] # [1] \"STRATA1\" \"A\" \"@content\" \"A\" # # [[2]] # [1] \"STRATA1\" \"A\" \"AGE\" \"max\" # # [[3]] # [1] \"STRATA1\" \"B\" \"@content\" \"B\" # # [[4]] # [1] \"STRATA1\" \"B\" \"AGE\" \"max\" # # [[5]] # [1] \"STRATA1\" \"C\" \"@content\" \"C\" # # [[6]] # [1] \"STRATA1\" \"C\" \"AGE\" \"max\" make_row_df(tbl, visible_only = FALSE)[, c(\"label\", \"name\", \"abs_rownumber\", \"path\", \"node_class\")] # label name abs_rownumber path node_class # 1 STRATA1 NA STRATA1 TableTree # 2 A NA STRATA1, A TableTree # 3 A@content NA STRATA1,.... ElementaryTable # 4 A A 1 STRATA1,.... ContentRow # 5 AGE NA STRATA1,.... ElementaryTable # 6 max max 2 STRATA1,.... DataRow # 7 B NA STRATA1, B TableTree # 8 B@content NA STRATA1,.... ElementaryTable # 9 B B 3 STRATA1,.... ContentRow # 10 AGE NA STRATA1,.... ElementaryTable # 11 max max 4 STRATA1,.... DataRow # 12 C NA STRATA1, C TableTree # 13 C@content NA STRATA1,.... ElementaryTable # 14 C C 5 STRATA1,.... ContentRow # 15 AGE NA STRATA1,.... ElementaryTable # 16 max max 6 STRATA1,.... DataRow make_col_df(tbl)[, c(\"label\", \"name\", \"abs_pos\", \"path\", \"leaf_indices\")] # label name abs_pos path leaf_indices # 1 S1 S1 1 ARMCD, A.... 1 # 2 S2 S2 2 ARMCD, A.... 2 # 3 S1 S1 3 ARMCD, A.... 3 # 4 S2 S2 4 ARMCD, A.... 4 # 5 S1 S1 5 ARMCD, A.... 5 # 6 S2 S2 6 ARMCD, A.... 6 # 7 All All 7 All, All 7 make_col_df(tbl, visible_only = FALSE)[, c(\"label\", \"name\", \"abs_pos\", \"path\", \"leaf_indices\")] # label name abs_pos path leaf_indices # 1 ARM A ARM A NA ARMCD, ARM A 1, 2 # 2 S1 S1 1 ARMCD, A.... 1 # 3 S2 S2 2 ARMCD, A.... 2 # 4 ARM B ARM B NA ARMCD, ARM B 3, 4 # 5 S1 S1 3 ARMCD, A.... 3 # 6 S2 S2 4 ARMCD, A.... 4 # 7 ARM C ARM C NA ARMCD, ARM C 5, 6 # 8 S1 S1 5 ARMCD, A.... 5 # 9 S2 S2 6 ARMCD, A.... 6 # 10 All All 7 All, All 7 col_paths(tbl) # [[1]] # [1] \"ARMCD\" \"ARM A\" \"STRATA2\" \"S1\" # # [[2]] # [1] \"ARMCD\" \"ARM A\" \"STRATA2\" \"S2\" # # [[3]] # [1] \"ARMCD\" \"ARM B\" \"STRATA2\" \"S1\" # # [[4]] # [1] \"ARMCD\" \"ARM B\" \"STRATA2\" \"S2\" # # [[5]] # [1] \"ARMCD\" \"ARM C\" \"STRATA2\" \"S1\" # # [[6]] # [1] \"ARMCD\" \"ARM C\" \"STRATA2\" \"S2\" # # [[7]] # [1] \"All\" \"All\" row_paths_summary(tbl) # rowname node_class path # ———————————————————————————————————————————————— # A ContentRow STRATA1, A, @content, A # max DataRow STRATA1, A, AGE, max # B ContentRow STRATA1, B, @content, B # max DataRow STRATA1, B, AGE, max # C ContentRow STRATA1, C, @content, C # max DataRow STRATA1, C, AGE, max col_paths_summary(tbl) # label path # —————————————————————————————————— # ARM A ARMCD, ARM A # S1 ARMCD, ARM A, STRATA2, S1 # S2 ARMCD, ARM A, STRATA2, S2 # ARM B ARMCD, ARM B # S1 ARMCD, ARM B, STRATA2, S1 # S2 ARMCD, ARM B, STRATA2, S2 # ARM C ARMCD, ARM C # S1 ARMCD, ARM C, STRATA2, S1 # S2 ARMCD, ARM C, STRATA2, S2 # All All, All"},{"path":"https://insightsengineering.github.io/rtables/articles/introspecting_tables.html","id":"insights-on-value-format-structure","dir":"Articles","previous_headings":"","what":"Insights on Value Format Structure","title":"Introspecting Tables","text":"can gain insight value formatting structure table using table_shell(), returns table output print() cell values replaced underlying format strings (e.g. instead 40.0, xx.x displayed, ). useful understanding structure table, debugging purposes. Another useful tool value_formats() function instead table returns matrix format strings cell value table. See printout examples:","code":"table_shell(tbl) # ARM A ARM B ARM C # S1 S2 S1 S2 S1 S2 All # (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) (N=400) # ———————————————————————————————————————————————————————————————————————————————————————————————— # A xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) # max xx.x xx.x xx.x xx.x xx.x xx.x xx.x # B xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) # max xx.x xx.x xx.x xx.x xx.x xx.x xx.x # C xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) # max xx.x xx.x xx.x xx.x xx.x xx.x xx.x value_formats(tbl) # ARM A.S1 ARM A.S2 ARM B.S1 ARM B.S2 ARM C.S1 # A \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" # B \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" # C \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" # ARM C.S2 All # A \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" # B \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" # C \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\""},{"path":"https://insightsengineering.github.io/rtables/articles/introspecting_tables.html","id":"applications","dir":"Articles","previous_headings":"","what":"Applications","title":"Introspecting Tables","text":"Knowing structure rtable object helpful retrieving specific values table. examples, see Path Based Cell Value Accessing section Subsetting Manipulating Table Contents vignette. Understanding table structure also important post-processing processes sorting pruning. details covered Pruning Sorting Tables vignette vignette.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/introspecting_tables.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introspecting Tables","text":"vignette learned number utility functions available examining underlying structure rtable objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/manual_table_construction.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Constructing rtables Manually","text":"main functions currently associated rtables Tables rtables can constructed via layout rtabulate tabulation frameworks also manually. Currently manual table construction way define column spans. main functions manual table constructions : rtable(): collection rrow() objects, column header default format rrow(): collection rcell() objects default format rcell(): collection data objects cell format","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/manual_table_construction.html","id":"simple-example","dir":"Articles","previous_headings":"","what":"Simple Example","title":"Constructing rtables Manually","text":"go explaining individual components used create table continue html conversion rtable() object: Next, [ operator lets access cell content. format cell run format_rcell(tbl[1,1])=. Note tbl[6, 1] tbl[6, 2] display rcell colspan.","code":"library(rtables) tbl <- rtable( header = c(\"Treatement\\nN=100\", \"Comparison\\nN=300\"), format = \"xx (xx.xx%)\", rrow(\"A\", c(104, .2), c(100, .4)), rrow(\"B\", c(23, .4), c(43, .5)), rrow(), rrow(\"this is a very long section header\"), rrow(\"estimate\", rcell(55.23, \"xx.xx\", colspan = 2)), rrow(\"95% CI\", indent = 1, rcell(c(44.8, 67.4), format = \"(xx.x, xx.x)\", colspan = 2)) ) as_html(tbl, width = \"80%\") tbl[1, 1] # Treatement # N=100 # ———————————————— # A 104 (20.00%)"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Pruning and Sorting Tables","text":"Often want filter reorder elements table ways take account table structure. example: Sorting subtables corresponding factor levels commonly observed levels occur first table. Sorting rows within single subtable Removing subtables represent 0 observations filtering contain 0 rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"a-table-in-need-of-attention","dir":"Articles","previous_headings":"","what":"A Table In Need of Attention","title":"Pruning and Sorting Tables","text":"","code":"library(rtables) library(dplyr) raw_lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% split_rows_by(\"RACE\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") raw_tbl <- build_table(raw_lyt, DM) raw_tbl # A: Drug X B: Placebo C: Combination # F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED # —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 0 (NA%) 0 (NA%) 37 (66.1%) 31 (62.0%) 0 (NA%) 0 (NA%) 40 (65.6%) 44 (64.7%) 0 (NA%) 0 (NA%) # A 15 (21.4%) 12 (23.5%) 0 (NA%) 0 (NA%) 14 (25.0%) 6 (12.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 30.40 34.42 NA NA 35.43 30.33 NA NA 37.40 36.25 NA NA # B 16 (22.9%) 8 (15.7%) 0 (NA%) 0 (NA%) 13 (23.2%) 16 (32.0%) 0 (NA%) 0 (NA%) 10 (16.4%) 12 (17.6%) 0 (NA%) 0 (NA%) # Mean 33.75 34.88 NA NA 32.46 30.94 NA NA 33.30 35.92 NA NA # C 13 (18.6%) 15 (29.4%) 0 (NA%) 0 (NA%) 10 (17.9%) 9 (18.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 36.92 35.60 NA NA 34.00 31.89 NA NA 33.47 31.38 NA NA # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 0 (NA%) 0 (NA%) 12 (21.4%) 12 (24.0%) 0 (NA%) 0 (NA%) 13 (21.3%) 14 (20.6%) 0 (NA%) 0 (NA%) # A 5 (7.1%) 1 (2.0%) 0 (NA%) 0 (NA%) 5 (8.9%) 2 (4.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.20 33.00 NA NA 28.00 30.00 NA NA 30.75 36.50 NA NA # B 7 (10.0%) 3 (5.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 6 (9.8%) 6 (8.8%) 0 (NA%) 0 (NA%) # Mean 36.14 34.33 NA NA 29.67 32.00 NA NA 36.33 31.00 NA NA # C 6 (8.6%) 6 (11.8%) 0 (NA%) 0 (NA%) 4 (7.1%) 7 (14.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.33 39.67 NA NA 34.50 34.00 NA NA 33.00 36.50 NA NA # WHITE 8 (11.4%) 6 (11.8%) 0 (NA%) 0 (NA%) 7 (12.5%) 7 (14.0%) 0 (NA%) 0 (NA%) 8 (13.1%) 10 (14.7%) 0 (NA%) 0 (NA%) # A 2 (2.9%) 1 (2.0%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 1 (1.6%) 5 (7.4%) 0 (NA%) 0 (NA%) # Mean 34.00 45.00 NA NA 29.33 33.33 NA NA 35.00 32.80 NA NA # B 4 (5.7%) 3 (5.9%) 0 (NA%) 0 (NA%) 1 (1.8%) 4 (8.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 1 (1.5%) 0 (NA%) 0 (NA%) # Mean 37.00 43.67 NA NA 48.00 36.75 NA NA 34.33 36.00 NA NA # C 2 (2.9%) 2 (3.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 0 (0.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 35.50 44.00 NA NA 44.67 NA NA NA 38.50 35.00 NA NA # AMERICAN INDIAN OR ALASKA NATIVE 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # MULTIPLE 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # OTHER 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # UNKNOWN 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"trimming-rows","dir":"Articles","previous_headings":"Trimming","what":"Trimming Rows","title":"Pruning and Sorting Tables","text":"Trimming represents convenience wrapper around simple, direct subsetting rows TableTree. use trim_rows() function table criteria function. rows criteria function returns TRUE removed, others retained. NOTE: row kept removed completely independently, awareness surrounding structure. means, example, subtree analysis rows removed removed . structure-aware filtering table, use pruning described next section. trimming function accepts TableRow object returns TRUE row removed. default trimming function removes rows columns values , .e. NA values 0 values:","code":"trim_rows(raw_tbl) # A: Drug X B: Placebo C: Combination # F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED # —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 0 (NA%) 0 (NA%) 37 (66.1%) 31 (62.0%) 0 (NA%) 0 (NA%) 40 (65.6%) 44 (64.7%) 0 (NA%) 0 (NA%) # A 15 (21.4%) 12 (23.5%) 0 (NA%) 0 (NA%) 14 (25.0%) 6 (12.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 30.40 34.42 NA NA 35.43 30.33 NA NA 37.40 36.25 NA NA # B 16 (22.9%) 8 (15.7%) 0 (NA%) 0 (NA%) 13 (23.2%) 16 (32.0%) 0 (NA%) 0 (NA%) 10 (16.4%) 12 (17.6%) 0 (NA%) 0 (NA%) # Mean 33.75 34.88 NA NA 32.46 30.94 NA NA 33.30 35.92 NA NA # C 13 (18.6%) 15 (29.4%) 0 (NA%) 0 (NA%) 10 (17.9%) 9 (18.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 36.92 35.60 NA NA 34.00 31.89 NA NA 33.47 31.38 NA NA # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 0 (NA%) 0 (NA%) 12 (21.4%) 12 (24.0%) 0 (NA%) 0 (NA%) 13 (21.3%) 14 (20.6%) 0 (NA%) 0 (NA%) # A 5 (7.1%) 1 (2.0%) 0 (NA%) 0 (NA%) 5 (8.9%) 2 (4.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.20 33.00 NA NA 28.00 30.00 NA NA 30.75 36.50 NA NA # B 7 (10.0%) 3 (5.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 6 (9.8%) 6 (8.8%) 0 (NA%) 0 (NA%) # Mean 36.14 34.33 NA NA 29.67 32.00 NA NA 36.33 31.00 NA NA # C 6 (8.6%) 6 (11.8%) 0 (NA%) 0 (NA%) 4 (7.1%) 7 (14.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.33 39.67 NA NA 34.50 34.00 NA NA 33.00 36.50 NA NA # WHITE 8 (11.4%) 6 (11.8%) 0 (NA%) 0 (NA%) 7 (12.5%) 7 (14.0%) 0 (NA%) 0 (NA%) 8 (13.1%) 10 (14.7%) 0 (NA%) 0 (NA%) # A 2 (2.9%) 1 (2.0%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 1 (1.6%) 5 (7.4%) 0 (NA%) 0 (NA%) # Mean 34.00 45.00 NA NA 29.33 33.33 NA NA 35.00 32.80 NA NA # B 4 (5.7%) 3 (5.9%) 0 (NA%) 0 (NA%) 1 (1.8%) 4 (8.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 1 (1.5%) 0 (NA%) 0 (NA%) # Mean 37.00 43.67 NA NA 48.00 36.75 NA NA 34.33 36.00 NA NA # C 2 (2.9%) 2 (3.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 0 (0.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 35.50 44.00 NA NA 44.67 NA NA NA 38.50 35.00 NA NA"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"trimming-columns","dir":"Articles","previous_headings":"Trimming","what":"Trimming Columns","title":"Pruning and Sorting Tables","text":"currently special utilities trimming columns can remove empty columns fairly straightforward column subsetting using col_counts() function: Now, interesting see table structured: deeper understanding fundamental structures rtables, suggest taking look slides 69-76 Slide deck. brief, important notice [TableTree] RACE root table split (split_rows_by(\"RACE\") %>%) two subtables: [TableTree] ASIAN [cont: 1 x 6] [TableTree] BLACK AFRICAN AMERICAN [cont: 1 x 6]. “described” summarize_row_groups() %>%, creates every split “content” table containing 1 row (1 cont: 1 x 6), rendered takes place LabelRow. two subtables contain STRATA1 table, representing split_rows_by(\"STRATA1\") layout, , similar RACE table, split subtables: one strata similar content tables; individual strata subtable, , contains ElementaryTable (whose children individual rows) generated analyze(\"AGE\") layout directive, .e. [ElementaryTable] AGE (1 x 6). subtable row structure important sorting pruning; values “content” (ContentRow) “value” (DataRow) rows use different access functions treated differently. Another interesting function can used understand connection row names representational path following:","code":"coltrimmed <- raw_tbl[, col_counts(raw_tbl) > 0] # Note: method with signature 'VTableTree#missing#ANY' chosen for function '[', # target signature 'TableTree#missing#logical'. # \"VTableTree#ANY#logical\" would also be valid h_coltrimmed <- head(coltrimmed, n = 14) h_coltrimmed # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 table_structure(h_coltrimmed) # [TableTree] RACE # [TableTree] ASIAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) row_paths_summary(h_coltrimmed) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN ContentRow RACE, ASIAN, @content, ASIAN # A ContentRow RACE, ASIAN, STRATA1, A, @content, A # Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean # B ContentRow RACE, ASIAN, STRATA1, B, @content, B # Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean # C ContentRow RACE, ASIAN, STRATA1, C, @content, C # Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean # BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN # A ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, @content, A # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean # B ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, @content, B # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean # C ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, @content, C # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"pruning","dir":"Articles","previous_headings":"","what":"Pruning","title":"Pruning and Sorting Tables","text":"Pruning similar outcome trimming, powerful complex, takes structure account. Pruning applied recursively, structural unit (subtable, row) applies pruning function level ’s children (user-specifiable maximum depth). default pruning function, example, determines subtree empty : Removing children contain single content row contains zeros NAs Removing rows contain either zeros NAs Removing full subtree unpruned children remain can also use low_obs_pruner() pruning function constructor create pruning function removes subtrees content summaries whose first entries column sum average specified number. (default summaries first entry per column count). Note pruning applied recursively, ASIAN subtree remains even though full BLACK AFRICAN AMERICAN subtree encompassed enough observations, strata within . can take care setting stop_depth pruning 1. can also see pruning lower number observations, say, total 16, stop_depth removes strata third race (WHITE).","code":"pruned <- prune_table(coltrimmed) pruned # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 pruned2 <- prune_table(coltrimmed, low_obs_pruner(10, \"mean\")) pruned2 # A: Drug X B: Placebo C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 pruned3 <- prune_table(coltrimmed, low_obs_pruner(10, \"sum\"), stop_depth = 1) pruned3 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 pruned4 <- prune_table(coltrimmed, low_obs_pruner(16, \"sum\")) pruned4 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"sorting-fundamentals","dir":"Articles","previous_headings":"Sorting","what":"Sorting Fundamentals","title":"Pruning and Sorting Tables","text":"Sorting rtables table done path, meaning sort operation occur particular location within table, direct children element path reordered. occurs whether children subtables , individual rows. Sorting done via sort_at_path() function, accepts (row) path scoring function. score function accepts subtree TableRow returns single orderable (typically numeric) value. Within subtable currently sorted, children reordered value score function. Importantly, “content” (ContentRow) “values” (DataRow) need treated differently scoring function retrieved: content subtable retrieved via content _table accessor. cont_n_allcols() scoring function provided rtables, works scoring subtables sum first elements first row subtable’s content table. Note function fails child scored content function (.e., summarize_row_groups() used corresponding point layout). can see ’s definition, : Therefore, fundamental difference pruning sorting sorting occurs particular places table, defined path. example, can sort strata values (ContentRow) observation counts within just ASIAN subtable:","code":"cont_n_allcols # function (tt) # { # ctab <- content_table(tt) # if (NROW(ctab) == 0) { # stop(\"cont_n_allcols score function used at subtable [\", # obj_name(tt), \"] that has no content table.\") # } # sum(sapply(row_values(tree_children(ctab)[[1]]), function(cv) cv[1])) # } # # sort_at_path(pruned, path = c(\"RACE\", \"ASIAN\", \"STRATA1\"), scorefun = cont_n_allcols) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # B and C are swapped as the global count (sum of all column counts) of strata C is higher than the one of strata B"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"wildcards-in-sort-paths","dir":"Articles","previous_headings":"Sorting","what":"Wildcards in Sort Paths","title":"Pruning and Sorting Tables","text":"Unlike uses pathing (currently), sorting path can contain “*“. indicates children subtable matching * element path sorted separately indicated remainder path * score function. Thus can extend sorting strata within ASIAN subtable race-specific subtables using wildcard: equivalent separately calling following: possible understand better pathing table_structure() highlights tree-like structure node names: row_paths_summary: Note latter see content rows paths following @content, e.g., ASIAN, @content, ASIAN. first given path (.e., , @content, <> rows used scoring functions begin cont_. can directly sort ethnicity observations increasing order: Within ethnicity separately, sort strata number females arm C (.e. column position 5):","code":"sort_at_path(pruned, path = c(\"RACE\", \"*\", \"STRATA1\"), scorefun = cont_n_allcols) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # All subtables, i.e. ASIAN, BLACK..., and WHITE, are reordered separately tmptbl <- sort_at_path(pruned, path = c(\"RACE\", \"ASIAN\", \"STRATA1\"), scorefun = cont_n_allcols) tmptbl <- sort_at_path(tmptbl, path = c(\"RACE\", \"BLACK OR AFRICAN AMERICAN\", \"STRATA1\"), scorefun = cont_n_allcols) tmptbl <- sort_at_path(tmptbl, path = c(\"RACE\", \"WHITE\", \"STRATA1\"), scorefun = cont_n_allcols) tmptbl # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 table_structure(pruned) # [TableTree] RACE # [TableTree] ASIAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] WHITE [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) row_paths_summary(pruned) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN ContentRow RACE, ASIAN, @content, ASIAN # A ContentRow RACE, ASIAN, STRATA1, A, @content, A # Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean # B ContentRow RACE, ASIAN, STRATA1, B, @content, B # Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean # C ContentRow RACE, ASIAN, STRATA1, C, @content, C # Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean # BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN # A ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, @content, A # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean # B ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, @content, B # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean # C ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, @content, C # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean # WHITE ContentRow RACE, WHITE, @content, WHITE # A ContentRow RACE, WHITE, STRATA1, A, @content, A # Mean DataRow RACE, WHITE, STRATA1, A, AGE, Mean # B ContentRow RACE, WHITE, STRATA1, B, @content, B # Mean DataRow RACE, WHITE, STRATA1, B, AGE, Mean # C ContentRow RACE, WHITE, STRATA1, C, @content, C # Mean DataRow RACE, WHITE, STRATA1, C, AGE, Mean ethsort <- sort_at_path(pruned, path = c(\"RACE\"), scorefun = cont_n_allcols, decreasing = FALSE) ethsort # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 sort_at_path(pruned, path = c(\"RACE\", \"*\", \"STRATA1\"), cont_n_onecol(5)) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"sorting-within-an-analysis-subtable","dir":"Articles","previous_headings":"Sorting","what":"Sorting Within an Analysis Subtable","title":"Pruning and Sorting Tables","text":"sorting within analysis subtable (e.g., subtable generated analysis function generates one row per group data), name subtable (generally name variable analyzed) must appear path, even variable label displayed table printed. show differences sorting analysis subtable (DataRow), content subtable (ContentRow), modify prune () similar raw table : now want sort median mean strata variables? need write custom score function ready-made ones moment work content nodes (content_table() access function cont_n_allcols() cont_n_onecol(), talk moment). , need think ordering, .e. need specify right path. suggest looking structure first table_structure() row_paths_summary(). see order AGE nodes need get something like : RACE, ASIAN, STRATA1, , AGE next level need sort. see now path sort first group. need wildcards: RACE, *, STRATA1, *, AGE. Now, found way select relevant paths want sort. want construct scoring function works median mean sort . , may want enter scoring function browser() see fed try retrieve single value returned sorting. allow user experiment , show possible solution considers summing column values retrieved row_values(tt) subtable fed function . Note score function defined subtable tt unique input parameter single numeric value output. help user visualize happening score function show example exploration debugging: can see powerful pragmatic might change sorting principles within custom scoring function. show selecting specific column sort. Looking pre-defined function cont_n_onecol() gives us insight proceed. see similar function cont_n_allcols() wrapped one allows parameter j used select specific column. selecting column want sort. table see mean median rows reordered values first column, compared raw table, desired. function can also columns nested within larger splits:","code":"more_analysis_fnc <- function(x) { in_rows( \"median\" = median(x), \"mean\" = mean(x), .formats = \"xx.x\" ) } raw_lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by( \"RACE\", split_fun = drop_and_remove_levels(\"WHITE\") # dropping WHITE levels ) %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\", afun = more_analysis_fnc) tbl <- build_table(raw_lyt, DM) %>% prune_table() %>% print() # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) # A 27 (22.3%) 20 (18.9%) 31 (24.0%) # median 30.0 33.0 36.0 # mean 32.2 33.9 36.8 # B 24 (19.8%) 29 (27.4%) 22 (17.1%) # median 32.5 32.0 34.0 # mean 34.1 31.6 34.7 # C 28 (23.1%) 19 (17.9%) 31 (24.0%) # median 36.5 34.0 33.0 # mean 36.2 33.0 32.4 # BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) # A 6 (5.0%) 7 (6.6%) 8 (6.2%) # median 32.0 29.0 32.5 # mean 31.5 28.6 33.6 # B 10 (8.3%) 6 (5.7%) 12 (9.3%) # median 33.0 30.0 33.5 # mean 35.6 30.8 33.7 # C 12 (9.9%) 11 (10.4%) 7 (5.4%) # median 33.0 36.0 32.0 # mean 35.5 34.2 35.0 table_structure(tbl) # Direct inspection into the tree-like structure of rtables # [TableTree] RACE # [TableTree] ASIAN [cont: 1 x 3] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] B [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] C [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 3] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] B [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] C [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) scorefun <- function(tt) { # Here we could use browser() sum(unlist(row_values(tt))) } sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun) # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) # A 27 (22.3%) 20 (18.9%) 31 (24.0%) # mean 32.2 33.9 36.8 # median 30.0 33.0 36.0 # B 24 (19.8%) 29 (27.4%) 22 (17.1%) # mean 34.1 31.6 34.7 # median 32.5 32.0 34.0 # C 28 (23.1%) 19 (17.9%) 31 (24.0%) # median 36.5 34.0 33.0 # mean 36.2 33.0 32.4 # BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) # A 6 (5.0%) 7 (6.6%) 8 (6.2%) # mean 31.5 28.6 33.6 # median 32.0 29.0 32.5 # B 10 (8.3%) 6 (5.7%) 12 (9.3%) # mean 35.6 30.8 33.7 # median 33.0 30.0 33.5 # C 12 (9.9%) 11 (10.4%) 7 (5.4%) # mean 35.5 34.2 35.0 # median 33.0 36.0 32.0 > sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun) Called from: scorefun(x) Browse[1]> tt ### THIS IS THE LEAF LEVEL -> DataRow ### [DataRow indent_mod 0]: median 30.0 33.0 36.0 Browse[1]> row_values(tt) ### Extraction of values -> It will be a named list! ### $`A: Drug X` [1] 30 $`B: Placebo` [1] 33 $`C: Combination` [1] 36 Browse[1]> sum(unlist(row_values(tt))) ### Final value we want to give back to sort_at_path ### [1] 99 cont_n_onecol # function (j) # { # function(tt) { # ctab <- content_table(tt) # if (NROW(ctab) == 0) { # stop(\"cont_n_allcols score function used at subtable [\", # obj_name(tt), \"] that has no content table.\") # } # row_values(tree_children(ctab)[[1]])[[j]][1] # } # } # # scorefun_onecol <- function(colpath) { function(tt) { # Here we could use browser() unlist(cell_values(tt, colpath = colpath), use.names = FALSE)[1] # Modified to lose the list names } } sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun_onecol(colpath = c(\"ARM\", \"A: Drug X\"))) # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) # A 27 (22.3%) 20 (18.9%) 31 (24.0%) # mean 32.2 33.9 36.8 # median 30.0 33.0 36.0 # B 24 (19.8%) 29 (27.4%) 22 (17.1%) # mean 34.1 31.6 34.7 # median 32.5 32.0 34.0 # C 28 (23.1%) 19 (17.9%) 31 (24.0%) # median 36.5 34.0 33.0 # mean 36.2 33.0 32.4 # BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) # A 6 (5.0%) 7 (6.6%) 8 (6.2%) # median 32.0 29.0 32.5 # mean 31.5 28.6 33.6 # B 10 (8.3%) 6 (5.7%) 12 (9.3%) # mean 35.6 30.8 33.7 # median 33.0 30.0 33.5 # C 12 (9.9%) 11 (10.4%) 7 (5.4%) # mean 35.5 34.2 35.0 # median 33.0 36.0 32.0 # Simpler table tbl <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_and_remove_levels(c(\"U\", \"UNDIFFERENTIATED\")) ) %>% analyze(\"AGE\", afun = more_analysis_fnc) %>% build_table(DM) %>% prune_table() %>% print() # A: Drug X B: Placebo C: Combination # F M F M F M # ————————————————————————————————————————————————————————— # median 32.0 35.0 33.0 31.0 35.0 32.0 # mean 33.7 36.5 33.8 32.1 34.9 34.3 sort_at_path(tbl, c(\"AGE\"), scorefun_onecol(colpath = c(\"ARM\", \"B: Placebo\", \"SEX\", \"F\"))) # A: Drug X B: Placebo C: Combination # F M F M F M # ————————————————————————————————————————————————————————— # mean 33.7 36.5 33.8 32.1 34.9 34.3 # median 32.0 35.0 33.0 31.0 35.0 32.0"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"writing-custom-pruning-criteria-and-scoring-functions","dir":"Articles","previous_headings":"","what":"Writing Custom Pruning Criteria and Scoring Functions","title":"Pruning and Sorting Tables","text":"Pruning criteria scoring functions map TableTree TableRow objects Boolean value (pruning criteria) sortable scalar value (scoring functions). currently need interact structure objects usual. Indeed, showed already sorting can complicated concept tree-like structure pathing well understood. important though mind following functions can used pruning sorting function retrieve relevant information table.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"useful-functions-and-accessors","dir":"Articles","previous_headings":"Writing Custom Pruning Criteria and Scoring Functions","what":"Useful Functions and Accessors","title":"Pruning and Sorting Tables","text":"accepts rowpath colpath restrict cell values returned obj_name() - Retrieves name object. Note can differ label displayed () printing. match element path. obj_label() - Retrieves display label object. Note can differ name appears path. content_table() - Retrieves TableTree object’s content table (contains summary rows). tree_children() - Retrieves TableTree object’s direct children (either subtables, rows possibly mix thereof, though happen practice)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"sort-by-a-character-score","dir":"Articles","previous_headings":"Writing Custom Pruning Criteria and Scoring Functions > Example Custom Scoring Functions","what":"Sort by a character “score”","title":"Pruning and Sorting Tables","text":"case, convenience/simplicity, use name table element logic returns single string used . sort ethnicity alphabetical order (practice undoing previous sorting ethnicity ). NOTE: Generally appropriately done using reorder_split_levels() function within layout rather sort post-processing step, character scorers may may map easily layouting directives.","code":"silly_name_scorer <- function(tt) { nm <- obj_name(tt) print(nm) nm } sort_at_path(ethsort, \"RACE\", silly_name_scorer) # Now, it is sorted alphabetically! # [1] \"WHITE\" # [1] \"BLACK OR AFRICAN AMERICAN\" # [1] \"ASIAN\" # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00"},{"path":"https://insightsengineering.github.io/rtables/articles/sorting_pruning.html","id":"sort-by-the-percent-difference-in-counts-between-genders-in-arm-c","dir":"Articles","previous_headings":"Writing Custom Pruning Criteria and Scoring Functions > Example Custom Scoring Functions","what":"Sort by the Percent Difference in Counts Between Genders in Arm C","title":"Pruning and Sorting Tables","text":"need F M percents, Arm C (.e. columns 5 6), differenced. sort strata within ethnicity percent difference counts males females arm C. Note: statistically meaningful , fact terrible idea reorders strata seemingly () random within race, illustrates various things need inside custom sorting functions.","code":"silly_gender_diffcount <- function(tt) { ## (1st) content row has same name as object (STRATA1 level) rpath <- c(obj_name(tt), \"@content\", obj_name(tt)) ## the [1] below is cause these are count (pct%) cells ## and we only want the count part! mcount <- unlist(cell_values( tt, rowpath = rpath, colpath = c(\"ARM\", \"C: Combination\", \"SEX\", \"M\") ))[1] fcount <- unlist(cell_values( tt, rowpath = rpath, colpath = c(\"ARM\", \"C: Combination\", \"SEX\", \"F\") ))[1] (mcount - fcount) / fcount } sort_at_path(pruned, c(\"RACE\", \"*\", \"STRATA1\"), silly_gender_diffcount) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"provided-functions","dir":"Articles","previous_headings":"Controlling Facet Levels","what":"Provided Functions","title":"Controlling Splitting Behavior","text":"default, split_*_by(varname, ...) generates facet level variable varname takes data - including unobserved ones factor case. behavior can customized various ways. straightforward way customize facets generated split one split functions split function families provided rtables. predefined split functions function factories implement commonly desired customization patterns splitting behavior (.e., faceting behavior). include: remove_split_levels - remove specified levels data facet generation. keep_split_levels - keep specified levels data facet generation (removing others). drop_split_levels - drop levels unobserved within data split, .e., associated parent facet. reorder_split_levels - reorder levels (thus generated facets) specified order. trim_levels_in_group - drop unobserved levels another variable independently within data associated facet generated current split. add_overall_level, add_combo_levels - add additional “virtual” levels combine two levels variable split. See following section. trim_levels_to_map - trim levels multiple variables pre-specified set value combinations. See following section. first four fairly self-describing brevity, refer readers ?split_funcs details including working examples.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"controlling-combinations-of-levels-across-multiple-variables","dir":"Articles","previous_headings":"Controlling Facet Levels","what":"Controlling Combinations of Levels Across Multiple Variables","title":"Controlling Splitting Behavior","text":"Often nested splitting involving multiple variables, values variables question logically nested; meaning certain values inner variable coherent combination specific value values outer variable. example, suppose variable vehicle_class, can take values \"automobile\", \"boat\", variable vehicle_type, can take values \"car\", \"truck\", \"suv\",\"sailboat\", \"cruiseliner\". combination (\"automobile\", \"cruiseliner\") make sense never occur (correctly cleaned) data set; combination (\"boat\", \"truck\"). showcase strategies deal next sections using following artificial data:","code":"set.seed(0) levs_type <- c(\"car\", \"truck\", \"suv\", \"sailboat\", \"cruiseliner\") vclass <- sample(c(\"auto\", \"boat\"), 1000, replace = TRUE) auto_inds <- which(vclass == \"auto\") vtype <- rep(NA_character_, 1000) vtype[auto_inds] <- sample( c(\"car\", \"truck\"), ## suv missing on purpose length(auto_inds), replace = TRUE ) vtype[-auto_inds] <- sample( c(\"sailboat\", \"cruiseliner\"), 1000 - length(auto_inds), replace = TRUE ) vehic_data <- data.frame( vehicle_class = factor(vclass), vehicle_type = factor(vtype, levels = levs_type), color = sample( c(\"white\", \"black\", \"red\"), 1000, prob = c(1, 2, 1), replace = TRUE ), cost = ifelse( vclass == \"boat\", rnorm(1000, 100000, sd = 5000), rnorm(1000, 40000, sd = 5000) ) ) head(vehic_data) ## vehicle_class vehicle_type color cost ## 1 boat sailboat black 100393.81 ## 2 auto car white 38150.17 ## 3 boat sailboat white 98696.13 ## 4 auto truck white 37677.16 ## 5 auto truck black 38489.27 ## 6 boat cruiseliner black 108709.72"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"trim_levels_in_group","dir":"Articles","previous_headings":"Controlling Facet Levels > Controlling Combinations of Levels Across Multiple Variables","what":"trim_levels_in_group","title":"Controlling Splitting Behavior","text":"trim_levels_in_group split function factory creates split functions deal issue empirically; combination observed data tabulated appear nested facets within table, , . use default level-based faceting, get several logically incoherent cells within table: obviously table want, majority space taken meaningless combinations. use trim_levels_in_group trim levels vehicle_type separately within level vehicle_class, get table meaningful combinations: Note, however, contain meaningful combinations, actually observed data; happens include perfectly valid \"auto\", \"suv\" combination. restrict level combinations valid regardless whether combination observed, must use trim_levels_to_map() instead.","code":"library(rtables) lyt <- basic_table() %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\") %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt, vehic_data) ## black white red ## ———————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 ## truck ## Mean 40061.70 40635.74 40024.41 ## suv ## Mean NA NA NA ## sailboat ## Mean NA NA NA ## cruiseliner ## Mean NA NA NA ## boat ## car ## Mean NA NA NA ## truck ## Mean NA NA NA ## suv ## Mean NA NA NA ## sailboat ## Mean 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100212.00 99340.25 100363.52 lyt2 <- basic_table() %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_in_group(\"vehicle_type\")) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt2, vehic_data) ## black white red ## ———————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 ## truck ## Mean 40061.70 40635.74 40024.41 ## boat ## sailboat ## Mean 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100212.00 99340.25 100363.52"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"trim_levels_to_map","dir":"Articles","previous_headings":"Controlling Facet Levels > Controlling Combinations of Levels Across Multiple Variables","what":"trim_levels_to_map","title":"Controlling Splitting Behavior","text":"trim_levels_to_map similar trim_levels_in_group purpose avoid combinatorial explosion nesting splitting logically nested variables. Unlike sibling function, however, trim_levels_to_map define exact set allowed combinations priori, exact set combinations produced resulting table, regardless whether observed . Now see \"auto\", \"suv\" combination present, even though populated NAs (data category), logically invalid combinations still absent.","code":"library(tibble) map <- tribble( ~vehicle_class, ~vehicle_type, \"auto\", \"truck\", \"auto\", \"suv\", \"auto\", \"car\", \"boat\", \"sailboat\", \"boat\", \"cruiseliner\" ) lyt3 <- basic_table() %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt3, vehic_data) ## black white red ## ———————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 ## truck ## Mean 40061.70 40635.74 40024.41 ## suv ## Mean NA NA NA ## boat ## sailboat ## Mean 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100212.00 99340.25 100363.52"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"combining-levels","dir":"Articles","previous_headings":"Controlling Facet Levels","what":"Combining Levels","title":"Controlling Splitting Behavior","text":"Another common manipulation faceting table context introduction combination levels explicitly modeled data. often, involves addition “overall” category, principle practice can involve arbitrary combination levels. rtables explicitly supports via add_overall_level (case) add_combo_levels split function factories.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"add_overall_level","dir":"Articles","previous_headings":"Controlling Facet Levels > Combining Levels","what":"add_overall_level","title":"Controlling Splitting Behavior","text":"add_overall_level accepts valname name new level, well label, first (whether come first, TRUE, last, FALSE, ordering). Building arbitrary vehicles table, can use create “colors” category: column counts turned , can see “Colors” column encompasses full 1000 (completely fake) vehicles data set. add arbitrary combinations, use add_combo_levels.","code":"lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\", split_fun = add_overall_level(\"allcolors\", label = \"All Colors\")) %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt4, vehic_data) ## All Colors black white red ## (N=1000) (N=521) (N=251) (N=228) ## ————————————————————————————————————————————————————————————— ## auto ## car ## Mean 40095.49 40431.92 40518.92 38713.14 ## truck ## Mean 40194.68 40061.70 40635.74 40024.41 ## suv ## Mean NA NA NA NA ## boat ## sailboat ## Mean 100133.22 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100036.76 100212.00 99340.25 100363.52"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"add_combo_levels","dir":"Articles","previous_headings":"Controlling Facet Levels > Combining Levels","what":"add_combo_levels","title":"Controlling Splitting Behavior","text":"add_combo_levels allows us add one arbitrary combination levels faceting structure table. defining combination data.frame describes levels want add. combination data.frame following columns one row combination add: valname - string indicating name value, appear paths. label - string indicating label displayed rendering. levelcombo - character vector individual levels combined combination level. exargs - list (usually list()) extra arguments passed analysis content functions tabulated within column row. Suppose wanted combinations levels non-white colors, white black colors. like :","code":"combodf <- tribble( ~valname, ~label, ~levelcombo, ~exargs, \"non-white\", \"Non-White\", c(\"black\", \"red\"), list(), \"blackwhite\", \"Black or White\", c(\"black\", \"white\"), list() ) lyt5 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\", split_fun = add_combo_levels(combodf)) %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt5, vehic_data) ## black white red Non-White Black or White ## (N=521) (N=251) (N=228) (N=749) (N=772) ## ————————————————————————————————————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 39944.93 40460.77 ## truck ## Mean 40061.70 40635.74 40024.41 40050.66 40243.57 ## suv ## Mean NA NA NA NA NA ## boat ## sailboat ## Mean 99349.69 99996.54 101865.73 100179.72 99567.50 ## cruiseliner ## Mean 100212.00 99340.25 100363.52 100258.56 99937.47"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"fully-customizing-split-facet-behavior","dir":"Articles","previous_headings":"","what":"Fully Customizing Split (Facet) Behavior","title":"Controlling Splitting Behavior","text":"Beyond ability select common splitting customizations split functions split function factories rtables provides, can also fully customize every aspect splitting behavior creating split functions. possible hand, primary way via make_split_fun() function, accepts functions implementing different component behaviors combines split function can used layout. Splitting, faceting done rtables, can thought combination 3 steps: preprocessing - transformation incoming data faceted e.g., dropping unused factor levels, etc. splitting - mapping incoming data set 1 subsets representing individual facets. postprocessing - operations facets - e.g., combining , removing , etc. make_split_fun() function allows us specify custom behaviors steps independently defining custom splitting behavior via pre, core_split, post arguments, dictate steps, respectively. pre argument accepts zero pre-processing functions, must accept: df, spl, vals, labels, can optionally accept .spl_context. manipulate df (incoming data split) return modified data.frame. modified data.frame must contain columns present incoming data.frame, can add columns necessary. Although, note new columns used layout split analysis variables, present validity checking done. pre-processing component useful things manipulating factor levels, e.g., trim unobserved ones reorder levels based observed counts, etc. detailed discussion custom split functions , example custom split function implemented via make_split_fun(), see ?custom_split_funs.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"an-example-custom-split-function","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior","what":"An Example Custom Split Function","title":"Controlling Splitting Behavior","text":"implement arbitrary, custom split function specify pre- post-processing instructions. unusual users need override core splitting logic - , fact, supported row space currently - leave example provide another narrow example usage .","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"an-illustrative-example-of-a-custom-split-function","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior > An Example Custom Split Function","what":"An Illustrative Example of A Custom Split Function","title":"Controlling Splitting Behavior","text":"First, define two aspects ‘pre-processing step’ behavior: function reverses order levels variable (retaining level associated observation), function factory creates function removes level data associated . Finally implement post-processing function. reorder facets based amount data represents. Finally, construct custom split function use create table:","code":"## reverse order of levels rev_lev <- function(df, spl, vals, labels, ...) { ## in the split_rows_by() and split_cols_by() cases, ## spl_variable() gives us the variable var <- spl_variable(spl) vec <- df[[var]] levs <- if (is.character(vec)) unique(vec) else levels(vec) df[[var]] <- factor(vec, levels = rev(levs)) df } rem_lev_facet <- function(torem) { function(df, spl, vals, labels, ...) { var <- spl_variable(spl) vec <- df[[var]] bad <- vec == torem df <- df[!bad, ] levs <- if (is.character(vec)) unique(vec) else levels(vec) df[[var]] <- factor(as.character(vec[!bad]), levels = setdiff(levs, torem)) df } } sort_them_facets <- function(splret, spl, fulldf, ...) { ord <- order(sapply(splret$datasplit, nrow)) make_split_result( splret$values[ord], splret$datasplit[ord], splret$labels[ord] ) } silly_splfun1 <- make_split_fun( pre = list( rev_lev, rem_lev_facet(\"white\") ), post = list(sort_them_facets) ) lyt6 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\", split_fun = silly_splfun1) %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt6, vehic_data) ## red black ## (N=228) (N=521) ## ————————————————————————————————————— ## auto ## car ## Mean 38713.14 40431.92 ## truck ## Mean 40024.41 40061.70 ## suv ## Mean NA NA ## boat ## sailboat ## Mean 101865.73 99349.69 ## cruiseliner ## Mean 100363.52 100212.00"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"overriding-the-core-split-function","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior > An Example Custom Split Function","what":"Overriding the Core Split Function","title":"Controlling Splitting Behavior","text":"Currently, overriding core split behavior supported functions used row splits. Next, write custom core-splitting function divides observations 4 groups: first 100, observations 101-500, observations 501-900, last hundred. claim test structural bias first last observations, really simply illustrate overriding core splitting machinery meaningful statistical purpose. can use construct splitting function. can combined pre- post-processing functions, stages performed independently, case, won’t, core splitting behavior pre- post-processing make much sense.","code":"silly_core_split <- function(spl, df, vals, labels, .spl_context) { make_split_result( c(\"first\", \"lowmid\", \"highmid\", \"last\"), datasplit = list( df[1:100, ], df[101:500, ], df[501:900, ], df[901:1000, ] ), labels = c( \"first 100\", \"obs 101-500\", \"obs 501-900\", \"last 100\" ) ) } even_sillier_splfun <- make_split_fun(core_split = silly_core_split) lyt7 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\", split_fun = even_sillier_splfun) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt7, vehic_data) ## black white red ## (N=521) (N=251) (N=228) ## ————————————————————————————————————————————————— ## first 100 ## car ## Mean 40496.05 37785.41 37623.17 ## truck ## Mean 41094.17 40437.29 37866.81 ## suv ## Mean NA NA NA ## sailboat ## Mean 100560.80 102017.05 101185.96 ## cruiseliner ## Mean 100838.12 96952.27 100610.71 ## obs 101-500 ## car ## Mean 39350.88 41185.98 37978.72 ## truck ## Mean 40166.87 41385.32 39885.72 ## suv ## Mean NA NA NA ## sailboat ## Mean 98845.47 99563.02 101462.79 ## cruiseliner ## Mean 101558.62 99039.91 97335.05 ## obs 501-900 ## car ## Mean 40721.82 40379.48 38681.26 ## truck ## Mean 39951.92 39846.89 39840.39 ## suv ## Mean NA NA NA ## sailboat ## Mean 99533.20 100347.18 102732.12 ## cruiseliner ## Mean 99140.43 100074.43 101994.99 ## last 100 ## car ## Mean 45204.44 40626.95 41214.33 ## truck ## Mean 38920.70 40620.47 42899.14 ## suv ## Mean NA NA NA ## sailboat ## Mean 99380.21 97644.77 101691.92 ## cruiseliner ## Mean 100017.53 99581.94 100751.30"},{"path":"https://insightsengineering.github.io/rtables/articles/split_functions.html","id":"design-of-pre--and-post-processing-functions-for-use-in-make_split_fun","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior > An Example Custom Split Function","what":"Design of Pre- and Post-Processing Functions For Use in make_split_fun","title":"Controlling Splitting Behavior","text":"Pre-processing post-processing functions custom-splitting context best thought (implemented ) independent, atomic building blocks desired overall behavior. allows reused flexible mix--match way. rtables provides several behavior components implemented either functions function factories: drop_facet_levels - drop unobserved levels variable split trim_levels_in_facets - provides trim_levels_in_group behavior add_overall_facet - add combination facet full data add_combo_facet - add single combination facet (can used single make_split_fun call)","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/subsetting_tables.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Subsetting and Manipulating Table Contents","text":"TableTree objects based tree data structure name indicates. package written user need walk trees many basic table manipulations. Walking trees still necessary certain manipulation subject different vignette. vignette show methods subset tables extract cell values. use following table illustrative purposes:","code":"library(rtables) library(dplyr) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(c(\"AGE\", \"STRATA1\")) tbl <- build_table(lyt, ex_adsl %>% filter(SEX %in% c(\"M\", \"F\"))) tbl # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————— # F # AGE # Mean 32.76 34.12 35.20 # STRATA1 # A 21 24 18 # B 25 27 21 # C 33 26 27 # M # AGE # Mean 35.57 37.44 35.38 # STRATA1 # A 16 19 20 # B 21 17 21 # C 14 19 19"},{"path":"https://insightsengineering.github.io/rtables/articles/subsetting_tables.html","id":"traditional-subsetting-and-modification-with","dir":"Articles","previous_headings":"","what":"Traditional Subsetting and modification with [","title":"Subsetting and Manipulating Table Contents","text":"[ [<- accessor functions operate largely data.frame cousins: particular means label rows treated rows empty cell values, rather rows without cells multiple values can specified row column position negative numeric positions supported, though like [.data.frame mixed positive ones [ always returns class object subset unless drop = TRUE [ , drop = TRUE returns raw (possibly multi-element) value associated cell. Known Differences [.data.frame - absolute position currently used reorder columns rows. Note general result ordering unlikely structurally valid. change order values, please read sorting pruning vignette relevant function (sort_at_path()). - character indices treated paths, vectors names [ [<- [ accessor function always returns TableTree object drop=TRUE set. first argument row indices second argument column indices. Alternatively logical subsetting can used. indices based visible rows tree structure. : table empty cell first row label row. need access cell actual cell data: retrieve value, use drop = TRUE: One can access multiple rows columns: Note repeat label rows descending children, e.g. show first row derived AGE. order repeat content/label information, one use pagination feature. Please read related vignette. Character indices interpreted paths (see ), elements matched names(tbl):","code":"tbl[1, 1] # A: Drug X # ————————————— # F tbl[3, 1] # A: Drug X # ———————————————— # Mean 32.76 tbl[3, 1, drop = TRUE] # [1] 32.75949 tbl[1:3, 1:2] # A: Drug X B: Placebo # ————————————————————————————————— # F # AGE # Mean 32.76 34.12 tbl[2:4, ] # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————— # AGE # Mean 32.76 34.12 35.20 # STRATA1 tbl[, c(\"ARM\", \"A: Drug X\")] # Note: method with signature 'VTableTree#missing#ANY' chosen for function '[', # target signature 'TableTree#missing#character'. # \"VTableTree#ANY#character\" would also be valid # A: Drug X # ————————————————————— # F # AGE # Mean 32.76 # STRATA1 # A 21 # B 25 # C 33 # M # AGE # Mean 35.57 # STRATA1 # A 16 # B 21 # C 14"},{"path":"https://insightsengineering.github.io/rtables/articles/subsetting_tables.html","id":"dealing-with-titles-foot-notes-and-top-left-information","dir":"Articles","previous_headings":"Traditional Subsetting and modification with [","what":"Dealing with titles, foot notes, and top left information","title":"Subsetting and Manipulating Table Contents","text":"standard additional information kept subsetting. , show complete table still possible keep (possibly) relevant information. Normal subsetting loses information showed . rows kept, top left information also kept. can also imposed adding keep_topleft = TRUE subsetting follows: referenced entry present subsetting, also referential footnote appear. Please consider reading relevant vignette referential footnotes. case subsetting, referential footnotes default indexed , produced table new one. Similar used keep top left information, can specify keep information original table. standard foot notes always present titles kept.","code":"top_left(tbl) <- \"SEX\" main_title(tbl) <- \"Table 1\" subtitles(tbl) <- c(\"Authors:\", \" - Abcd Zabcd\", \" - Cde Zbcd\") main_footer(tbl) <- \"Please regard this table as an example of smart subsetting\" prov_footer(tbl) <- \"Do remember where you read this though\" fnotes_at_path(tbl, rowpath = c(\"M\", \"AGE\", \"Mean\"), colpath = c(\"ARM\", \"A: Drug X\")) <- \"Very important mean\" tbl[3, 3] # C: Combination # ————————————————————— # Mean 35.20 tbl[, 2:3] # SEX B: Placebo C: Combination # ——————————————————————————————————————— # F # AGE # Mean 34.12 35.20 # STRATA1 # A 24 18 # B 27 21 # C 26 27 # M # AGE # Mean 37.44 35.38 # STRATA1 # A 19 20 # B 17 21 # C 19 19 tbl[1:3, 3, keep_topleft = TRUE] # SEX C: Combination # ————————————————————————— # F # AGE # Mean 35.20 tbl[10, 1] # A: Drug X # ———————————————— # Mean 35.57 {1} # ———————————————— # # {1} - Very important mean # ———————————————— col_paths_summary(tbl) # Use these to find the right path to value or label # label path # ————————————————————————————————————— # A: Drug X ARM, A: Drug X # B: Placebo ARM, B: Placebo # C: Combination ARM, C: Combination row_paths_summary(tbl) # # rowname node_class path # ————————————————————————————————————————————— # F LabelRow SEX, F # AGE LabelRow SEX, F, AGE # Mean DataRow SEX, F, AGE, Mean # STRATA1 LabelRow SEX, F, STRATA1 # A DataRow SEX, F, STRATA1, A # B DataRow SEX, F, STRATA1, B # C DataRow SEX, F, STRATA1, C # M LabelRow SEX, M # AGE LabelRow SEX, M, AGE # Mean DataRow SEX, M, AGE, Mean # STRATA1 LabelRow SEX, M, STRATA1 # A DataRow SEX, M, STRATA1, A # B DataRow SEX, M, STRATA1, B # C DataRow SEX, M, STRATA1, C # To select column value, use `NULL` for `rowpath` fnotes_at_path(tbl, rowpath = NULL, colpath = c(\"ARM\", \"A: Drug X\")) <- \"Interesting\" tbl[3, 1] # A: Drug X {1} # ———————————————————— # Mean 32.76 # ———————————————————— # # {1} - Interesting # ———————————————————— # reindexing of {2} as {1} fnotes_at_path(tbl, rowpath = c(\"M\", \"AGE\", \"Mean\"), colpath = NULL) <- \"THIS mean\" tbl # {1}, {2}, and {3} are present # Table 1 # Authors: # - Abcd Zabcd # - Cde Zbcd # # —————————————————————————————————————————————————————————— # SEX A: Drug X {1} B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # AGE # Mean 32.76 34.12 35.20 # STRATA1 # A 21 24 18 # B 25 27 21 # C 33 26 27 # M # AGE # Mean {2} 35.57 {3} 37.44 35.38 # STRATA1 # A 16 19 20 # B 21 17 21 # C 14 19 19 # —————————————————————————————————————————————————————————— # # {1} - Interesting # {2} - THIS mean # {3} - Very important mean # —————————————————————————————————————————————————————————— # # Please regard this table as an example of smart subsetting # # Do remember where you read this though tbl[10, 2] # only {1} which was previously {2} # B: Placebo # ————————————————————— # Mean {1} 37.44 # ————————————————————— # # {1} - THIS mean # ————————————————————— tbl[1:3, 2:3, keep_titles = TRUE] # Table 1 # Authors: # - Abcd Zabcd # - Cde Zbcd # # —————————————————————————————————————— # B: Placebo C: Combination # —————————————————————————————————————— # F # AGE # Mean 34.12 35.20 # —————————————————————————————————————— # # Please regard this table as an example of smart subsetting # # Do remember where you read this though tbl[1:3, 2:3, keep_titles = FALSE, keep_footers = TRUE] # B: Placebo C: Combination # —————————————————————————————————————— # F # AGE # Mean 34.12 35.20 # —————————————————————————————————————— # # Please regard this table as an example of smart subsetting # # Do remember where you read this though # Referential footnotes are not influenced by `keep_footers = FALSE` tbl[1:3, keep_titles = TRUE, keep_footers = FALSE] # Table 1 # Authors: # - Abcd Zabcd # - Cde Zbcd # # —————————————————————————————————————————————————————— # A: Drug X {1} B: Placebo C: Combination # —————————————————————————————————————————————————————— # F # AGE # Mean 32.76 34.12 35.20 # —————————————————————————————————————————————————————— # # {1} - Interesting # ——————————————————————————————————————————————————————"},{"path":"https://insightsengineering.github.io/rtables/articles/subsetting_tables.html","id":"path-based-cell-value-accessing","dir":"Articles","previous_headings":"","what":"Path Based Cell Value Accessing:","title":"Subsetting and Manipulating Table Contents","text":"Tables can subset modified structurally aware manner via pathing. Paths define semantically meaningful positions within constructed table correspond logic layout used create . path ordered set split names, names subgroups generated split, @content directive, steps position’s content (row group summary) table. can see row column paths existing table via row_paths(), col_paths(), row_paths_summary(), col_paths_summary(), functions, portion general make_row_df() function output. column paths follows: row paths follows: get semantically meaningful subset table, , can use [ (tt_at_path() underlies ) can also retrieve individual cell-values via value_at() convenience function, takes pair row column paths resolve together individual cell, e.g. average age Asian female patients arm : can also request information non-cell specific paths cell_values() function: Note return value cell_values() always list even specify path cell:","code":"lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% analyze(c(\"AGE\", \"STRATA1\")) tbl2 <- build_table(lyt2, ex_adsl %>% filter(SEX %in% c(\"M\", \"F\") & RACE %in% (levels(RACE)[1:3]))) tbl2 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # BLACK OR AFRICAN AMERICAN 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # WHITE 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 col_paths_summary(tbl2) # label path # ————————————————————————————————————————————— # A: Drug X ARM, A: Drug X # F ARM, A: Drug X, SEX, F # M ARM, A: Drug X, SEX, M # B: Placebo ARM, B: Placebo # F ARM, B: Placebo, SEX, F # M ARM, B: Placebo, SEX, M # C: Combination ARM, C: Combination # F ARM, C: Combination, SEX, F # M ARM, C: Combination, SEX, M row_paths_summary(tbl2) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN ContentRow RACE, ASIAN, @content, ASIAN # AGE LabelRow RACE, ASIAN, AGE # Mean DataRow RACE, ASIAN, AGE, Mean # STRATA1 LabelRow RACE, ASIAN, STRATA1 # A DataRow RACE, ASIAN, STRATA1, A # B DataRow RACE, ASIAN, STRATA1, B # C DataRow RACE, ASIAN, STRATA1, C # BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN # AGE LabelRow RACE, BLACK OR AFRICAN AMERICAN, AGE # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean # STRATA1 LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1 # A DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A # B DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B # C DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C # WHITE ContentRow RACE, WHITE, @content, WHITE # AGE LabelRow RACE, WHITE, AGE # Mean DataRow RACE, WHITE, AGE, Mean # STRATA1 LabelRow RACE, WHITE, STRATA1 # A DataRow RACE, WHITE, STRATA1, A # B DataRow RACE, WHITE, STRATA1, B # C DataRow RACE, WHITE, STRATA1, C tbl2[c(\"RACE\", \"ASIAN\"), c(\"ARM\", \"C: Combination\")] # C: Combination # F M # ——————————————————————————————————— # ASIAN 39 (60.9%) 32 (57.1%) # AGE # Mean 36.44 37.66 # STRATA1 # A 11 7 # B 11 14 # C 17 11 value_at(tbl2, c(\"RACE\", \"ASIAN\", \"AGE\", \"Mean\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\")) # [1] 31.21951 cell_values(tbl2, c(\"RACE\", \"ASIAN\", \"AGE\", \"Mean\"), c(\"ARM\", \"A: Drug X\")) # $`A: Drug X.F` # [1] 31.21951 # # $`A: Drug X.M` # [1] 34.6 cell_values(tbl2, c(\"RACE\", \"ASIAN\", \"AGE\", \"Mean\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\")) # $`A: Drug X.F` # [1] 31.21951"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_concepts.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Tabulation Concepts","text":"vignette introduce theory behind using layouts table creation. Much theory also holds true using table packages. vignette use following packages: data use following, created random number generators:","code":"library(dplyr) library(tibble) library(rtables) add_subgroup <- function(x) paste0(tolower(x), sample(1:3, length(x), TRUE)) set.seed(1) df <- tibble( x = rnorm(100), c1 = factor(sample(c(\"A\", \"B\", \"C\"), 100, replace = TRUE), levels = c(\"A\", \"B\", \"C\")), r1 = factor(sample(c(\"U\", \"V\", \"W\"), 100, replace = TRUE), levels = c(\"U\", \"V\", \"W\")) ) %>% mutate( c2 = add_subgroup(c1), r2 = add_subgroup(r1), y = as.numeric(2 * as.numeric(c1) - 3 * as.numeric(r1)) ) %>% select(c1, c2, r1, r2, x, y) df # # A tibble: 100 × 6 # c1 c2 r1 r2 x y # # 1 B b2 U u3 -0.626 1 # 2 A a3 V v2 0.184 -4 # 3 B b1 V v2 -0.836 -2 # 4 B b3 V v2 1.60 -2 # 5 B b1 U u1 0.330 1 # 6 C c1 U u3 -0.820 3 # 7 A a3 U u3 0.487 -1 # 8 B b1 U u3 0.738 1 # 9 C c3 V v2 0.576 0 # 10 C c3 U u2 -0.305 3 # # ℹ 90 more rows"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_concepts.html","id":"building-a-table-row-by-row","dir":"Articles","previous_headings":"","what":"Building A Table Row By Row","title":"Tabulation Concepts","text":"Let’s look table 3 columns 3 rows. row represents different analysis (functions foo, bar, zoo return rcell() object): data passed analysis functions subset defined respective column : Let’s concrete data analyze(): wanted x variable instead data frame: : function passed afun evaluated using argument matching. afun argument x analysis variable specified vars analyze() passed function, afun argument df subset dataset passed afun: Note also possible function returns multiple rows in_rows(): recommend specify row names explicitly.","code":"A B C ------------------------------------------------ foo_label foo(df_A) foo(df_B) foo(df_C) bar_label bar(df_A) bar(df_B) bar(df_C) zoo_label zoo(df_A) zoo(df_B) zoo(df_C) df_A <- df %>% filter(c1 == \"A\") df_B <- df %>% filter(c1 == \"B\") df_C <- df %>% filter(c1 == \"C\") foo <- prod bar <- sum zoo <- mean lyt <- basic_table() %>% split_cols_by(\"c1\") %>% analyze(\"x\", function(df) foo(df$x), var_labels = \"foo label\", format = \"xx.xx\") %>% analyze(\"x\", function(df) bar(df$x), var_labels = \"bar label\", format = \"xx.xx\") %>% analyze(\"x\", function(df) zoo(df$x), var_labels = \"zoo label\", format = \"xx.xx\") tbl <- build_table(lyt, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: root tbl # A B C # —————————————————————————————————— # foo label # foo label 0.00 -0.00 -0.00 # bar label # bar label 1.87 4.37 4.64 # zoo label # zoo label 0.05 0.13 0.18 A B C ------------------------------------------------ foo_label foo(x_A) foo(x_B) foo(x_C) bar_label bar(x_A) bar(x_B) bar(x_C) zoo_label zoo(x_A) zoo(x_B) zoo(x_C) x_A <- df_A$x x_B <- df_B$x x_C <- df_C$x lyt2 <- basic_table() %>% split_cols_by(\"c1\") %>% analyze(\"x\", foo, var_labels = \"foo label\", format = \"xx.xx\") %>% analyze(\"x\", bar, var_labels = \"bar label\", format = \"xx.xx\") %>% analyze(\"x\", zoo, var_labels = \"zoo label\", format = \"xx.xx\") tbl2 <- build_table(lyt2, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: root tbl2 # A B C # ———————————————————————————————— # foo label # foo 0.00 -0.00 -0.00 # bar label # bar 1.87 4.37 4.64 # zoo label # zoo 0.05 0.13 0.18 lyt3 <- basic_table() %>% split_cols_by(\"c1\") %>% analyze(\"x\", function(x) { in_rows( \"row 1\" = rcell(mean(x), format = \"xx.xx\"), \"row 2\" = rcell(sd(x), format = \"xx.xxx\") ) }, var_labels = \"foo label\") %>% analyze(\"x\", function(x) { in_rows( \"more rows 1\" = rcell(median(x), format = \"xx.x\"), \"even more rows 1\" = rcell(IQR(x), format = \"xx.xx\") ) }, var_labels = \"bar label\", format = \"xx.xx\") tbl3 <- build_table(lyt3, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: root tbl3 # A B C # —————————————————————————————————————————— # foo label # row 1 0.05 0.13 0.18 # row 2 0.985 0.815 0.890 # bar label # more rows 1 -0.0 0.2 0.3 # even more rows 1 1.20 1.15 1.16"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_concepts.html","id":"tabulation-with-row-structure","dir":"Articles","previous_headings":"","what":"Tabulation With Row Structure","title":"Tabulation Concepts","text":"Let’s say like create following table: df_* subsets df follows: note df_* class df, .e. tibbles. Hence foo aggregates subset data cell value. Given function foo (ignore ... now): can start calculating cell values individually: Now still missing table structure: rtables type tabulation done layouts: want see foo label use: now row labels disappeared. cfun needs define row label. let’s redefine foo:","code":"A B C -------------------------------------- U foo(df_UA) foo(df_UB) foo(df_UC) V foo(df_VA) foo(df_VB) foo(df_VC) W foo(df_WA) foo(df_WB) foo(df_WC) df_UA <- df %>% filter(r1 == \"U\", c1 == \"A\") df_VA <- df %>% filter(r1 == \"V\", c1 == \"A\") df_WA <- df %>% filter(r1 == \"W\", c1 == \"A\") df_UB <- df %>% filter(r1 == \"U\", c1 == \"B\") df_VB <- df %>% filter(r1 == \"V\", c1 == \"B\") df_WB <- df %>% filter(r1 == \"W\", c1 == \"C\") df_UC <- df %>% filter(r1 == \"U\", c1 == \"C\") df_VC <- df %>% filter(r1 == \"V\", c1 == \"C\") df_WC <- df %>% filter(r1 == \"W\", c1 == \"C\") foo <- function(df, labelstr = \"\", ...) { paste(dim(df), collapse = \" x \") } foo(df_UA) # [1] \"17 x 6\" foo(df_VA) # [1] \"9 x 6\" foo(df_WA) # [1] \"14 x 6\" foo(df_UB) # [1] \"13 x 6\" foo(df_VB) # [1] \"15 x 6\" foo(df_WB) # [1] \"11 x 6\" foo(df_UC) # [1] \"10 x 6\" foo(df_VC) # [1] \"5 x 6\" foo(df_WC) # [1] \"11 x 6\" matrix( list( foo(df_UA), foo(df_VA), foo(df_WA), foo(df_UB), foo(df_VB), foo(df_WB), foo(df_UC), foo(df_VC), foo(df_WC) ), byrow = FALSE, ncol = 3 ) # [,1] [,2] [,3] # [1,] \"17 x 6\" \"13 x 6\" \"10 x 6\" # [2,] \"9 x 6\" \"15 x 6\" \"5 x 6\" # [3,] \"14 x 6\" \"11 x 6\" \"11 x 6\" lyt4 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"x\", foo) tbl4 <- build_table(lyt4, df) tbl4 # A B C # ———————————————————————————————— # U # foo 17 x 6 13 x 6 10 x 6 # V # foo 9 x 6 15 x 6 5 x 6 # W # foo 14 x 6 6 x 6 11 x 6 lyt5 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = foo, format = \"xx\") tbl5 <- build_table(lyt5, df) tbl5 # A B C # ——————————————————————————— # 17 x 6 13 x 6 10 x 6 # 9 x 6 15 x 6 5 x 6 # 14 x 6 6 x 6 11 x 6 foo <- function(df, labelstr) { rcell(paste(dim(df), collapse = \" x \"), format = \"xx\", label = labelstr) } lyt6 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = foo) tbl6 <- build_table(lyt6, df) tbl6 # A B C # ———————————————————————————— # U 17 x 6 13 x 6 10 x 6 # V 9 x 6 15 x 6 5 x 6 # W 14 x 6 6 x 6 11 x 6"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_concepts.html","id":"calculating-the-mean","dir":"Articles","previous_headings":"Tabulation With Row Structure","what":"Calculating the Mean","title":"Tabulation Concepts","text":"Now let’s calculate mean df$y pattern : Note foo variable information hard-encoded function body. Let’s try alternatives returning analyze(): Note subset y variable passed x argument mean(). also get data.frame instead variable: contrast : function receives subset y.","code":"foo <- function(df, labelstr) { rcell(mean(df$y), label = labelstr, format = \"xx.xx\") } lyt7 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = foo) tbl7 <- build_table(lyt7, df) tbl7 # A B C # ————————————————————————— # U -1.00 1.00 3.00 # V -4.00 -2.00 0.00 # W -7.00 -5.00 -3.00 lyt8 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"y\", afun = mean) tbl8 <- build_table(lyt8, df) tbl8 # A B C # ————————————————————— # U # mean -1 1 3 # V # mean -4 -2 0 # W # mean -7 -5 -3 lyt9 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"y\", afun = function(df) mean(df$y)) tbl9 <- build_table(lyt9, df) tbl9 # A B C # —————————————————— # U # y -1 1 3 # V # y -4 -2 0 # W # y -7 -5 -3 lyt10 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"y\", afun = function(x) mean(x)) tbl10 <- build_table(lyt10, df) tbl10 # A B C # —————————————————— # U # y -1 1 3 # V # y -4 -2 0 # W # y -7 -5 -3"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_concepts.html","id":"group-summaries","dir":"Articles","previous_headings":"Tabulation With Row Structure","what":"Group Summaries","title":"Tabulation Concepts","text":"Pattern interesting one can add row structure (splits). Consider following table: <> represents data represented cell. cell U > u1, subset: . can get table follows: , wanted calculate two summaries per row split: following structure: rows U, u1, u2, …, W, w1, w2, w3 label rows rows (mean_sd range) data rows. Currently content rows table. Content rows summarize data defined splitting (.e. V > v1, B). wanted add content rows r2 split level get: s_cfun_2 content function either returns one row via rcell() multiple rows via in_rows(). data represented <> content rows data ’s descendant, .e. U > u1, content row cell df %>% filter(r1 == \"U\", r2 == \"u1\", c1 == \"\"). Note content functions cfun operate data frames vectors/variables must take df argument. , cfun must also labelstr argument split level. way, cfun can define row name. order get table can use layout framework follows: manner, want content rows r1 split can follows: pagination, content rows label rows get repeated page split descendant content row. , example, split following table ***: get following two tables: ","code":"A B C -------------------------------------- U u1 foo(<>) foo(<>) foo(<>) u2 foo(<>) foo(<>) foo(<>) u3 foo(<>) foo(<>) foo(<>) V v1 foo(<>) foo(<>) foo(<>) v2 foo(<>) foo(<>) foo(<>) v3 foo(<>) foo(<>) foo(<>) W w1 foo(<>) foo(<>) foo(<>) w2 foo(<>) foo(<>) foo(<>) w3 foo(<>) foo(<>) foo(<>) df %>% filter(r1 == \"U\", r2 == \"u1\", c1 == \"A\") # # A tibble: 2 × 6 # c1 c2 r1 r2 x y # # 1 A a2 U u1 1.12 -1 # 2 A a1 U u1 0.594 -1 lyt11 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% split_rows_by(\"r2\") %>% summarize_row_groups(cfun = function(df, labelstr) { rcell(mean(df$x), format = \"xx.xx\", label = paste(\"mean x for\", labelstr)) }) tbl11 <- build_table(lyt11, df) tbl11 # A B C # ——————————————————————————————————————— # U # mean x for u3 -0.04 0.36 -0.25 # mean x for u1 0.86 0.32 NA # mean x for u2 -0.28 0.38 0.08 # V # mean x for v2 0.01 0.55 0.60 # mean x for v3 -0.03 -0.30 1.06 # mean x for v1 0.56 -0.27 -0.54 # W # mean x for w1 -0.58 0.42 0.67 # mean x for w3 0.56 0.69 -0.39 # mean x for w2 -1.99 -0.10 0.53 s_mean_sd <- function(x) { in_rows(\"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\")) } s_range <- function(x) { in_rows(\"range\" = rcell(range(x), format = \"xx.xx - xx.xx\")) } lyt12 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% split_rows_by(\"r2\") %>% analyze(\"x\", s_mean_sd, show_labels = \"hidden\") %>% analyze(\"x\", s_range, show_labels = \"hidden\") tbl12 <- build_table(lyt12, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u3] # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w2] tbl12 # A B C # ——————————————————————————————————————————————————————————— # U # u3 # mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72) # range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56 # u1 # mean (sd) 0.86 (0.38) 0.32 (0.51) NA # range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf # u2 # mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91) # range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51 # V # v2 # mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03) # range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62 # v3 # mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA) # range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06 # v1 # mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18) # range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29 # W # w1 # mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39) # range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21 # w3 # mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68) # range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10 # w2 # mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60) # range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16 A B C --------------------------------------------------------- U u1 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u2 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u3 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) V v1 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v2 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v3 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) W w1 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w2 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w3 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) A B C --------------------------------------------------------- U u1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) V v1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) W w1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) s_mean_sd <- function(x) { in_rows(\"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\")) } s_range <- function(x) { in_rows(\"range\" = rcell(range(x), format = \"xx.xx - xx.xx\")) } s_cfun_2 <- function(df, labelstr) { rcell(nrow(df), format = \"xx\", label = paste(labelstr, \"(n)\")) } lyt13 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% split_rows_by(\"r2\") %>% summarize_row_groups(cfun = s_cfun_2) %>% analyze(\"x\", s_mean_sd, show_labels = \"hidden\") %>% analyze(\"x\", s_range, show_labels = \"hidden\") tbl13 <- build_table(lyt13, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u3] # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w2] tbl13 # A B C # ——————————————————————————————————————————————————————————— # U # u3 (n) 6 5 3 # mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72) # range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56 # u1 (n) 2 5 0 # mean (sd) 0.86 (0.38) 0.32 (0.51) NA # range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf # u2 (n) 9 3 7 # mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91) # range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51 # V # v2 (n) 2 4 2 # mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03) # range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62 # v3 (n) 3 4 1 # mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA) # range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06 # v1 (n) 4 7 2 # mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18) # range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29 # W # w1 (n) 4 1 4 # mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39) # range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21 # w3 (n) 9 1 3 # mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68) # range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10 # w2 (n) 1 4 4 # mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60) # range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16 lyt14 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = s_cfun_2) %>% split_rows_by(\"r2\") %>% summarize_row_groups(cfun = s_cfun_2) %>% analyze(\"x\", s_mean_sd, show_labels = \"hidden\") %>% analyze(\"x\", s_range, show_labels = \"hidden\") tbl14 <- build_table(lyt14, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u3] # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w2] tbl14 # A B C # ——————————————————————————————————————————————————————————— # U (n) 17 13 10 # u3 (n) 6 5 3 # mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72) # range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56 # u1 (n) 2 5 0 # mean (sd) 0.86 (0.38) 0.32 (0.51) NA # range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf # u2 (n) 9 3 7 # mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91) # range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51 # V (n) 9 15 5 # v2 (n) 2 4 2 # mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03) # range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62 # v3 (n) 3 4 1 # mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA) # range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06 # v1 (n) 4 7 2 # mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18) # range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29 # W (n) 14 6 11 # w1 (n) 4 1 4 # mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39) # range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21 # w3 (n) 9 1 3 # mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68) # range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10 # w2 (n) 1 4 4 # mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60) # range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16 A B C --------------------------------------------------------- U u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) *** range s_range(<>) s_range(<>) s_range(<>) u2 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) A B C --------------------------------------------------------- U u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) A B C --------------------------------------------------------- U u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) range s_range(<>) s_range(<>) s_range(<>) u2 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>)"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_concepts.html","id":"pattern-iii","dir":"Articles","previous_headings":"Tabulation With Row Structure","what":"Pattern III","title":"Tabulation Concepts","text":"Let’s consider following tabulation pattern: discuss future release rtables.","code":"A B C ------------------------------------------------ label 1 foo(x_A) bar(x_B) zoo(x_C) label 2 foo(x_A) bar(x_B) zoo(x_C) label 3 foo(x_A) bar(x_B) zoo(x_C)"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_dplyr.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Comparison with dplyr Tabulation","text":"vignette, like discuss similarities differences dplyr rtable. Much rtables framework focuses tabulation/summarizing data visualization table. vignette, focus summarizing data using dplyr contrast rtables. won’t pay attention table visualization/markup just derive cell content. Using dplyr summarize data gt visualize table good way tabulation certain nature complexity. However, tables table created introduction vignette take effort create dplyr. Part effort due fact using dplyr table data stored data.frames tibbles natural way represent table show vignette. know elegant way deriving table content dplyr please let us know update vignette. table data used introduction vignette:","code":"library(rtables) library(dplyr) n <- 400 set.seed(1) df <- tibble( arm = factor(sample(c(\"Arm A\", \"Arm B\"), n, replace = TRUE), levels = c(\"Arm A\", \"Arm B\")), country = factor(sample(c(\"CAN\", \"USA\"), n, replace = TRUE, prob = c(.55, .45)), levels = c(\"CAN\", \"USA\")), gender = factor(sample(c(\"Female\", \"Male\"), n, replace = TRUE), levels = c(\"Female\", \"Male\")), handed = factor(sample(c(\"Left\", \"Right\"), n, prob = c(.6, .4), replace = TRUE), levels = c(\"Left\", \"Right\")), age = rchisq(n, 30) + 10 ) %>% mutate( weight = 35 * rnorm(n, sd = .5) + ifelse(gender == \"Female\", 140, 180) ) lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # (N=96) (N=105) (N=92) (N=107) # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.9 40.4 40.3 37.7 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.4 39.7 39.2 40.1 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_dplyr.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting Started","title":"Comparison with dplyr Tabulation","text":"start deriving first data cell row 3 (note, row 1 2 content cells, see introduction vignette). Cell 3,1 contains mean age left handed & female Canadians “Arm ”: dplyr: , dplyr gives us verbs easily get average age left handed Canadians group defined 4 columns: can get average age cell values : rtable syntax, need following code get content: mentioned introduction vignette, please ignore difference arranging formatting data: ’s possible condense rtable possible make tibble look like reference table using gt R package. terms tabulation example arguably much added rtables dplyr.","code":"mean(df$age[df$country == \"CAN\" & df$arm == \"Arm A\" & df$gender == \"Female\" & df$handed == \"Left\"]) # [1] 38.86979 df %>% filter(country == \"CAN\", arm == \"Arm A\", gender == \"Female\", handed == \"Left\") %>% summarise(mean_age = mean(age)) # # A tibble: 1 × 1 # mean_age # # 1 38.9 df %>% group_by(arm, gender) %>% filter(country == \"CAN\", handed == \"Left\") %>% summarise(mean_age = mean(age)) # `summarise()` has grouped output by 'arm'. You can override using the `.groups` # argument. # # A tibble: 4 × 3 # # Groups: arm [2] # arm gender mean_age # # 1 Arm A Female 38.9 # 2 Arm A Male 40.4 # 3 Arm B Female 40.3 # 4 Arm B Male 37.7 average_age <- df %>% group_by(arm, gender, country, handed) %>% summarise(mean_age = mean(age)) # `summarise()` has grouped output by 'arm', 'gender', 'country'. You can # override using the `.groups` argument. average_age # # A tibble: 16 × 5 # # Groups: arm, gender, country [8] # arm gender country handed mean_age # # 1 Arm A Female CAN Left 38.9 # 2 Arm A Female CAN Right 36.6 # 3 Arm A Female USA Left 40.4 # 4 Arm A Female USA Right 36.9 # 5 Arm A Male CAN Left 40.4 # 6 Arm A Male CAN Right 40.2 # 7 Arm A Male USA Left 39.7 # 8 Arm A Male USA Right 39.8 # 9 Arm B Female CAN Left 40.3 # 10 Arm B Female CAN Right 40.2 # 11 Arm B Female USA Left 39.2 # 12 Arm B Female USA Right 38.5 # 13 Arm B Male CAN Left 37.7 # 14 Arm B Male CAN Right 40.6 # 15 Arm B Male USA Left 40.1 # 16 Arm B Male USA Right 39.0 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% split_rows_by(\"handed\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————— # CAN # Left # mean 38.9 40.4 40.3 37.7 # Right # mean 36.6 40.2 40.2 40.6 # USA # Left # mean 40.4 39.7 39.2 40.1 # Right # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_dplyr.html","id":"content-information","dir":"Articles","previous_headings":"","what":"Content Information","title":"Comparison with dplyr Tabulation","text":"Unlike rtables different levels summarization discrete computations dplyr need combine first focus count percentage information handedness within country (arm-gender pair), along analysis row mean values: 16 rows (cells) like average_age data frame defined . Next, derive group information countries: Finally, left_join() two levels summary get data.frame containing full set values make body table (note, however, order): Alternatively, calculate counts c_h_df, use mutate() left_join() divide counts n_col values naturally calculated within c_df. simplify c_h_df’s creation somewhat requiring explicit ungroup(), prevents level summarization self-contained set computations. rtables call contrast : can now spot check values , rtable syntax hopefully also become bit straightforward derive cell values dplyr particular table.","code":"c_h_df <- df %>% group_by(arm, gender, country, handed) %>% summarize(mean = mean(age), c_h_count = n()) %>% ## we need the sum below to *not* be by country, so that we're dividing by the column counts ungroup(country) %>% # now the `handed` grouping has been removed, therefore we can calculate percent now: mutate(n_col = sum(c_h_count), c_h_percent = c_h_count / n_col) # `summarise()` has grouped output by 'arm', 'gender', 'country'. You can # override using the `.groups` argument. c_h_df # # A tibble: 16 × 8 # # Groups: arm, gender [4] # arm gender country handed mean c_h_count n_col c_h_percent # # 1 Arm A Female CAN Left 38.9 32 96 0.333 # 2 Arm A Female CAN Right 36.6 13 96 0.135 # 3 Arm A Female USA Left 40.4 34 96 0.354 # 4 Arm A Female USA Right 36.9 17 96 0.177 # 5 Arm A Male CAN Left 40.4 42 105 0.4 # 6 Arm A Male CAN Right 40.2 22 105 0.210 # 7 Arm A Male USA Left 39.7 19 105 0.181 # 8 Arm A Male USA Right 39.8 22 105 0.210 # 9 Arm B Female CAN Left 40.3 26 92 0.283 # 10 Arm B Female CAN Right 40.2 20 92 0.217 # 11 Arm B Female USA Left 39.2 25 92 0.272 # 12 Arm B Female USA Right 38.5 21 92 0.228 # 13 Arm B Male CAN Left 37.7 37 107 0.346 # 14 Arm B Male CAN Right 40.6 25 107 0.234 # 15 Arm B Male USA Left 40.1 25 107 0.234 # 16 Arm B Male USA Right 39.0 20 107 0.187 c_df <- df %>% group_by(arm, gender, country) %>% summarize(c_count = n()) %>% # now the `handed` grouping has been removed, therefore we can calculate percent now: mutate(n_col = sum(c_count), c_percent = c_count / n_col) # `summarise()` has grouped output by 'arm', 'gender'. You can override using the # `.groups` argument. c_df # # A tibble: 8 × 6 # # Groups: arm, gender [4] # arm gender country c_count n_col c_percent # # 1 Arm A Female CAN 45 96 0.469 # 2 Arm A Female USA 51 96 0.531 # 3 Arm A Male CAN 64 105 0.610 # 4 Arm A Male USA 41 105 0.390 # 5 Arm B Female CAN 46 92 0.5 # 6 Arm B Female USA 46 92 0.5 # 7 Arm B Male CAN 62 107 0.579 # 8 Arm B Male USA 45 107 0.421 full_dplyr <- left_join(c_h_df, c_df) %>% ungroup() # Joining with `by = join_by(arm, gender, country, n_col)` lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # (N=96) (N=105) (N=92) (N=107) # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.9 40.4 40.3 37.7 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.4 39.7 39.2 40.1 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.9 39.8 38.5 39.0 frm_rtables_h <- cell_values( tbl, rowpath = c(\"country\", \"CAN\", \"handed\", \"Right\", \"@content\"), colpath = c(\"arm\", \"Arm B\", \"gender\", \"Female\") )[[1]] frm_rtables_h # [1] 20.0000000 0.2173913 frm_dplyr_h <- full_dplyr %>% filter(country == \"CAN\" & handed == \"Right\" & arm == \"Arm B\" & gender == \"Female\") %>% select(c_h_count, c_h_percent) frm_dplyr_h # # A tibble: 1 × 2 # c_h_count c_h_percent # # 1 20 0.217 frm_rtables_c <- cell_values( tbl, rowpath = c(\"country\", \"CAN\", \"@content\"), colpath = c(\"arm\", \"Arm A\", \"gender\", \"Male\") )[[1]] frm_rtables_c # [1] 64.0000000 0.6095238 frm_dplyr_c <- full_dplyr %>% filter(country == \"CAN\" & arm == \"Arm A\" & gender == \"Male\") %>% select(c_count, c_percent) frm_dplyr_c # # A tibble: 2 × 2 # c_count c_percent # # 1 64 0.610 # 2 64 0.610"},{"path":"https://insightsengineering.github.io/rtables/articles/tabulation_dplyr.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Comparison with dplyr Tabulation","text":"vignette learned : dplyr keeps simple things simple tables group summaries repeating information required rtables streamlines construction complex tables recommend continue reading clinical_trials vignette create number advanced tables using layouts.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/title_footer.html","id":"titles-and-non-referential-footer-materials","dir":"Articles","previous_headings":"","what":"Titles and Non-Referential Footer Materials","title":"Titles, Footers, and Referential Footnotes","text":"rtables table can annotated three types header (title) information, well three types footer information. Header information comes two forms specified directly (main title subtitles), well one populated automatically necessary (page title, see next section). Similarly, footer materials come two directly specified components: main footer provenance footer, addition one computed necessary: referential footnotes. basic_table() accepts values static title footer element layout construction:","code":"library(rtables) library(dplyr) lyt <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, format = \"xx.x\") tbl <- build_table(lyt, DM) cat(export_as_txt(tbl, paginate = TRUE, page_break = \"\\n\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/articles/title_footer.html","id":"page-by-splitting","dir":"Articles","previous_headings":"","what":"Page-by splitting","title":"Titles, Footers, and Referential Footnotes","text":"often want split tables based values one variables (e.g., lab measurement) paginate separately within table subsections. rtables via page row splits. Row splits can declared page splits setting page_by = TRUE split_rows_by*() call, . page splits present, page titles generated automatically appending split value (typically factor level, though need ), page_prefix, separated :. default, page_prefix name variable split. Page row splits can nested, within page_by splits, nested within traditional row splits. case, page title page split present every resulting page, seen :","code":"lyt2 <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", page_by = TRUE, page_prefix = \"Patient Subset - Gender\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, format = \"xx.x\") tbl2 <- build_table(lyt2, DM) cat(export_as_txt(tbl2, paginate = TRUE, page_break = \"\\n\\n~~~~ Page Break ~~~~\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a lyt3 <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", page_by = TRUE, page_prefix = \"Patient Subset - Gender\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\", page_by = TRUE, page_prefix = \"Stratification - Strata\") %>% analyze(\"AGE\", mean, format = \"xx.x\") tbl3 <- build_table(lyt3, DM) cat(export_as_txt(tbl3, paginate = TRUE, page_break = \"\\n\\n~~~~ Page Break ~~~~\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # Stratification - Strata: A # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 30.9 32.9 36.0 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # Stratification - Strata: B # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 34.9 32.9 34.4 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # Stratification - Strata: C # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 35.2 36.0 34.3 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # Stratification - Strata: A # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 35.1 31.1 35.6 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # Stratification - Strata: B # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 36.6 32.1 34.4 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # Stratification - Strata: C # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/articles/title_footer.html","id":"referential-footnotes","dir":"Articles","previous_headings":"","what":"Referential Footnotes","title":"Titles, Footers, and Referential Footnotes","text":"Referential footnotes footnotes associated particular component table: column, row, cell. can added tabulation via analysis functions, can also added post-hoc table created. rendered number within curly braces within table body, row, column labels, followed message associated number printed table rendering.","code":""},{"path":"https://insightsengineering.github.io/rtables/articles/title_footer.html","id":"adding-cell--and-analysis-row-referential-footnotes-at-tabulation-time","dir":"Articles","previous_headings":"Referential Footnotes","what":"Adding Cell- and Analysis-row Referential Footnotes At Tabulation Time","title":"Titles, Footers, and Referential Footnotes","text":"note typically type footnote added within analysis function dependent computations done calculate cell value(s), e.g., model converging. simply use context information illustrative proxy . procedure adding footnotes content (summary row) rows cells identical , done within content function.","code":"afun <- function(df, .var, .spl_context) { val <- .spl_context$value[NROW(.spl_context)] rw_fnotes <- if (val == \"C\") list(\"This is strata level C for these patients\") else list() cl_fnotes <- if (val == \"B\" && df[1, \"ARM\", drop = TRUE] == \"C: Combination\") { list(\"these Strata B patients got the drug combination\") } else { list() } in_rows( mean = mean(df[[.var]]), .row_footnotes = rw_fnotes, .cell_footnotes = cl_fnotes, .formats = c(mean = \"xx.x\") ) } lyt <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", page_by = TRUE, page_prefix = \"Patient Subset - Gender\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", afun, format = \"xx.x\") tbl <- build_table(lyt, DM) cat(export_as_txt(tbl, paginate = TRUE, page_break = \"\\n\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # # —————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————— # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 {1} # C # mean {2} 35.2 36.0 34.3 # —————————————————————————————————————————————————————— # # {1} - these Strata B patients got the drug combination # {2} - This is strata level C for these patients # —————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # # —————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————— # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 {1} # C # mean {2} 37.4 32.8 32.8 # —————————————————————————————————————————————————————— # # {1} - these Strata B patients got the drug combination # {2} - This is strata level C for these patients # —————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/articles/title_footer.html","id":"annotating-an-existing-table-with-referential-footnotes","dir":"Articles","previous_headings":"Referential Footnotes","what":"Annotating an Existing Table with Referential Footnotes","title":"Titles, Footers, and Referential Footnotes","text":"addition inserting referential footnotes tabulation time within analysis functions, can also annotate tables post-hoc. also way add footnotes column labels, controlled within analysis content function. fnotes_at_path<- function accepts row path, column path, value full set footnotes defined locations (NULL character vector). non-NULL row path NULL column path specifies footnote(s) attached row, NULL row path non-NULL column path indicates go column. non-NULL indicates cell (must resolve individual cell). Note step content row must add path, even though didn’t need put footnote full row. Currently, content rows default named label rather name corresponding facet. reflected output , e.g., row_paths_summary. can add footnotes cell like :","code":"## from ?tolower example slightly modified .simpleCap <- function(x) { if (length(x) > 1) { return(sapply(x, .simpleCap)) } s <- strsplit(tolower(x), \" \")[[1]] paste(toupper(substring(s, 1, 1)), substring(s, 2), sep = \"\", collapse = \" \") } adsl2 <- ex_adsl %>% filter(SEX %in% c(\"M\", \"F\") & RACE %in% (levels(RACE)[1:3])) %>% ## we trim the level names here solely due to space considerations mutate(ethnicity = .simpleCap(gsub(\"(.*)OR.*\", \"\\\\1\", RACE)), RACE = factor(RACE)) lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"RACE\", labels_var = \"ethnicity\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% analyze(c(\"AGE\", \"STRATA1\")) tbl2 <- build_table(lyt2, adsl2) tbl2 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————— # Asian 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 fnotes_at_path(tbl2, c(\"RACE\", \"ASIAN\")) <- c(\"hi\", \"there\") tbl2 # A: Drug X B: Placebo C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————————— # Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 # —————————————————————————————————————————————————————————————————————————————————————————— # # {1} - hi # {2} - there # —————————————————————————————————————————————————————————————————————————————————————————— fnotes_at_path(tbl2, rowpath = NULL, c(\"ARM\", \"B: Placebo\")) <- c(\"this is a placebo\") tbl2 # A: Drug X B: Placebo {NA} C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————————— # Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 # —————————————————————————————————————————————————————————————————————————————————————————— # # {1} - hi # {2} - there # {NA} - this is a placebo # —————————————————————————————————————————————————————————————————————————————————————————— row_paths_summary(tbl2) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————— # Asian ContentRow RACE, ASIAN, @content, Asian # AGE LabelRow RACE, ASIAN, AGE # Mean DataRow RACE, ASIAN, AGE, Mean # STRATA1 LabelRow RACE, ASIAN, STRATA1 # A DataRow RACE, ASIAN, STRATA1, A # B DataRow RACE, ASIAN, STRATA1, B # C DataRow RACE, ASIAN, STRATA1, C # Black ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, Black # AGE LabelRow RACE, BLACK OR AFRICAN AMERICAN, AGE # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean # STRATA1 LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1 # A DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A # B DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B # C DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C # White ContentRow RACE, WHITE, @content, White # AGE LabelRow RACE, WHITE, AGE # Mean DataRow RACE, WHITE, AGE, Mean # STRATA1 LabelRow RACE, WHITE, STRATA1 # A DataRow RACE, WHITE, STRATA1, A # B DataRow RACE, WHITE, STRATA1, B # C DataRow RACE, WHITE, STRATA1, C fnotes_at_path( tbl2, rowpath = c(\"RACE\", \"ASIAN\", \"@content\", \"Asian\"), colpath = c(\"ARM\", \"B: Placebo\", \"SEX\", \"F\") ) <- \"These asian women got placebo treatments\" tbl2 # A: Drug X B: Placebo {NA} C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————————————— # Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) {3} 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 # —————————————————————————————————————————————————————————————————————————————————————————————— # # {1} - hi # {2} - there # {3} - These asian women got placebo treatments # {NA} - this is a placebo # ——————————————————————————————————————————————————————————————————————————————————————————————"},{"path":"https://insightsengineering.github.io/rtables/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"define authors actively maintaining code base, contributors made significant contribution past. acknowledgements, see eponymous section Home Page. Gabriel Becker. Author. Original creator package Adrian Waddell. Author. Daniel Sabanés Bové. Contributor. Maximilian Mordig. Contributor. Davide Garolini. Contributor. Emily de la Rua. Contributor. Abinaya Yogasekaram. Contributor. Joe Zhu. Contributor, maintainer. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/rtables/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Becker G, Waddell (2024). rtables: Reporting Tables. R package version 0.6.6.9008, https://insightsengineering.github.io/rtables/, https://github.com/insightsengineering/rtables.","code":"@Manual{, title = {rtables: Reporting Tables}, author = {Gabriel Becker and Adrian Waddell}, year = {2024}, note = {R package version 0.6.6.9008, https://insightsengineering.github.io/rtables/}, url = {https://github.com/insightsengineering/rtables}, }"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"reporting-tables-with-r","dir":"","previous_headings":"","what":"Reporting tables with R","title":"Reporting Tables","text":"rtables R package designed create display complex tables R. cells rtable may contain high-dimensional data structure can displayed cell-specific formatting instructions. Currently, rtables can outputted ascii html, pdf, well Power Point (via conversion flextable objects). rtf support development future release. rtables developed copy written F. Hoffmann-La Roche released open source Apache License Version 2. rtables development driven need create regulatory ready tables health authority review. key requirements undertaking listed : values need programmatically accessible non-rounded state cross-checking multiple values displayed within cell flexible tabulation framework flexible formatting (cell spans, rounding, alignment, etc.) multiple output formats (html, ascii, latex, pdf, xml) flexible pagination horizontal vertical directions distinguish name label data structure work CDISC standards title, footnotes, cell cell/row/column references rtables currently covers virtually requirements, advances remain active development.","code":""},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Reporting Tables","text":"rtables available CRAN can install latest released version : can install latest development version directly GitHub : Note might need set GITHUB_PAT environment variable order able install GitHub. Packaged releases (CRAN official CRAN releases) can found releases list.","code":"install.packages(\"rtables\") remotes::install_github(\"insightsengineering/formatters\") remotes::install_github(\"insightsengineering/rtables\")"},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Reporting Tables","text":"first demonstrate demographic table-like example show creation complex table.","code":"library(rtables) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR1\", \"BMRKR2\"), function(x, ...) { if (is.numeric(x)) { in_rows( \"Mean (sd)\" = c(mean(x), sd(x)), \"Median\" = median(x), \"Min - Max\" = range(x), .formats = c(\"xx.xx (xx.xx)\", \"xx.xx\", \"xx.xx - xx.xx\") ) } else if (is.factor(x) || is.character(x)) { in_rows(.list = list_wrap_x(table)(x)) } else { stop(\"type not supported\") } }) build_table(lyt, ex_adsl) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————— #> AGE #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> Median 33.00 35.00 35.00 #> Min - Max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 #> BMRKR1 #> Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) #> Median 5.39 4.81 4.61 #> Min - Max 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 library(rtables) library(dplyr) ## for simplicity grab non-sparse subset ADSL <- ex_adsl %>% filter(RACE %in% levels(RACE)[1:3]) biomarker_ave <- function(x, ...) { val <- if(length(x) > 0) round(mean(x), 2) else \"no data\" in_rows( \"Biomarker 1 (mean)\" = rcell(val) ) } basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_cols_by(\"BMRKR2\") %>% split_rows_by(\"RACE\", split_fun = trim_levels_in_group(\"SEX\")) %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% analyze(\"BMRKR1\", biomarker_ave) %>% build_table(ADSL) #> A: Drug X B: Placebo C: Combination #> LOW MEDIUM HIGH LOW MEDIUM HIGH LOW MEDIUM HIGH #> (N=45) (N=35) (N=46) (N=42) (N=48) (N=31) (N=40) (N=39) (N=47) #> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN #> F 13 (28.9%) 9 (25.7%) 19 (41.3%) 9 (21.4%) 18 (37.5%) 9 (29.0%) 13 (32.5%) 9 (23.1%) 17 (36.2%) #> Biomarker 1 (mean) 5.23 6.17 5.38 5.64 5.55 4.33 5.46 5.48 5.19 #> M 8 (17.8%) 7 (20.0%) 10 (21.7%) 12 (28.6%) 10 (20.8%) 8 (25.8%) 5 (12.5%) 11 (28.2%) 16 (34.0%) #> Biomarker 1 (mean) 6.77 6.06 5.54 4.9 4.98 6.81 6.53 5.47 4.98 #> U 1 (2.2%) 1 (2.9%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (3.2%) 0 (0.0%) 1 (2.6%) 1 (2.1%) #> Biomarker 1 (mean) 4.68 7.7 no data no data no data 6.97 no data 11.93 9.01 #> BLACK OR AFRICAN AMERICAN #> F 6 (13.3%) 3 (8.6%) 9 (19.6%) 6 (14.3%) 8 (16.7%) 2 (6.5%) 7 (17.5%) 4 (10.3%) 3 (6.4%) #> Biomarker 1 (mean) 5.01 7.2 6.79 6.15 5.26 8.57 5.72 5.76 4.58 #> M 5 (11.1%) 5 (14.3%) 2 (4.3%) 3 (7.1%) 5 (10.4%) 4 (12.9%) 4 (10.0%) 5 (12.8%) 5 (10.6%) #> Biomarker 1 (mean) 6.92 5.82 11.66 4.46 6.14 8.47 6.16 5.25 4.83 #> U 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (2.5%) 1 (2.6%) 0 (0.0%) #> Biomarker 1 (mean) no data no data no data no data no data no data 2.79 9.82 no data #> UNDIFFERENTIATED 1 (2.2%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 2 (5.0%) 0 (0.0%) 0 (0.0%) #> Biomarker 1 (mean) 9.48 no data no data no data no data no data 6.46 no data no data #> WHITE #> F 6 (13.3%) 7 (20.0%) 4 (8.7%) 5 (11.9%) 6 (12.5%) 6 (19.4%) 6 (15.0%) 3 (7.7%) 2 (4.3%) #> Biomarker 1 (mean) 4.43 7.83 4.52 6.42 5.07 7.83 6.71 5.87 10.7 #> M 4 (8.9%) 3 (8.6%) 2 (4.3%) 6 (14.3%) 1 (2.1%) 1 (3.2%) 2 (5.0%) 5 (12.8%) 3 (6.4%) #> Biomarker 1 (mean) 5.81 7.23 1.39 4.72 4.58 12.87 2.3 5.1 5.98 #> U 1 (2.2%) 0 (0.0%) 0 (0.0%) 1 (2.4%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Biomarker 1 (mean) 3.94 no data no data 3.77 no data no data no data no data no data"},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"Reporting Tables","text":"like thank everyone made rtables better project providing feedback improving examples & vignettes. following list contributors alphabetical: Maximo Carreras, Francois Collins, Saibah Chohan, Tadeusz Lewandowski, Nick Paszty, Nina Qi, Jana Stoilova, Heng Wang, Godwin Yung","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"advanced-rtables-training","dir":"","previous_headings":"Presentations","what":"Advanced rtables Training","title":"Reporting Tables","text":"Part 1 Slides Part 2 Slides","code":""},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"rinpharma-workshop-creating-submission-quality-clinical-trial-reporting-tables-in-r-with-rtables","dir":"","previous_headings":"Presentations","what":"RinPharma Workshop: Creating Submission-Quality Clinical Trial Reporting Tables in R with rtables","title":"Reporting Tables","text":"Slides","code":""},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"r-adoption-series","dir":"","previous_headings":"Presentations","what":"R Adoption Series","title":"Reporting Tables","text":"R Adoption Series presentation 2022 Slides","code":""},{"path":"https://insightsengineering.github.io/rtables/index.html","id":"new-current-layouting-and-tabulation-framework-v03","dir":"","previous_headings":"Presentations","what":"New (Current) Layouting and Tabulation Framework (v.0.3+)","title":"Reporting Tables","text":"useR!2020 Presentation (v0.3.1.1) July 2020","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/CellValue.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell Value constructor — CellValue","title":"Cell Value constructor — CellValue","text":"Cell Value constructor","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/CellValue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell Value constructor — CellValue","text":"","code":"CellValue( val, format = NULL, colspan = 1L, label = NULL, indent_mod = NULL, footnotes = NULL, align = NULL, format_na_str = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/CellValue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell Value constructor — CellValue","text":"val . value cell exactly passed formatter returned extracted format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. colspan integer(1). Column span value. label character(1). label (confused name) object/structure. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. footnotes list NULL. Referential footnote messages cell. align character(1) NULL. Alignment value rendered . defaults \"center\" NULL used. See formatters::list_valid_aligns() currently supported alignments. format_na_str character(1). String displayed formatted cell's value(s) NA.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/CellValue.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell Value constructor — CellValue","text":"object representing value within single cell within populated table. underlying structure object implementation detail relied upon beyond calling accessors class.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/EmptyColInfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty table, column, split objects — EmptyColInfo","title":"Empty table, column, split objects — EmptyColInfo","text":"Empty objects various types compare efficiently.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ManualSplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Manually defined split — ManualSplit","title":"Manually defined split — ManualSplit","text":"Manually defined split","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ManualSplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manually defined split — ManualSplit","text":"","code":"ManualSplit( levels, label, name = \"manual\", extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", page_prefix = NA_character_, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/ManualSplit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manually defined split — ManualSplit","text":"levels character. Levels split (.e. children manual split) label character(1). label (confused name) object/structure. name character(1). Name split/table/row created. Defaults corresponding label, required . extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ManualSplit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manually defined split — ManualSplit","text":"ManualSplit object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ManualSplit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manually defined split — ManualSplit","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/MultiVarSplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split between two or more different variables — MultiVarSplit","title":"Split between two or more different variables — MultiVarSplit","text":"Split two different variables","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/MultiVarSplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split between two or more different variables — MultiVarSplit","text":"","code":"MultiVarSplit( vars, split_label = \"\", varlabels = NULL, varnames = NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = \"multivars\", child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", split_fun = NULL, page_prefix = NA_character_, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/MultiVarSplit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split between two or more different variables — MultiVarSplit","text":"vars character vector. Multiple variable names. split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). varlabels character vector. Labels vars varnames character vector. Names vars appear pathing. vars unique variable names. , variable names suffixes necessary enforce uniqueness. cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. split_name string. Name associated split (pathing, etc) child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. split_fun function/NULL. custom splitting function See custom_split_funs page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/MultiVarSplit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split between two or more different variables — MultiVarSplit","text":"MultiVarSplit object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/MultiVarSplit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split between two or more different variables — MultiVarSplit","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/VarLevelSplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split on levels within a variable — VarLevelSplit-class","title":"Split on levels within a variable — VarLevelSplit-class","text":"Split levels within variable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/VarLevelSplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split on levels within a variable — VarLevelSplit-class","text":"","code":"VarLevelSplit( var, split_label, labels_var = NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_fun = NULL, split_format = NULL, split_na_str = NA_character_, valorder = NULL, split_name = var, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), indent_mod = 0L, label_pos = c(\"topleft\", \"hidden\", \"visible\"), cindent_mod = 0L, cvar = \"\", cextra_args = list(), page_prefix = NA_character_, section_div = NA_character_ ) VarLevWBaselineSplit( var, ref_group, labels_var = var, split_label, split_fun = NULL, label_fstr = \"%s - %s\", cfun = NULL, cformat = NULL, cna_str = NA_character_, cvar = \"\", split_format = NULL, split_na_str = NA_character_, valorder = NULL, split_name = var, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/reference/VarLevelSplit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split on levels within a variable — VarLevelSplit-class","text":"var string, variable name split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). labels_var string, name variable containing labels displayed values var cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_fun function/NULL. custom splitting function See custom_split_funs split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. valorder character vector. Order split children appear resulting table. split_name string. Name associated split (pathing, etc) child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. ref_group character. Value var taken ref_group/control compared . label_fstr string. sprintf style format string containing. non-comparison splits, can contain one \"%s\" takes current split value generates row/column label. Comparison-based splits can contain two \"%s\".","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/VarLevelSplit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split on levels within a variable — VarLevelSplit-class","text":"VarLevelSplit object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/VarLevelSplit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split on levels within a variable — VarLevelSplit-class","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/Viewer.html","id":null,"dir":"Reference","previous_headings":"","what":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"table displayed using bootstrap styling tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/Viewer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"","code":"Viewer(x, y = NULL, ...)"},{"path":"https://insightsengineering.github.io/rtables/reference/Viewer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"x object class rtable shiny.tag (defined htmltools package) y optional second argument type x ... arguments passed as_html","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/Viewer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"meaningful. Called side effect opening browser viewer pane.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/Viewer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"","code":"if (interactive()) { sl5 <- factor(iris$Sepal.Length > 5, levels = c(TRUE, FALSE), labels = c(\"S.L > 5\", \"S.L <= 5\") ) df <- cbind(iris, sl5 = sl5) lyt <- basic_table() %>% split_cols_by(\"sl5\") %>% analyze(\"Sepal.Length\") tbl <- build_table(lyt, df) Viewer(tbl) Viewer(tbl, tbl) tbl2 <- htmltools::tags$div( class = \"table-responsive\", as_html(tbl, class_table = \"table\") ) Viewer(tbl, tbl2) }"},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the column population counts to the header — add_colcounts","title":"Add the column population counts to the header — add_colcounts","text":"Add data derived column counts.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the column population counts to the header — add_colcounts","text":"","code":"add_colcounts(lyt, format = \"(N=xx)\")"},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the column population counts to the header — add_colcounts","text":"lyt layout object pre-data used tabulation format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the column population counts to the header — add_colcounts","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the column population counts to the header — add_colcounts","text":"often case column counts derived input data build_table representative population counts. example, events counted table header display number subjects total number events. case use col_count argument build_table control counts displayed table header.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add the column population counts to the header — add_colcounts","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_colcounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the column population counts to the header — add_colcounts","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze(\"AGE\", afun = function(x) list(min = min(x), max = max(x))) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> RACE (lvls) -> AGE (** analysis **) #> tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> ——————————————————————————————————————————————————————————————————— #> ASIAN #> min 20 21 22 #> max 58 55 53 #> BLACK OR AFRICAN AMERICAN #> min 23 21 24 #> max 60 42 51 #> WHITE #> min 30 25 28 #> max 47 55 47"},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_facet.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a combination facet in postprocessing — add_combo_facet","title":"Add a combination facet in postprocessing — add_combo_facet","text":"Add combination facet postprocessing stage custom split fun.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_facet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a combination facet in postprocessing — add_combo_facet","text":"","code":"add_combo_facet(name, label = name, levels, extra = list()) add_overall_facet(name, label, extra = list())"},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_facet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a combination facet in postprocessing — add_combo_facet","text":"name character(1). Name resulting facet (use pathing, etc). label character(1). Label resulting facet. levels character. Vector levels combine within resulting facet. extra list. Extra arguments passed analysis functions applied within resulting facet.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_facet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a combination facet in postprocessing — add_combo_facet","text":"function can used within post argument make_split_fun.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_facet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a combination facet in postprocessing — add_combo_facet","text":"add_combo_facet, data associated resulting facet data associated facets level levels, rbound together. particular, means levels overlapping, data appears duplicated.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_facet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a combination facet in postprocessing — add_combo_facet","text":"","code":"mysplfun <- make_split_fun(post = list( add_combo_facet(\"A_B\", label = \"Arms A+B\", levels = c(\"A: Drug X\", \"B: Placebo\") ), add_overall_facet(\"ALL\", label = \"All Arms\") )) lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = mysplfun) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM)"},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Combination Levels to split — select_all_levels","title":"Add Combination Levels to split — select_all_levels","text":"Add Combination Levels split","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Combination Levels to split — select_all_levels","text":"","code":"select_all_levels add_combo_levels(combosdf, trim = FALSE, first = FALSE, keep_levels = NULL)"},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Add Combination Levels to split — select_all_levels","text":"object class AllLevelsSentinel length 0.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Combination Levels to split — select_all_levels","text":"combosdf data.frame/tbl_df. Columns valname, label, levelcombo, exargs. levelcombo exargs list columns. Passing select_all_levels object value comblevels column indicates overall/-observations level created. trim logical(1). splits corresponding 0 observations kept tabulating. first logical(1). created split level placed first levels (TRUE) last (FALSE, default). keep_levels character NULL. non-NULL, levels retain across combination individual levels.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Combination Levels to split — select_all_levels","text":"closure suitable use splitting function (splfun) creating table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add Combination Levels to split — select_all_levels","text":"Analysis summary functions order matters never used within tabulation framework.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_combo_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Combination Levels to split — select_all_levels","text":"","code":"library(tibble) combodf <- tribble( ~valname, ~label, ~levelcombo, ~exargs, \"A_B\", \"Arms A+B\", c(\"A: Drug X\", \"B: Placebo\"), list(), \"A_C\", \"Arms A+C\", c(\"A: Drug X\", \"C: Combination\"), list() ) lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf)) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination Arms A+B Arms A+C #> (N=121) (N=106) (N=129) (N=227) (N=250) #> ———————————————————————————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 34.03 34.73 lyt1 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf, keep_levels = c( \"A_B\", \"A_C\" ) ) ) %>% analyze(\"AGE\") tbl1 <- build_table(lyt1, DM) tbl1 #> Arms A+B Arms A+C #> (N=227) (N=250) #> —————————————————————————— #> Mean 34.03 34.73 smallerDM <- droplevels(subset(DM, SEX %in% c(\"M\", \"F\") & grepl(\"^(A|B)\", ARM))) lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf[1, ])) %>% split_cols_by(\"SEX\", split_fun = add_overall_level(\"SEX_ALL\", \"All Genders\") ) %>% analyze(\"AGE\") lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf)) %>% split_rows_by(\"SEX\", split_fun = add_overall_level(\"SEX_ALL\", \"All Genders\") ) %>% summarize_row_groups() %>% analyze(\"AGE\") tbl3 <- build_table(lyt3, smallerDM) tbl3 #> A: Drug X B: Placebo Arms A+B Arms A+C #> (N=121) (N=106) (N=227) (N=121) #> ——————————————————————————————————————————————————————————————————————— #> All Genders 121 (100.0%) 106 (100.0%) 227 (100.0%) 121 (100.0%) #> Mean 34.91 33.02 34.03 34.91 #> F 70 (57.9%) 56 (52.8%) 126 (55.5%) 70 (57.9%) #> Mean 33.71 33.84 33.77 33.71 #> M 51 (42.1%) 50 (47.2%) 101 (44.5%) 51 (42.1%) #> Mean 36.55 32.10 34.35 36.55"},{"path":"https://insightsengineering.github.io/rtables/reference/add_existing_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an already calculated table to the layout — add_existing_table","title":"Add an already calculated table to the layout — add_existing_table","text":"Add already calculated table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_existing_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an already calculated table to the layout — add_existing_table","text":"","code":"add_existing_table(lyt, tt, indent_mod = 0)"},{"path":"https://insightsengineering.github.io/rtables/reference/add_existing_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an already calculated table to the layout — add_existing_table","text":"lyt layout object pre-data used tabulation tt TableTree (related class). TableTree object representing populated table. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_existing_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an already calculated table to the layout — add_existing_table","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_existing_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add an already calculated table to the layout — add_existing_table","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_existing_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an already calculated table to the layout — add_existing_table","text":"","code":"lyt1 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = mean, format = \"xx.xx\") tbl1 <- build_table(lyt1, DM) tbl1 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————— #> mean 34.91 33.02 34.57 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = sd, format = \"xx.xx\") %>% add_existing_table(tbl1) tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————— #> sd 7.79 6.34 6.50 #> mean 34.91 33.02 34.57 table_structure(tbl2) #> [TableTree] root #> [ElementaryTable] AGE (1 x 3) #> [ElementaryTable] AGE (1 x 3) row_paths_summary(tbl2) #> rowname node_class path #> ———————————————————————————————————————— #> sd DataRow root, AGE, sd #> mean DataRow root, AGE, mean"},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Overall Column — add_overall_col","title":"Add Overall Column — add_overall_col","text":"function add overall column top level splitting, within existing column splits. See add_overall_level recommended way add overall columns generally within existing splits.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Overall Column — add_overall_col","text":"","code":"add_overall_col(lyt, label)"},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Overall Column — add_overall_col","text":"lyt layout object pre-data used tabulation label character(1). label (confused name) object/structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Overall Column — add_overall_col","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Overall Column — add_overall_col","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze(\"AGE\") lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> (all obs) #> #> Row-Split Structure: #> AGE (** analysis **) #> tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination All Patients #> ————————————————————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 34.22"},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an virtual 'overall' level to split — add_overall_level","title":"Add an virtual 'overall' level to split — add_overall_level","text":"Add virtual 'overall' level split","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an virtual 'overall' level to split — add_overall_level","text":"","code":"add_overall_level( valname = \"Overall\", label = valname, extra_args = list(), first = TRUE, trim = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an virtual 'overall' level to split — add_overall_level","text":"valname character(1). 'Value' assigned implicit -observations split level. Defaults \"Overall\" label character(1). label (confused name) object/structure. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. first logical(1). implicit level appear first (TRUE) last FALSE. Defaults TRUE. trim logical(1). splits corresponding 0 observations kept tabulating.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an virtual 'overall' level to split — add_overall_level","text":"closure suitable use splitting function (splfun) creating table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/add_overall_level.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an virtual 'overall' level to split — add_overall_level","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE )) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination All Patients #> ————————————————————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 34.22 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", split_fun = add_overall_level(\"All Ethnicities\") ) %>% summarize_row_groups(label_fstr = \"%s (n)\") %>% analyze(\"AGE\") lyt2 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> RACE (lvls) -> AGE (** analysis **) #> tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————————————————————————————— #> All Ethnicities (n) 121 (100.0%) 106 (100.0%) 129 (100.0%) #> Mean 34.91 33.02 34.57 #> ASIAN (n) 79 (65.3%) 68 (64.2%) 84 (65.1%) #> Mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN (n) 28 (23.1%) 24 (22.6%) 27 (20.9%) #> Mean 34.68 31.71 34.00 #> WHITE (n) 14 (11.6%) 14 (13.2%) 18 (14.0%) #> Mean 39.36 36.93 35.11 #> AMERICAN INDIAN OR ALASKA NATIVE (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> MULTIPLE (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> OTHER (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> UNKNOWN (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA"},{"path":"https://insightsengineering.github.io/rtables/reference/additional_fun_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","title":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","text":"possible add specific parameters afun cfun, analyze summarize_row_groups respectively. parameters grant access relevant information like row split structure (see spl_context) predefined baseline (.ref_group).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/additional_fun_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","text":"list describe parameters can added custom analysis function: .N_col column-wise N (column count) full column tabulated within .N_total overall N (observation count, defined sum column counts) tabulation .N_row row-wise N (row group count) group observations analyzed (.e. column-based subsetting) .df_row data.frame observations row group analyzed (.e. column-based subsetting) .var variable analyzed .ref_group data.frame vector subset corresponding ref_group column including subsetting defined row-splitting. Optional required/meaningful ref_group column defined .ref_full data.frame vector subset corresponding ref_group column without subsetting defined row-splitting. Optional required/meaningful ref_group column defined .in_ref_col boolean indicates calculation done cells within reference column .spl_context data.frame, row gives information previous/'ancestor' split state. See spl_context .alt_df_row data.frame, .e. alt_count_df row splitting. can used .all_col_exprs .spl_context information retrieve current faceting, alt_count_df. can empty table entries filtered . .alt_df data.frame, .alt_df_row filtered columns expression. data present faceting main data df. also filters NAs related parameters set (e.g. inclNAs analyze). Similarly .alt_df_row, can empty table entries filtered . .all_col_exprs list expressions. represents different column splitting. .all_col_counts vector integers. represents global count column. differs alt_counts_df used (see build_table).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/additional_fun_params.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","text":"formals specified incorrectly present tabulation machinery, missing. example.ref_group missing baseline previously defined data splitting (via ref_group parameters , e.g., split_rows_by). Similarly, alt_counts_df provided build_table, .alt_df_row .alt_df present.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Rows Analyzing Variables Across Columns — analyze","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"Adding analyzed variables table layout defines primary tabulation performed. adding calls analyze /analyze_colvars layout pipeline. adding splitting, tabulation occur current/next level nesting default.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"","code":"analyze( lyt, vars, afun = simple_analysis, var_labels = vars, table_names = vars, format = NULL, na_str = NA_character_, nested = TRUE, inclNAs = FALSE, extra_args = list(), show_labels = c(\"default\", \"visible\", \"hidden\"), indent_mod = 0L, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"lyt layout object pre-data used tabulation vars character vector. Multiple variable names. afun function. Analysis function, must take x df first parameter. Can optionally take parameters populated tabulation framework. See Details analyze. var_labels character. Variable labels 1 variables table_names character. Names tables representing atomic analysis. Defaults var. format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. show_labels character(1). variable labels corresponding variable(s) vars visible resulting table. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"non-NULL format used specify formats generated rows, can character vector, function, list functions. repped number rows known tabulation process, overridden formats specified within rcell calls afun. analysis function (afun) take first parameter either x df. function accepts changes behavior tabulation performed. afun's first parameter x, receive corresponding subset vector data relevant column (var ) raw data used build table. afun's first parameter df, receive corresponding subset data.frame (.e. columns) raw data tabulated addition differentiation first argument, analysis function can optionally accept number parameters , present formals passed function tabulation machinery. listed described additional_fun_params.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"None arguments described Details section can overridden via extra_args calling make_afun. .N_col .N_total can overridden via col_counts argument build_table. Alternative values others must calculated within afun based combination extra arguments unmodified values provided tabulation framework.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> AGE (** analysis **) #> tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————— #> Min. 20.00 21.00 22.00 #> 1st Qu. 29.00 29.00 30.00 #> Median 33.00 32.00 33.00 #> Mean 34.91 33.02 34.57 #> 3rd Qu. 39.00 37.00 38.00 #> Max. 60.00 55.00 53.00 lyt2 <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(head(names(iris), -1), afun = function(x) { list( \"mean / sd\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = rcell(diff(range(x)), format = \"xx.xx\") ) }) lyt2 #> A Pre-data Table Layout #> #> Column-Split Structure: #> Species (lvls) #> #> Row-Split Structure: #> Sepal.Length:Sepal.Width:Petal.Length:Petal.Width (** multivar analysis **) #> tbl2 <- build_table(lyt2, iris) tbl2 #> setosa versicolor virginica #> —————————————————————————————————————————————————————— #> Sepal.Length #> mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) #> range 1.50 2.10 3.00 #> Sepal.Width #> mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32) #> range 2.10 1.40 1.60 #> Petal.Length #> mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55) #> range 0.90 2.10 2.40 #> Petal.Width #> mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) #> range 0.50 0.80 1.10"},{"path":"https://insightsengineering.github.io/rtables/reference/analyze_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"Generate Rows Analyzing Different Variables Across Columns","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"","code":"analyze_colvars( lyt, afun, format = NULL, na_str = NA_character_, nested = TRUE, extra_args = list(), indent_mod = 0L, inclNAs = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/analyze_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"lyt layout object pre-data used tabulation afun function list. Function(s) used calculate values column. list repped needed matched position columns tabulation. functions accepts parameters analyze like afun format. information see additional_fun_params. format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/analyze_colvars.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/analyze_colvars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"","code":"library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:testthat’: #> #> matches #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union ANL <- DM %>% mutate(value = rnorm(n()), pctdiff = runif(n())) ## toy example where we take the mean of the first variable and the ## count of >.5 for the second. colfuns <- list( function(x) rcell(mean(x), format = \"xx.x\"), function(x) rcell(sum(x > .5), format = \"xx\") ) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"value\", \"pctdiff\")) %>% split_rows_by(\"RACE\", split_label = \"ethnicity\", split_fun = drop_split_levels ) %>% summarize_row_groups() %>% analyze_colvars(afun = colfuns) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> value:pctdiff (vars) #> #> Row-Split Structure: #> RACE (lvls) -> NA (** col-var analysis **) #> tbl <- build_table(lyt, ANL) tbl #> A: Drug X B: Placebo C: Combination #> value pctdiff value pctdiff value pctdiff #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 79 (65.3%) 68 (64.2%) 68 (64.2%) 84 (65.1%) 84 (65.1%) #> 0.0 32 0.2 28 0.1 42 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 28 (23.1%) 24 (22.6%) 24 (22.6%) 27 (20.9%) 27 (20.9%) #> -0.0 15 0.1 9 0.1 15 #> WHITE 14 (11.6%) 14 (11.6%) 14 (13.2%) 14 (13.2%) 18 (14.0%) 18 (14.0%) #> -0.2 10 0.3 7 -0.3 12 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"value\", \"pctdiff\"), varlabels = c(\"Measurement\", \"Pct Diff\") ) %>% split_rows_by(\"RACE\", split_label = \"ethnicity\", split_fun = drop_split_levels ) %>% summarize_row_groups() %>% analyze_colvars(afun = mean, format = \"xx.xx\") tbl2 <- build_table(lyt2, ANL) tbl2 #> A: Drug X B: Placebo C: Combination #> Measurement Pct Diff Measurement Pct Diff Measurement Pct Diff #> —————————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 79 (65.3%) 68 (64.2%) 68 (64.2%) 84 (65.1%) 84 (65.1%) #> mean 0.04 0.45 0.19 0.44 0.14 0.51 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 28 (23.1%) 24 (22.6%) 24 (22.6%) 27 (20.9%) 27 (20.9%) #> mean -0.04 0.53 0.13 0.46 0.06 0.57 #> WHITE 14 (11.6%) 14 (11.6%) 14 (13.2%) 14 (13.2%) 18 (14.0%) 18 (14.0%) #> mean -0.19 0.61 0.33 0.48 -0.27 0.55"},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":null,"dir":"Reference","previous_headings":"","what":"Append a description to the 'top-left' materials for the layout — append_topleft","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"function adds newlines current set \"top-left materials\".","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"","code":"append_topleft(lyt, newlines)"},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"lyt layout object pre-data used tabulation newlines character. new line(s) added materials","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"Adds newlines set strings representing 'top-left' materials declared layout (content displayed left column labels resulting tables printed). Top-left material strings stored displayed exactly , structure indenting applied either added displayed.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"Currently, construction layout called makes difference, independent actual splitting keywords. may change future. function experimental, name details behavior subject change future versions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/append_topleft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"","code":"library(dplyr) DM2 <- DM %>% mutate(RACE = factor(RACE), SEX = factor(SEX)) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% split_rows_by(\"RACE\") %>% append_topleft(\"Ethnicity\") %>% analyze(\"AGE\") %>% append_topleft(\" Age\") tbl <- build_table(lyt, DM2) tbl #> Ethnicity A: Drug X B: Placebo C: Combination #> Age F M F M F M #> ————————————————————————————————————————————————————————————————————————————— #> ASIAN #> Mean 33.55 35.03 34.00 31.10 34.90 34.39 #> BLACK OR AFRICAN AMERICAN #> Mean 33.17 37.40 30.58 32.83 33.85 34.14 #> WHITE #> Mean 35.88 44.00 38.57 35.29 36.50 34.00"},{"path":"https://insightsengineering.github.io/rtables/reference/as_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an rtable object to a shiny.tag html object — as_html","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"returned html object can immediately used shiny rmarkdown.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/as_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"","code":"as_html( x, width = NULL, class_table = \"table table-condensed table-hover\", class_tr = NULL, class_th = NULL, link_label = NULL, bold = c(\"header\"), header_sep_line = TRUE, no_spaces_between_cells = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/as_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"x rtable object width string indicate desired width table. Common input formats include percentage viewer window width (e.g. \"100%\") distance value (e.g. \"300px\"). Defaults NULL. class_table class table tag class_tr class tr tag class_th class th tag link_label link anchor label (including tab: prefix) table. bold elements table output bold. Options \"main_title\", \"subtitles\", \"header\", \"row_names\", \"label_rows\", \"content_rows\" (includes non-label rows). Defaults \"header\". header_sep_line whether black line printed table header. Defaults TRUE. no_spaces_between_cells whether spaces table cells collapsed. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/as_html.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"shiny.tag object representing x HTML.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/as_html.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"","code":"tbl <- rtable( header = LETTERS[1:3], format = \"xx\", rrow(\"r1\", 1, 2, 3), rrow(\"r2\", 4, 3, 2, indent = 1), rrow(\"r3\", indent = 2) ) as_html(tbl) #>
    #>
    #>
    #>

    <\/p> #> <\/div> #>

    <\/div> #> <\/div> #> #> #> #> #> #>
    <\/th> #> A<\/th> #> B<\/th> #> C<\/th> #> <\/tr> #>
    r1<\/td> #> 1<\/td> #> 2<\/td> #> 3<\/td> #> <\/tr> #>
    r2<\/td> #> 4<\/td> #> 3<\/td> #> 2<\/td> #> <\/tr> #>
    r3<\/td> #> <\/td> #> <\/td> #> <\/td> #> <\/tr> #>
    <\/caption> #> <\/table> #>
    <\/div> #> <\/div> as_html(tbl, class_table = \"table\", class_tr = \"row\") #>
    #>
    #>
    #>

    <\/p> #> <\/div> #>

    <\/div> #> <\/div> #> #> #> #> #> #>
    <\/th> #> A<\/th> #> B<\/th> #> C<\/th> #> <\/tr> #>
    r1<\/td> #> 1<\/td> #> 2<\/td> #> 3<\/td> #> <\/tr> #>
    r2<\/td> #> 4<\/td> #> 3<\/td> #> 2<\/td> #> <\/tr> #>
    r3<\/td> #> <\/td> #> <\/td> #> <\/td> #> <\/tr> #>
    <\/caption> #> <\/table> #>
    <\/div> #> <\/div> as_html(tbl, bold = c(\"header\", \"row_names\")) #>
    #>
    #>
    #>

    <\/p> #> <\/div> #>

    <\/div> #> <\/div> #> #> #> #> #> #>
    <\/th> #> A<\/th> #> B<\/th> #> C<\/th> #> <\/tr> #>
    r1<\/td> #> 1<\/td> #> 2<\/td> #> 3<\/td> #> <\/tr> #>
    r2<\/td> #> 4<\/td> #> 3<\/td> #> 2<\/td> #> <\/tr> #>
    r3<\/td> #> <\/td> #> <\/td> #> <\/td> #> <\/tr> #>
    <\/caption> #> <\/table> #>
    <\/div> #> <\/div> if (FALSE) { Viewer(tbl) }"},{"path":"https://insightsengineering.github.io/rtables/reference/asvec.html","id":null,"dir":"Reference","previous_headings":"","what":"convert to a vector — asvec","title":"convert to a vector — asvec","text":"Convert rtables framework object vector, possible. unlikely useful realistic scenarios.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/asvec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"convert to a vector — asvec","text":"","code":"# S4 method for VTableTree as.vector(x, mode = \"any\")"},{"path":"https://insightsengineering.github.io/rtables/reference/asvec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"convert to a vector — asvec","text":"x . object converted vector mode character(1). Passed .vector","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/asvec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"convert to a vector — asvec","text":"vector chosen mode (error raised one row present).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/asvec.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"convert to a vector — asvec","text":"works table single row row object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/avarspl.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a subset tabulation/analysis — AnalyzeVarSplit","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"Define subset tabulation/analysis Define subset tabulation/analysis","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/avarspl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"","code":"AnalyzeVarSplit( var, split_label = var, afun, defrowlab = \"\", cfun = NULL, cformat = NULL, split_format = NULL, split_na_str = NA_character_, inclNAs = FALSE, split_name = var, extra_args = list(), indent_mod = 0L, label_pos = \"default\", cvar = \"\", section_div = NA_character_ ) AnalyzeColVarSplit( afun, defrowlab = \"\", cfun = NULL, cformat = NULL, split_format = NULL, split_na_str = NA_character_, inclNAs = FALSE, split_name = \"\", extra_args = list(), indent_mod = 0L, label_pos = \"default\", cvar = \"\", section_div = NA_character_ ) AnalyzeMultiVars( var, split_label = \"\", afun, defrowlab = \"\", cfun = NULL, cformat = NULL, split_format = NULL, split_na_str = NA_character_, inclNAs = FALSE, .payload = NULL, split_name = NULL, extra_args = list(), indent_mod = 0L, child_labels = c(\"default\", \"topleft\", \"visible\", \"hidden\"), child_names = var, cvar = \"\", section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/avarspl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"var string, variable name split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). afun function. Analysis function, must take x df first parameter. Can optionally take parameters populated tabulation framework. See Details analyze. defrowlab character. Default row labels specified return value afun cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE split_name string. Name associated split (pathing, etc) extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cvar character(1). variable, , content function accept. Defaults NA. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. .payload Used internally, intended set end users. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. child_names character. Names given sub splits contained compound split (typically AnalyzeMultiVars split object).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/avarspl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"AnalyzeVarSplit object. AnalyzeMultiVars split object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/avarspl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Layout with 1 column and zero rows — basic_table","title":"Layout with 1 column and zero rows — basic_table","text":"Every layout must start basic table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layout with 1 column and zero rows — basic_table","text":"","code":"basic_table( title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), show_colcounts = FALSE, colcount_format = \"(N=xx)\", header_section_div = NA_character_, top_level_section_div = NA_character_, inset = 0L )"},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layout with 1 column and zero rows — basic_table","text":"title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. show_colcounts logical(1). column counts displayed resulting table layout applied data colcount_format character(1). Format use displaying column counts. Must 1d, 2d one component percent. See details. header_section_div character(1). String used divide header table. See header_section_div() getter setter . Please consider changing last element section_div() concatenating tables need divider . top_level_section_div character(1). assigned single character, first (top level) split division table highlighted line made character. See section_div information. inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layout with 1 column and zero rows — basic_table","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Layout with 1 column and zero rows — basic_table","text":"colcount_format ignored show_colcounts FALSE (default). show_colcounts TRUE, colcount_format 2-dimensional percent component, value component percent always populated 1 (.e. 100%). 1d formats used render counts exactly normally , 2d formats include percent, 3d formats result error. Formats form functions supported colcount format. See formatters::list_valid_format_labels() list valid format labels select .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layout with 1 column and zero rows — basic_table","text":"percent components colcount_format always populated value 1, can get arguably strange results, individual arm columns combined \"patients\" column list \"100%\" percentage, even though individual arm columns represent strict subsets patients column. Note subtitles (subtitles()) footers (main_footer() prov_footer()) spans one line can supplied character vector maintain indentation multiple lines.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/basic_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layout with 1 column and zero rows — basic_table","text":"","code":"lyt <- basic_table() %>% analyze(\"AGE\", afun = mean) tbl <- build_table(lyt, DM) tbl #> all obs #> ——————————————————————— #> mean 34.2219101123596 lyt2 <- basic_table( title = \"Title of table\", subtitles = c(\"a number\", \"of subtitles\"), main_footer = \"test footer\", prov_footer = paste( \"test.R program, executed at\", Sys.time() ) ) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", mean) tbl2 <- build_table(lyt2, DM) tbl2 #> Title of table #> a number #> of subtitles #> #> ————————————————————————————————————————————————————————————— #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————— #> mean 34.9090909090909 33.0188679245283 34.5658914728682 #> ————————————————————————————————————————————————————————————— #> #> test footer #> #> test.R program, executed at 2024-02-22 20:28:20.014627 lyt3 <- basic_table( show_colcounts = TRUE, colcount_format = \"xx. (xx.%)\" ) %>% split_cols_by(\"ARM\")"},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve and assign elements of a TableTree — brackets","title":"Retrieve and assign elements of a TableTree — brackets","text":"Retrieve assign elements TableTree","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve and assign elements of a TableTree — brackets","text":"","code":"# S4 method for VTableTree,ANY,ANY,list [(x, i, j, ...) <- value # S4 method for VTableTree,logical,logical [(x, i, j, ..., drop = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve and assign elements of a TableTree — brackets","text":"x TableTree index j index ... Includes keep_topleft logical(1) ([ ) top-left material table retained subsetting. Defaults TRUE rows included (.e. subsetting column), drops otherwise. keep_titles logical(1) title information retained. Defaults FALSE. keep_footers logical(1) non-referential footer information retained. Defaults keep_titles. reindex_refs logical(1). referential footnotes re-indexed resulting subset entire table. Defaults TRUE. value Replacement value (list, TableRow, TableTree) drop logical(1). value cell returned one cell selected combination j. possible return vector values. please consider using cell_values(). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve and assign elements of a TableTree — brackets","text":"TableTree (ElementaryTable) object, unless single cell selected drop=TRUE, case (possibly multi-valued) fully stripped raw value selected cell.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve and assign elements of a TableTree — brackets","text":"default, subsetting drops information title, subtitle, main footer, provenance footer, topleft. column selected rows kept, topleft information remains default. referential footnote kept whenever subset table contains referenced element.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Retrieve and assign elements of a TableTree — brackets","text":"subsetting always preserve original order, even provided indexes preserve . sorting needed, please consider using sort_at_path(). Also note character indices treated paths, vectors names [ [<-.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/brackets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve and assign elements of a TableTree — brackets","text":"","code":"lyt <- basic_table( title = \"Title\", subtitles = c(\"Sub\", \"titles\"), prov_footer = \"prov footer\", main_footer = \"main footer\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\")) tbl <- build_table(lyt, DM) top_left(tbl) <- \"Info\" tbl #> Title #> Sub #> titles #> #> —————————————————————————————————————————————————————————— #> Info A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89 #> M #> Mean 36.55 32.10 34.28 #> U #> Mean NA NA NA #> UNDIFFERENTIATED #> Mean NA NA NA #> —————————————————————————————————————————————————————————— #> #> main footer #> #> prov footer # As default header, footer, and topleft information is lost tbl[1, ] #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————— #> F tbl[1:2, 2] #> B: Placebo #> ——————————————————— #> F #> Mean 33.84 # Also boolean filters can work tbl[, c(FALSE, TRUE, FALSE)] #> Note: method with signature ‘VTableTree#missing#ANY’ chosen for function ‘[’, #> target signature ‘TableTree#missing#logical’. #> \"VTableTree#ANY#logical\" would also be valid #> Info B: Placebo #> ————————————————————————————— #> F #> Mean 33.84 #> M #> Mean 32.10 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA # If drop = TRUE, the content values are directly retrieved tbl[2, 1] #> A: Drug X #> ———————————————— #> Mean 33.71 tbl[2, 1, drop = TRUE] #> [1] 33.71429 # Drop works also if vectors are selected, but not matrices tbl[, 1, drop = TRUE] #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> Info A: Drug X #> ———————————————————————————— #> F #> Mean 33.71 #> M #> Mean 36.55 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA tbl[2, , drop = TRUE] #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————— #> Mean 33.71 33.84 34.89 tbl[1, 1, drop = TRUE] # NULL because it is a label row #> Warning: The value selected with drop = TRUE belongs to a label row. NULL will be returned #> NULL tbl[2, 1:2, drop = TRUE] # vectors can be returned only with cell_values() #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> A: Drug X B: Placebo #> ————————————————————————————— #> Mean 33.71 33.84 tbl[1:2, 1:2, drop = TRUE] # no dropping because it is a matrix #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> A: Drug X B: Placebo #> ——————————————————————————————— #> F #> Mean 33.71 33.84 # If all rows are selected, topleft is kept by default tbl[, 2] #> Info B: Placebo #> ————————————————————————————— #> F #> Mean 33.84 #> M #> Mean 32.10 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA tbl[, 1] #> Info A: Drug X #> ———————————————————————————— #> F #> Mean 33.71 #> M #> Mean 36.55 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA # It is possible to deselect values tbl[-2, ] #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> M #> Mean 36.55 32.10 34.28 #> U #> Mean NA NA NA #> UNDIFFERENTIATED #> Mean NA NA NA tbl[, -1] #> Info B: Placebo C: Combination #> —————————————————————————————————————————————— #> F #> Mean 33.84 34.89 #> M #> Mean 32.10 34.28 #> U #> Mean NA NA #> UNDIFFERENTIATED #> Mean NA NA # Values can be reassigned tbl[2, 1] <- rcell(999) tbl[2, ] <- list(rrow(\"FFF\", 888, 666, 777)) tbl[6, ] <- list(-111, -222, -333) tbl #> Title #> Sub #> titles #> #> —————————————————————————————————————————————————————————— #> Info A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> FFF 888 666 777 #> M #> Mean 36.55 32.10 34.28 #> U #> Mean -111 -222 -333 #> UNDIFFERENTIATED #> Mean NA NA NA #> —————————————————————————————————————————————————————————— #> #> main footer #> #> prov footer # We can keep some information from the original table if we need tbl[1, 2, keep_titles = TRUE] #> Title #> Sub #> titles #> #> —————————————— #> B: Placebo #> —————————————— #> F #> —————————————— #> #> main footer #> #> prov footer tbl[1, 2, keep_footers = TRUE, keep_titles = FALSE] #> B: Placebo #> —————————————— #> F #> —————————————— #> #> main footer #> #> prov footer tbl[1, 2, keep_footers = FALSE, keep_titles = TRUE] #> Title #> Sub #> titles #> #> —————————————— #> B: Placebo #> —————————————— #> F tbl[1, 2, keep_footers = TRUE] #> B: Placebo #> —————————————— #> F #> —————————————— #> #> main footer #> #> prov footer tbl[1, 2, keep_topleft = TRUE] #> Info B: Placebo #> ————————————————— #> F # Keeps the referential footnotes when subset contains them fnotes_at_path(tbl, rowpath = c(\"SEX\", \"M\", \"AGE\", \"Mean\")) <- \"important\" tbl[4, 1] #> A: Drug X #> ———————————————————— #> Mean {1} 36.55 #> ———————————————————— #> #> {1} - important #> ———————————————————— #> tbl[2, 1] # None present #> A: Drug X #> ——————————————— #> FFF 888 # We can reindex referential footnotes, so that the new table does not depend # on the original one fnotes_at_path(tbl, rowpath = c(\"SEX\", \"U\", \"AGE\", \"Mean\")) <- \"important\" tbl[, 1] # both present #> Info A: Drug X #> ———————————————————————————— #> F #> FFF 888 #> M #> Mean {1} 36.55 #> U #> Mean {1} -111 #> UNDIFFERENTIATED #> Mean NA #> ———————————————————————————— #> #> {1} - important #> ———————————————————————————— #> tbl[5:6, 1] # {1} because it has been indexed again #> A: Drug X #> —————————————————————— #> U #> Mean {1} -111 #> —————————————————————— #> #> {1} - important #> —————————————————————— #> tbl[5:6, 1, reindex_refs = FALSE] # {2} -> not reindexed #> A: Drug X #> —————————————————————— #> U #> Mean {1} -111 #> —————————————————————— #> #> {1} - important #> —————————————————————— #> # Note that order can not be changed with subsetting tbl[c(4, 3, 1), c(3, 1)] # It preserves order and wanted selection #> A: Drug X C: Combination #> ——————————————————————————————————————— #> F #> M #> Mean {1} 34.28 36.55 #> ——————————————————————————————————————— #> #> {1} - important #> ——————————————————————————————————————— #>"},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a table from a layout and data — build_table","title":"Create a table from a layout and data — build_table","text":"Layouts used describe table pre-data. build_table used create table using layout dataset.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a table from a layout and data — build_table","text":"","code":"build_table( lyt, df, alt_counts_df = NULL, col_counts = NULL, col_total = if (is.null(alt_counts_df)) nrow(df) else nrow(alt_counts_df), topleft = NULL, hsep = default_hsep(), ... )"},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a table from a layout and data — build_table","text":"lyt layout object pre-data used tabulation df dataset (data.frame tibble) alt_counts_df dataset (data.frame tibble). Alternative full data rtables framework use () calculating column counts. col_counts numeric (NULL). Deprecated. non-null, column counts override calculated automatically tabulation. Must specify \"counts\" resulting columns non-NULL. NA elements replaced automatically calculated counts. col_total integer(1). total observations across columns. Defaults nrow(df). topleft character. Override values \"top left\" material displayed printing. hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. ... currently ignored.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a table from a layout and data — build_table","text":"TableTree ElementaryTable object representing table created performing tabulations declared lyt data df.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a table from a layout and data — build_table","text":"alt_counts_df specified, column counts calculated applying exact column subsetting expressions determined applying column splitting main data (df) alt_counts_df counting observations resulting subset. particular, means case splitting based cuts data, dynamic cuts calculated based df simply re-used count calculation.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a table from a layout and data — build_table","text":"overriding column counts totals care must taken , e.g., length() nrow() called within tabulation functions, give overridden counts. Writing/using tabulation functions accept .N_col .N_total rely column counts (even implicitly) way ensure overridden counts fully respected.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a table from a layout and data — build_table","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/build_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a table from a layout and data — build_table","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(\"Sepal.Length\", afun = function(x) { list( \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = diff(range(x)) ) }) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> Species (lvls) #> #> Row-Split Structure: #> Sepal.Length (** analysis **) #> tbl <- build_table(lyt, iris) tbl #> setosa versicolor virginica #> ——————————————————————————————————————————————————— #> mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) #> range 1.5 2.1 3 # analyze multiple variables lyt2 <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = function(x) { list( \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = diff(range(x)) ) }) tbl2 <- build_table(lyt2, iris) tbl2 #> setosa versicolor virginica #> —————————————————————————————————————————————————————— #> Sepal.Length #> mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) #> range 1.5 2.1 3 #> Petal.Width #> mean (sd) 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) #> range 0.5 0.8 1.1 # an example more relevant for clinical trials with column counts lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = function(x) { setNames(as.list(fivenum(x)), c( \"minimum\", \"lower-hinge\", \"median\", \"upper-hinge\", \"maximum\" )) }) tbl3 <- build_table(lyt3, DM) tbl3 #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> ————————————————————————————————————————————————————— #> minimum 20 21 22 #> lower-hinge 29 29 30 #> median 33 32 33 #> upper-hinge 39 37 38 #> maximum 60 55 53 tbl4 <- build_table(lyt3, subset(DM, AGE > 40)) tbl4 #> A: Drug X B: Placebo C: Combination #> (N=25) (N=10) (N=21) #> ————————————————————————————————————————————————————— #> minimum 41 41 41 #> lower-hinge 43 42 43 #> median 45 45.5 45 #> upper-hinge 49 48 47 #> maximum 60 55 53 # with column counts calculated based on different data miniDM <- DM[sample(1:NROW(DM), 100), ] tbl5 <- build_table(lyt3, DM, alt_counts_df = miniDM) tbl5 #> A: Drug X B: Placebo C: Combination #> (N=37) (N=30) (N=33) #> ————————————————————————————————————————————————————— #> minimum 20 21 22 #> lower-hinge 29 29 30 #> median 33 32 33 #> upper-hinge 39 37 38 #> maximum 60 55 53 tbl6 <- build_table(lyt3, DM, col_counts = 1:3) tbl6 #> A: Drug X B: Placebo C: Combination #> (N=1) (N=2) (N=3) #> ————————————————————————————————————————————————————— #> minimum 20 21 22 #> lower-hinge 29 29 30 #> median 33 32 33 #> upper-hinge 39 37 38 #> maximum 60 55 53"},{"path":"https://insightsengineering.github.io/rtables/reference/cbind_rtables.html","id":null,"dir":"Reference","previous_headings":"","what":"cbind two rtables — cbind_rtables","title":"cbind two rtables — cbind_rtables","text":"cbind two rtables","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cbind_rtables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cbind two rtables — cbind_rtables","text":"","code":"cbind_rtables(x, ...)"},{"path":"https://insightsengineering.github.io/rtables/reference/cbind_rtables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cbind two rtables — cbind_rtables","text":"x table row object ... 1 objects class x","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cbind_rtables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cbind two rtables — cbind_rtables","text":"formal table object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cbind_rtables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"cbind two rtables — cbind_rtables","text":"","code":"x <- rtable(c(\"A\", \"B\"), rrow(\"row 1\", 1, 2), rrow(\"row 2\", 3, 4)) y <- rtable(\"C\", rrow(\"row 1\", 5), rrow(\"row 2\", 6)) z <- rtable(\"D\", rrow(\"row 1\", 9), rrow(\"row 2\", 10)) t1 <- cbind_rtables(x, y) t1 #> A B C #> ————————————————— #> row 1 1 2 5 #> row 2 3 4 6 t2 <- cbind_rtables(x, y, z) t2 #> A B C D #> —————————————————————— #> row 1 1 2 5 9 #> row 2 3 4 6 10 col_paths_summary(t1) #> label path #> —————————————————— #> A manual, A #> B manual, B #> C manual, C col_paths_summary(t2) #> label path #> —————————————————— #> A manual, A #> B manual, B #> C manual, C #> D manual, D"},{"path":"https://insightsengineering.github.io/rtables/reference/cell_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve cell values by row and column path — cell_values","title":"Retrieve cell values by row and column path — cell_values","text":"Retrieve cell values row column path","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cell_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve cell values by row and column path — cell_values","text":"","code":"cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) value_at(tt, rowpath = NULL, colpath = NULL) # S4 method for VTableTree value_at(tt, rowpath = NULL, colpath = NULL)"},{"path":"https://insightsengineering.github.io/rtables/reference/cell_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve cell values by row and column path — cell_values","text":"tt TableTree (related class). TableTree object representing populated table. rowpath character. Path row-split space desired row(s). Can include \"@content\". colpath character. Path column-split space desired column(s). Can include \"*\". omit_labrows logical(1). label rows underneath rowpath omitted (TRUE, default), return empty lists cell \"values\" (FALSE).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cell_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve cell values by row and column path — cell_values","text":"cell_values, list (regardless type value cells hold). rowpath defines path single row, cell_values returns list cell values row, otherwise list lists, one row captured underneath rowpath. occurs subsetting colpath occurred. value_at \"unwrapped\" value single cell, error, combination rowpath colpath define location single cell tt.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cell_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Retrieve cell values by row and column path — cell_values","text":"cell_values return single cell's value wrapped list. Use value_at receive \"bare\" cell value.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cell_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve cell values by row and column path — cell_values","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% split_rows_by(\"RACE\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\") library(dplyr) ## for mutate tbl <- build_table(lyt, DM %>% mutate(SEX = droplevels(SEX), RACE = droplevels(RACE))) row_paths_summary(tbl) #> rowname node_class path #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN ContentRow RACE, ASIAN, @content, ASIAN #> A LabelRow RACE, ASIAN, STRATA1, A #> Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean #> B LabelRow RACE, ASIAN, STRATA1, B #> Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean #> C LabelRow RACE, ASIAN, STRATA1, C #> Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean #> BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN #> A LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A #> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean #> B LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B #> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean #> C LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C #> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean #> WHITE ContentRow RACE, WHITE, @content, WHITE #> A LabelRow RACE, WHITE, STRATA1, A #> Mean DataRow RACE, WHITE, STRATA1, A, AGE, Mean #> B LabelRow RACE, WHITE, STRATA1, B #> Mean DataRow RACE, WHITE, STRATA1, B, AGE, Mean #> C LabelRow RACE, WHITE, STRATA1, C #> Mean DataRow RACE, WHITE, STRATA1, C, AGE, Mean col_paths_summary(tbl) #> label path #> ————————————————————————————————————————————— #> A: Drug X ARM, A: Drug X #> F ARM, A: Drug X, SEX, F #> M ARM, A: Drug X, SEX, M #> B: Placebo ARM, B: Placebo #> F ARM, B: Placebo, SEX, F #> M ARM, B: Placebo, SEX, M #> C: Combination ARM, C: Combination #> F ARM, C: Combination, SEX, F #> M ARM, C: Combination, SEX, M cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\") ) #> $`A: Drug X.F` #> [1] 33.75 #> # it's also possible to access multiple values by being less specific cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\") ) #> $A.AGE.Mean #> $A.AGE.Mean$`A: Drug X.F` #> [1] 30.4 #> #> #> $B.AGE.Mean #> $B.AGE.Mean$`A: Drug X.F` #> [1] 33.75 #> #> #> $C.AGE.Mean #> $C.AGE.Mean$`A: Drug X.F` #> [1] 36.92308 #> #> cell_values(tbl, c(\"RACE\", \"ASIAN\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"M\")) #> $ASIAN #> $ASIAN$`A: Drug X.M` #> [1] 35.0000000 0.6862745 #> #> #> $STRATA1.A.AGE.Mean #> $STRATA1.A.AGE.Mean$`A: Drug X.M` #> [1] 34.41667 #> #> #> $STRATA1.B.AGE.Mean #> $STRATA1.B.AGE.Mean$`A: Drug X.M` #> [1] 34.875 #> #> #> $STRATA1.C.AGE.Mean #> $STRATA1.C.AGE.Mean$`A: Drug X.M` #> [1] 35.6 #> #> ## any arm, male columns from the ASIAN content (i.e. summary) row cell_values( tbl, c(\"RACE\", \"ASIAN\", \"@content\"), c(\"ARM\", \"B: Placebo\", \"SEX\", \"M\") ) #> $`B: Placebo.M` #> [1] 31.00 0.62 #> cell_values( tbl, c(\"RACE\", \"ASIAN\", \"@content\"), c(\"ARM\", \"*\", \"SEX\", \"M\") ) #> $`A: Drug X.M` #> [1] 35.0000000 0.6862745 #> #> $`B: Placebo.M` #> [1] 31.00 0.62 #> #> $`C: Combination.M` #> [1] 44.0000000 0.6470588 #> ## all columns cell_values(tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\")) #> $`A: Drug X.F` #> [1] 33.75 #> #> $`A: Drug X.M` #> [1] 34.875 #> #> $`B: Placebo.F` #> [1] 32.46154 #> #> $`B: Placebo.M` #> [1] 30.9375 #> #> $`C: Combination.F` #> [1] 33.3 #> #> $`C: Combination.M` #> [1] 35.91667 #> ## all columns for the Combination arm cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\"), c(\"ARM\", \"C: Combination\") ) #> $`C: Combination.F` #> [1] 33.3 #> #> $`C: Combination.M` #> [1] 35.91667 #> cvlist <- cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\", \"AGE\", \"Mean\"), c(\"ARM\", \"B: Placebo\", \"SEX\", \"M\") ) cvnolist <- value_at( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\", \"AGE\", \"Mean\"), c(\"ARM\", \"B: Placebo\", \"SEX\", \"M\") ) stopifnot(identical(cvlist[[1]], cvnolist))"},{"path":"https://insightsengineering.github.io/rtables/reference/cinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"InstantiatedColumnInfo","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"","code":"InstantiatedColumnInfo( treelyt = LayoutColTree(), csubs = list(expression(TRUE)), extras = list(list()), cnts = NA_integer_, total_cnt = NA_integer_, dispcounts = FALSE, countformat = \"(N=xx)\", count_na_str = \"\", topleft = character() )"},{"path":"https://insightsengineering.github.io/rtables/reference/cinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"treelyt LayoutColTree. csubs list. List subsetting expressions extras list. Extra arguments associated columns cnts integer. Counts. total_cnt integer(1). Total observations represented across columns. dispcounts logical(1). counts displayed header info associated table printed. countformat character(1). Format counts displayed count_na_str character. NA string used formatting counts. Defaults \"\". topleft character. Override values \"top left\" material displayed printing.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cinfo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"InstantiateadColumnInfo object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/clear_imods.html","id":null,"dir":"Reference","previous_headings":"","what":"Clear All Indent Mods from a Table — clear_indent_mods","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"Clear Indent Mods Table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/clear_imods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"","code":"clear_indent_mods(tt) # S4 method for VTableTree clear_indent_mods(tt) # S4 method for TableRow clear_indent_mods(tt)"},{"path":"https://insightsengineering.github.io/rtables/reference/clear_imods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"tt TableTree (related class). TableTree object representing populated table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/clear_imods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"class tt, indent mods set zero.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/clear_imods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"","code":"lyt1 <- basic_table() %>% summarize_row_groups(\"STUDYID\", label_fstr = \"overall summary\") %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\") %>% summarize_row_groups(\"STUDYID\", label = \"subgroup summary\") %>% analyze(\"AGE\", indent_mod = -1L) tbl1 <- build_table(lyt1, ex_adae) tbl1 #> all obs #> ———————————————————————————————————— #> overall summary 1934 (100.0%) #> cl A.1 #> subgroup summary 422 (21.8%) #> Mean 34.70 #> cl B.1 #> subgroup summary 178 (9.2%) #> Mean 35.86 #> cl B.2 #> subgroup summary 410 (21.2%) #> Mean 35.42 #> cl C.1 #> subgroup summary 182 (9.4%) #> Mean 33.83 #> cl C.2 #> subgroup summary 166 (8.6%) #> Mean 33.89 #> cl D.1 #> subgroup summary 368 (19.0%) #> Mean 34.39 #> cl D.2 #> subgroup summary 208 (10.8%) #> Mean 34.83 clear_indent_mods(tbl1) #> all obs #> ———————————————————————————————————— #> overall summary 1934 (100.0%) #> cl A.1 #> subgroup summary 422 (21.8%) #> Mean 34.70 #> cl B.1 #> subgroup summary 178 (9.2%) #> Mean 35.86 #> cl B.2 #> subgroup summary 410 (21.2%) #> Mean 35.42 #> cl C.1 #> subgroup summary 182 (9.4%) #> Mean 33.83 #> cl C.2 #> subgroup summary 166 (8.6%) #> Mean 33.89 #> cl D.1 #> subgroup summary 368 (19.0%) #> Mean 34.39 #> cl D.2 #> subgroup summary 208 (10.8%) #> Mean 34.83"},{"path":"https://insightsengineering.github.io/rtables/reference/col_accessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Column information/structure accessors — clayout","title":"Column information/structure accessors — clayout","text":"Column information/structure accessors","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/col_accessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Column information/structure accessors — clayout","text":"","code":"clayout(obj) # S4 method for VTableNodeInfo clayout(obj) # S4 method for PreDataTableLayouts clayout(obj) # S4 method for ANY clayout(obj) clayout(object) <- value # S4 method for PreDataTableLayouts clayout(object) <- value col_info(obj) # S4 method for VTableNodeInfo col_info(obj) col_info(obj) <- value # S4 method for TableRow col_info(obj) <- value # S4 method for ElementaryTable col_info(obj) <- value # S4 method for TableTree col_info(obj) <- value coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for InstantiatedColumnInfo coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for PreDataTableLayouts coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for PreDataColLayout coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for LayoutColTree coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for VTableTree coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for TableRow coltree(obj, df = NULL, rtpos = TreePos()) col_exprs(obj, df = NULL) # S4 method for PreDataTableLayouts col_exprs(obj, df = NULL) # S4 method for PreDataColLayout col_exprs(obj, df = NULL) # S4 method for InstantiatedColumnInfo col_exprs(obj, df = NULL) col_counts(obj, path = NULL) # S4 method for InstantiatedColumnInfo col_counts(obj, path = NULL) # S4 method for VTableNodeInfo col_counts(obj, path = NULL) col_counts(obj, path = NULL) <- value # S4 method for InstantiatedColumnInfo col_counts(obj, path = NULL) <- value # S4 method for VTableNodeInfo col_counts(obj, path = NULL) <- value col_total(obj) # S4 method for InstantiatedColumnInfo col_total(obj) # S4 method for VTableNodeInfo col_total(obj) col_total(obj) <- value # S4 method for InstantiatedColumnInfo col_total(obj) <- value # S4 method for VTableNodeInfo col_total(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/col_accessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Column information/structure accessors — clayout","text":"obj . object accessor access modify object object modify -place value new value df data.frame/NULL. Data use column information generated Pre-Data layout object rtpos TreePos. Root position. path character NULL. col_counts getter setter . Path (column structure).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/col_accessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Column information/structure accessors — clayout","text":"LayoutColTree object. Various column information, depending accessor used.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/collect_leaves.html","id":null,"dir":"Reference","previous_headings":"","what":"Collect leaves of a table tree — collect_leaves","title":"Collect leaves of a table tree — collect_leaves","text":"Collect leaves table tree","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/collect_leaves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collect leaves of a table tree — collect_leaves","text":"","code":"collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/reference/collect_leaves.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collect leaves of a table tree — collect_leaves","text":"tt TableTree (related class). TableTree object representing populated table. incl.cont logical. Include rows content tables within tree. Defaults TRUE add.labrows logical. Include label rows. Defaults FALSE","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/collect_leaves.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collect leaves of a table tree — collect_leaves","text":"list TableRow objects rows table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compare_rtables.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare two rtables — compare_rtables","title":"Compare two rtables — compare_rtables","text":"Prints matrix . means cell matches, X means cell cells match, + cell (row) missing, - cell (row) . structure set TRUE, C indicates columnar structure mismatch, R indicates row-structure mismatch, S indicates mismatch row column structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compare_rtables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare two rtables — compare_rtables","text":"","code":"compare_rtables( object, expected, tol = 0.1, comp.attr = TRUE, structure = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/compare_rtables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare two rtables — compare_rtables","text":"object rtable test expected rtable expected tol numerical tolerance comp.attr boolean. Compare format cells. attributes silently ignored. structure boolean. structure (form column row paths cells) compared. Currently defaults FALSE, subject change future versions.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compare_rtables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare two rtables — compare_rtables","text":"matrix class \"rtables_diff\" representing differences object expected described .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compare_rtables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compare two rtables — compare_rtables","text":"current form compare_rtables take structure account, row cell position.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compare_rtables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare two rtables — compare_rtables","text":"","code":"t1 <- rtable(header = c(\"A\", \"B\"), format = \"xx\", rrow(\"row 1\", 1, 2)) t2 <- rtable(header = c(\"A\", \"B\", \"C\"), format = \"xx\", rrow(\"row 1\", 1, 2, 3)) compare_rtables(object = t1, expected = t2) #> 1 2 3 #> 1 \".\" \".\" \"-\" #> attr(,\"info\") #> [1] \"column names are not the same\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" if (interactive()) { Viewer(t1, t2) } expected <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.xx, xx.xx)\")) ) expected #> ARM A ARM B #> N=100 N=200 #> ——————————————————————————————— #> row 1 10 15 #> #> section title #> row colspan (0.35, 0.44) object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.xx, xx.xx)\")) ) compare_rtables(object, expected, comp.attr = FALSE) #> 1 2 #> 1 \".\" \".\" #> 2 \".\" \".\" #> 3 \"X\" \"X\" #> 4 \"-\" \"-\" #> attr(,\"info\") #> [1] \"cell attributes have not been compared\" #> [2] \"row labels are not the same\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(), rrow(\"section title\") ) compare_rtables(object, expected) #> 1 2 #> 1 \".\" \".\" #> 2 \".\" \".\" #> 3 \".\" \".\" #> 4 \"-\" \"-\" #> attr(,\"info\") #> [1] \"row labels are not the same\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 14, 15.03), rrow(), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.xx, xx.xx)\")) ) compare_rtables(object, expected) #> [,1] [,2] #> [1,] \"X\" \".\" #> [2,] \".\" \".\" #> [3,] \".\" \".\" #> [4,] \"X\" \"X\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.x, xx.x)\")) ) compare_rtables(object, expected) #> [,1] [,2] #> [1,] \".\" \".\" #> [2,] \".\" \".\" #> [3,] \".\" \".\" #> [4,] \"X\" \"X\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\""},{"path":"https://insightsengineering.github.io/rtables/reference/compat_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Compatibility Arg Conventions — compat_args","title":"Compatibility Arg Conventions — compat_args","text":"Compatibility Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compat_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compatibility Arg Conventions — compat_args","text":"","code":"compat_args(.lst, row.name, format, indent, label, inset)"},{"path":"https://insightsengineering.github.io/rtables/reference/compat_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compatibility Arg Conventions — compat_args","text":".lst list. already-collected list arguments used instead elements .... Arguments passed via ... ignored specified. row.name NULL empty string used row.name rrow. format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. indent deprecated. label character(1). label (confused name) object/structure. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/compat_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compatibility Arg Conventions — compat_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/constr_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor Arg Conventions — constr_args","title":"Constructor Arg Conventions — constr_args","text":"Constructor Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/constr_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor Arg Conventions — constr_args","text":"","code":"constr_args( kids, cont, lev, iscontent, cinfo, labelrow, vals, cspan, label_pos, cindent_mod, cvar, label, cextra_args, child_names, title, subtitles, main_footer, prov_footer, footnotes, page_title, page_prefix, section_div, trailing_section_div, split_na_str, cna_str, inset, table_inset, header_section_div )"},{"path":"https://insightsengineering.github.io/rtables/reference/constr_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor Arg Conventions — constr_args","text":"kids list. List direct children. cont ElementaryTable. Content table. lev integer. Nesting level (roughly, indentation level practical terms). iscontent logical. TableTree/ElementaryTable constructed content table another TableTree. cinfo InstantiatedColumnInfo (NULL). Column structure object created. labelrow LabelRow. LabelRow object assign Table. Constructed label default specified. vals list. cell values row cspan integer. Column span. 1 indicates spanning. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. label character(1). label (confused name) object/structure. cextra_args list. Extra arguments passed content function tabulating row group summaries. child_names character. Names given sub splits contained compound split (typically AnalyzeMultiVars split object). title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. footnotes list NULL. Referential footnotes applied current level. post-processing, can achieved fnotes_at_path<-. page_title character. Page specific title(s). page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. trailing_section_div character(1). String used section divider printing last row contained (sub)-table, unless row also last table row printed overall, NA_character_ none (default). generated via layouting, correspond section_div split table represents single facet. split_na_str character. NA string vector use split_format. cna_str character. NA string use cformat content table. inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). table_inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). header_section_div character(1). String used divide header table. See header_section_div() getter setter . Please consider changing last element section_div() concatenating tables need divider .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/constr_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor Arg Conventions — constr_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/content_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve or set Content Table from a TableTree — content_table","title":"Retrieve or set Content Table from a TableTree — content_table","text":"Returns content table obj TableTree object, NULL otherwise","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/content_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve or set Content Table from a TableTree — content_table","text":"","code":"content_table(obj) content_table(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/content_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve or set Content Table from a TableTree — content_table","text":"obj TableTree. TableTree value ElementaryTable. new content table obj.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/content_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve or set Content Table from a TableTree — content_table","text":"ElementaryTable containing (top level) content rows obj ( NULL obj formal table object).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/counts_wpcts.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"Analysis function count levels factor percentage column total","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/counts_wpcts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"","code":"counts_wpcts(x, .N_col)"},{"path":"https://insightsengineering.github.io/rtables/reference/counts_wpcts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"x factor. Vector data, provided rtables pagination machinery .N_col integer(1). Total count column, provided rtables pagination machinery","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/counts_wpcts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"RowsVerticalSection object counts (percents) level factor","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/counts_wpcts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"","code":"counts_wpcts(DM$SEX, 400) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 F 187 (46.8%) 0 F #> 2 M 169 (42.2%) 0 M #> 3 U 0 (0.0%) 0 U #> 4 UNDIFFERENTIATED 0 (0.0%) 0 UNDIFFERENTIATED"},{"path":"https://insightsengineering.github.io/rtables/reference/custom_split_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom Split Functions — custom_split_funs","title":"Custom Split Functions — custom_split_funs","text":"Split functions provide work-horse rtables's generalized partitioning. functions accept (sub)set incoming data, split object, return 'splits' data.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/custom_split_funs.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Custom Split Functions — custom_split_funs","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/custom_split_funs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Split Functions — custom_split_funs","text":"","code":"# Example of a picky split function. The number of values in the column variable # var decrees if we are going to print also the column with all observation # or not. picky_splitter <- function(var) { # Main layout function function(df, spl, vals, labels, trim) { orig_vals <- vals # Check for number of levels if all are selected if (is.null(vals)) { vec <- df[[var]] vals <- unique(vec) } # Do a split with or without All obs if (length(vals) == 1) { do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim) } else { fnc_tmp <- add_overall_level(\"Overall\", label = \"All Obs\", first = FALSE) fnc_tmp(df = df, spl = spl, vals = orig_vals, trim = trim) } } } # Data sub-set d1 <- subset(ex_adsl, ARM == \"A: Drug X\" | (ARM == \"B: Placebo\" & SEX == \"F\")) d1 <- subset(d1, SEX %in% c(\"M\", \"F\")) d1$SEX <- factor(d1$SEX) # This table uses the number of values in the SEX column to add the overall col or not lyt <- basic_table() %>% split_cols_by(\"ARM\", split_fun = drop_split_levels) %>% split_cols_by(\"SEX\", split_fun = picky_splitter(\"SEX\")) %>% analyze(\"AGE\", show_labels = \"visible\") tbl <- build_table(lyt, d1) tbl #> A: Drug X B: Placebo #> F M All Obs F #> ————————————————————————————————————————————— #> AGE #> Mean 32.76 35.57 33.86 34.12"},{"path":"https://insightsengineering.github.io/rtables/reference/cutsplits.html","id":null,"dir":"Reference","previous_headings":"","what":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"Splits cutting values numeric variable Create static cut static cumulative cut split","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cutsplits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"","code":"make_static_cut_split( var, split_label, cuts, cutlabels = NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = var, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", cumulative = FALSE, page_prefix = NA_character_, section_div = NA_character_ ) VarDynCutSplit( var, split_label, cutfun, cutlabelfun = function(x) NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = var, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), cumulative = FALSE, indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", page_prefix = NA_character_, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/cutsplits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"var string, variable name split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). cuts numeric. Cuts use cutlabels character (NULL). Labels cuts cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. split_name string. Name associated split (pathing, etc) child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cumulative logical. cuts treated cumulative. Defaults FALSE page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. cutfun function. Function accepts full vector var values returns cut points used (via cut) splitting data tabulation cutlabelfun function. Function returns either labels cuts NULL passed return value cutfun","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/cutsplits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"VarStaticCutSplit, CumulativeCutSplit object make_static_cut_split, VarDynCutSplit object VarDynCutSplit()","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a Result Data Frame — data.frame_export","title":"Generate a Result Data Frame — data.frame_export","text":"Collection utilities exctract data.frame TableTree objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a Result Data Frame — data.frame_export","text":"","code":"as_result_df(tt, spec = \"v0_experimental\", simplify = FALSE, ...) result_df_specs() path_enriched_df(tt, path_fun = collapse_path, value_fun = collapse_values)"},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a Result Data Frame — data.frame_export","text":"tt TableTree (related class). TableTree object representing populated table. spec character(1). specification use extract result data frame. See details simplify logical(1). TRUE, result data frame visible labels result columns. ... Passed spec-specific result data frame conversion function. Currently can one following parameters (valid v0_experimental spec now): expand_colnames: TRUE, result data frame expanded column names usual output. useful result data frame used processing. simplify: TRUE, result data frame visible labels result columns. as_strings: TRUE, result data frame values strings, appear final table (can also retrieved matrix_form(tt)$strings). also true column counts expand_colnames = TRUE. as_viewer: TRUE, result data frame values appear final table, .e. precision numbers, easy--use numeric form. keep_label_rows: TRUE, result data frame labels appear final table. as_is: TRUE, result data frame values appear final table, without information row structure. Row labels assigned rows work well df_to_tt(). path_fun function. Function transform paths single-string row/column names. value_fun function. Function transform cell values cells data.frame. Defaults collapse_values creates strings multi-valued cells collapsed together, separated |.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a Result Data Frame — data.frame_export","text":"result_df_specs(): returns named list result data frame extraction functions \"specification\". path_enriched_df(): returns data frame tt's cell values (processed value_fun, columns named full column paths (processed path_fun additional row_path column row paths (processed path_fun).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate a Result Data Frame — data.frame_export","text":"as_result_df(): Result data frame specifications may differ exact information include form represent . Specifications whose names end \"_experimental\" subject change without notice, specifications without \"_experimental\" suffix remain available including bugs construction indefinitely.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Generate a Result Data Frame — data.frame_export","text":"result_df_specs(): list functions extract result data frames TableTrees. path_enriched_df(): transform TableTree object Path-Enriched data.frame.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/data.frame_export.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a Result Data Frame — data.frame_export","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\") %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl) as_result_df(tbl) #> spl_var_1 spl_value_1 avar_name row_name label_name row_num is_group_summary #> 1 STRATA1 A AGE Mean Mean 3 FALSE #> 2 STRATA1 A BMRKR2 LOW LOW 5 FALSE #> 3 STRATA1 A BMRKR2 MEDIUM MEDIUM 6 FALSE #> 4 STRATA1 A BMRKR2 HIGH HIGH 7 FALSE #> 5 STRATA1 B AGE Mean Mean 10 FALSE #> 6 STRATA1 B BMRKR2 LOW LOW 12 FALSE #> 7 STRATA1 B BMRKR2 MEDIUM MEDIUM 13 FALSE #> 8 STRATA1 B BMRKR2 HIGH HIGH 14 FALSE #> 9 STRATA1 C AGE Mean Mean 17 FALSE #> 10 STRATA1 C BMRKR2 LOW LOW 19 FALSE #> 11 STRATA1 C BMRKR2 MEDIUM MEDIUM 20 FALSE #> 12 STRATA1 C BMRKR2 HIGH HIGH 21 FALSE #> node_class A: Drug X B: Placebo C: Combination #> 1 DataRow 33.07895 35.11364 34.225 #> 2 DataRow 12 16 14 #> 3 DataRow 10 17 13 #> 4 DataRow 16 11 13 #> 5 DataRow 33.85106 36 36.32558 #> 6 DataRow 19 13 10 #> 7 DataRow 13 22 16 #> 8 DataRow 15 10 17 #> 9 DataRow 34.22449 35.17778 35.63265 #> 10 DataRow 19 16 16 #> 11 DataRow 14 17 13 #> 12 DataRow 16 12 20 result_df_specs() #> $v0_experimental #> function(tt, #> as_viewer = FALSE, #> as_strings = FALSE, #> expand_colnames = FALSE, #> keep_label_rows = FALSE, #> as_is = FALSE) { #> checkmate::assert_flag(as_viewer) #> checkmate::assert_flag(as_strings) #> checkmate::assert_flag(expand_colnames) #> checkmate::assert_flag(keep_label_rows) #> checkmate::assert_flag(as_is) #> #> if (as_is) { #> keep_label_rows <- TRUE #> expand_colnames <- FALSE #> } #> #> raw_cvals <- cell_values(tt) #> ## if the table has one row and multiple columns, sometimes the cell values returns a list of the cell values #> ## rather than a list of length 1 representing the single row. This is bad but may not be changeable #> ## at this point. #> if (nrow(tt) == 1 && length(raw_cvals) > 1) { #> raw_cvals <- list(raw_cvals) #> } #> #> # Flatten the list of lists (rows) of cell values into a data frame #> cellvals <- as.data.frame(do.call(rbind, raw_cvals)) #> row.names(cellvals) <- NULL #> #> if (nrow(tt) == 1 && ncol(tt) == 1) { #> colnames(cellvals) <- names(raw_cvals) #> } #> #> if (as_viewer || as_strings) { #> # we keep previous calculations to check the format of the data #> mf_tt <- matrix_form(tt) #> mf_result_chars <- mf_strings(mf_tt)[-seq_len(mf_nlheader(mf_tt)), -1] #> mf_result_chars <- .remove_empty_elements(mf_result_chars) #> mf_result_numeric <- as.data.frame( #> .make_numeric_char_mf(mf_result_chars) #> ) #> mf_result_chars <- as.data.frame(mf_result_chars) #> if (!setequal(dim(mf_result_numeric), dim(cellvals)) || #> !setequal(dim(mf_result_chars), dim(cellvals))) { #> stop( #> \"The extracted numeric data.frame does not have the same dimension of the\", #> \" cell values extracted with cell_values(). This is a bug. Please report it.\" #> ) # nocov #> } #> if (as_strings) { #> colnames(mf_result_chars) <- colnames(cellvals) #> cellvals <- mf_result_chars #> } else { #> colnames(mf_result_numeric) <- colnames(cellvals) #> cellvals <- mf_result_numeric #> } #> } #> #> rdf <- make_row_df(tt) #> #> df <- rdf[, c(\"name\", \"label\", \"abs_rownumber\", \"path\", \"reprint_inds\", \"node_class\")] #> # Removing initial root elements from path (out of the loop -> right maxlen) #> df$path <- lapply(df$path, .remove_root_elems_from_path, #> which_root_name = c(\"root\", \"rbind_root\"), #> all = TRUE #> ) #> maxlen <- max(lengths(df$path)) #> #> # Loop for metadata (path and details from make_row_df) #> metadf <- do.call( #> rbind.data.frame, #> lapply( #> seq_len(NROW(df)), #> function(ii) { #> handle_rdf_row(df[ii, ], maxlen = maxlen) #> } #> ) #> ) #> #> # Should we keep label rows with NAs instead of values? #> if (keep_label_rows) { #> cellvals_mat_struct <- as.data.frame( #> matrix(NA, nrow = nrow(rdf), ncol = ncol(cellvals)) #> ) #> colnames(cellvals_mat_struct) <- colnames(cellvals) #> cellvals_mat_struct[metadf$node_class != \"LabelRow\", ] <- cellvals #> ret <- cbind(metadf, cellvals_mat_struct) #> } else { #> ret <- cbind( #> metadf[metadf$node_class != \"LabelRow\", ], #> cellvals #> ) #> } #> #> # If we want to expand colnames #> if (expand_colnames) { #> col_name_structure <- .get_formatted_colnames(clayout(tt)) #> number_of_non_data_cols <- which(colnames(ret) == \"node_class\") #> if (NCOL(ret) - number_of_non_data_cols != NCOL(col_name_structure)) { #> stop( #> \"When expanding colnames structure, we were not able to find the same\", #> \" number of columns as in the result data frame. This is a bug. Please report it.\" #> ) # nocov #> } #> #> buffer_rows_for_colnames <- matrix( #> rep(\"\", number_of_non_data_cols * NROW(col_name_structure)), #> nrow = NROW(col_name_structure) #> ) #> #> header_colnames_matrix <- cbind(buffer_rows_for_colnames, data.frame(col_name_structure)) #> colnames(header_colnames_matrix) <- colnames(ret) #> #> count_row <- NULL #> if (disp_ccounts(tt)) { #> ccounts <- col_counts(tt) #> if (as_strings) { #> ccounts <- mf_strings(mf_tt)[mf_nlheader(mf_tt), ] #> ccounts <- .remove_empty_elements(ccounts) #> } #> count_row <- c(rep(\"\", number_of_non_data_cols), ccounts) #> header_colnames_matrix <- rbind(header_colnames_matrix, count_row) #> } #> ret <- rbind(header_colnames_matrix, ret) #> } #> #> # Using only labels for row names and losing information about paths #> if (as_is) { #> tmp_rownames <- ret$label_name #> ret <- ret[, -seq_len(which(colnames(ret) == \"node_class\"))] #> if (length(unique(tmp_rownames)) == length(tmp_rownames)) { #> rownames(ret) <- tmp_rownames #> } else { #> ret <- cbind(\"label_name\" = tmp_rownames, ret) #> rownames(ret) <- NULL #> } #> } else { #> rownames(ret) <- NULL #> } #> #> ret #> } #> #> lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl) path_enriched_df(tbl) #> row_path ARM|A: Drug X ARM|B: Placebo ARM|C: Combination #> 1 ma_AGE_BMRKR2|AGE|Mean 33.76866 35.43284 35.43182 #> 2 ma_AGE_BMRKR2|BMRKR2|LOW 50.00000 45.00000 40.00000 #> 3 ma_AGE_BMRKR2|BMRKR2|MEDIUM 37.00000 56.00000 42.00000 #> 4 ma_AGE_BMRKR2|BMRKR2|HIGH 47.00000 33.00000 50.00000"},{"path":"https://insightsengineering.github.io/rtables/reference/df_to_tt.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ElementaryTable from data.frame — df_to_tt","title":"Create ElementaryTable from data.frame — df_to_tt","text":"Create ElementaryTable data.frame","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/df_to_tt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ElementaryTable from data.frame — df_to_tt","text":"","code":"df_to_tt(df)"},{"path":"https://insightsengineering.github.io/rtables/reference/df_to_tt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ElementaryTable from data.frame — df_to_tt","text":"df data.frame.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/df_to_tt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ElementaryTable from data.frame — df_to_tt","text":"ElementaryTable object unnested columns corresponding names(df) row labels corresponding row.names(df).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/df_to_tt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ElementaryTable from data.frame — df_to_tt","text":"row names defined df (simple numbers), row names taken column label_name, exists. label_name exists, also removed original data. Remember behavior compatible as_result_df(), as_is = TRUE row names unique.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/df_to_tt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ElementaryTable from data.frame — df_to_tt","text":"","code":"df_to_tt(mtcars) #> mpg cyl disp hp drat wt qsec vs am gear carb #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 #> Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 #> Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 #> Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 #> Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 #> Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 #> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 #> Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 #> Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 #> Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2"},{"path":"https://insightsengineering.github.io/rtables/reference/dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Table Dimensions — nrow,VTableTree-method","title":"Table Dimensions — nrow,VTableTree-method","text":"Table Dimensions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table Dimensions — nrow,VTableTree-method","text":"","code":"# S4 method for VTableTree nrow(x) # S4 method for VTableNodeInfo ncol(x) # S4 method for VTableNodeInfo dim(x)"},{"path":"https://insightsengineering.github.io/rtables/reference/dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table Dimensions — nrow,VTableTree-method","text":"x TableTree ElementaryTable object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table Dimensions — nrow,VTableTree-method","text":"number rows (nrow), columns (ncol) (dim) object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table Dimensions — nrow,VTableTree-method","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"SEX\", \"AGE\")) tbl <- build_table(lyt, ex_adsl) dim(tbl) #> [1] 7 3 nrow(tbl) #> [1] 7 ncol(tbl) #> [1] 3 NROW(tbl) #> [1] 7 NCOL(tbl) #> [1] 3"},{"path":"https://insightsengineering.github.io/rtables/reference/do_base_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"function intended use inside custom split functions. applies current split custom splitting function default splits can manipulated.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/do_base_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"","code":"do_base_split(spl, df, vals = NULL, labels = NULL, trim = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/reference/do_base_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"spl Split object defining partitioning analysis/tabulation data. df dataset (data.frame tibble) vals . Already calculated/known values split. Generally left NULL. labels character. Labels associated vals. NULL vals , almost always case. trim logical(1). groups corresponding empty data subsets removed. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/do_base_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"result split applied custom split function, see custom_split_funs","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/do_base_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"","code":"uneven_splfun <- function(df, spl, vals = NULL, labels = NULL, trim = FALSE) { ret <- do_base_split(spl, df, vals, labels, trim) if (NROW(df) == 0) { ret <- lapply(ret, function(x) x[1]) } ret } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"USUBJID\", \"AESEQ\", \"BMRKR1\"), varlabels = c(\"N\", \"E\", \"BMR1\"), split_fun = uneven_splfun ) %>% analyze_colvars(list( USUBJID = function(x, ...) length(unique(x)), AESEQ = max, BMRKR1 = mean )) tbl <- build_table(lyt, subset(ex_adae, as.numeric(ARM) <= 2)) tbl #> A: Drug X B: Placebo C: Combination #> N E BMR1 N E BMR1 N #> ————————————————————————————————————————————————————————————————————————————— #> 122 10 6.09356345928374 123 10 5.86496605625578 0"},{"path":"https://insightsengineering.github.io/rtables/reference/drop_facet_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","title":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","text":"function intended use preprocessing component make_split_fun, called directly end users.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/drop_facet_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","text":"","code":"drop_facet_levels(df, spl, ...)"},{"path":"https://insightsengineering.github.io/rtables/reference/drop_facet_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","text":"df data.frame. incoming data corresponding parent facet spl Split. ... dots. used internally pass parameters.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/export_as_docx.html","id":null,"dir":"Reference","previous_headings":"","what":"Export as word document — export_as_docx","title":"Export as word document — export_as_docx","text":"table, produce self-contained word document attach template word file (template_file). function based tt_to_flextable() transformer officer package.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/export_as_docx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export as word document — export_as_docx","text":"","code":"export_as_docx( tt, file, doc_metadata = NULL, titles_as_header = FALSE, footers_as_text = TRUE, template_file = NULL, section_properties = NULL ) section_properties_portrait() section_properties_landscape() margins_potrait() margins_landscape()"},{"path":"https://insightsengineering.github.io/rtables/reference/export_as_docx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export as word document — export_as_docx","text":"tt TableTree (related class). TableTree object representing populated table. file character(1). String indicates final file output. needs .docx extension. doc_metadata list character(1)s. value can used metadata ?officer::set_doc_properties. Important text values title, subject, creator, description created date object. titles_as_header logical(1). Defaults TRUE tt_to_flextable(), table self-contained makes additional header rows main_title() string subtitles() character vector (one per element). FALSE suggested export_as_docx(). adds titles subtitles text paragraph table. style applied. footers_as_text logical(1). Defaults FALSE tt_to_flextable(), table self-contained flextable definition footnotes. TRUE used export_as_docx() add footers new paragraph table. style applied, smaller font. template_file character(1). Template file officer use starting point final document. attach table use defaults defined template file. Output doc file nonetheless. section_properties officer::prop_section object. can set margins page size.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/export_as_docx.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Export as word document — export_as_docx","text":"section_properties_portrait(): helper function defines standard portrait properties tables. section_properties_landscape(): helper function defines standard landscape properties tables. margins_potrait(): helper function defines standard portrait margins tables. margins_landscape(): helper function defines standard landscape margins tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/export_as_docx.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Export as word document — export_as_docx","text":"export_as_docx() many options available. suggest, need specific formats details use tt_to_flextable() first export_as_docx. title_as_header footer_as_text need specified changed tt_to_flextable().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/export_as_docx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Export as word document — export_as_docx","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\", \"COUNTRY\")) tbl <- build_table(lyt, ex_adsl) # See how section_properties_portrait function is built for custom if (FALSE) { tf <- tempfile(fileext = \".docx\") export_as_docx(tbl, file = tf, section_properties = section_properties_portrait()) }"},{"path":"https://insightsengineering.github.io/rtables/reference/find_degen_struct.html","id":null,"dir":"Reference","previous_headings":"","what":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"Find degenerate (sub)structures within table (Experimental)","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/find_degen_struct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"","code":"find_degen_struct(tt)"},{"path":"https://insightsengineering.github.io/rtables/reference/find_degen_struct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"tt TableTree function returns list row-paths structural subtables contain data rows ( even associated content rows).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/find_degen_struct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"list character vectors representing row paths, , degenerate substructures within table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/find_degen_struct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"","code":"find_degen_struct(rtable(\"hi\")) #> [[1]] #> [1] \"\" #>"},{"path":"https://insightsengineering.github.io/rtables/reference/format_rcell.html","id":null,"dir":"Reference","previous_headings":"","what":"Format rcell — format_rcell","title":"Format rcell — format_rcell","text":"wrapper around formatters::format_value use CellValue objects","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/format_rcell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format rcell — format_rcell","text":"","code":"format_rcell( x, format, output = c(\"ascii\", \"html\"), na_str = obj_na_str(x) %||% \"NA\", pr_row_format = NULL, pr_row_na_str = NULL, shell = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/format_rcell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format rcell — format_rcell","text":"x object class CellValue, raw value. format character(1) function. format label (string) formatter function apply x. output character(1). Output type. na_str character(1). String displayed value x missing. Defaults \"NA\". pr_row_format list default format coming general row. pr_row_na_str list default \"NA\" string coming general row. shell logical(1). formats returned instead values formats applied. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/format_rcell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format rcell — format_rcell","text":"formatted text representing cell","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/format_rcell.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format rcell — format_rcell","text":"","code":"cll <- CellValue(pi, format = \"xx.xxx\") format_rcell(cll) #> [1] \"3.142\" # Cell values precedes the row values cll <- CellValue(pi, format = \"xx.xxx\") format_rcell(cll, pr_row_format = \"xx.x\") #> [1] \"3.142\" # Similarly for NA values cll <- CellValue(NA, format = \"xx.xxx\", format_na_str = \"This is THE NA\") format_rcell(cll, pr_row_na_str = \"This is NA\") #> [1] \"This is THE NA\""},{"path":"https://insightsengineering.github.io/rtables/reference/formatters_methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"See formatters documentation descriptions generics.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/formatters_methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"","code":"# S4 method for VNodeInfo obj_name(obj) # S4 method for Split obj_name(obj) # S4 method for VNodeInfo obj_name(obj) <- value # S4 method for Split obj_name(obj) <- value # S4 method for Split obj_label(obj) # S4 method for TableRow obj_label(obj) # S4 method for VTableTree obj_label(obj) # S4 method for ValueWrapper obj_label(obj) # S4 method for Split obj_label(obj) <- value # S4 method for TableRow obj_label(obj) <- value # S4 method for ValueWrapper obj_label(obj) <- value # S4 method for VTableTree obj_label(obj) <- value # S4 method for VTableNodeInfo obj_format(obj) # S4 method for CellValue obj_format(obj) # S4 method for Split obj_format(obj) # S4 method for VTableNodeInfo obj_format(obj) <- value # S4 method for Split obj_format(obj) <- value # S4 method for CellValue obj_format(obj) <- value # S4 method for Split obj_na_str(obj) # S4 method for VTitleFooter main_title(obj) # S4 method for VTitleFooter main_title(obj) <- value # S4 method for TableRow main_title(obj) # S4 method for VTitleFooter subtitles(obj) # S4 method for VTitleFooter subtitles(obj) <- value # S4 method for TableRow subtitles(obj) # S4 method for VTitleFooter main_footer(obj) # S4 method for VTitleFooter main_footer(obj) <- value # S4 method for TableRow main_footer(obj) # S4 method for VTitleFooter prov_footer(obj) # S4 method for VTitleFooter prov_footer(obj) <- value # S4 method for TableRow prov_footer(obj) # S4 method for VTableNodeInfo table_inset(obj) # S4 method for PreDataTableLayouts table_inset(obj) # S4 method for VTableNodeInfo table_inset(obj) <- value # S4 method for PreDataTableLayouts table_inset(obj) <- value # S4 method for InstantiatedColumnInfo table_inset(obj) <- value # S4 method for TableRow nlines(x, colwidths = NULL, max_width = NULL) # S4 method for LabelRow nlines(x, colwidths = NULL, max_width = NULL) # S4 method for RefFootnote nlines(x, colwidths = NULL, max_width = NULL) # S4 method for InstantiatedColumnInfo nlines(x, colwidths = NULL, max_width = NULL) # S4 method for VTableTree make_row_df( tt, colwidths = NULL, visible_only = TRUE, rownum = 0, indent = 0L, path = character(), incontent = FALSE, repr_ext = 0L, repr_inds = integer(), sibpos = NA_integer_, nsibs = NA_integer_, max_width = NULL ) # S4 method for TableRow make_row_df( tt, colwidths = NULL, visible_only = TRUE, rownum = 0, indent = 0L, path = \"root\", incontent = FALSE, repr_ext = 0L, repr_inds = integer(), sibpos = NA_integer_, nsibs = NA_integer_, max_width = NULL ) # S4 method for LabelRow make_row_df( tt, colwidths = NULL, visible_only = TRUE, rownum = 0, indent = 0L, path = \"root\", incontent = FALSE, repr_ext = 0L, repr_inds = integer(), sibpos = NA_integer_, nsibs = NA_integer_, max_width = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/formatters_methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"obj . object accessor access modify value new value x object colwidths numeric vector. Column widths use vertical pagination. max_width numeric(1). Width strings wrapped determining many lines require. tt TableTree (related class). TableTree object representing populated table. visible_only logical(1). visible aspects table structure reflected summary. Defaults TRUE. May supported methods. rownum numeric(1). Internal detail set manually. indent integer(1). Internal detail set manually. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. incontent logical(1). Internal detail set manually. repr_ext integer(1). Internal detail set manually. repr_inds integer. Internal detail set manually. sibpos integer(1). Internal detail set manually. nsibs integer(1). Internal detail set manually.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/formatters_methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"getters, current value component accessed obj, setters, modified copy obj new value.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/formatters_methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":" visible_only TRUE ( default), methods return data.frame exactly one row per visible row table-like object. useful reasoning table print, reflect full pathing space structure (though paths given work ). supported, visible_only FALSE, every structural element table (row-space) reflected returned data.frame, meaning full pathing-space represented rows layout summary represent printed rows table displayed. arguments beyond tt visible_only present make_row_df methods can call make_row_df recursively retain information, set top-level call","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/formatters_methods.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"technically present root tree node excluded summary returned make_row_df make_col_df (see rtables::make_col_df), simply row/column structure tt thus useful pathing pagination.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/gen_args.html","id":null,"dir":"Reference","previous_headings":"","what":"General Argument Conventions — gen_args","title":"General Argument Conventions — gen_args","text":"General Argument Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/gen_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General Argument Conventions — gen_args","text":"","code":"gen_args( df, alt_counts_df, spl, pos, tt, tr, verbose, colwidths, obj, x, value, object, path, label, label_pos, cvar, topleft, page_prefix, hsep, indent_size, section_div, na_str, inset, table_inset, ... )"},{"path":"https://insightsengineering.github.io/rtables/reference/gen_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General Argument Conventions — gen_args","text":"df dataset (data.frame tibble) alt_counts_df dataset (data.frame tibble). Alternative full data rtables framework use () calculating column counts. spl Split object defining partitioning analysis/tabulation data. pos numeric. top-level set nested splits new layout feature added . Defaults current tt TableTree (related class). TableTree object representing populated table. tr TableRow (related class). TableRow object representing single row within populated table. verbose logical(1). extra debugging messages shown. Defaults FALSE. colwidths numeric vector. Column widths use vertical pagination. obj . object accessor access modify x object value new value object object modify -place path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. label character(1). label (confused name) object/structure. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cvar character(1). variable, , content function accept. Defaults NA. topleft character. Override values \"top left\" material displayed printing. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. indent_size numeric(1). Number spaces use per indent level. Defaults 2 section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. na_str character(1). String displayed value x missing. Defaults \"NA\". inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). table_inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). ... Passed methods tabulation functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/gen_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General Argument Conventions — gen_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/gfc.html","id":null,"dir":"Reference","previous_headings":"","what":"get formatted cells — get_formatted_cells","title":"get formatted cells — get_formatted_cells","text":"get formatted cells","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/gfc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get formatted cells — get_formatted_cells","text":"","code":"get_formatted_cells(obj, shell = FALSE) # S4 method for TableTree get_formatted_cells(obj, shell = FALSE) # S4 method for ElementaryTable get_formatted_cells(obj, shell = FALSE) # S4 method for TableRow get_formatted_cells(obj, shell = FALSE) # S4 method for LabelRow get_formatted_cells(obj, shell = FALSE) get_cell_aligns(obj) # S4 method for TableTree get_cell_aligns(obj) # S4 method for ElementaryTable get_cell_aligns(obj) # S4 method for TableRow get_cell_aligns(obj) # S4 method for LabelRow get_cell_aligns(obj)"},{"path":"https://insightsengineering.github.io/rtables/reference/gfc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get formatted cells — get_formatted_cells","text":"obj . object accessor access modify shell logical(1). formats returned instead values formats applied. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/gfc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get formatted cells — get_formatted_cells","text":"formatted print-strings (body) cells obj.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/gfc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get formatted cells — get_formatted_cells","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() tbl <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\") %>% build_table(iris2) get_formatted_cells(tbl) #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] \"\" \"\" \"\" \"\" \"\" \"\" #> [2,] \"4.40\" \"4.30\" \"5.00\" \"4.90\" \"4.90\" \"5.60\" #> [3,] \"4.80\" \"4.80\" \"5.60\" \"5.60\" \"6.20\" \"6.30\" #> [4,] \"5.00\" \"5.00\" \"5.90\" \"5.90\" \"6.50\" \"6.50\" #> [5,] \"5.02\" \"4.99\" \"5.99\" \"5.88\" \"6.50\" \"6.67\" #> [6,] \"5.30\" \"5.10\" \"6.40\" \"6.10\" \"6.70\" \"7.20\" #> [7,] \"5.80\" \"5.70\" \"7.00\" \"6.70\" \"7.70\" \"7.90\" #> [8,] \"\" \"\" \"\" \"\" \"\" \"\" #> [9,] \"0.10\" \"0.10\" \"1.00\" \"1.00\" \"1.40\" \"1.50\" #> [10,] \"0.20\" \"0.20\" \"1.20\" \"1.20\" \"1.90\" \"1.80\" #> [11,] \"0.20\" \"0.20\" \"1.30\" \"1.30\" \"2.10\" \"2.00\" #> [12,] \"0.23\" \"0.26\" \"1.35\" \"1.30\" \"2.08\" \"1.98\" #> [13,] \"0.20\" \"0.30\" \"1.50\" \"1.40\" \"2.30\" \"2.20\" #> [14,] \"0.40\" \"0.60\" \"1.80\" \"1.70\" \"2.50\" \"2.50\""},{"path":"https://insightsengineering.github.io/rtables/reference/head_tail.html","id":null,"dir":"Reference","previous_headings":"","what":"Head and tail methods — head","title":"Head and tail methods — head","text":"Head tail methods","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/head_tail.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Head and tail methods — head","text":"","code":"head(x, ...) # S4 method for VTableTree head( x, n = 6, ..., keep_topleft = TRUE, keep_titles = TRUE, keep_footers = keep_titles, reindex_refs = FALSE ) tail(x, ...) # S4 method for VTableTree tail( x, n = 6, ..., keep_topleft = TRUE, keep_titles = TRUE, keep_footers = keep_titles, reindex_refs = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/head_tail.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Head and tail methods — head","text":"x object ... arguments passed methods. n integer vector length dim(x) (1, non-dimensioned objects). logical silently coerced integer. Values specify indices selected corresponding dimension (along length) object. positive value n[] includes first/last n[] indices dimension, negative value excludes last/first abs(n[]), including remaining indices. NA non-specified values (length(n) < length(dim(x))) select indices dimension. Must contain least one non-missing value. keep_topleft logical(1). TRUE (default), top_left material table carried subset. keep_titles logical(1). TRUE (default), title material table carried subset. keep_footers logical(1). TRUE, footer material table carried subset. defaults keep_titles. reindex_refs logical(1). Defaults FALSE. TRUE, referential footnotes reindexed subset.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/horizontal_sep.html","id":null,"dir":"Reference","previous_headings":"","what":"Access or recursively set header-body separator for tables — horizontal_sep","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"Access recursively set header-body separator tables","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/horizontal_sep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"","code":"horizontal_sep(obj) # S4 method for VTableTree horizontal_sep(obj) horizontal_sep(obj) <- value # S4 method for VTableTree horizontal_sep(obj) <- value # S4 method for TableRow horizontal_sep(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/horizontal_sep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"obj . object accessor access modify value character(1). String use new header/body separator.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/horizontal_sep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"horizontal_sep string acting header separator. horizontal_sep<-, obj, new header separator applied recursively subtables.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/in_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Create multiple rows in analysis or summary functions — in_rows","title":"Create multiple rows in analysis or summary functions — in_rows","text":"define cells get placed multiple rows afun","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/in_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create multiple rows in analysis or summary functions — in_rows","text":"","code":"in_rows( ..., .list = NULL, .names = NULL, .labels = NULL, .formats = NULL, .indent_mods = NULL, .cell_footnotes = list(NULL), .row_footnotes = list(NULL), .aligns = NULL, .format_na_strs = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/in_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create multiple rows in analysis or summary functions — in_rows","text":"... single row defining expressions .list list. list cell content, usually rcells, .list concatenated ... .names character NULL. Names returned list/structure. .labels character NULL. labels defined rows .formats character NULL. Formats values .indent_mods integer NULL. Indent modifications defined rows. .cell_footnotes list. Referential footnote messages associated name cells. .row_footnotes list. Referential footnotes messages associated name rows. .aligns character NULL. Alignments cells. Standard NULL \"center\". See formatters::list_valid_aligns() currently supported alignments. .format_na_strs character NULL. NA strings cells","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/in_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create multiple rows in analysis or summary functions — in_rows","text":"RowsVerticalSection object (NULL). details object considered internal implementation detail.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/in_rows.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create multiple rows in analysis or summary functions — in_rows","text":"post-processing, referential footnotes can also added using row column paths fnotes_at_path<-.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/in_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create multiple rows in analysis or summary functions — in_rows","text":"","code":"in_rows(1, 2, 3, .names = c(\"a\", \"b\", \"c\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c in_rows(1, 2, 3, .labels = c(\"a\", \"b\", \"c\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c in_rows(1, 2, 3, .names = c(\"a\", \"b\", \"c\"), .labels = c(\"AAA\", \"BBB\", \"CCC\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 AAA #> 2 b 2 0 BBB #> 3 c 3 0 CCC in_rows(.list = list(a = 1, b = 2, c = 3)) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c in_rows(1, 2, .list = list(3), .names = c(\"a\", \"b\", \"c\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx.xx - xx.xx\") ) }) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00"},{"path":"https://insightsengineering.github.io/rtables/reference/indent.html","id":null,"dir":"Reference","previous_headings":"","what":"Change indentation of all rrows in an rtable — indent","title":"Change indentation of all rrows in an rtable — indent","text":"Change indentation rrows rtable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/indent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change indentation of all rrows in an rtable — indent","text":"","code":"indent(x, by = 1)"},{"path":"https://insightsengineering.github.io/rtables/reference/indent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change indentation of all rrows in an rtable — indent","text":"x rtable object integer increase indentation rows. Can negative. final indentation smaller 0 indentation set 0.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/indent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change indentation of all rrows in an rtable — indent","text":"x indent modifier incremented .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/indent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change indentation of all rrows in an rtable — indent","text":"","code":"is_setosa <- iris$Species == \"setosa\" m_tbl <- rtable( header = rheader( rrow(row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2)), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"All Species\", mean(iris$Sepal.Length), median(iris$Sepal.Length), mean(iris$Petal.Length), median(iris$Petal.Length), format = \"xx.xx\" ), rrow( row.name = \"Setosa\", mean(iris$Sepal.Length[is_setosa]), median(iris$Sepal.Length[is_setosa]), mean(iris$Petal.Length[is_setosa]), median(iris$Petal.Length[is_setosa]), format = \"xx.xx\" ) ) indent(m_tbl) #> Sepal.Length Petal.Length #> mean median mean median #> ————————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.01 5.00 1.46 1.50 indent(m_tbl, 2) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.01 5.00 1.46 1.50"},{"path":"https://insightsengineering.github.io/rtables/reference/indent_string.html","id":null,"dir":"Reference","previous_headings":"","what":"Indent Strings — indent_string","title":"Indent Strings — indent_string","text":"Used rtables indent row names ASCII output.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/indent_string.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indent Strings — indent_string","text":"","code":"indent_string(x, indent = 0, incr = 2, including_newline = TRUE)"},{"path":"https://insightsengineering.github.io/rtables/reference/indent_string.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indent Strings — indent_string","text":"x character vector indent vector length length(x) non-negative integers incr non-negative integer: number spaces per indent level including_newline boolean: newlines also indented","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/indent_string.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indent Strings — indent_string","text":"x indented left-padding indent*incr white-spaces.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/indent_string.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indent Strings — indent_string","text":"","code":"indent_string(\"a\", 0) #> [1] \"a\" indent_string(\"a\", 1) #> [1] \" a\" indent_string(letters[1:3], 0:2) #> [1] \"a\" \" b\" \" c\" indent_string(paste0(letters[1:3], \"\\n\", LETTERS[1:3]), 0:2) #> [1] \"a\\nA\" \" b\\n B\" \" c\\n C\""},{"path":"https://insightsengineering.github.io/rtables/reference/insert_row_at_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Insert Row at Path — insert_row_at_path","title":"Insert Row at Path — insert_row_at_path","text":"Insert row existing table directly directly existing data (.e., non-content non-label) row, specified path.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/insert_row_at_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insert Row at Path — insert_row_at_path","text":"","code":"insert_row_at_path(tt, path, value, after = FALSE) # S4 method for VTableTree,DataRow insert_row_at_path(tt, path, value, after = FALSE) # S4 method for VTableTree,ANY insert_row_at_path(tt, path, value)"},{"path":"https://insightsengineering.github.io/rtables/reference/insert_row_at_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Insert Row at Path — insert_row_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. value new value logical(1). value added row directly (FALSE, default) (TRUE) row specified path.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/insert_row_at_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insert Row at Path — insert_row_at_path","text":"","code":"lyt <- basic_table() %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(c(\"CHN\", \"USA\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl2 <- insert_row_at_path( tbl, c(\"COUNTRY\", \"CHN\", \"AGE\", \"Mean\"), rrow(\"new row\", 555) ) tbl2 #> all obs #> ——————————————————— #> CHN #> new row 555 #> Mean 34.64 #> USA #> Mean 35.30 tbl3 <- insert_row_at_path(tbl2, c(\"COUNTRY\", \"CHN\", \"AGE\", \"Mean\"), rrow(\"new row redux\", 888), after = TRUE ) tbl3 #> all obs #> ————————————————————————— #> CHN #> new row 555 #> Mean 34.64 #> new row redux 888 #> USA #> Mean 35.30"},{"path":"https://insightsengineering.github.io/rtables/reference/insert_rrow.html","id":null,"dir":"Reference","previous_headings":"","what":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"function deprecated removed future release rtables. Please use insert_row_at_path label_at_path instead.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/insert_rrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"","code":"insert_rrow(tbl, rrow, at = 1, ascontent = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/reference/insert_rrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"tbl rtable rrow rrow append rtable position put rrow, defaults beginning (.e. 1) ascontent logical. Currently ignored.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/insert_rrow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"TableTree specific class tbl","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/insert_rrow.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"Label rows (.e. row data values, row.name) can inserted positions already contain label row non-trivial nested row structure tbl","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/insert_rrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"","code":"o <- options(warn = 0) lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(\"Sepal.Length\") tbl <- build_table(lyt, iris) insert_rrow(tbl, rrow(\"Hello World\")) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ————————————————————————————————————————————— #> Hello World #> Mean 5.01 5.94 6.59 insert_rrow(tbl, rrow(\"Hello World\"), at = 2) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ————————————————————————————————————————————— #> Mean 5.01 5.94 6.59 #> Hello World lyt2 <- basic_table() %>% split_cols_by(\"Species\") %>% split_rows_by(\"Species\") %>% analyze(\"Sepal.Length\") tbl2 <- build_table(lyt2, iris) insert_rrow(tbl2, rrow(\"Hello World\")) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ————————————————————————————————————————————— #> Hello World #> setosa #> Mean 5.01 NA NA #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"Hello World\"), at = 2) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ——————————————————————————————————————————————— #> setosa #> Hello World #> Mean 5.01 NA NA #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"Hello World\"), at = 4) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ——————————————————————————————————————————————— #> setosa #> Mean 5.01 NA NA #> versicolor #> Hello World #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"new row\", 5, 6, 7)) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ———————————————————————————————————————————— #> new row 5 6 7 #> setosa #> Mean 5.01 NA NA #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"new row\", 5, 6, 7), at = 3) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ———————————————————————————————————————————— #> setosa #> Mean 5.01 NA NA #> new row 5 6 7 #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 options(o)"},{"path":"https://insightsengineering.github.io/rtables/reference/int_methods.html","id":null,"dir":"Reference","previous_headings":"","what":"combine SplitVector objects — internal_methods","title":"combine SplitVector objects — internal_methods","text":"internal methods documented satisfy R CMD check. End users pay attention documentation.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/int_methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine SplitVector objects — internal_methods","text":"","code":"# S4 method for SplitVector c(x, ...) split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for NULL split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for PreDataRowLayout split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for SplitVector split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for PreDataTableLayouts split_rows(lyt, spl, pos) # S4 method for ANY split_rows(lyt, spl, pos) cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for NULL cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for PreDataRowLayout cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for SplitVector cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for PreDataTableLayouts cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for ANY cmpnd_last_rowsplit(lyt, spl, constructor) split_cols(lyt = NULL, spl, pos) # S4 method for NULL split_cols(lyt = NULL, spl, pos) # S4 method for PreDataColLayout split_cols(lyt = NULL, spl, pos) # S4 method for SplitVector split_cols(lyt = NULL, spl, pos) # S4 method for PreDataTableLayouts split_cols(lyt = NULL, spl, pos) # S4 method for ANY split_cols(lyt = NULL, spl, pos) .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for PreDataTableLayouts .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for PreDataRowLayout .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for SplitVector .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for Split .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) fix_dyncuts(spl, df) # S4 method for Split fix_dyncuts(spl, df) # S4 method for VarDynCutSplit fix_dyncuts(spl, df) # S4 method for VTableTree fix_dyncuts(spl, df) # S4 method for PreDataRowLayout fix_dyncuts(spl, df) # S4 method for PreDataColLayout fix_dyncuts(spl, df) # S4 method for SplitVector fix_dyncuts(spl, df) # S4 method for PreDataTableLayouts fix_dyncuts(spl, df) summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for TableTree summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for ElementaryTable summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for TableRow summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for LabelRow summarize_rows_inner(obj, depth = 0, indent = 0) table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) str(object, ...) # S4 method for VTableTree str(object, max.level = 3L, ...) # S4 method for TableTree table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for ElementaryTable table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for TableRow table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for LabelRow table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for TableRow nrow(x) # S4 method for TableRow ncol(x) # S4 method for LabelRow ncol(x) # S4 method for InstantiatedColumnInfo ncol(x) # S4 method for VTree tree_children(x) # S4 method for VTableTree tree_children(x) # S4 method for ANY tree_children(x) # S4 method for VTree tree_children(x) <- value # S4 method for VTableTree tree_children(x) <- value # S4 method for TableTree content_table(obj) # S4 method for ANY content_table(obj) # S4 method for TableTree,ElementaryTable content_table(obj) <- value next_rpos(obj, nested = TRUE, for_analyze = FALSE) # S4 method for PreDataTableLayouts next_rpos(obj, nested = TRUE, for_analyze = FALSE) # S4 method for PreDataRowLayout next_rpos(obj, nested = TRUE, for_analyze = FALSE) # S4 method for ANY next_rpos(obj, nested) next_cpos(obj, nested = TRUE) # S4 method for PreDataTableLayouts next_cpos(obj, nested = TRUE) # S4 method for PreDataColLayout next_cpos(obj, nested = TRUE) # S4 method for ANY next_cpos(obj, nested = TRUE) last_rowsplit(obj) # S4 method for NULL last_rowsplit(obj) # S4 method for SplitVector last_rowsplit(obj) # S4 method for PreDataRowLayout last_rowsplit(obj) # S4 method for PreDataTableLayouts last_rowsplit(obj) rlayout(obj) # S4 method for PreDataTableLayouts rlayout(obj) # S4 method for ANY rlayout(obj) rlayout(object) <- value # S4 method for PreDataTableLayouts rlayout(object) <- value tree_pos(obj) # S4 method for VLayoutNode tree_pos(obj) pos_subset(obj) # S4 method for TreePos pos_subset(obj) # S4 method for VLayoutNode pos_subset(obj) pos_splits(obj) # S4 method for TreePos pos_splits(obj) # S4 method for VLayoutNode pos_splits(obj) pos_splvals(obj) # S4 method for TreePos pos_splvals(obj) # S4 method for VLayoutNode pos_splvals(obj) pos_splval_labels(obj) # S4 method for TreePos pos_splval_labels(obj) spl_payload(obj) # S4 method for Split spl_payload(obj) spl_payload(obj) <- value # S4 method for Split spl_payload(obj) <- value spl_label_var(obj) # S4 method for VarLevelSplit spl_label_var(obj) # S4 method for Split spl_label_var(obj) tt_labelrow(obj) # S4 method for VTableTree tt_labelrow(obj) tt_labelrow(obj) <- value # S4 method for VTableTree,LabelRow tt_labelrow(obj) <- value labelrow_visible(obj) # S4 method for VTableTree labelrow_visible(obj) # S4 method for LabelRow labelrow_visible(obj) # S4 method for VAnalyzeSplit labelrow_visible(obj) labelrow_visible(obj) <- value # S4 method for VTableTree labelrow_visible(obj) <- value # S4 method for LabelRow labelrow_visible(obj) <- value # S4 method for VAnalyzeSplit labelrow_visible(obj) <- value label_kids(spl) # S4 method for Split label_kids(spl) label_kids(spl) <- value # S4 method for Split,character label_kids(spl) <- value # S4 method for Split,logical label_kids(spl) <- value vis_label(spl) # S4 method for Split vis_label(spl) label_position(spl) # S4 method for Split label_position(spl) # S4 method for VAnalyzeSplit label_position(spl) label_position(spl) <- value # S4 method for Split label_position(spl) <- value content_fun(obj) # S4 method for Split content_fun(obj) content_fun(object) <- value # S4 method for Split content_fun(object) <- value analysis_fun(obj) # S4 method for AnalyzeVarSplit analysis_fun(obj) # S4 method for AnalyzeColVarSplit analysis_fun(obj) split_fun(obj) # S4 method for CustomizableSplit split_fun(obj) # S4 method for Split split_fun(obj) split_fun(obj) <- value # S4 method for CustomizableSplit split_fun(obj) <- value # S4 method for Split split_fun(obj) <- value content_extra_args(obj) # S4 method for Split content_extra_args(obj) content_extra_args(object) <- value # S4 method for Split content_extra_args(object) <- value content_var(obj) # S4 method for Split content_var(obj) content_var(object) <- value # S4 method for Split content_var(object) <- value avar_inclNAs(obj) # S4 method for VAnalyzeSplit avar_inclNAs(obj) avar_inclNAs(obj) <- value # S4 method for VAnalyzeSplit avar_inclNAs(obj) <- value spl_labelvar(obj) # S4 method for VarLevelSplit spl_labelvar(obj) spl_child_order(obj) # S4 method for VarLevelSplit spl_child_order(obj) spl_child_order(obj) <- value # S4 method for VarLevelSplit spl_child_order(obj) <- value # S4 method for ManualSplit spl_child_order(obj) # S4 method for MultiVarSplit spl_child_order(obj) # S4 method for AllSplit spl_child_order(obj) # S4 method for VarStaticCutSplit spl_child_order(obj) root_spl(obj) # S4 method for PreDataAxisLayout root_spl(obj) root_spl(obj) <- value # S4 method for PreDataAxisLayout root_spl(obj) <- value spanned_values(obj) # S4 method for TableRow spanned_values(obj) # S4 method for LabelRow spanned_values(obj) spanned_cells(obj) # S4 method for TableRow spanned_cells(obj) # S4 method for LabelRow spanned_cells(obj) spanned_values(obj) <- value # S4 method for TableRow spanned_values(obj) <- value # S4 method for LabelRow spanned_values(obj) <- value # S4 method for CellValue obj_na_str(obj) <- value # S4 method for VTableNodeInfo obj_na_str(obj) <- value # S4 method for Split obj_na_str(obj) <- value # S4 method for VTableNodeInfo obj_na_str(obj) set_format_recursive(obj, format, na_str, override = FALSE) # S4 method for TableRow set_format_recursive(obj, format, na_str, override = FALSE) # S4 method for LabelRow set_format_recursive(obj, format, override = FALSE) content_format(obj) # S4 method for Split content_format(obj) content_format(obj) <- value # S4 method for Split content_format(obj) <- value content_na_str(obj) # S4 method for Split content_na_str(obj) content_na_str(obj) <- value # S4 method for Split content_na_str(obj) <- value # S4 method for TableTree collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for ElementaryTable collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for VTree collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for VLeaf collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for NULL collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for ANY collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) row_cspans(obj) # S4 method for TableRow row_cspans(obj) # S4 method for LabelRow row_cspans(obj) row_cspans(obj) <- value # S4 method for TableRow row_cspans(obj) <- value # S4 method for LabelRow row_cspans(obj) <- value cell_cspan(obj) # S4 method for CellValue cell_cspan(obj) cell_cspan(obj) <- value # S4 method for CellValue cell_cspan(obj) <- value cell_align(obj) # S4 method for CellValue cell_align(obj) cell_align(obj) <- value # S4 method for CellValue cell_align(obj) <- value tt_level(obj) # S4 method for VNodeInfo tt_level(obj) tt_level(obj) <- value # S4 method for VNodeInfo tt_level(obj) <- value # S4 method for VTableTree tt_level(obj) <- value indent_mod(obj) # S4 method for Split indent_mod(obj) # S4 method for VTableNodeInfo indent_mod(obj) # S4 method for ANY indent_mod(obj) # S4 method for RowsVerticalSection indent_mod(obj) indent_mod(obj) <- value # S4 method for Split indent_mod(obj) <- value # S4 method for VTableNodeInfo indent_mod(obj) <- value # S4 method for CellValue indent_mod(obj) <- value # S4 method for RowsVerticalSection indent_mod(obj) <- value content_indent_mod(obj) # S4 method for Split content_indent_mod(obj) # S4 method for VTableNodeInfo content_indent_mod(obj) content_indent_mod(obj) <- value # S4 method for Split content_indent_mod(obj) <- value # S4 method for VTableNodeInfo content_indent_mod(obj) <- value rawvalues(obj) # S4 method for ValueWrapper rawvalues(obj) # S4 method for LevelComboSplitValue rawvalues(obj) # S4 method for list rawvalues(obj) # S4 method for ANY rawvalues(obj) # S4 method for CellValue rawvalues(obj) # S4 method for TreePos rawvalues(obj) # S4 method for RowsVerticalSection rawvalues(obj) value_names(obj) # S4 method for ANY value_names(obj) # S4 method for TreePos value_names(obj) # S4 method for list value_names(obj) # S4 method for ValueWrapper value_names(obj) # S4 method for LevelComboSplitValue value_names(obj) # S4 method for RowsVerticalSection value_names(obj) value_labels(obj) # S4 method for ANY value_labels(obj) # S4 method for TreePos value_labels(obj) # S4 method for list value_labels(obj) # S4 method for RowsVerticalSection value_labels(obj) # S4 method for ValueWrapper value_labels(obj) # S4 method for LevelComboSplitValue value_labels(obj) # S4 method for MultiVarSplit value_labels(obj) spl_varlabels(obj) # S4 method for MultiVarSplit spl_varlabels(obj) spl_varlabels(object) <- value # S4 method for MultiVarSplit spl_varlabels(object) <- value splv_extra(obj) # S4 method for SplitValue splv_extra(obj) splv_extra(obj) <- value # S4 method for SplitValue splv_extra(obj) <- value split_exargs(obj) # S4 method for Split split_exargs(obj) split_exargs(obj) <- value # S4 method for Split split_exargs(obj) <- value col_extra_args(obj, df = NULL) # S4 method for InstantiatedColumnInfo col_extra_args(obj, df = NULL) # S4 method for PreDataTableLayouts col_extra_args(obj, df = NULL) # S4 method for PreDataColLayout col_extra_args(obj, df = NULL) # S4 method for LayoutColTree col_extra_args(obj, df = NULL) # S4 method for LayoutColLeaf col_extra_args(obj, df = NULL) disp_ccounts(obj) # S4 method for VTableTree disp_ccounts(obj) # S4 method for InstantiatedColumnInfo disp_ccounts(obj) # S4 method for PreDataTableLayouts disp_ccounts(obj) # S4 method for PreDataColLayout disp_ccounts(obj) disp_ccounts(obj) <- value # S4 method for VTableTree disp_ccounts(obj) <- value # S4 method for InstantiatedColumnInfo disp_ccounts(obj) <- value # S4 method for PreDataColLayout disp_ccounts(obj) <- value # S4 method for LayoutColTree disp_ccounts(obj) <- value # S4 method for PreDataTableLayouts disp_ccounts(obj) <- value colcount_format(obj) # S4 method for InstantiatedColumnInfo colcount_format(obj) # S4 method for VTableNodeInfo colcount_format(obj) # S4 method for PreDataColLayout colcount_format(obj) # S4 method for PreDataTableLayouts colcount_format(obj) colcount_format(obj) <- value # S4 method for InstantiatedColumnInfo colcount_format(obj) <- value # S4 method for VTableNodeInfo colcount_format(obj) <- value # S4 method for PreDataColLayout colcount_format(obj) <- value # S4 method for PreDataTableLayouts colcount_format(obj) <- value # S4 method for TableRow as.vector(x, mode = \"any\") # S4 method for ElementaryTable as.vector(x, mode = \"any\") spl_cuts(obj) # S4 method for VarStaticCutSplit spl_cuts(obj) spl_cutlabels(obj) # S4 method for VarStaticCutSplit spl_cutlabels(obj) spl_cutfun(obj) # S4 method for VarDynCutSplit spl_cutfun(obj) spl_cutlabelfun(obj) # S4 method for VarDynCutSplit spl_cutlabelfun(obj) spl_is_cmlcuts(obj) # S4 method for VarDynCutSplit spl_is_cmlcuts(obj) spl_varnames(obj) # S4 method for MultiVarSplit spl_varnames(obj) spl_varnames(object) <- value # S4 method for MultiVarSplit spl_varnames(object) <- value # S4 method for TableRow row_footnotes(obj) # S4 method for RowsVerticalSection row_footnotes(obj) # S4 method for TableRow row_footnotes(obj) <- value # S4 method for VTableTree row_footnotes(obj) # S4 method for CellValue cell_footnotes(obj) # S4 method for TableRow cell_footnotes(obj) # S4 method for LabelRow cell_footnotes(obj) # S4 method for VTableTree cell_footnotes(obj) # S4 method for CellValue cell_footnotes(obj) <- value # S4 method for DataRow cell_footnotes(obj) <- value # S4 method for ContentRow cell_footnotes(obj) <- value # S4 method for ANY col_fnotes_here(obj) <- value # S4 method for LayoutColTree col_footnotes(obj) # S4 method for LayoutColLeaf col_footnotes(obj) # S4 method for LayoutColTree col_footnotes(obj) <- value # S4 method for LayoutColLeaf col_footnotes(obj) <- value # S4 method for VTableTree col_footnotes(obj) # S4 method for RefFootnote ref_index(obj) # S4 method for RefFootnote ref_index(obj) <- value # S4 method for RefFootnote ref_symbol(obj) # S4 method for RefFootnote ref_symbol(obj) <- value # S4 method for RefFootnote ref_msg(obj) # S4 method for VTableTree,character fnotes_at_path(obj, rowpath = NULL, colpath = NULL, reset_idx = TRUE) <- value # S4 method for VTableTree,NULL fnotes_at_path(obj, rowpath = NULL, colpath = NULL, reset_idx = TRUE) <- value # S4 method for VTableNodeInfo,missing rbind2(x, y) # S4 method for VTableTree tt_at_path(tt, path, ...) # S4 method for VTableTree,ANY,VTableTree tt_at_path(tt, path, ...) <- value # S4 method for VTableTree,ANY,NULL tt_at_path(tt, path, ...) <- value # S4 method for VTableTree,ANY,TableRow tt_at_path(tt, path, ...) <- value # S4 method for VTableTree,ANY,ANY,CellValue [(x, i, j, ...) <- value # S4 method for VTableTree,logical,ANY [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,logical,missing [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,ANY,logical [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,ANY,missing [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,missing,ANY [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,ANY,character [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,character,ANY [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,character,character [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,missing,numeric [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,numeric,numeric [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) # S4 method for TableRow cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) # S4 method for LabelRow cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) # S4 method for TableRow value_at(tt, rowpath = NULL, colpath = NULL) # S4 method for LabelRow value_at(tt, rowpath = NULL, colpath = NULL) # S4 method for VTableTree print(x, ...) # S4 method for VTableTree show(object)"},{"path":"https://insightsengineering.github.io/rtables/reference/int_methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine SplitVector objects — internal_methods","text":"x object. ... Splits SplitVector objects lyt layout object pre-data used tabulation spl Split. split. pos numeric(1). Intended internal use. cmpnd_fun function. Intended internal use. constructor function. label character(1). label (confused name) object/structure. cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. cformat format spec. Format content rows cna_str character. NA string use cformat content table. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cvar character(1). variable, , content function accept. Defaults NA. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. df dataset (data.frame tibble) obj object. depth depth tree indent indent print_indent indent print object table object max.level numeric(1). Passed utils::str. Defaults 3 VTableTree method, unlike underlying default NA. NA appropriate VTableTree objects. value new value nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. for_analyze logical(1). format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". override logical(1). tt TableTree (related class). TableTree object representing populated table. incl.cont logical. Include rows content tables within tree. Defaults TRUE add.labrows logical. Include label rows. Defaults FALSE mode character(1). Passed .vector rowpath character NULL. Path within row structure. NULL indicates footnote go column rather cell. colpath character NULL. Path within column structure. NULL indicates footnote go row rather cell reset_idx logical(1). numbering referential footnotes immediately recalculated. Defaults TRUE. y Second element rbound via rbind2 path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. index j index drop logical(1). value cell returned one cell selected combination j. possible return vector values. please consider using cell_values(). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/int_methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine SplitVector objects — internal_methods","text":"Various, considered implementation details.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/int_methods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"combine SplitVector objects — internal_methods","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) tbl <- build_table(lyt, iris2) indent_mod(tbl) #> [1] 0 indent_mod(tbl) <- 1L tbl #> setosa versicolor virginica #> a b a b a b #> ——————————————————————————————————————————————————————————— #> Sepal.Length #> Min. 4.40 4.30 5.00 4.90 4.90 5.60 #> 1st Qu. 4.80 4.80 5.60 5.60 6.20 6.30 #> Median 5.00 5.00 5.90 5.90 6.50 6.50 #> Mean 5.02 4.99 5.99 5.88 6.50 6.67 #> 3rd Qu. 5.30 5.10 6.40 6.10 6.70 7.20 #> Max. 5.80 5.70 7.00 6.70 7.70 7.90 #> Petal.Width #> Min. 0.10 0.10 1.00 1.00 1.40 1.50 #> 1st Qu. 0.20 0.20 1.20 1.20 1.90 1.80 #> Median 0.20 0.20 1.30 1.30 2.10 2.00 #> Mean 0.23 0.26 1.35 1.30 2.08 1.98 #> 3rd Qu. 0.20 0.30 1.50 1.40 2.30 2.20 #> Max. 0.40 0.60 1.80 1.70 2.50 2.50"},{"path":"https://insightsengineering.github.io/rtables/reference/is_rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a valid rtable — is_rtable","title":"Check if an object is a valid rtable — is_rtable","text":"Check object valid rtable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/is_rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a valid rtable — is_rtable","text":"","code":"is_rtable(x)"},{"path":"https://insightsengineering.github.io/rtables/reference/is_rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a valid rtable — is_rtable","text":"x object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/is_rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if an object is a valid rtable — is_rtable","text":"TRUE x formal Table object, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/is_rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if an object is a valid rtable — is_rtable","text":"","code":"is_rtable(build_table(basic_table(), iris)) #> [1] TRUE"},{"path":"https://insightsengineering.github.io/rtables/reference/label_at_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Label at Path — label_at_path","title":"Label at Path — label_at_path","text":"Gets sets label path","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/label_at_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Label at Path — label_at_path","text":"","code":"label_at_path(tt, path) label_at_path(tt, path) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/label_at_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Label at Path — label_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/label_at_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Label at Path — label_at_path","text":"path resolves single row, label row retrieved set. , instead, path resolves subtable, text row-label associated path retrieved set. subtable case, label text set non-NA value, labelrow set visible, even . Similarly, label row text subtable set NA, label row bet set non-visible, row appear table printed.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/label_at_path.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Label at Path — label_at_path","text":"changing row labels content rows, important path way row. Paths ending \"@content\" exhibit behavior want, thus error. See row_paths help determining full paths content rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/label_at_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Label at Path — label_at_path","text":"","code":"lyt <- basic_table() %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(c(\"CHN\", \"USA\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) label_at_path(tbl, c(\"COUNTRY\", \"CHN\")) #> [1] \"CHN\" label_at_path(tbl, c(\"COUNTRY\", \"USA\")) <- \"United States\" tbl #> all obs #> ——————————————————————— #> CHN #> Mean 34.64 #> United States #> Mean 35.30"},{"path":"https://insightsengineering.github.io/rtables/reference/length-CellValue-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Length of a Cell value — length,CellValue-method","title":"Length of a Cell value — length,CellValue-method","text":"Length Cell value","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/length-CellValue-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Length of a Cell value — length,CellValue-method","text":"","code":"# S4 method for CellValue length(x)"},{"path":"https://insightsengineering.github.io/rtables/reference/length-CellValue-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Length of a Cell value — length,CellValue-method","text":"x x.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/length-CellValue-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Length of a Cell value — length,CellValue-method","text":"Always returns 1L","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a function that coerces the return values of f to a list — list_wrap_x","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"Returns function coerces return values f list","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"","code":"list_wrap_x(f) list_wrap_df(f)"},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"f function wrap.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"function calls f converts result list CellValue objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"list_wrap_x generates wrapper takes x first argument, list_wrap_df generates otherwise identical wrapper function whose first argument named df. provide using functions tabulation analyze, functions take df first argument passed full subset dataframe, accept anything else notably including x passed relevant subset variable analyzed.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/list_wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"","code":"summary(iris$Sepal.Length) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 4.300 5.100 5.800 5.843 6.400 7.900 f <- list_wrap_x(summary) f(x = iris$Sepal.Length) #> $Min. #> [1] 4.3 #> #> $`1st Qu.` #> [1] 5.1 #> #> $Median #> [1] 5.8 #> #> $Mean #> [1] 5.843333 #> #> $`3rd Qu.` #> [1] 6.4 #> #> $Max. #> [1] 7.9 #> f2 <- list_wrap_df(summary) f2(df = iris$Sepal.Length) #> $Min. #> [1] 4.3 #> #> $`1st Qu.` #> [1] 5.1 #> #> $Median #> [1] 5.8 #> #> $Mean #> [1] 5.843333 #> #> $`3rd Qu.` #> [1] 6.4 #> #> $Max. #> [1] 7.9 #>"},{"path":"https://insightsengineering.github.io/rtables/reference/lyt_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Layouting Function Arg Conventions — lyt_args","title":"Layouting Function Arg Conventions — lyt_args","text":"Layouting Function Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/lyt_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layouting Function Arg Conventions — lyt_args","text":"","code":"lyt_args( lyt, var, vars, label, labels_var, varlabels, varnames, split_format, split_na_str, nested, format, cfun, cformat, cna_str, split_fun, split_name, split_label, afun, inclNAs, valorder, ref_group, compfun, label_fstr, child_labels, extra_args, name, cuts, cutlabels, cutfun, cutlabelfun, cumulative, indent_mod, show_labels, label_pos, var_labels, cvar, table_names, topleft, align, page_by, page_prefix, format_na_str, section_div, na_str )"},{"path":"https://insightsengineering.github.io/rtables/reference/lyt_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layouting Function Arg Conventions — lyt_args","text":"lyt layout object pre-data used tabulation var string, variable name vars character vector. Multiple variable names. label character(1). label (confused name) object/structure. labels_var string, name variable containing labels displayed values var varlabels character vector. Labels vars varnames character vector. Names vars appear pathing. vars unique variable names. , variable names suffixes necessary enforce uniqueness. split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_fun function/NULL. custom splitting function See custom_split_funs split_name string. Name associated split (pathing, etc) split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). afun function. Analysis function, must take x df first parameter. Can optionally take parameters populated tabulation framework. See Details analyze. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE valorder character vector. Order split children appear resulting table. ref_group character. Value var taken ref_group/control compared . compfun function/string. comparison function accepts analysis function outputs two different partitions returns single value. Defaults subtraction. string, taken name function. label_fstr string. sprintf style format string containing. non-comparison splits, can contain one \"%s\" takes current split value generates row/column label. Comparison-based splits can contain two \"%s\". child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. name character(1). Name split/table/row created. Defaults corresponding label, required . cuts numeric. Cuts use cutlabels character (NULL). Labels cuts cutfun function. Function accepts full vector var values returns cut points used (via cut) splitting data tabulation cutlabelfun function. Function returns either labels cuts NULL passed return value cutfun cumulative logical. cuts treated cumulative. Defaults FALSE indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. show_labels character(1). variable labels corresponding variable(s) vars visible resulting table. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. var_labels character. Variable labels 1 variables cvar character(1). variable, , content function accept. Defaults NA. table_names character. Names tables representing atomic analysis. Defaults var. topleft character. Override values \"top left\" material displayed printing. align character(1) NULL. Alignment value rendered . defaults \"center\" NULL used. See formatters::list_valid_aligns() currently supported alignments. page_by logical(1). pagination forced different children resulting form split. error rise selected split contain least one value NA. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table format_na_str character(1). String displayed formatted cell's value(s) NA. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. na_str character(1). String displayed value x missing. Defaults \"NA\".","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/lyt_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layouting Function Arg Conventions — lyt_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/make_afun.html","id":null,"dir":"Reference","previous_headings":"","what":"Create custom analysis function wrapping existing function — make_afun","title":"Create custom analysis function wrapping existing function — make_afun","text":"Create custom analysis function wrapping existing function","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_afun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create custom analysis function wrapping existing function — make_afun","text":"","code":"make_afun( fun, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL, .ungroup_stats = NULL, .format_na_strs = NULL, ..., .null_ref_cells = \".in_ref_col\" %in% names(formals(fun)) )"},{"path":"https://insightsengineering.github.io/rtables/reference/make_afun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create custom analysis function wrapping existing function — make_afun","text":"fun function. function wrapped new customized analysis fun. return named list. .stats character. Names elements keep fun's full output. .formats . vector/list formats override defaults applied fun. .labels character. Vector labels override defaults returned fun .indent_mods integer. Named vector indent modifiers generated rows. .ungroup_stats character. Vector names, must match elements .stats .format_na_strs . vector/list na strings override defaults applied fun. ... dots. Additional arguments fun effectively become new defaults. can still overridden extra_args within split. .null_ref_cells logical(1). cells reference column NULL-ed returned analysis function. Defaults TRUE fun accepts .in_ref_col formal argument. Note argument occurs ... must fully specified name set.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_afun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create custom analysis function wrapping existing function — make_afun","text":"function suitable use analyze element selection, reformatting, relabeling performed automatically.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_afun.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create custom analysis function wrapping existing function — make_afun","text":"setting .ungroup_stats non-null changes structure value(s) returned fun, rather just labeling (.labels), formatting (.formats), selecting amongst (.stats) . means subsequent make_afun calls customize output can must operate new structure, original structure returned fun. See final pair examples .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/make_afun.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create custom analysis function wrapping existing function — make_afun","text":"","code":"s_summary <- function(x) { stopifnot(is.numeric(x)) list( n = sum(!is.na(x)), mean_sd = c(mean = mean(x), sd = sd(x)), min_max = range(x) ) } s_summary(iris$Sepal.Length) #> $n #> [1] 150 #> #> $mean_sd #> mean sd #> 5.8433333 0.8280661 #> #> $min_max #> [1] 4.3 7.9 #> a_summary <- make_afun( fun = s_summary, .formats = c(n = \"xx\", mean_sd = \"xx.xx (xx.xx)\", min_max = \"xx.xx - xx.xx\"), .labels = c(n = \"n\", mean_sd = \"Mean (sd)\", min_max = \"min - max\") ) a_summary(x = iris$Sepal.Length) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 150 0 n #> 2 mean_sd 5.84 (0.83) 0 Mean (sd) #> 3 min_max 4.30 - 7.90 0 min - max a_summary2 <- make_afun(a_summary, .stats = c(\"n\", \"mean_sd\")) a_summary2(x = iris$Sepal.Length) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 150 0 n #> 2 mean_sd 5.84 (0.83) 0 Mean (sd) a_summary3 <- make_afun(a_summary, .formats = c(mean_sd = \"(xx.xxx, xx.xxx)\")) s_foo <- function(df, .N_col, a = 1, b = 2) { list( nrow_df = nrow(df), .N_col = .N_col, a = a, b = b ) } s_foo(iris, 40) #> $nrow_df #> [1] 150 #> #> $.N_col #> [1] 40 #> #> $a #> [1] 1 #> #> $b #> [1] 2 #> a_foo <- make_afun(s_foo, b = 4, .formats = c(nrow_df = \"xx.xx\", \".N_col\" = \"xx.\", a = \"xx\", b = \"xx.x\"), .labels = c( nrow_df = \"Nrow df\", \".N_col\" = \"n in cols\", a = \"a value\", b = \"b value\" ), .indent_mods = c(nrow_df = 2L, a = 1L) ) a_foo(iris, .N_col = 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150.00 2 Nrow df #> 2 .N_col 40 0 n in cols #> 3 a 1 1 a value #> 4 b 4.0 0 b value a_foo2 <- make_afun(a_foo, .labels = c(nrow_df = \"Number of Rows\")) a_foo2(iris, .N_col = 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150.00 2 Number of Rows #> 2 .N_col 40 0 n in cols #> 3 a 1 1 a value #> 4 b 4.0 0 b value # grouping and further customization s_grp <- function(df, .N_col, a = 1, b = 2) { list( nrow_df = nrow(df), .N_col = .N_col, letters = list( a = a, b = b ) ) } a_grp <- make_afun(s_grp, b = 3, .labels = c( nrow_df = \"row count\", .N_col = \"count in column\" ), .formats = c(nrow_df = \"xx.\", .N_col = \"xx.\"), .indent_mod = c(letters = 1L), .ungroup_stats = \"letters\" ) a_grp(iris, 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150 0 row count #> 2 .N_col 40 0 count in column #> 3 a 1 1 a #> 4 b 3 1 b a_aftergrp <- make_afun(a_grp, .stats = c(\"nrow_df\", \"b\"), .formats = c(b = \"xx.\") ) a_aftergrp(iris, 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150 0 row count #> 2 b 3 0 b s_ref <- function(x, .in_ref_col, .ref_group) { list( mean_diff = mean(x) - mean(.ref_group) ) } a_ref <- make_afun(s_ref, .labels = c(mean_diff = \"Mean Difference from Ref\") ) a_ref(iris$Sepal.Length, .in_ref_col = TRUE, 1:10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 mean_diff 0 Mean Difference from Ref a_ref(iris$Sepal.Length, .in_ref_col = FALSE, 1:10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 mean_diff 0.343333333333334 0 Mean Difference from Ref"},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Column Layout Summary — make_col_df","title":"Column Layout Summary — make_col_df","text":"Generate structural summary columns rtables table return data.frame.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Column Layout Summary — make_col_df","text":"","code":"make_col_df(tt, colwidths = NULL, visible_only = TRUE)"},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Column Layout Summary — make_col_df","text":"tt . Object representing table-like object summarized. colwidths numeric. Internal detail set manually. visible_only logical(1). visible aspects table structure reflected summary. Defaults TRUE. May supported methods.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Column Layout Summary — make_col_df","text":"Used Pagination","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_row_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Return List with Table Row/Col Paths — row_paths","title":"Return List with Table Row/Col Paths — row_paths","text":"Return List Table Row/Col Paths","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_row_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return List with Table Row/Col Paths — row_paths","text":"","code":"row_paths(x) col_paths(x)"},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_row_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return List with Table Row/Col Paths — row_paths","text":"x rtable object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_row_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return List with Table Row/Col Paths — row_paths","text":"list paths row/column within x","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/make_col_row_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return List with Table Row/Col Paths — row_paths","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"SEX\", \"AGE\")) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————— #> SEX #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 #> AGE #> Mean 33.77 35.43 35.43 row_paths(tbl) #> [[1]] #> [1] \"ma_SEX_AGE\" \"SEX\" #> #> [[2]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"F\" #> #> [[3]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"M\" #> #> [[4]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"U\" #> #> [[5]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"UNDIFFERENTIATED\" #> #> [[6]] #> [1] \"ma_SEX_AGE\" \"AGE\" #> #> [[7]] #> [1] \"ma_SEX_AGE\" \"AGE\" \"Mean\" #> col_paths(tbl) #> [[1]] #> [1] \"ARM\" \"A: Drug X\" #> #> [[2]] #> [1] \"ARM\" \"B: Placebo\" #> #> [[3]] #> [1] \"ARM\" \"C: Combination\" #> cell_values(tbl, c(\"AGE\", \"Mean\"), c(\"ARM\", \"B: Placebo\")) #> $`B: Placebo` #> [1] 35.43284 #>"},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_fun.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Custom Splitting Function — make_split_fun","title":"Create a Custom Splitting Function — make_split_fun","text":"Create Custom Splitting Function","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_fun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Custom Splitting Function — make_split_fun","text":"","code":"make_split_fun(pre = list(), core_split = NULL, post = list())"},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_fun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Custom Splitting Function — make_split_fun","text":"pre list. Zero functions operate incoming data return new data frame split via core_split. called data order appear list. core_split function NULL. NULL, function accepts arguments do_base_split , returns type named list. Custom functions override behavior used column splits. post list. Zero functions called list output splitting.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_fun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Custom Splitting Function — make_split_fun","text":"function use custom split function.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_fun.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Custom Splitting Function — make_split_fun","text":"Custom split functions can thought () 3 different types manipulations splitting process Preprocessing incoming data split (Row-splitting ) Customization core mapping incoming data facets, Postprocessing operations set facets (groups) generated split. function provides interface create custom split functions implementing specifying sets operations classes customization independently. Preprocessing functions (1), must accept: df, spl, vals, labels, can optionally accept .spl_context. manipulate df ( incoming data split) return modified data.frame. modified data.frame must contain columns present incoming data.frame, can add columns necessary (though note new columns used layout split analysis variables, present validity checking done). preprocessing component useful things manipulating factor levels, e.g., trim unobserved ones reorder levels based observed counts, etc. Customization core splitting (2) currently supported row splits. Core splitting functions override fundamental splitting procedure, necessary rare cases. must accept spl, df, vals, labels, can optionally accept .spl_context. must return named list elements, length, follows: - datasplit (containing list data.frames), - values containing values associated facets, must character SplitValue objects. values appear paths resulting table. - labels containing character labels associated values Postprocessing functions (3) must accept result core split first argument ( writing can anything), addition spl, fulldf, can optionally accept .spl_context. must return modified version structure specified core splitting. pre- post-processing cases, multiple functions can specified. happens, applied sequentially, order appear list passed relevant argument (pre post, respectively).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_fun.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Custom Splitting Function — make_split_fun","text":"","code":"mysplitfun <- make_split_fun( pre = list(drop_facet_levels), post = list(add_overall_facet(\"ALL\", \"All Arms\")) ) basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = mysplitfun) %>% analyze(\"AGE\") %>% build_table(subset(DM, ARM %in% c(\"B: Placebo\", \"C: Combination\"))) #> B: Placebo C: Combination All Arms #> (N=106) (N=129) (N=235) #> ————————————————————————————————————————————— #> Mean 33.02 34.57 33.87 ## post (and pre) arguments can take multiple functions, here ## we add an overall facet and the reorder the facets reorder_facets <- function(splret, spl, fulldf, ...) { ord <- order(names(splret$values)) make_split_result( splret$values[ord], splret$datasplit[ord], splret$labels[ord] ) } mysplitfun2 <- make_split_fun( pre = list(drop_facet_levels), post = list( add_overall_facet(\"ALL\", \"All Arms\"), reorder_facets ) ) basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = mysplitfun2) %>% analyze(\"AGE\") %>% build_table(subset(DM, ARM %in% c(\"B: Placebo\", \"C: Combination\"))) #> All Arms B: Placebo C: Combination #> (N=235) (N=106) (N=129) #> ————————————————————————————————————————————— #> Mean 33.87 33.02 34.57 very_stupid_core <- function(spl, df, vals, labels, .spl_context) { make_split_result(c(\"stupid\", \"silly\"), datasplit = list(df[1:10, ], df[11:30, ]), labels = c(\"first 10\", \"second 20\") ) } dumb_30_facet <- add_combo_facet(\"dumb\", label = \"thirty patients\", levels = c(\"stupid\", \"silly\") ) nonsense_splfun <- make_split_fun( core_split = very_stupid_core, post = list(dumb_30_facet) ) ## recall core split overriding is not supported in column space ## currently, but we can see it in action in row space lyt_silly <- basic_table() %>% split_rows_by(\"ARM\", split_fun = nonsense_splfun) %>% summarize_row_groups() %>% analyze(\"AGE\") silly_table <- build_table(lyt_silly, DM) silly_table #> all obs #> ——————————————————————————— #> first 10 10 (2.8%) #> Mean 31.10 #> second 20 20 (5.6%) #> Mean 34.25 #> thirty patients 30 (8.4%) #> Mean 33.20"},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_result.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct split result object — make_split_result","title":"Construct split result object — make_split_result","text":"functions can used create add split result functions implement core splitting post-processing within custom split function.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_result.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct split result object — make_split_result","text":"","code":"make_split_result(values, datasplit, labels, extras = NULL) add_to_split_result(splres, values, datasplit, labels, extras = NULL)"},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_result.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct split result object — make_split_result","text":"values character list(SplitValue). values associated facet datasplit list(data.frame). facet data facet generated split labels character. labels associated facet extras NULL list. Extra values associated facets passed analysis functions applied within facet. splres list. list representing result splitting.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_result.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct split result object — make_split_result","text":"named list representing facets generated split elements values, datasplit, labels, length correspond elementwise.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_result.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct split result object — make_split_result","text":"functions various housekeeping ensure split result list rtables internals expect , relevant end users.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/make_split_result.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct split result object — make_split_result","text":"","code":"splres <- make_split_result( values = c(\"hi\", \"lo\"), datasplit = list(hi = mtcars, lo = mtcars[1:10, ]), labels = c(\"more data\", \"less data\") ) splres2 <- add_to_split_result(splres, values = \"med\", datasplit = list(med = mtcars[1:20, ]), labels = \"kinda some data\" )"},{"path":"https://insightsengineering.github.io/rtables/reference/manual_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Manual column declaration — manual_cols","title":"Manual column declaration — manual_cols","text":"Manual column declaration","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/manual_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manual column declaration — manual_cols","text":"","code":"manual_cols(..., .lst = list(...))"},{"path":"https://insightsengineering.github.io/rtables/reference/manual_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manual column declaration — manual_cols","text":"... One vectors levels appear column space. one set levels given, values second nested within value first, . .lst list sets levels, default populated via list(...).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/manual_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manual column declaration — manual_cols","text":"InstantiatedColumnInfo object, suitable use declaring column structure manually constructed table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/manual_cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manual column declaration — manual_cols","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/manual_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manual column declaration — manual_cols","text":"","code":"# simple one level column space rows <- lapply(1:5, function(i) { DataRow(rep(i, times = 3)) }) tbl <- TableTree(kids = rows, cinfo = manual_cols(split = c(\"a\", \"b\", \"c\"))) tbl #> a b c #> ———————————— #> 1 1 1 #> 2 2 2 #> 3 3 3 #> 4 4 4 #> 5 5 5 # manually declared nesting tbl2 <- TableTree( kids = list(DataRow(as.list(1:4))), cinfo = manual_cols( Arm = c(\"Arm A\", \"Arm B\"), Gender = c(\"M\", \"F\") ) ) tbl2 #> Arm A Arm B #> M F M F #> —————————————————————— #> 1 2 3 4"},{"path":"https://insightsengineering.github.io/rtables/reference/matrix_form-VTableTree-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"Although rtables represented tree data structure outputting table ASCII HTML useful map rtable state formatted cells matrix form.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/matrix_form-VTableTree-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"","code":"# S4 method for VTableTree matrix_form( obj, indent_rownames = FALSE, expand_newlines = TRUE, indent_size = 2 )"},{"path":"https://insightsengineering.github.io/rtables/reference/matrix_form-VTableTree-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"obj . object accessor access modify indent_rownames logical(1), TRUE column row names strings matrix indented row names (strings pre-fixed) expand_newlines logical(1). matrix form generated expand rows whose values contain newlines multiple 'physical' rows ( appear rendered ASCII). Defaults TRUE indent_size numeric(1). Number spaces use per indent level. Defaults 2","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/matrix_form-VTableTree-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"list following elements: strings content, printed, top-left material, column headers, row labels , cell values tt spans column-span information print-string strings matrix aligns text alignment print-string strings matrix display Whether print-string strings matrix printed row_info data.frame generated make_row_df additional nrow_header attribute indicating number pseudo \"rows\" column structure defines.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/matrix_form-VTableTree-method.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"strings return object defined follows: row labels determined make_row_df cell values determined using get_formatted_cells. (Column labels calculated using non-exported internal function.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/matrix_form-VTableTree-method.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> Species (lvls) -> group (lvls) #> #> Row-Split Structure: #> Sepal.Length:Petal.Width (** multivar analysis **) #> tbl <- build_table(lyt, iris2) matrix_form(tbl) #> $strings #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] \"\" \"setosa\" \"setosa\" \"versicolor\" \"versicolor\" \"virginica\" #> [2,] \"\" \"a\" \"b\" \"a\" \"b\" \"a\" #> [3,] \"Sepal.Length\" \"\" \"\" \"\" \"\" \"\" #> [4,] \"Min.\" \"4.40\" \"4.30\" \"5.00\" \"4.90\" \"4.90\" #> [5,] \"1st Qu.\" \"4.80\" \"4.80\" \"5.60\" \"5.60\" \"6.20\" #> [6,] \"Median\" \"5.00\" \"5.00\" \"5.90\" \"5.90\" \"6.50\" #> [7,] \"Mean\" \"5.02\" \"4.99\" \"5.99\" \"5.88\" \"6.50\" #> [8,] \"3rd Qu.\" \"5.30\" \"5.10\" \"6.40\" \"6.10\" \"6.70\" #> [9,] \"Max.\" \"5.80\" \"5.70\" \"7.00\" \"6.70\" \"7.70\" #> [10,] \"Petal.Width\" \"\" \"\" \"\" \"\" \"\" #> [11,] \"Min.\" \"0.10\" \"0.10\" \"1.00\" \"1.00\" \"1.40\" #> [12,] \"1st Qu.\" \"0.20\" \"0.20\" \"1.20\" \"1.20\" \"1.90\" #> [13,] \"Median\" \"0.20\" \"0.20\" \"1.30\" \"1.30\" \"2.10\" #> [14,] \"Mean\" \"0.23\" \"0.26\" \"1.35\" \"1.30\" \"2.08\" #> [15,] \"3rd Qu.\" \"0.20\" \"0.30\" \"1.50\" \"1.40\" \"2.30\" #> [16,] \"Max.\" \"0.40\" \"0.60\" \"1.80\" \"1.70\" \"2.50\" #> [,7] #> [1,] \"virginica\" #> [2,] \"b\" #> [3,] \"\" #> [4,] \"5.60\" #> [5,] \"6.30\" #> [6,] \"6.50\" #> [7,] \"6.67\" #> [8,] \"7.20\" #> [9,] \"7.90\" #> [10,] \"\" #> [11,] \"1.50\" #> [12,] \"1.80\" #> [13,] \"2.00\" #> [14,] \"1.98\" #> [15,] \"2.20\" #> [16,] \"2.50\" #> #> $spans #> setosa.a setosa.b versicolor.a versicolor.b virginica.a virginica.b #> [1,] 1 2 2 2 2 2 2 #> [2,] 1 1 1 1 1 1 1 #> [3,] 1 1 1 1 1 1 1 #> [4,] 1 1 1 1 1 1 1 #> [5,] 1 1 1 1 1 1 1 #> [6,] 1 1 1 1 1 1 1 #> [7,] 1 1 1 1 1 1 1 #> [8,] 1 1 1 1 1 1 1 #> [9,] 1 1 1 1 1 1 1 #> [10,] 1 1 1 1 1 1 1 #> [11,] 1 1 1 1 1 1 1 #> [12,] 1 1 1 1 1 1 1 #> [13,] 1 1 1 1 1 1 1 #> [14,] 1 1 1 1 1 1 1 #> [15,] 1 1 1 1 1 1 1 #> [16,] 1 1 1 1 1 1 1 #> #> $aligns #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [2,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [3,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [4,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [5,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [6,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [7,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [8,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [9,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [10,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [11,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [12,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [13,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [14,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [15,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [16,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> #> $display #> virginica.b1 virginica.b2 virginica.b3 virginica.b4 virginica.b5 #> [1,] TRUE TRUE FALSE TRUE FALSE TRUE #> [2,] TRUE TRUE TRUE TRUE TRUE TRUE #> [3,] TRUE TRUE TRUE TRUE TRUE TRUE #> [4,] TRUE TRUE TRUE TRUE TRUE TRUE #> [5,] TRUE TRUE TRUE TRUE TRUE TRUE #> [6,] TRUE TRUE TRUE TRUE TRUE TRUE #> [7,] TRUE TRUE TRUE TRUE TRUE TRUE #> [8,] TRUE TRUE TRUE TRUE TRUE TRUE #> [9,] TRUE TRUE TRUE TRUE TRUE TRUE #> [10,] TRUE TRUE TRUE TRUE TRUE TRUE #> [11,] TRUE TRUE TRUE TRUE TRUE TRUE #> [12,] TRUE TRUE TRUE TRUE TRUE TRUE #> [13,] TRUE TRUE TRUE TRUE TRUE TRUE #> [14,] TRUE TRUE TRUE TRUE TRUE TRUE #> [15,] TRUE TRUE TRUE TRUE TRUE TRUE #> [16,] TRUE TRUE TRUE TRUE TRUE TRUE #> virginica.b6 #> [1,] FALSE #> [2,] TRUE #> [3,] TRUE #> [4,] TRUE #> [5,] TRUE #> [6,] TRUE #> [7,] TRUE #> [8,] TRUE #> [9,] TRUE #> [10,] TRUE #> [11,] TRUE #> [12,] TRUE #> [13,] TRUE #> [14,] TRUE #> [15,] TRUE #> [16,] TRUE #> #> $formats #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] \"\" \"\" \"\" \"\" \"\" \"\" \"\" #> [2,] \"\" \"\" \"\" \"\" \"\" \"\" \"\" #> [3,] \"\" \"-\" \"-\" \"-\" \"-\" \"-\" \"-\" #> [4,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [5,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [6,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [7,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [8,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [9,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [10,] \"\" \"-\" \"-\" \"-\" \"-\" \"-\" \"-\" #> [11,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [12,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [13,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [14,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [15,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [16,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> #> $row_info #> label name abs_rownumber path pos_in_siblings #> 1 Sepal.Length Sepal.Length 1 ma_Sepal.... NA #> 2 Min. Min. 2 ma_Sepal.... 1 #> 3 1st Qu. 1st Qu. 3 ma_Sepal.... 2 #> 4 Median Median 4 ma_Sepal.... 3 #> 5 Mean Mean 5 ma_Sepal.... 4 #> 6 3rd Qu. 3rd Qu. 6 ma_Sepal.... 5 #> 7 Max. Max. 7 ma_Sepal.... 6 #> 8 Petal.Width Petal.Width 8 ma_Sepal.... NA #> 9 Min. Min. 9 ma_Sepal.... 1 #> 10 1st Qu. 1st Qu. 10 ma_Sepal.... 2 #> 11 Median Median 11 ma_Sepal.... 3 #> 12 Mean Mean 12 ma_Sepal.... 4 #> 13 3rd Qu. 3rd Qu. 13 ma_Sepal.... 5 #> 14 Max. Max. 14 ma_Sepal.... 6 #> n_siblings self_extent par_extent reprint_inds node_class indent nrowrefs #> 1 NA 1 0 LabelRow 0 0 #> 2 6 1 1 1 DataRow 1 0 #> 3 6 1 1 1 DataRow 1 0 #> 4 6 1 1 1 DataRow 1 0 #> 5 6 1 1 1 DataRow 1 0 #> 6 6 1 1 1 DataRow 1 0 #> 7 6 1 1 1 DataRow 1 0 #> 8 NA 1 0 LabelRow 0 0 #> 9 6 1 1 8 DataRow 1 0 #> 10 6 1 1 8 DataRow 1 0 #> 11 6 1 1 8 DataRow 1 0 #> 12 6 1 1 8 DataRow 1 0 #> 13 6 1 1 8 DataRow 1 0 #> 14 6 1 1 8 DataRow 1 0 #> ncellrefs nreflines force_page page_title trailing_sep #> 1 0 0 FALSE #> 2 0 0 FALSE #> 3 0 0 FALSE #> 4 0 0 FALSE #> 5 0 0 FALSE #> 6 0 0 FALSE #> 7 0 0 FALSE #> 8 0 0 FALSE #> 9 0 0 FALSE #> 10 0 0 FALSE #> 11 0 0 FALSE #> 12 0 0 FALSE #> 13 0 0 FALSE #> 14 0 0 FALSE #> #> $line_grouping #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 #> #> $ref_footnotes #> character(0) #> #> $main_title #> [1] \"\" #> #> $subtitles #> character(0) #> #> $page_titles #> character(0) #> #> $main_footer #> character(0) #> #> $prov_footer #> character(0) #> #> $header_section_div #> [1] NA #> #> $horizontal_sep #> [1] \"—\" #> #> $col_gap #> [1] 3 #> #> $table_inset #> [1] 0 #> #> $has_topleft #> [1] TRUE #> #> $indent_size #> [1] 2 #> #> $col_widths #> [1] 12 4 4 5 5 5 4 #> #> $ref_fnote_df #> [1] row_path col_path row col symbol ref_index msg #> [8] nlines #> <0 rows> (or 0-length row.names) #> #> attr(,\"nrow_header\") #> [1] 2 #> attr(,\"ncols\") #> [1] 6 #> attr(,\"class\") #> [1] \"MatrixPrintForm\" \"list\""},{"path":"https://insightsengineering.github.io/rtables/reference/names.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of a TableTree — names,VTableNodeInfo-method","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"Names TableTree","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"","code":"# S4 method for VTableNodeInfo names(x) # S4 method for InstantiatedColumnInfo names(x) # S4 method for LayoutColTree names(x) # S4 method for VTableTree row.names(x)"},{"path":"https://insightsengineering.github.io/rtables/reference/names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"x object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"column names x, defined details .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"TableTrees one level splitting columns, names defined top-level split values repped across columns span.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/no_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Exported for use in tern — no_colinfo","title":"Exported for use in tern — no_colinfo","text":"table/row/InstantiatedColumnInfo object contain column structure information?","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/no_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exported for use in tern — no_colinfo","text":"","code":"no_colinfo(obj) # S4 method for VTableNodeInfo no_colinfo(obj) # S4 method for InstantiatedColumnInfo no_colinfo(obj)"},{"path":"https://insightsengineering.github.io/rtables/reference/no_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exported for use in tern — no_colinfo","text":"obj . object accessor access modify","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/no_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exported for use in tern — no_colinfo","text":"TRUE object /empty instantiated column information, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":null,"dir":"Reference","previous_headings":"","what":"Pagination of a TableTree — pag_tt_indices","title":"Pagination of a TableTree — pag_tt_indices","text":"Paginate rtables table vertical / horizontal direction, required specified page size.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pagination of a TableTree — pag_tt_indices","text":"","code":"pag_tt_indices( tt, lpp = 15, min_siblings = 2, nosplitin = character(), colwidths = NULL, max_width = NULL, verbose = FALSE ) paginate_table( tt, page_type = \"letter\", font_family = \"Courier\", font_size = 8, lineheight = 1, landscape = FALSE, pg_width = NULL, pg_height = NULL, margins = c(top = 0.5, bottom = 0.5, left = 0.75, right = 0.75), lpp = NA_integer_, cpp = NA_integer_, min_siblings = 2, nosplitin = character(), colwidths = NULL, tf_wrap = FALSE, max_width = NULL, verbose = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pagination of a TableTree — pag_tt_indices","text":"tt TableTree (related class). TableTree object representing populated table. lpp numeric. Maximum lines per page including (re)printed header context rows min_siblings numeric. Minimum sibling rows must appear either side pagination row mid-subtable split valid. Defaults 2. nosplitin character. List names sub-tables page-breaks allowed, regardless considerations. Defaults none. colwidths numeric vector. Column widths use vertical pagination. max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE. verbose logical(1). extra debugging messages shown. Defaults FALSE. page_type character(1). Name page type. See page_types. Ignored pg_width pg_height set directly. font_family character(1). Name font family. error thrown family named monospaced. Defaults Courier. font_size numeric(1). Font size, defaults 12. lineheight numeric(1). Line height, defaults 1. landscape logical(1). dimensions page_type inverted landscape? Defaults FALSE, ignored pg_width pg_height set directly. pg_width numeric(1). Page width inches. pg_height numeric(1). Page height inches. margins numeric(4). Named numeric vector containing 'bottom', 'left', 'top', 'right' margins inches. Defaults .5 inches vertical margins .75 horizontal margins. cpp numeric(1) NULL. Width (characters) pages horizontal pagination. NA (default) indicates cpp inferred page size; NULL indicates horizontal pagination done regardless page size. tf_wrap logical(1). texts title, subtitle, footnotes wrapped?","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pagination of a TableTree — pag_tt_indices","text":"pag_tt_indices list paginated-groups row-indices tt. paginate_table, subtables defined subsetting indices defined pag_tt_indices.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pagination of a TableTree — pag_tt_indices","text":"rtables pagination context aware, meaning label rows row-group summaries (content rows) repeated (vertical) pagination, appropriate. allows reader immediately understand table turning new page, also mean rendered, paginated table take lines text rendering table without pagination . Pagination also takes account word-wrapping title, footer, column-label, formatted cell value content. Vertical pagination information (pagination data.frame) created using (make_row_df) Horizontal pagination performed creating pagination dataframe columns, applying algorithm used vertical pagination . physical page size font information specified, used derive lines-per-page (lpp) characters-per-page (cpp) values. full multi-direction pagination algorithm follows: Adjust lpp cpp account rendered elements rows (columns) titles/footers/column labels, horizontal dividers vertical pagination case row-labels, table_inset, top-left materials horizontal case Perform 'forced pagination' representing page-row splits, generating 1 tables Perform vertical pagination separately table generated (1) Perform horizontal pagination entire table apply results table page generated (1)-(2) Return list subtables representing full bi-directional pagination Pagination directions done using Core Pagination Algorithm implemented formatters package:","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":"pagination-algorithm","dir":"Reference","previous_headings":"","what":"Pagination Algorithm","title":"Pagination of a TableTree — pag_tt_indices","text":"Pagination performed independently vertical horizontal directions based solely pagination data.frame, includes following information row/column: number lines/characters rendering row take word-wrapping (self_extent) indices (reprint_inds) number lines (par_extent) rows act context row row's number siblings position within siblings Given lpp (cpp) already adjusted rendered elements rows/columns dataframe pagination information, pagination performed via following algorithm, start = 1: Core Pagination Algorithm: Initial guess pagination point start + lpp (start + cpp) guess valid pagination position, guess > start, decrement guess repeat error thrown possible pagination positions start start + lpp (start + cpp) ever < start decrementing Retain pagination index pagination point less NROW(tt) (ncol(tt)), set start pos + 1, repeat steps (1) - (4). Validating pagination position: Given (already adjusted) lpp cpp value, pagination invalid : rows/columns page take (adjusted) lpp lines/cpp characters render including word-wrapping (vertical ) context repetition (vertical ) footnote messages section divider lines take many lines rendering rows (vertical ) row label content (row-group summary) row (vertical ) row pagination point siblings, less min_siblings preceding following siblings pagination occur within sub-table listed nosplitin","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/paginate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pagination of a TableTree — pag_tt_indices","text":"","code":"s_summary <- function(x) { if (is.numeric(x)) { in_rows( \"n\" = rcell(sum(!is.na(x)), format = \"xx\"), \"Mean (sd)\" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = \"xx.xx (xx.xx)\" ), \"IQR\" = rcell(IQR(x, na.rm = TRUE), format = \"xx.xx\"), \"min - max\" = rcell(range(x, na.rm = TRUE), format = \"xx.xx - xx.xx\") ) } else if (is.factor(x)) { vs <- as.list(table(x)) do.call(in_rows, lapply(vs, rcell, format = \"xx\")) } else { ( stop(\"type not supported\") ) } } lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze(c(\"AGE\", \"SEX\", \"BEP01FL\", \"BMRKR1\", \"BMRKR2\", \"COUNTRY\"), afun = s_summary) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> AGE #> n 134 134 132 #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> IQR 11.00 10.00 10.00 #> min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 #> SEX #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 #> BEP01FL #> Y 68 63 66 #> N 66 71 66 #> BMRKR1 #> n 134 134 132 #> Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) #> IQR 4.16 4.06 3.88 #> min - max 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 #> COUNTRY #> CHN 74 81 64 #> USA 10 13 17 #> BRA 13 7 10 #> PAK 12 9 10 #> NGA 8 7 11 #> RUS 5 8 6 #> JPN 5 4 9 #> GBR 4 3 2 #> CAN 3 2 3 #> CHE 0 0 0 nrow(tbl) #> [1] 33 row_paths_summary(tbl) #> rowname node_class path #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> AGE LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE #> n DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, n #> Mean (sd) DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, Mean (sd) #> IQR DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, IQR #> min - max DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, min - max #> SEX LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX #> F DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, F #> M DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, M #> U DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, U #> UNDIFFERENTIATED DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, UNDIFFERENTIATED #> BEP01FL LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BEP01FL #> Y DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BEP01FL, Y #> N DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BEP01FL, N #> BMRKR1 LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1 #> n DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, n #> Mean (sd) DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, Mean (sd) #> IQR DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, IQR #> min - max DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, min - max #> BMRKR2 LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2 #> LOW DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2, LOW #> MEDIUM DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2, MEDIUM #> HIGH DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2, HIGH #> COUNTRY LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY #> CHN DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, CHN #> USA DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, USA #> BRA DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, BRA #> PAK DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, PAK #> NGA DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, NGA #> RUS DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, RUS #> JPN DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, JPN #> GBR DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, GBR #> CAN DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, CAN #> CHE DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, CHE tbls <- paginate_table(tbl, lpp = 15) mf <- matrix_form(tbl, indent_rownames = TRUE) w_tbls <- propose_column_widths(mf) # so that we have the same column widths tmp <- lapply(tbls, function(tbli) { cat(toString(tbli, widths = w_tbls)) cat(\"\\n\\n\") cat(\"~~~~ PAGE BREAK ~~~~\") cat(\"\\n\\n\") }) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> AGE #> n 134 134 132 #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> IQR 11.00 10.00 10.00 #> min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 #> SEX #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 #> BEP01FL #> Y 68 63 66 #> N 66 71 66 #> #> #> ~~~~ PAGE BREAK ~~~~ #> #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> BMRKR1 #> n 134 134 132 #> Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) #> IQR 4.16 4.06 3.88 #> min - max 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 #> COUNTRY #> CHN 74 81 64 #> USA 10 13 17 #> BRA 13 7 10 #> #> #> ~~~~ PAGE BREAK ~~~~ #> #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> COUNTRY #> PAK 12 9 10 #> NGA 8 7 11 #> RUS 5 8 6 #> JPN 5 4 9 #> GBR 4 3 2 #> CAN 3 2 3 #> CHE 0 0 0 #> #> #> ~~~~ PAGE BREAK ~~~~ #>"},{"path":"https://insightsengineering.github.io/rtables/reference/prune_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Recursively prune a TableTree — prune_table","title":"Recursively prune a TableTree — prune_table","text":"Recursively prune TableTree","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/prune_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recursively prune a TableTree — prune_table","text":"","code":"prune_table( tt, prune_func = prune_empty_level, stop_depth = NA_real_, depth = 0 )"},{"path":"https://insightsengineering.github.io/rtables/reference/prune_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recursively prune a TableTree — prune_table","text":"tt TableTree (related class). TableTree object representing populated table. prune_func function. Function called subtree returns TRUE entire subtree removed. stop_depth numeric(1). depth subtrees checked pruning. Defaults NA indicates pruning happen levels depth numeric(1). Used internally, intended set end user.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/prune_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recursively prune a TableTree — prune_table","text":"TableTree pruned via recursive application prune_func.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/prune_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recursively prune a TableTree — prune_table","text":"","code":"adsl <- ex_adsl levels(adsl$SEX) <- c(levels(ex_adsl$SEX), \"OTHER\") tbl_to_prune <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") %>% build_table(adsl) tbl_to_prune %>% prune_table() #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 44.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 28.00 NA 46.00"},{"path":"https://insightsengineering.github.io/rtables/reference/qtable_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Generalized Frequency Table — qtable_layout","title":"Generalized Frequency Table — qtable_layout","text":"function provides convenience interface generating generalizations 2-way frequency table. Row column space can facetted variables, analysis function can specified. function builds layout specified layout applies data provided.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/qtable_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generalized Frequency Table — qtable_layout","text":"","code":"qtable_layout( data, row_vars = character(), col_vars = character(), avar = NULL, row_labels = NULL, afun = NULL, summarize_groups = FALSE, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), show_colcounts = TRUE, drop_levels = TRUE, ..., .default_rlabel = NULL ) qtable( data, row_vars = character(), col_vars = character(), avar = NULL, row_labels = NULL, afun = NULL, summarize_groups = FALSE, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), show_colcounts = TRUE, drop_levels = TRUE, ... )"},{"path":"https://insightsengineering.github.io/rtables/reference/qtable_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generalized Frequency Table — qtable_layout","text":"data data.frame. data tabulate. row_vars character. names variables used row facetting. col_vars character. names variables used column facetting. avar character(1). variable analyzed. Defaults first variable data. row_labels character NULL. Row label(s) applied analysis rows. length must match number rows generated afun. See details. afun function. function generate analysis row cell values. can proper analysis function, function returns vector list. Vectors taken multi-valued single cells, whereas lists interpreted multiple cells. summarize_groups logical(1). level nesting include marginal summary rows. Defaults FALSE title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. show_colcounts logical(1). column counts displayed resulting table layout applied data drop_levels logical(1). unobserved factor levels dropped facetting. Defaults TRUE. ... passed afun, specified. Otherwise ignored. .default_rlabel character(1). implementation detail set end users.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/qtable_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generalized Frequency Table — qtable_layout","text":"qtable built TableTree object representing desired table, qtable_layout, PreDataTableLayouts object declaring structure desired table, suitable passing build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/qtable_layout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generalized Frequency Table — qtable_layout","text":"function creates table single top-level structure row column dimensions involving faceting 0 variables . display table depends certain details tabulation. case afun returns single cell's contents (either scalar vector 2 3 elements), label rows deepest-nested row facets hidden labels used used analysis row labels. case afun returns list (corresponding multiple cells), names list used analysis row labels deepest-nested facet row labels visible. table annotated top-left area informative label displaying analysis variable (avar), set, function used (captured via substitute) possible, 'count' . One exception user may directly modify top-left area (via row_labels) case table row facets afun returns single row.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/qtable_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generalized Frequency Table — qtable_layout","text":"","code":"qtable(ex_adsl) #> all obs #> (N=400) #> ——————————————— #> count 400 qtable(ex_adsl, row_vars = \"ARM\") #> all obs #> count (N=400) #> ———————————————————————— #> A: Drug X 134 #> B: Placebo 134 #> C: Combination 132 qtable(ex_adsl, col_vars = \"ARM\") #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————— #> count 134 134 132 qtable(ex_adsl, row_vars = \"SEX\", col_vars = \"ARM\") #> A: Drug X B: Placebo C: Combination #> count (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————— #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 qtable(ex_adsl, row_vars = c(\"COUNTRY\", \"SEX\"), col_vars = c(\"ARM\", \"STRATA1\")) #> A: Drug X B: Placebo C: Combination #> A B C A B C A B C #> count (N=38) (N=47) (N=49) (N=44) (N=45) (N=45) (N=40) (N=43) (N=49) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> CHN #> F 11 15 18 12 15 18 8 9 12 #> M 9 7 11 8 12 15 10 10 10 #> U 1 0 1 1 0 0 1 0 2 #> UNDIFFERENTIATED 0 0 1 0 0 0 1 0 1 #> USA #> F 1 3 3 1 4 3 2 4 4 #> M 1 2 0 3 1 1 1 3 3 #> BRA #> F 3 1 1 2 1 0 0 3 2 #> M 1 7 0 4 0 0 2 3 0 #> PAK #> F 3 1 4 4 0 0 2 1 1 #> M 2 1 1 2 2 1 1 3 2 #> NGA #> F 0 1 2 2 1 2 1 0 4 #> M 2 1 1 0 1 1 4 1 1 #> U 0 1 0 0 0 0 0 0 0 #> RUS #> F 0 2 2 2 1 2 1 1 2 #> M 1 0 0 1 1 1 0 0 1 #> U 0 0 0 0 0 0 0 1 0 #> JPN #> F 1 1 2 0 2 1 3 2 2 #> M 0 1 0 0 0 0 1 0 1 #> U 0 0 0 0 1 0 0 0 0 #> GBR #> F 1 0 1 0 2 0 0 1 0 #> M 0 1 1 1 0 0 0 0 1 #> CAN #> F 1 1 0 1 1 0 1 0 0 #> M 0 1 0 0 0 0 1 1 0 qtable(ex_adsl, row_vars = c(\"COUNTRY\", \"SEX\"), col_vars = c(\"ARM\", \"STRATA1\"), avar = \"AGE\", afun = mean ) #> A: Drug X B: Placebo C: Combination #> A B C A B C A B C #> AGE - mean (N=38) (N=47) (N=49) (N=44) (N=45) (N=45) (N=40) (N=43) (N=49) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> CHN #> F 31.09 30.67 32.56 33.08 35.73 35.28 35.25 33.89 35.75 #> M 34.44 36.43 35.73 38.38 35.25 37.60 30.80 35.20 38.60 #> U 33.00 NA 34.00 27.00 NA NA 38.00 NA 33.00 #> UNDIFFERENTIATED NA NA 28.00 NA NA NA 44.00 NA 46.00 #> USA #> F 24.00 38.00 35.00 46.00 40.75 32.33 36.50 30.50 36.00 #> M 40.00 34.50 NA 38.67 53.00 30.00 47.00 38.00 29.00 #> BRA #> F 35.33 38.00 44.00 27.50 25.00 NA NA 46.33 33.00 #> M 43.00 35.14 NA 36.00 NA NA 28.50 40.33 NA #> PAK #> F 28.00 38.00 29.50 30.75 NA NA 33.00 23.00 49.00 #> M 39.00 37.00 31.00 41.50 28.50 33.00 40.00 35.33 35.50 #> NGA #> F NA 25.00 35.00 26.50 37.00 32.00 31.00 NA 32.75 #> M 29.50 29.00 50.00 NA 40.00 24.00 34.50 32.00 28.00 #> U NA 28.00 NA NA NA NA NA NA NA #> RUS #> F NA 36.50 39.50 31.00 36.00 39.50 30.00 44.00 26.00 #> M 40.00 NA NA 36.00 58.00 29.00 NA NA 28.00 #> U NA NA NA NA NA NA NA 37.00 NA #> JPN #> F 29.00 34.00 37.50 NA 29.00 28.00 32.33 47.50 34.00 #> M NA 48.00 NA NA NA NA 45.00 NA 26.00 #> U NA NA NA NA 35.00 NA NA NA NA #> GBR #> F 28.00 NA 36.00 NA 28.00 NA NA 40.00 NA #> M NA 27.00 28.00 62.00 NA NA NA NA 69.00 #> CAN #> F 41.00 39.00 NA 34.00 43.00 NA 37.00 NA NA #> M NA 31.00 NA NA NA NA 36.00 32.00 NA summary_list <- function(x, ...) as.list(summary(x)) qtable(ex_adsl, row_vars = \"SEX\", col_vars = \"ARM\", avar = \"AGE\", afun = summary_list) #> A: Drug X B: Placebo C: Combination #> AGE - summary_list (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————— #> F #> Min. 21.00 23.00 21.00 #> 1st Qu. 28.00 29.00 31.25 #> Median 32.00 32.00 35.00 #> Mean 32.76 34.12 35.20 #> 3rd Qu. 37.00 37.00 38.00 #> Max. 47.00 58.00 64.00 #> M #> Min. 23.00 21.00 20.00 #> 1st Qu. 29.50 32.50 29.00 #> Median 37.00 37.00 33.50 #> Mean 35.57 37.44 35.38 #> 3rd Qu. 40.50 41.50 40.00 #> Max. 50.00 62.00 69.00 #> U #> Min. 28.00 27.00 31.00 #> 1st Qu. 30.50 29.00 34.00 #> Median 33.00 31.00 36.00 #> Mean 31.67 31.00 35.25 #> 3rd Qu. 33.50 33.00 37.25 #> Max. 34.00 35.00 38.00 #> UNDIFFERENTIATED #> Min. 28.00 NA 44.00 #> 1st Qu. 28.00 NA 44.50 #> Median 28.00 NA 45.00 #> Mean 28.00 NA 45.00 #> 3rd Qu. 28.00 NA 45.50 #> Max. 28.00 NA 46.00 suppressWarnings(qtable(ex_adsl, row_vars = \"SEX\", col_vars = \"ARM\", avar = \"AGE\", afun = range )) #> A: Drug X B: Placebo C: Combination #> AGE - range (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————— #> F 21.0 / 47.0 23.0 / 58.0 21.0 / 64.0 #> M 23.0 / 50.0 21.0 / 62.0 20.0 / 69.0 #> U 28.0 / 34.0 27.0 / 35.0 31.0 / 38.0 #> UNDIFFERENTIATED 28.0 / 28.0 Inf / -Inf 44.0 / 46.0"},{"path":"https://insightsengineering.github.io/rtables/reference/rbind.html","id":null,"dir":"Reference","previous_headings":"","what":"rbind TableTree and related objects — rbindl_rtables","title":"rbind TableTree and related objects — rbindl_rtables","text":"rbind TableTree related objects","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rbind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"rbind TableTree and related objects — rbindl_rtables","text":"","code":"rbindl_rtables(x, gap = 0, check_headers = TRUE) # S4 method for VTableNodeInfo rbind(..., deparse.level = 1) # S4 method for VTableNodeInfo,ANY rbind2(x, y)"},{"path":"https://insightsengineering.github.io/rtables/reference/rbind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"rbind TableTree and related objects — rbindl_rtables","text":"x VTableNodeInfo. TableTree, ElementaryTable TableRow object. gap deprecated. Ignored. check_headers deprecated. Ignored. ... . Elements stacked. deparse.level numeric(1). Currently Ignored. y VTableNodeInfo. TableTree, ElementaryTable TableRow object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rbind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"rbind TableTree and related objects — rbindl_rtables","text":"formal table object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rbind.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"rbind TableTree and related objects — rbindl_rtables","text":"objects rbinded, titles footer information retained first object (exists) objects titles/footers identical titles/footers. Otherwise, titles/footers removed must set bound table via main_title(), subtitles(), main_footer(), prov_footer() functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rbind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"rbind TableTree and related objects — rbindl_rtables","text":"","code":"mtbl <- rtable( header = rheader( rrow(row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2)), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"All Species\", mean(iris$Sepal.Length), median(iris$Sepal.Length), mean(iris$Petal.Length), median(iris$Petal.Length), format = \"xx.xx\" ) ) mtbl2 <- with(subset(iris, Species == \"setosa\"), rtable( header = rheader( rrow(row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2)), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"Setosa\", mean(Sepal.Length), median(Sepal.Length), mean(Petal.Length), median(Petal.Length), format = \"xx.xx\" ) )) rbind(mtbl, mtbl2) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.01 5.00 1.46 1.50 rbind(mtbl, rrow(), mtbl2) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> #> Setosa 5.01 5.00 1.46 1.50 rbind(mtbl, rrow(\"aaa\"), indent(mtbl2)) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> aaa #> Setosa 5.01 5.00 1.46 1.50"},{"path":"https://insightsengineering.github.io/rtables/reference/rcell.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell value constructors — rcell","title":"Cell value constructors — rcell","text":"Construct cell value associate formatting, labeling, indenting, column spanning information .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rcell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell value constructors — rcell","text":"","code":"rcell( x, format = NULL, colspan = 1L, label = NULL, indent_mod = NULL, footnotes = NULL, align = NULL, format_na_str = NULL ) non_ref_rcell( x, is_ref, format = NULL, colspan = 1L, label = NULL, indent_mod = NULL, refval = NULL, align = \"center\", format_na_str = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/rcell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell value constructors — rcell","text":"x . Cell value. format character(1) function. format label (string) formatters function apply x. See formatters::list_valid_format_labels() currently supported format labels. colspan integer(1). Column span value. label character(1). Label NULL. non-null, looked determining row labels. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. footnotes list NULL. Referential footnote messages cell. align character(1) NULL. Alignment value rendered . defaults \"center\" NULL used. See formatters::list_valid_aligns() currently supported alignments. format_na_str character(1). String displayed formatted cell's value(s) NA. is_ref logical(1). reference column (.e. .in_ref_col passed argument) refval . Value use reference column. Defaults NULL","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rcell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell value constructors — rcell","text":"object representing value within single cell within populated table. underlying structure object implementation detail relied upon beyond calling accessors class.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rcell.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cell value constructors — rcell","text":"non_ref_rcell provides common blank cells reference column, value otherwise, passed value .in_ref_col used.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rcell.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cell value constructors — rcell","text":"Currently column spanning supported defining header structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. formatters export_as_pdf, export_as_txt","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/reexports.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Objects exported from other packages — reexports","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\", \"COUNTRY\")) tbl <- build_table(lyt, ex_adsl) cat(export_as_txt(tbl, file = NULL, paginate = TRUE, lpp = 8)) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> AGE #> Mean 33.77 35.43 35.43 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 #> \\s\\n A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> COUNTRY #> CHN 74 81 64 #> USA 10 13 17 #> BRA 13 7 10 #> PAK 12 9 10 #> NGA 8 7 11 #> \\s\\n A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> COUNTRY #> RUS 5 8 6 #> JPN 5 4 9 #> GBR 4 3 2 #> CAN 3 2 3 #> CHE 0 0 0 if (FALSE) { tf <- tempfile(fileext = \".txt\") export_as_txt(tbl, file = tf) system2(\"cat\", tf) } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\", \"COUNTRY\")) tbl <- build_table(lyt, ex_adsl) if (FALSE) { tf <- tempfile(fileext = \".pdf\") export_as_pdf(tbl, file = tf, pg_height = 4) tf <- tempfile(fileext = \".pdf\") export_as_pdf(tbl, file = tf, lpp = 8) }"},{"path":"https://insightsengineering.github.io/rtables/reference/ref_fnotes.html","id":null,"dir":"Reference","previous_headings":"","what":"Referential Footnote Accessors — row_footnotes","title":"Referential Footnote Accessors — row_footnotes","text":"Get set referential footnotes aspects built table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ref_fnotes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Referential Footnote Accessors — row_footnotes","text":"","code":"row_footnotes(obj) row_footnotes(obj) <- value cell_footnotes(obj) cell_footnotes(obj) <- value col_fnotes_here(obj) # S4 method for ANY col_fnotes_here(obj) col_fnotes_here(obj) <- value col_footnotes(obj) col_footnotes(obj) <- value ref_index(obj) ref_index(obj) <- value ref_symbol(obj) ref_symbol(obj) <- value ref_msg(obj) fnotes_at_path(obj, rowpath = NULL, colpath = NULL, reset_idx = TRUE) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/ref_fnotes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Referential Footnote Accessors — row_footnotes","text":"obj . object accessor access modify value new value rowpath character NULL. Path within row structure. NULL indicates footnote go column rather cell. colpath character NULL. Path within column structure. NULL indicates footnote go row rather cell reset_idx logical(1). numbering referential footnotes immediately recalculated. Defaults TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/ref_fnotes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Referential Footnote Accessors — row_footnotes","text":"","code":"# How to add referencial footnotes after having created a table lyt <- basic_table() %>% split_rows_by(\"SEX\", page_by = TRUE) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl <- trim_rows(tbl) # Check the row and col structure to add precise references # row_paths(tbl) # col_paths(t) # row_paths_summary(tbl) # col_paths_summary(tbl) # Add the citation numbers on the table and relative references in the footnotes fnotes_at_path(tbl, rowpath = c(\"SEX\", \"F\", \"AGE\", \"Mean\")) <- \"Famous paper 1\" fnotes_at_path(tbl, rowpath = c(\"SEX\", \"UNDIFFERENTIATED\")) <- \"Unfamous paper 2\" # tbl"},{"path":"https://insightsengineering.github.io/rtables/reference/rheader.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a header — rheader","title":"Create a header — rheader","text":"Create header","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rheader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a header — rheader","text":"","code":"rheader(..., format = \"xx\", .lst = NULL)"},{"path":"https://insightsengineering.github.io/rtables/reference/rheader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a header — rheader","text":"... row specifications (either character vectors output rrow DataRow, LabelRow, etc. format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. .lst list. already-collected list arguments used instead elements .... Arguments passed via ... ignored specified.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rheader.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a header — rheader","text":"InstantiatedColumnInfo object.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/rheader.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a header — rheader","text":"","code":"h1 <- rheader(c(\"A\", \"B\", \"C\")) h2 <- rheader( rrow(NULL, rcell(\"group 1\", colspan = 2), rcell(\"group 2\", colspan = 2)), rrow(NULL, \"A\", \"B\", \"A\", \"B\") ) h1 #> An InstantiatedColumnInfo object #> Columns: #> A (mnl) #> B (mnl) #> C (mnl) #> h2 #> An InstantiatedColumnInfo object #> Columns: #> group 1 (mnl) -> A (mnl) #> group 1 (mnl) -> B (mnl) #> group 2 (mnl) -> A (mnl) #> group 2 (mnl) -> B (mnl) #>"},{"path":"https://insightsengineering.github.io/rtables/reference/row_accessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Row attribute accessors — obj_avar","title":"Row attribute accessors — obj_avar","text":"Row attribute accessors","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/row_accessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row attribute accessors — obj_avar","text":"","code":"obj_avar(obj) # S4 method for TableRow obj_avar(obj) # S4 method for ElementaryTable obj_avar(obj) row_cells(obj) # S4 method for TableRow row_cells(obj) row_cells(obj) <- value # S4 method for TableRow row_cells(obj) <- value row_values(obj) # S4 method for TableRow row_values(obj) row_values(obj) <- value # S4 method for TableRow row_values(obj) <- value # S4 method for LabelRow row_values(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/row_accessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row attribute accessors — obj_avar","text":"obj . object accessor access modify value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/row_accessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row attribute accessors — obj_avar","text":"various, depending accessor called.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/row_paths_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Row/Col Paths Summary — row_paths_summary","title":"Print Row/Col Paths Summary — row_paths_summary","text":"Print Row/Col Paths Summary","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/row_paths_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Row/Col Paths Summary — row_paths_summary","text":"","code":"row_paths_summary(x) col_paths_summary(x)"},{"path":"https://insightsengineering.github.io/rtables/reference/row_paths_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Row/Col Paths Summary — row_paths_summary","text":"x rtable object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/row_paths_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print Row/Col Paths Summary — row_paths_summary","text":"data.frame summarizing row- column-structure x.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/row_paths_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Row/Col Paths Summary — row_paths_summary","text":"","code":"library(dplyr) ex_adsl_MF <- ex_adsl %>% filter(SEX %in% c(\"M\", \"F\")) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl_MF) tbl #> A: Drug X B: Placebo C: Combination #> F M F M F M #> ———————————————————————————————————————————————————————————— #> AGE #> Mean 32.76 35.57 34.12 37.44 35.20 35.38 #> BMRKR2 #> LOW 26 21 21 23 26 11 #> MEDIUM 21 15 38 18 17 23 #> HIGH 32 15 18 14 23 26 df <- row_paths_summary(tbl) #> rowname node_class path #> ——————————————————————————————————————————————————————— #> AGE LabelRow ma_AGE_BMRKR2, AGE #> Mean DataRow ma_AGE_BMRKR2, AGE, Mean #> BMRKR2 LabelRow ma_AGE_BMRKR2, BMRKR2 #> LOW DataRow ma_AGE_BMRKR2, BMRKR2, LOW #> MEDIUM DataRow ma_AGE_BMRKR2, BMRKR2, MEDIUM #> HIGH DataRow ma_AGE_BMRKR2, BMRKR2, HIGH df #> label indent node_class path #> 1 AGE 0 LabelRow ma_AGE_B.... #> 2 Mean 1 DataRow ma_AGE_B.... #> 3 BMRKR2 0 LabelRow ma_AGE_B.... #> 4 LOW 1 DataRow ma_AGE_B.... #> 5 MEDIUM 1 DataRow ma_AGE_B.... #> 6 HIGH 1 DataRow ma_AGE_B.... col_paths_summary(tbl) #> label path #> ————————————————————————————————————————————— #> A: Drug X ARM, A: Drug X #> F ARM, A: Drug X, SEX, F #> M ARM, A: Drug X, SEX, M #> B: Placebo ARM, B: Placebo #> F ARM, B: Placebo, SEX, F #> M ARM, B: Placebo, SEX, M #> C: Combination ARM, C: Combination #> F ARM, C: Combination, SEX, F #> M ARM, C: Combination, SEX, M # manually constructed table tbl2 <- rtable( rheader( rrow( \"row 1\", rcell(\"a\", colspan = 2), rcell(\"b\", colspan = 2) ), rrow(\"h2\", \"a\", \"b\", \"c\", \"d\") ), rrow(\"r1\", 1, 2, 1, 2), rrow(\"r2\", 3, 4, 2, 1) ) col_paths_summary(tbl2) #> label path #> ————————————————————————————— #> a manual, a #> a manual, a, manual, a #> b manual, a, manual, b #> b manual, b #> c manual, b, manual, c #> d manual, b, manual, d"},{"path":"https://insightsengineering.github.io/rtables/reference/rowclasses.html","id":null,"dir":"Reference","previous_headings":"","what":"Row classes and constructors — LabelRow","title":"Row classes and constructors — LabelRow","text":"Row classes constructors Row constructors Classes","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rowclasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row classes and constructors — LabelRow","text":"","code":"LabelRow( lev = 1L, label = \"\", name = label, vis = !is.na(label) && nzchar(label), cinfo = EmptyColInfo, indent_mod = 0L, table_inset = 0L, trailing_section_div = NA_character_ ) .tablerow( vals = list(), name = \"\", lev = 1L, label = name, cspan = rep(1L, length(vals)), cinfo = EmptyColInfo, var = NA_character_, format = NULL, na_str = NA_character_, klass, indent_mod = 0L, footnotes = list(), table_inset = 0L, trailing_section_div = NA_character_ ) DataRow(...) ContentRow(...)"},{"path":"https://insightsengineering.github.io/rtables/reference/rowclasses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row classes and constructors — LabelRow","text":"lev integer. Nesting level (roughly, indentation level practical terms). label character(1). label (confused name) object/structure. name character(1). Name split/table/row created. Defaults corresponding label, required . vis logical. row visible (LabelRow ). cinfo InstantiatedColumnInfo (NULL). Column structure object created. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. table_inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). trailing_section_div character(1). String used section divider printing last row contained (sub)-table, unless row also last table row printed overall, NA_character_ none (default). generated via layouting, correspond section_div split table represents single facet. vals list. cell values row cspan integer. Column span. 1 indicates spanning. var string, variable name format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". klass Internal detail. footnotes list NULL. Referential footnotes applied current level. post-processing, can achieved fnotes_at_path<-. ... passed shared constructor (.tablerow).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rowclasses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row classes and constructors — LabelRow","text":"formal object representing table row constructed type.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rowclasses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row classes and constructors — LabelRow","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rrow.html","id":null,"dir":"Reference","previous_headings":"","what":"row — rrow","title":"row — rrow","text":"row","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"row — rrow","text":"","code":"rrow(row.name = \"\", ..., format = NULL, indent = 0, inset = 0L)"},{"path":"https://insightsengineering.github.io/rtables/reference/rrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"row — rrow","text":"row.name NULL empty string used row.name rrow. ... cell values format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. indent deprecated. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rrow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"row — rrow","text":"row object context-appropriate type (label data)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/rrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"row — rrow","text":"","code":"rrow(\"ABC\", c(1, 2), c(3, 2), format = \"xx (xx.%)\") #> [DataRow indent_mod 0]: ABC 1 (200%) 3 (200%) rrow(\"\") #> [LabelRow indent_mod 0]:"},{"path":"https://insightsengineering.github.io/rtables/reference/rrowl.html","id":null,"dir":"Reference","previous_headings":"","what":"rrowl — rrowl","title":"rrowl — rrowl","text":"rrowl","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rrowl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"rrowl — rrowl","text":"","code":"rrowl(row.name, ..., format = NULL, indent = 0, inset = 0L)"},{"path":"https://insightsengineering.github.io/rtables/reference/rrowl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"rrowl — rrowl","text":"row.name NULL empty string used row.name rrow. ... values vector/list form format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. indent deprecated. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rrowl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"rrowl — rrowl","text":"row object context-appropriate type (label data)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/rrowl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"rrowl — rrowl","text":"","code":"rrowl(\"a\", c(1, 2, 3), format = \"xx\") #> [DataRow indent_mod 0]: a 1 2 3 rrowl(\"a\", c(1, 2, 3), c(4, 5, 6), format = \"xx\") #> [DataRow indent_mod 0]: a 1 2 3 4 5 6 rrowl(\"N\", table(iris$Species)) #> [DataRow indent_mod 0]: N 50 50 50 rrowl(\"N\", table(iris$Species), format = \"xx\") #> [DataRow indent_mod 0]: N 50 50 50 x <- tapply(iris$Sepal.Length, iris$Species, mean, simplify = FALSE) rrow(row.name = \"row 1\", x) #> [DataRow indent_mod 0]: row 1 5.006, 5.936, 6.588 rrow(\"ABC\", 2, 3) #> [DataRow indent_mod 0]: ABC 2 3 rrowl(row.name = \"row 1\", c(1, 2), c(3, 4)) #> [DataRow indent_mod 0]: row 1 1 2 3 4 rrow(row.name = \"row 2\", c(1, 2), c(3, 4)) #> [DataRow indent_mod 0]: row 2 1, 2 3, 4"},{"path":"https://insightsengineering.github.io/rtables/reference/rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Table — rtable","title":"Create a Table — rtable","text":"Create Table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Table — rtable","text":"","code":"rtable(header, ..., format = NULL, hsep = default_hsep(), inset = 0L) rtablel(header, ..., format = NULL, hsep = default_hsep(), inset = 0L)"},{"path":"https://insightsengineering.github.io/rtables/reference/rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Table — rtable","text":"header Information defining header (column structure) table. can row objects (legacy), character vectors InstantiatedColumnInfo object. ... Rows place table. format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Table — rtable","text":"formal table object appropriate type (ElementaryTable TableTree)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Table — rtable","text":"","code":"rtable( header = LETTERS[1:3], rrow(\"one to three\", 1, 2, 3), rrow(\"more stuff\", rcell(pi, format = \"xx.xx\"), \"test\", \"and more\") ) #> A B C #> ————————————————————————————————————— #> one to three 1 2 3 #> more stuff 3.14 test and more # Table with multirow header sel <- iris$Species == \"setosa\" mtbl <- rtable( header = rheader( rrow( row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2) ), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"All Species\", mean(iris$Sepal.Length), median(iris$Sepal.Length), mean(iris$Petal.Length), median(iris$Petal.Length), format = \"xx.xx\" ), rrow( row.name = \"Setosa\", mean(iris$Sepal.Length[sel]), median(iris$Sepal.Length[sel]), mean(iris$Petal.Length[sel]), median(iris$Petal.Length[sel]) ) ) mtbl #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.006 5 1.462 1.5 names(mtbl) # always first row of header #> [1] \"Sepal.Length\" \"Sepal.Length\" \"Petal.Length\" \"Petal.Length\" # Single row header tbl <- rtable( header = c(\"Treatement\\nN=100\", \"Comparison\\nN=300\"), format = \"xx (xx.xx%)\", rrow(\"A\", c(104, .2), c(100, .4)), rrow(\"B\", c(23, .4), c(43, .5)), rrow(\"\"), rrow(\"this is a very long section header\"), rrow(\"estimate\", rcell(55.23, \"xx.xx\", colspan = 2)), rrow(\"95% CI\", indent = 1, rcell(c(44.8, 67.4), format = \"(xx.x, xx.x)\", colspan = 2)) ) tbl #> Treatement Comparison #> N=100 N=300 #> ———————————————————————————————————————————————————————————————— #> A 104 (20.00%) 100 (40.00%) #> B 23 (40.00%) 43 (50.00%) #> #> this is a very long section header #> estimate 55.23 #> 95% CI (44.8, 67.4) row.names(tbl) #> [1] \"A\" \"B\" #> [3] \"\" \"this is a very long section header\" #> [5] \"estimate\" \"95% CI\" names(tbl) #> [1] \"Treatement\" \"Comparison\" # Subsetting tbl[1, ] #> Treatement Comparison #> N=100 N=300 #> ——————————————————————————————— #> A 104 (20.00%) 100 (40.00%) tbl[, 1] #> Treatement #> N=100 #> ————————————————————————————————————————————————— #> A 104 (20.00%) #> B 23 (40.00%) #> #> this is a very long section header #> estimate 55.23 #> 95% CI (44.8, 67.4) tbl[1, 2] #> Comparison #> N=300 #> ———————————————— #> A 100 (40.00%) tbl[2, 1] #> Treatement #> N=100 #> ——————————————— #> B 23 (40.00%) tbl[3, 2] #> Comparison #> N=300 #> ————————————— #> tbl[5, 1] #> Treatement #> N=100 #> ————————————————————— #> estimate 55.23 tbl[5, 2] #> Comparison #> N=300 #> ————————————————————— #> estimate 55.23 # # Data Structure methods dim(tbl) #> [1] 6 2 nrow(tbl) #> [1] 6 ncol(tbl) #> [1] 2 names(tbl) #> [1] \"Treatement\" \"Comparison\" # Colspans tbl2 <- rtable( c(\"A\", \"B\", \"C\", \"D\", \"E\"), format = \"xx\", rrow(\"r1\", 1, 2, 3, 4, 5), rrow(\"r2\", rcell(\"sp2\", colspan = 2), \"sp1\", rcell(\"sp2-2\", colspan = 2)) ) tbl2 #> A B C D E #> ———————————————————————————— #> r1 1 2 3 4 5 #> r2 sp2 sp1 sp2-2"},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":null,"dir":"Reference","previous_headings":"","what":"Default tabulation — simple_analysis","title":"Default tabulation — simple_analysis","text":"function used analyze invoked","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default tabulation — simple_analysis","text":"","code":"simple_analysis(x, ...) # S4 method for numeric simple_analysis(x, ...) # S4 method for logical simple_analysis(x, ...) # S4 method for factor simple_analysis(x, ...) # S4 method for ANY simple_analysis(x, ...)"},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default tabulation — simple_analysis","text":"x already split data tabulated particular cell/set cells ... passed directly","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default tabulation — simple_analysis","text":"RowsVerticalSection object (NULL). details object considered internal implementation detail.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Default tabulation — simple_analysis","text":"function following behavior given particular types inputs: numeric calls mean x logical calls sum x factor calls length x in_rows called resulting value(s). classes input currently lead error.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default tabulation — simple_analysis","text":"Gabriel Becker Adrian Waddell","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/rtinner.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Default tabulation — simple_analysis","text":"","code":"simple_analysis(1:3) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Mean 2.00 0 Mean simple_analysis(iris$Species) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 setosa 50 0 setosa #> 2 versicolor 50 0 versicolor #> 3 virginica 50 0 virginica simple_analysis(iris$Species == \"setosa\") #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Count 50 0 Count"},{"path":"https://insightsengineering.github.io/rtables/reference/sanitize_table_struct.html","id":null,"dir":"Reference","previous_headings":"","what":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"Experimental function correct structure degenerate tables adding messaging rows empty sub-structures.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sanitize_table_struct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"","code":"sanitize_table_struct(tt, empty_msg = \"-- This Section Contains No Data --\")"},{"path":"https://insightsengineering.github.io/rtables/reference/sanitize_table_struct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"tt TableTree empty_msg character(1). string spanned across inserted empty rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sanitize_table_struct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"tt already valid, returned unmodified. tt degenerate, modified, non-degenerate version table returned.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sanitize_table_struct.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"function locates degenerate portions table (including table overall case table data rows) inserts row spans columns message empty_msg one, generating table guaranteed non-degenerate.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sanitize_table_struct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"","code":"sanitize_table_struct(rtable(\"cool beans\")) #> cool beans #> —————————————————————————————————————— #> -- This Section Contains No Data -- lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() ## Degenerate because it doesn't have any analyze calls -> no data rows badtab <- build_table(lyt, DM) sanitize_table_struct(badtab) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————— #> F 70 (57.9%) 56 (52.8%) 61 (47.3%) #> -- This Section Contains No Data -- #> M 51 (42.1%) 50 (47.2%) 68 (52.7%) #> -- This Section Contains No Data -- #> U 0 (0.0%) 0 (0.0%) 0 (0.0%) #> -- This Section Contains No Data -- #> UNDIFFERENTIATED 0 (0.0%) 0 (0.0%) 0 (0.0%) #> -- This Section Contains No Data --"},{"path":"https://insightsengineering.github.io/rtables/reference/score_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Score functions for sorting TableTrees — cont_n_allcols","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"Score functions sorting TableTrees","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/score_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"","code":"cont_n_allcols(tt) cont_n_onecol(j)"},{"path":"https://insightsengineering.github.io/rtables/reference/score_funs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"tt TableTree (related class). TableTree object representing populated table. j numeric(1). Number column used scoring.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/score_funs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"single numeric value indicating score according relevant metric tt, used sorting.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/section_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Section dividers getter and setter — section_div","title":"Section dividers getter and setter — section_div","text":"section_div can used set get section divider table object produced build_table(). assigned post-processing (section_div<-) table can section divider every row, assigned independently. assigning layout creation, split_rows_by() (related row-wise splits) analyze() section_div parameter produce separators split sections data subgroups, respectively.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/section_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Section dividers getter and setter — section_div","text":"","code":"section_div(obj) # S4 method for VTableTree section_div(obj) # S4 method for list section_div(obj) # S4 method for TableRow section_div(obj) section_div(obj, only_sep_sections = FALSE) <- value # S4 method for VTableTree section_div(obj, only_sep_sections = FALSE) <- value # S4 method for list section_div(obj, only_sep_sections = FALSE) <- value # S4 method for TableRow section_div(obj, only_sep_sections = FALSE) <- value # S4 method for LabelRow section_div(obj, only_sep_sections = FALSE) <- value header_section_div(obj) # S4 method for PreDataTableLayouts header_section_div(obj) # S4 method for VTableTree header_section_div(obj) header_section_div(obj) <- value # S4 method for PreDataTableLayouts header_section_div(obj) <- value # S4 method for VTableTree header_section_div(obj) <- value top_level_section_div(obj) # S4 method for PreDataTableLayouts top_level_section_div(obj) top_level_section_div(obj) <- value # S4 method for PreDataTableLayouts top_level_section_div(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/section_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Section dividers getter and setter — section_div","text":"obj Table object. can class inherits VTableTree TableRow/LabelRow. only_sep_sections logical(1). Defaults FALSE section_div<-. Allows set section divider sections splits analyses number values less number rows table. TRUE, section divider set rows table. value character. Vector single characters use section dividers. character repeated section dividers span width table. character NA_character_ produce trailing separator row table. value length reflect number rows, 1 number splits/levels. See Details section information.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/section_div.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Section dividers getter and setter — section_div","text":"section divider string. line trailing separator NA_character_ section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/section_div.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Section dividers getter and setter — section_div","text":"Assigned value section divider must character vector. value NA_character_ section divider absent row section. want affect sections splits, please use only_sep_sections provide shorter vector number rows. Ideally, length vector less number splits , eventually, leaf-level, .e. DataRow analyze results . Note one value inserted, first split affected. only_sep_sections = TRUE, default section_div() produced table construction, section divider set splits eventually analyses, header row table. can set header_section_div basic_table() , eventually, hsep build_table(). FALSE, section divider set rows table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/section_div.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Section dividers getter and setter — section_div","text":"","code":"# Data df <- data.frame( cat = c( \"really long thing its so \", \"long\" ), value = c(6, 3, 10, 1) ) fast_afun <- function(x) list(\"m\" = rcell(mean(x), format = \"xx.\"), \"m/2\" = max(x) / 2) tbl <- basic_table() %>% split_rows_by(\"cat\", section_div = \"~\") %>% analyze(\"value\", afun = fast_afun, section_div = \" \") %>% build_table(df) # Getter section_div(tbl) #> [1] NA \" \" \"~\" NA \" \" \"~\" # Setter section_div(tbl) <- letters[seq_len(nrow(tbl))] tbl #> all obs #> ——————————————————————————————————— #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5 # last letter can appear if there is another table rbind(tbl, tbl) #> all obs #> ——————————————————————————————————— #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5 #> fffffffffffffffffffffffffffffffffff #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5 # header_section_div header_section_div(tbl) <- \"+\" tbl #> all obs #> ——————————————————————————————————— #> +++++++++++++++++++++++++++++++++++ #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5"},{"path":"https://insightsengineering.github.io/rtables/reference/sf_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Function Arg Conventions — sf_args","title":"Split Function Arg Conventions — sf_args","text":"Split Function Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sf_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Function Arg Conventions — sf_args","text":"","code":"sf_args(trim, label, first)"},{"path":"https://insightsengineering.github.io/rtables/reference/sf_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Function Arg Conventions — sf_args","text":"trim logical(1). splits corresponding 0 observations kept tabulating. label character(1). label (confused name) object/structure. first logical(1). created split level placed first levels (TRUE) last (FALSE, default).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sf_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Function Arg Conventions — sf_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/sort_at_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorting a Table at a Specific Path — sort_at_path","title":"Sorting a Table at a Specific Path — sort_at_path","text":"Main sorting function order substructure TableTree particular Path table tree.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sort_at_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorting a Table at a Specific Path — sort_at_path","text":"","code":"sort_at_path( tt, path, scorefun, decreasing = NA, na.pos = c(\"omit\", \"last\", \"first\"), .prev_path = character() )"},{"path":"https://insightsengineering.github.io/rtables/reference/sort_at_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorting a Table at a Specific Path — sort_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. scorefun function. Scoring function, accept type children directly position path (either VTableTree, VTableRow, VTableNodeInfo, covers ) return numeric value sorted. decreasing logical(1). scores generated scorefun sorted decreasing order. unset (default NA), set TRUE generated scores numeric FALSE characters. na.pos character(1). done children (sub-trees/rows) NA scores. Defaults \"omit\", removes , allowed values \"last\" \"first\" indicate placed order. .prev_path character. Internal detail, set manually.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sort_at_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorting a Table at a Specific Path — sort_at_path","text":"TableTree structure tt exception requested sorting done path.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/sort_at_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sorting a Table at a Specific Path — sort_at_path","text":"path can include \"wildcard\" \"*\" step, translates roughly node/branching element means child step separately sorted based scorefun remaining path entries. can occur multiple times path. Note sorting needs deeper understanding table structure rtables. Please consider reading related vignette (Sorting Pruning) explore table structure useful functions like table_structure() row_paths_summary(). also important understand difference \"content\" rows \"data\" rows. first one analyzes describes split variable generally generated summarize_row_groups(), second one commonly produced calling one various analyze() instances. Built-score functions cont_n_allcols() cont_n_onecol(). working content rows (coming summarize_row_groups()) custom score function needs used DataRows. , useful descriptor accessor functions (coming related vignette): cell_values() - Retrieves named list TableRow TableTree object's values. obj_name() - Retrieves name object. Note can differ label displayed () printing. obj_label() - Retrieves display label object. Note can differ name appears path. content_table() - Retrieves TableTree object's content table (contains summary rows). tree_children() - Retrieves TableTree object's direct children (either subtables, rows possibly mix thereof, though happen practice).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/sort_at_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sorting a Table at a Specific Path — sort_at_path","text":"","code":"# Creating a table to sort # Function that gives two statistics per table-tree \"leaf\" more_analysis_fnc <- function(x) { in_rows( \"median\" = median(x), \"mean\" = mean(x), .formats = \"xx.x\" ) } # Main layout of the table raw_lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by( \"RACE\", split_fun = drop_and_remove_levels(\"WHITE\") # dropping WHITE levels ) %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\", afun = more_analysis_fnc) # Creating the table and pruning empty and NAs tbl <- build_table(raw_lyt, DM) %>% prune_table() # Peek at the table structure to understand how it is built table_structure(tbl) #> [TableTree] RACE #> [TableTree] ASIAN [cont: 1 x 3] #> [TableTree] STRATA1 #> [TableTree] A [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] B [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] C [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 3] #> [TableTree] STRATA1 #> [TableTree] A [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] B [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] C [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) # Sorting only ASIAN sub-table, or, in other words, sorting STRATA elements for # the ASIAN group/row-split. This uses content_table() accessor function as it # is a \"ContentRow\". In this case, we also base our sorting only on the second column. sort_at_path(tbl, c(\"ASIAN\", \"STRATA1\"), cont_n_onecol(2)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> median 32.5 32.0 34.0 #> mean 34.1 31.6 34.7 #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> median 30.0 33.0 36.0 #> mean 32.2 33.9 36.8 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> median 36.5 34.0 33.0 #> mean 36.2 33.0 32.4 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> median 32.0 29.0 32.5 #> mean 31.5 28.6 33.6 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> median 33.0 30.0 33.5 #> mean 35.6 30.8 33.7 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> median 33.0 36.0 32.0 #> mean 35.5 34.2 35.0 # Custom scoring function that is working on \"DataRow\"s scorefun <- function(tt) { # Here we could use browser() sum(unlist(row_values(tt))) # Different accessor function } # Sorting mean and median for all the AGE leaves! sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> mean 32.2 33.9 36.8 #> median 30.0 33.0 36.0 #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> mean 34.1 31.6 34.7 #> median 32.5 32.0 34.0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> median 36.5 34.0 33.0 #> mean 36.2 33.0 32.4 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> mean 31.5 28.6 33.6 #> median 32.0 29.0 32.5 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> mean 35.6 30.8 33.7 #> median 33.0 30.0 33.5 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> mean 35.5 34.2 35.0 #> median 33.0 36.0 32.0"},{"path":"https://insightsengineering.github.io/rtables/reference/spl_context.html","id":null,"dir":"Reference","previous_headings":"","what":".spl_context within analysis and split functions — spl_context","title":".spl_context within analysis and split functions — spl_context","text":".spl_context optional parameter rtables' special functions, afun (analysis function analyze), cfun (content label function summarize_row_groups), split_fun (e.g. split_rows_by).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_context.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":".spl_context within analysis and split functions — spl_context","text":".spl_context data.frame gives information subsets data corresponding splits within-current analyze action nested. Taken together, correspond path resulting (set ) rows analysis function creating, although information slightly different form. split (correspond groups rows resulting table), well initial 'root' \"split\", represented via following columns: split name split (often variable split simple case) value string representation value split full_parent_df dataframe containing full data (.e. across columns) corresponding path defined combination split value row rows row all_cols_n number observations corresponding row grouping (union columns) (row-split analyze contexts ) <1 column column table structure list columns (named names(col_exprs(tab))) contain logical vectors corresponding subset row's full_parent_df corresponding column cur_col_id Identifier current column. may internal name, constructed pasting column path together cur_col_subset List column containing logical vectors indicating subset row's full_parent_df column currently created analysis function cur_col_expr List current column expression. may used filter .alt_df_row external data column. Filtering .alt_df_row columns produces .alt_df. cur_col_n integer column containing observation counts split cur_col_split Current column split names. recovered current column path cur_col_split_val Current column split values. recovered current column path note Within analysis functions accept .spl_context, all_cols_n cur_col_n columns dataframe contain 'true' observation counts corresponding row-group row-group x column subsets data. numbers , currently , reflect alternate column observation counts provided alt_counts_df, col_counts col_total arguments build_table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_context_to_disp_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"Translate spl_context Path display error messages","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_context_to_disp_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"","code":"spl_context_to_disp_path(ctx)"},{"path":"https://insightsengineering.github.io/rtables/reference/spl_context_to_disp_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"ctx data.frame. spl_context data.frame error occurred","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_context_to_disp_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"character string containing description row path corresponding ctx","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_variable.html","id":null,"dir":"Reference","previous_headings":"","what":"Variable Associated With a Split — spl_variable","title":"Variable Associated With a Split — spl_variable","text":"function intended use writing custom splitting logic. cases split associated single variable, name variable returned. time writing includes splits generated via split_rows_by, split_cols_by, split_rows_by_cuts, split_cols_by_cuts, split_rows_by_cutfun, split_cols_by_cutfun layout directives.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_variable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variable Associated With a Split — spl_variable","text":"","code":"spl_variable(spl) # S4 method for VarLevelSplit spl_variable(spl) # S4 method for VarDynCutSplit spl_variable(spl) # S4 method for VarStaticCutSplit spl_variable(spl) # S4 method for Split spl_variable(spl)"},{"path":"https://insightsengineering.github.io/rtables/reference/spl_variable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variable Associated With a Split — spl_variable","text":"spl Split. split object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/spl_variable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variable Associated With a Split — spl_variable","text":"splits single variable associated , split, others, error raised.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Declaring a column-split based on levels of a variable — split_cols_by","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"generate children subset categorical variable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"","code":"split_cols_by( lyt, var, labels_var = var, split_label = var, split_fun = NULL, format = NULL, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), ref_group = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"lyt layout object pre-data used tabulation var string, variable name labels_var string, name variable containing labels displayed values var split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). split_fun function/NULL. custom splitting function See custom_split_funs format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. ref_group character(1) NULL. Level var considered ref_group/reference","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> AGE #> Mean 33.77 35.43 35.43 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 # Let's look at the splits in more detail lyt1 <- basic_table() %>% split_cols_by(\"ARM\") lyt1 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> () #> # add an analysis (summary) lyt2 <- lyt1 %>% analyze(c(\"AGE\", \"COUNTRY\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) lyt2 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> AGE:COUNTRY (** multivar analysis **) #> tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> AGE #> Min. 20.00 21.00 22.00 #> 1st Qu. 29.00 29.00 30.00 #> Median 33.00 32.00 33.00 #> Mean 34.91 33.02 34.57 #> 3rd Qu. 39.00 37.00 38.00 #> Max. 60.00 55.00 53.00 #> COUNTRY #> CHN 62.00 48.00 69.00 #> USA 13.00 14.00 17.00 #> BRA 9.00 13.00 7.00 #> PAK 8.00 8.00 12.00 #> NGA 10.00 5.00 9.00 #> RUS 9.00 5.00 6.00 #> JPN 5.00 8.00 5.00 #> GBR 2.00 3.00 2.00 #> CAN 3.00 2.00 2.00 #> CHE 0.00 0.00 0.00 # By default sequentially adding layouts results in nesting library(dplyr) DM_MF <- DM %>% filter(SEX %in% c(\"M\", \"F\")) %>% mutate(SEX = droplevels(SEX)) lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% analyze(c(\"AGE\", \"COUNTRY\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) lyt3 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> SEX (lvls) #> #> Row-Split Structure: #> AGE:COUNTRY (** multivar analysis **) #> tbl3 <- build_table(lyt3, DM_MF) tbl3 #> A: Drug X B: Placebo C: Combination #> F M F M F M #> ————————————————————————————————————————————————————————————— #> AGE #> Min. 20.00 24.00 21.00 21.00 22.00 25.00 #> 1st Qu. 29.00 31.00 29.00 28.00 30.00 29.00 #> Median 32.00 35.00 33.00 31.00 35.00 32.00 #> Mean 33.71 36.55 33.84 32.10 34.89 34.28 #> 3rd Qu. 38.00 41.50 38.00 35.75 39.00 38.00 #> Max. 58.00 60.00 55.00 47.00 53.00 53.00 #> COUNTRY #> CHN 34.00 28.00 29.00 19.00 31.00 38.00 #> USA 8.00 5.00 6.00 8.00 10.00 7.00 #> BRA 6.00 3.00 6.00 7.00 3.00 4.00 #> PAK 2.00 6.00 5.00 3.00 5.00 7.00 #> NGA 6.00 4.00 2.00 3.00 5.00 4.00 #> RUS 7.00 2.00 1.00 4.00 2.00 4.00 #> JPN 2.00 3.00 3.00 5.00 4.00 1.00 #> GBR 2.00 0.00 3.00 0.00 1.00 1.00 #> CAN 3.00 0.00 1.00 1.00 0.00 2.00 #> CHE 0.00 0.00 0.00 0.00 0.00 0.00 # nested=TRUE vs not lyt4 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze(\"AGE\") lyt4 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> SEX (lvls) -> RACE (lvls) -> AGE (** analysis **) #> tbl4 <- build_table(lyt4, DM) tbl4 #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————————————— #> F #> ASIAN #> Mean 33.55 34.00 34.90 #> BLACK OR AFRICAN AMERICAN #> Mean 33.17 30.58 33.85 #> WHITE #> Mean 35.88 38.57 36.50 #> M #> ASIAN #> Mean 35.03 31.10 34.39 #> BLACK OR AFRICAN AMERICAN #> Mean 37.40 32.83 34.14 #> WHITE #> Mean 44.00 35.29 34.00 lyt5 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"AGE\") %>% split_rows_by(\"RACE\", nested = FALSE, split_fun = drop_split_levels) %>% analyze(\"AGE\") lyt5 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> SEX (lvls) -> AGE (** analysis **) #> RACE (lvls) -> AGE (** analysis **) #> tbl5 <- build_table(lyt5, DM) tbl5 #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89 #> M #> Mean 36.55 32.10 34.28 #> ASIAN #> Mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN #> Mean 34.68 31.71 34.00 #> WHITE #> Mean 39.36 36.93 35.11"},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by_multivar.html","id":null,"dir":"Reference","previous_headings":"","what":"Associate Multiple Variables with Columns — split_cols_by_multivar","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"cases, variable ultimately analyzed naturally defined column, row basis. need columns reflect different variables entirely, rather different levels single variable, use split_cols_by_multivar","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by_multivar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"","code":"split_cols_by_multivar( lyt, vars, split_fun = NULL, varlabels = vars, varnames = NULL, nested = TRUE, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by_multivar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"lyt layout object pre-data used tabulation vars character vector. Multiple variable names. split_fun function/NULL. custom splitting function See custom_split_funs varlabels character vector. Labels vars varnames character vector. Names vars appear pathing. vars unique variable names. , variable names suffixes necessary enforce uniqueness. nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by_multivar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by_multivar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_cols_by_multivar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"","code":"library(dplyr) ANL <- DM %>% mutate(value = rnorm(n()), pctdiff = runif(n())) ## toy example where we take the mean of the first variable and the ## count of >.5 for the second. colfuns <- list( function(x) in_rows(mean = mean(x), .formats = \"xx.x\"), function(x) in_rows(\"# x > 5\" = sum(x > .5), .formats = \"xx\") ) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"value\", \"pctdiff\")) %>% split_rows_by(\"RACE\", split_label = \"ethnicity\", split_fun = drop_split_levels ) %>% summarize_row_groups() %>% analyze_colvars(afun = colfuns) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> value:pctdiff (vars) #> #> Row-Split Structure: #> RACE (lvls) -> NA (** col-var analysis **) #> tbl <- build_table(lyt, ANL) tbl #> A: Drug X B: Placebo C: Combination #> value pctdiff value pctdiff value pctdiff #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 79 (65.3%) 68 (64.2%) 68 (64.2%) 84 (65.1%) 84 (65.1%) #> mean 0.0 39 -0.1 31 -0.0 33 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 28 (23.1%) 24 (22.6%) 24 (22.6%) 27 (20.9%) 27 (20.9%) #> mean 0.2 11 0.1 10 -0.3 10 #> WHITE 14 (11.6%) 14 (11.6%) 14 (13.2%) 14 (13.2%) 18 (14.0%) 18 (14.0%) #> mean 0.3 8 0.4 7 -0.2 9"},{"path":"https://insightsengineering.github.io/rtables/reference/split_funcs.html","id":null,"dir":"Reference","previous_headings":"","what":"Split functions — split_funcs","title":"Split functions — split_funcs","text":"Split functions","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_funcs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split functions — split_funcs","text":"","code":"remove_split_levels(excl) keep_split_levels(only, reorder = TRUE) drop_split_levels(df, spl, vals = NULL, labels = NULL, trim = FALSE) drop_and_remove_levels(excl) reorder_split_levels(neworder, newlabels = neworder, drlevels = TRUE) trim_levels_in_group(innervar, drop_outlevs = TRUE)"},{"path":"https://insightsengineering.github.io/rtables/reference/split_funcs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split functions — split_funcs","text":"excl character. Levels excluded (reflected resulting table structure regardless presence data). character. Levels retain (others dropped). reorder logical(1). order used order children split. defaults TRUE df dataset (data.frame tibble) spl Split object defining partitioning analysis/tabulation data. vals . internal use . labels character. Labels use remaining levels instead existing ones. trim logical(1). splits corresponding 0 observations kept tabulating. neworder character. New order factor levels. newlabels character. Labels (new order ) factor levels drlevels logical(1). levels data appear neworder dropped. Defaults TRUE innervar character(1). Variable whose factor levels trimmed (e.g., empty levels dropped) separately within grouping defined point structure drop_outlevs logical(1). empty levels variable split (.e. 'outer' variable, innervar) dropped? Defaults TRUE","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_funcs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split functions — split_funcs","text":"closure suitable use splitting function (splfun) creating table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_funcs.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Split functions — split_funcs","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_funcs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split functions — split_funcs","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = remove_split_levels(c( \"USA\", \"CAN\", \"CHE\", \"BRA\" )) ) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> CHN #> Mean 36.08 34.12 33.71 #> PAK #> Mean 35.38 33.12 36.75 #> NGA #> Mean 31.20 31.40 35.78 #> RUS #> Mean 33.33 34.20 33.00 #> JPN #> Mean 31.20 32.50 36.20 #> GBR #> Mean 32.00 29.00 30.00 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(c(\"USA\", \"CAN\", \"BRA\")) ) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> USA #> Mean 36.77 32.57 36.41 #> CAN #> Mean 36.00 34.00 29.50 #> BRA #> Mean 31.78 30.62 36.14 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89 #> M #> Mean 36.55 32.10 34.28 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_and_remove_levels(c(\"M\", \"U\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89"},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Rows according to levels of a variable — split_rows_by","title":"Add Rows according to levels of a variable — split_rows_by","text":"Add Rows according levels variable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Rows according to levels of a variable — split_rows_by","text":"","code":"split_rows_by( lyt, var, labels_var = var, split_label = var, split_fun = NULL, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), label_pos = \"hidden\", indent_mod = 0L, page_by = FALSE, page_prefix = split_label, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Rows according to levels of a variable — split_rows_by","text":"lyt layout object pre-data used tabulation var string, variable name labels_var string, name variable containing labels displayed values var split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). split_fun function/NULL. custom splitting function See custom_split_funs format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. page_by logical(1). pagination forced different children resulting form split. error rise selected split contain least one value NA. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Rows according to levels of a variable — split_rows_by","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add Rows according to levels of a variable — split_rows_by","text":"var factor empty unobserved levels labels_var specified, must also factor number levels var. Currently error occurs case informative, change future.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Add Rows according to levels of a variable — split_rows_by","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Rows according to levels of a variable — split_rows_by","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Rows according to levels of a variable — split_rows_by","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze(\"AGE\", mean, var_labels = \"Age\", format = \"xx.xx\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> ASIAN #> mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN #> mean 34.68 31.71 34.00 #> WHITE #> mean 39.36 36.93 35.11 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\") %>% analyze(\"AGE\", mean, var_labels = \"Age\", format = \"xx.xx\") tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————————————————————— #> ASIAN #> mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN #> mean 34.68 31.71 34.00 #> WHITE #> mean 39.36 36.93 35.11 #> AMERICAN INDIAN OR ALASKA NATIVE #> mean NA NA NA #> MULTIPLE #> mean NA NA NA #> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER #> mean NA NA NA #> OTHER #> mean NA NA NA #> UNKNOWN #> mean NA NA NA lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% summarize_row_groups(label_fstr = \"Overall (N)\") %>% split_rows_by(\"RACE\", split_label = \"Ethnicity\", labels_var = \"ethn_lab\", split_fun = drop_split_levels ) %>% summarize_row_groups(\"RACE\", label_fstr = \"%s (n)\") %>% analyze(\"AGE\", var_labels = \"Age\", afun = mean, format = \"xx.xx\") lyt3 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> SEX (lvls) #> #> Row-Split Structure: #> RACE (lvls) -> AGE (** analysis **) #> library(dplyr) DM2 <- DM %>% filter(SEX %in% c(\"M\", \"F\")) %>% mutate( SEX = droplevels(SEX), gender_lab = c( \"F\" = \"Female\", \"M\" = \"Male\", \"U\" = \"Unknown\", \"UNDIFFERENTIATED\" = \"Undifferentiated\" )[SEX], ethn_lab = c( \"ASIAN\" = \"Asian\", \"BLACK OR AFRICAN AMERICAN\" = \"Black or African American\", \"WHITE\" = \"White\", \"AMERICAN INDIAN OR ALASKA NATIVE\" = \"American Indian or Alaska Native\", \"MULTIPLE\" = \"Multiple\", \"NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\" = \"Native Hawaiian or Other Pacific Islander\", \"OTHER\" = \"Other\", \"UNKNOWN\" = \"Unknown\" )[RACE] ) tbl3 <- build_table(lyt3, DM2) tbl3 #> A: Drug X B: Placebo C: Combination #> F M F M F M #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Overall (N) 70 (100.0%) 51 (100.0%) 56 (100.0%) 50 (100.0%) 61 (100.0%) 68 (100.0%) #> Asian (n) 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) #> mean 33.55 35.03 34.00 31.10 34.90 34.39 #> Black or African American (n) 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) #> mean 33.17 37.40 30.58 32.83 33.85 34.14 #> White (n) 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) #> mean 35.88 44.00 38.57 35.29 36.50 34.00"},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by_multivar.html","id":null,"dir":"Reference","previous_headings":"","what":"Associate Multiple Variables with Rows — split_rows_by_multivar","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"need rows reflect different variables rather different levels single variable, use split_rows_by_multivar.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by_multivar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"","code":"split_rows_by_multivar( lyt, vars, split_fun = NULL, split_label = \"\", varlabels = vars, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), indent_mod = 0L, section_div = NA_character_, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by_multivar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"lyt layout object pre-data used tabulation vars character vector. Multiple variable names. split_fun function/NULL. custom splitting function See custom_split_funs split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). varlabels character vector. Labels vars format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by_multivar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/split_rows_by_multivar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by_multivar(c(\"SEX\", \"STRATA1\")) %>% summarize_row_groups() %>% analyze(c(\"AGE\", \"SEX\")) tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————————————— #> #> SEX 121 (100.0%) 106 (100.0%) 129 (100.0%) #> AGE #> Mean 34.91 33.02 34.57 #> SEX #> F 70 56 61 #> M 51 50 68 #> U 0 0 0 #> UNDIFFERENTIATED 0 0 0 #> STRATA1 121 (100.0%) 106 (100.0%) 129 (100.0%) #> AGE #> Mean 34.91 33.02 34.57 #> SEX #> F 70 56 61 #> M 51 50 68 #> U 0 0 0 #> UNDIFFERENTIATED 0 0 0"},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a content row of summary counts — summarize_row_groups","title":"Add a content row of summary counts — summarize_row_groups","text":"Add content row summary counts","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a content row of summary counts — summarize_row_groups","text":"","code":"summarize_row_groups( lyt, var = \"\", label_fstr = \"%s\", format = \"xx (xx.x%)\", na_str = \"-\", cfun = NULL, indent_mod = 0L, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a content row of summary counts — summarize_row_groups","text":"lyt layout object pre-data used tabulation var string, variable name label_fstr string. sprintf style format string containing. non-comparison splits, can contain one \"%s\" takes current split value generates row/column label. Comparison-based splits can contain two \"%s\". format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a content row of summary counts — summarize_row_groups","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a content row of summary counts — summarize_row_groups","text":"format expects 1 value (.e. specified format string xx appears two values (.e. xx appears twice format string) specified function, raw percent column total counts calculated. format format string xx appears one time, raw counts used. cfun must accept x df first argument. df argument cfun receive subset data.frame corresponding row- column-splitting cell calculated. Must accept labelstr second parameter, accepts label level parent split currently summarized. Can additionally take optional argument supported analysis functions. (see analyze). addition, complex custom functions needed, suggest checking available additional_fun_params apply afun.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add a content row of summary counts — summarize_row_groups","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_row_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a content row of summary counts — summarize_row_groups","text":"","code":"DM2 <- subset(DM, COUNTRY %in% c(\"USA\", \"CAN\", \"CHN\")) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = drop_split_levels) %>% summarize_row_groups(label_fstr = \"%s (n)\") %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> COUNTRY (lvls) -> AGE (** analysis **) #> tbl <- build_table(lyt, DM2) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————— #> CHN (n) 62 (79.5%) 48 (75.0%) 69 (78.4%) #> Min. 22.00 25.00 24.00 #> 1st Qu. 29.25 30.00 30.00 #> Median 34.00 33.50 33.00 #> Mean 36.08 34.12 33.71 #> 3rd Qu. 41.00 38.00 37.00 #> Max. 60.00 55.00 51.00 #> USA (n) 13 (16.7%) 14 (21.9%) 17 (19.3%) #> Min. 23.00 24.00 22.00 #> 1st Qu. 31.00 28.00 31.00 #> Median 36.00 30.00 37.00 #> Mean 36.77 32.57 36.41 #> 3rd Qu. 41.00 37.50 41.00 #> Max. 58.00 47.00 51.00 #> CAN (n) 3 (3.8%) 2 (3.1%) 2 (2.3%) #> Min. 29.00 30.00 28.00 #> 1st Qu. 32.50 32.00 28.75 #> Median 36.00 34.00 29.50 #> Mean 36.00 34.00 29.50 #> 3rd Qu. 39.50 36.00 30.25 #> Max. 43.00 38.00 31.00 row_paths_summary(tbl) # summary count is a content table #> rowname node_class path #> —————————————————————————————————————————————————————————— #> CHN (n) ContentRow COUNTRY, CHN, @content, CHN (n) #> Min. DataRow COUNTRY, CHN, AGE, Min. #> 1st Qu. DataRow COUNTRY, CHN, AGE, 1st Qu. #> Median DataRow COUNTRY, CHN, AGE, Median #> Mean DataRow COUNTRY, CHN, AGE, Mean #> 3rd Qu. DataRow COUNTRY, CHN, AGE, 3rd Qu. #> Max. DataRow COUNTRY, CHN, AGE, Max. #> USA (n) ContentRow COUNTRY, USA, @content, USA (n) #> Min. DataRow COUNTRY, USA, AGE, Min. #> 1st Qu. DataRow COUNTRY, USA, AGE, 1st Qu. #> Median DataRow COUNTRY, USA, AGE, Median #> Mean DataRow COUNTRY, USA, AGE, Mean #> 3rd Qu. DataRow COUNTRY, USA, AGE, 3rd Qu. #> Max. DataRow COUNTRY, USA, AGE, Max. #> CAN (n) ContentRow COUNTRY, CAN, @content, CAN (n) #> Min. DataRow COUNTRY, CAN, AGE, Min. #> 1st Qu. DataRow COUNTRY, CAN, AGE, 1st Qu. #> Median DataRow COUNTRY, CAN, AGE, Median #> Mean DataRow COUNTRY, CAN, AGE, Mean #> 3rd Qu. DataRow COUNTRY, CAN, AGE, 3rd Qu. #> Max. DataRow COUNTRY, CAN, AGE, Max. ## use a cfun and extra_args to customize summarization ## behavior sfun <- function(x, labelstr, trim) { in_rows( c(mean(x, trim = trim), trim), .formats = \"xx.x (xx.x%)\", .labels = sprintf( \"%s (Trimmed mean and trim %%)\", labelstr ) ) } lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = drop_split_levels) %>% summarize_row_groups(\"AGE\", cfun = sfun, extra_args = list(trim = .2) ) %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") %>% append_topleft(c(\"Country\", \" Age\")) tbl2 <- build_table(lyt2, DM2) tbl2 #> Country A: Drug X B: Placebo C: Combination #> Age (N=78) (N=64) (N=88) #> ———————————————————————————————————————————————————————————————————————————— #> CHN (Trimmed mean and trim %) 35.1 (20.0%) 33.4 (20.0%) 33.4 (20.0%) #> Min. 22.00 25.00 24.00 #> 1st Qu. 29.25 30.00 30.00 #> Median 34.00 33.50 33.00 #> Mean 36.08 34.12 33.71 #> 3rd Qu. 41.00 38.00 37.00 #> Max. 60.00 55.00 51.00 #> USA (Trimmed mean and trim %) 36.1 (20.0%) 31.9 (20.0%) 36.1 (20.0%) #> Min. 23.00 24.00 22.00 #> 1st Qu. 31.00 28.00 31.00 #> Median 36.00 30.00 37.00 #> Mean 36.77 32.57 36.41 #> 3rd Qu. 41.00 37.50 41.00 #> Max. 58.00 47.00 51.00 #> CAN (Trimmed mean and trim %) 36.0 (20.0%) 34.0 (20.0%) 29.5 (20.0%) #> Min. 29.00 30.00 28.00 #> 1st Qu. 32.50 32.00 28.75 #> Median 36.00 34.00 29.50 #> Mean 36.00 34.00 29.50 #> 3rd Qu. 39.50 36.00 30.25 #> Max. 43.00 38.00 31.00"},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"summarize_rows — summarize_rows","title":"summarize_rows — summarize_rows","text":"summarize_rows deprecated favor make_row_df.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"summarize_rows — summarize_rows","text":"","code":"summarize_rows(obj)"},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"summarize_rows — summarize_rows","text":"obj VTableTree.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/summarize_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"summarize_rows — summarize_rows","text":"data.frame summarizing rows obj.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tabclasses.html","id":null,"dir":"Reference","previous_headings":"","what":"TableTree classes — ElementaryTable-class","title":"TableTree classes — ElementaryTable-class","text":"TableTree classes Table Constructors Classes","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tabclasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TableTree classes — ElementaryTable-class","text":"","code":"ElementaryTable( kids = list(), name = \"\", lev = 1L, label = \"\", labelrow = LabelRow(lev = lev, label = label, vis = !isTRUE(iscontent) && !is.na(label) && nzchar(label)), rspans = data.frame(), cinfo = NULL, iscontent = NA, var = NA_character_, format = NULL, na_str = NA_character_, indent_mod = 0L, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), header_section_div = NA_character_, hsep = default_hsep(), trailing_section_div = NA_character_, inset = 0L ) TableTree( kids = list(), name = if (!is.na(var)) var else \"\", cont = EmptyElTable, lev = 1L, label = name, labelrow = LabelRow(lev = lev, label = label, vis = nrow(cont) == 0 && !is.na(label) && nzchar(label)), rspans = data.frame(), iscontent = NA, var = NA_character_, cinfo = NULL, format = NULL, na_str = NA_character_, indent_mod = 0L, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), page_title = NA_character_, hsep = default_hsep(), header_section_div = NA_character_, trailing_section_div = NA_character_, inset = 0L )"},{"path":"https://insightsengineering.github.io/rtables/reference/tabclasses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TableTree classes — ElementaryTable-class","text":"kids list. List direct children. name character(1). Name split/table/row created. Defaults corresponding label, required . lev integer. Nesting level (roughly, indentation level practical terms). label character(1). label (confused name) object/structure. labelrow LabelRow. LabelRow object assign Table. Constructed label default specified. rspans data.frame. Currently stored otherwise ignored. cinfo InstantiatedColumnInfo (NULL). Column structure object created. iscontent logical. TableTree/ElementaryTable constructed content table another TableTree. var string, variable name format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. header_section_div character(1). String used divide header table. See header_section_div() getter setter . Please consider changing last element section_div() concatenating tables need divider . hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. trailing_section_div character(1). String used section divider printing last row contained (sub)-table, unless row also last table row printed overall, NA_character_ none (default). generated via layouting, correspond section_div split table represents single facet. inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). cont ElementaryTable. Content table. page_title character. Page specific title(s).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tabclasses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TableTree classes — ElementaryTable-class","text":"formal object representing populated table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tabclasses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"TableTree classes — ElementaryTable-class","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/table_shell.html","id":null,"dir":"Reference","previous_headings":"","what":"Table shells — table_shell","title":"Table shells — table_shell","text":"table shell rendering table maintains structure, display values, rather displaying formatting instructions cell.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/table_shell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table shells — table_shell","text":"","code":"table_shell( tt, widths = NULL, col_gap = 3, hsep = default_hsep(), tf_wrap = FALSE, max_width = NULL ) table_shell_str( tt, widths = NULL, col_gap = 3, hsep = default_hsep(), tf_wrap = FALSE, max_width = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/table_shell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table shells — table_shell","text":"tt TableTree (related class). TableTree object representing populated table. widths numeric ( NULL). (proposed) widths columns x. expected length numeric vector can retrieved ncol() + 1 column row names must also considered. col_gap numeric(1). Space (characters) columns hsep character(1). Characters repeat create header/body separator line. NULL, object value used. \" \", empty separator printed. Check default_hsep() information. tf_wrap logical(1). texts title, subtitle, footnotes wrapped? max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/table_shell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table shells — table_shell","text":"table_shell_str string representing table shell, table_shell, NULL, function called side effect printing shell console.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/table_shell.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table shells — table_shell","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\") tbl <- build_table(lyt, iris2) table_shell(tbl) #> setosa versicolor virginica #> a b a b a b #> ———————————————————————————————————————————————————————————— #> Sepal.Length - - - - - - #> Min. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 1st Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Median xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Mean xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 3rd Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Max. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Petal.Width - - - - - - #> Min. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 1st Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Median xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Mean xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 3rd Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Max. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx"},{"path":"https://insightsengineering.github.io/rtables/reference/table_structure.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Table — table_structure","title":"Summarize Table — table_structure","text":"Summarize Table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/table_structure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize Table — table_structure","text":"","code":"table_structure(x, detail = c(\"subtable\", \"row\"))"},{"path":"https://insightsengineering.github.io/rtables/reference/table_structure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize Table — table_structure","text":"x table object detail either row subtable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/table_structure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize Table — table_structure","text":"currently return value. Called side-effect printing row- subtable-structure summary x.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/table_structure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize Table — table_structure","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) tbl <- build_table(lyt, iris2) tbl #> setosa versicolor virginica #> a b a b a b #> ————————————————————————————————————————————————————————— #> Sepal.Length #> Min. 4.40 4.30 5.00 4.90 4.90 5.60 #> 1st Qu. 4.80 4.80 5.60 5.60 6.20 6.30 #> Median 5.00 5.00 5.90 5.90 6.50 6.50 #> Mean 5.02 4.99 5.99 5.88 6.50 6.67 #> 3rd Qu. 5.30 5.10 6.40 6.10 6.70 7.20 #> Max. 5.80 5.70 7.00 6.70 7.70 7.90 #> Petal.Width #> Min. 0.10 0.10 1.00 1.00 1.40 1.50 #> 1st Qu. 0.20 0.20 1.20 1.20 1.90 1.80 #> Median 0.20 0.20 1.30 1.30 2.10 2.00 #> Mean 0.23 0.26 1.35 1.30 2.08 1.98 #> 3rd Qu. 0.20 0.30 1.50 1.40 2.30 2.20 #> Max. 0.40 0.60 1.80 1.70 2.50 2.50 row_paths(tbl) #> [[1]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> #> [[2]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Min.\" #> #> [[3]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"1st Qu.\" #> #> [[4]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Median\" #> #> [[5]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Mean\" #> #> [[6]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"3rd Qu.\" #> #> [[7]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Max.\" #> #> [[8]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> #> [[9]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Min.\" #> #> [[10]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"1st Qu.\" #> #> [[11]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Median\" #> #> [[12]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Mean\" #> #> [[13]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"3rd Qu.\" #> #> [[14]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Max.\" #> table_structure(tbl) #> [TableTree] ma_Sepal.Length_Petal.Width #> [ElementaryTable] Sepal.Length (6 x 6) #> [ElementaryTable] Petal.Width (6 x 6) table_structure(tbl, detail = \"row\") #> TableTree: [ma_Sepal.Length_Petal.Width] () #> labelrow: [] () - #> children: #> ElementaryTable: [Sepal.Length] (Sepal.Length) #> labelrow: [Sepal.Length] (Sepal.Length) #> children: #> DataRow: [Min.] (Min.) #> DataRow: [1st Qu.] (1st Qu.) #> DataRow: [Median] (Median) #> DataRow: [Mean] (Mean) #> DataRow: [3rd Qu.] (3rd Qu.) #> DataRow: [Max.] (Max.) #> ElementaryTable: [Petal.Width] (Petal.Width) #> labelrow: [Petal.Width] (Petal.Width) #> children: #> DataRow: [Min.] (Min.) #> DataRow: [1st Qu.] (1st Qu.) #> DataRow: [Median] (Median) #> DataRow: [Mean] (Mean) #> DataRow: [3rd Qu.] (3rd Qu.) #> DataRow: [Max.] (Max.)"},{"path":"https://insightsengineering.github.io/rtables/reference/top_left.html","id":null,"dir":"Reference","previous_headings":"","what":"Top Left Material (Experimental) — top_left","title":"Top Left Material (Experimental) — top_left","text":"TableTree object can top left material sequence strings printed area table column header display label first row. functions access modify material.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/top_left.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Top Left Material (Experimental) — top_left","text":"","code":"top_left(obj) # S4 method for VTableTree top_left(obj) # S4 method for InstantiatedColumnInfo top_left(obj) # S4 method for PreDataTableLayouts top_left(obj) top_left(obj) <- value # S4 method for VTableTree top_left(obj) <- value # S4 method for InstantiatedColumnInfo top_left(obj) <- value # S4 method for PreDataTableLayouts top_left(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/top_left.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Top Left Material (Experimental) — top_left","text":"obj . object accessor access modify value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/top_left.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Top Left Material (Experimental) — top_left","text":"character vector representing top-left material obj (obj modification, case setter).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tostring.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an rtable object to a string — tostring","title":"Convert an rtable object to a string — tostring","text":"Transform complex object string representation ready printed written plain-text file objects printed console pass toString. function allows fundamental formatting specifications final output, like column widths relative wrapping (width), title footer wrapping (tf_wrap = TRUE max_width), horizontal separator character (e.g. hsep = \"+\").","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tostring.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an rtable object to a string — tostring","text":"","code":"# S4 method for VTableTree toString( x, widths = NULL, col_gap = 3, hsep = horizontal_sep(x), indent_size = 2, tf_wrap = FALSE, max_width = NULL )"},{"path":"https://insightsengineering.github.io/rtables/reference/tostring.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an rtable object to a string — tostring","text":"x . Object prepared rendering. widths numeric ( NULL). (proposed) widths columns x. expected length numeric vector can retrieved ncol() + 1 column row names must also considered. col_gap numeric(1). Space (characters) columns hsep character(1). Characters repeat create header/body separator line. NULL, object value used. \" \", empty separator printed. Check default_hsep() information. indent_size numeric(1). Number spaces use per indent level. Defaults 2 tf_wrap logical(1). texts title, subtitle, footnotes wrapped? max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tostring.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an rtable object to a string — tostring","text":"string representation x appears printed.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tostring.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert an rtable object to a string — tostring","text":"Manual insertion newlines supported tf_wrap result warning undefined wrapping behavior. Passing vectors already split strings remains supported, however case string word-wrapped separately behavior described .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/tostring.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an rtable object to a string — tostring","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\") tbl <- build_table(lyt, iris2) cat(toString(tbl, col_gap = 3)) #> setosa versicolor virginica #> a b a b a b #> ————————————————————————————————————————————————————————— #> Sepal.Length #> Min. 4.40 4.30 5.00 4.90 4.90 5.60 #> 1st Qu. 4.80 4.80 5.60 5.60 6.20 6.30 #> Median 5.00 5.00 5.90 5.90 6.50 6.50 #> Mean 5.02 4.99 5.99 5.88 6.50 6.67 #> 3rd Qu. 5.30 5.10 6.40 6.10 6.70 7.20 #> Max. 5.80 5.70 7.00 6.70 7.70 7.90 #> Petal.Width #> Min. 0.10 0.10 1.00 1.00 1.40 1.50 #> 1st Qu. 0.20 0.20 1.20 1.20 1.90 1.80 #> Median 0.20 0.20 1.30 1.30 2.10 2.00 #> Mean 0.23 0.26 1.35 1.30 2.08 1.98 #> 3rd Qu. 0.20 0.30 1.50 1.40 2.30 2.20 #> Max. 0.40 0.60 1.80 1.70 2.50 2.50"},{"path":"https://insightsengineering.github.io/rtables/reference/tree_children.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve or set the direct children of a Tree-style object — tree_children","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"Retrieve set direct children Tree-style object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tree_children.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"","code":"tree_children(x) tree_children(x) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/tree_children.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"x object Tree structure value New list children.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tree_children.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"List direct children x","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_in_facets.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"Trim Levels Another Variable Facet (Postprocessing split step)","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_in_facets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"","code":"trim_levels_in_facets(innervar)"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_in_facets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"innervar character. variable(s) trim (remove unobserved levels) independently within facet.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_in_facets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"function suitable use pre (list) argument make_split_fun","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_to_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim Levels to map — trim_levels_to_map","title":"Trim Levels to map — trim_levels_to_map","text":"split function constructor creates split function trims levels variable reflect restrictions possible combinations two variables split (along axis) within layout.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_to_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim Levels to map — trim_levels_to_map","text":"","code":"trim_levels_to_map(map = NULL)"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_to_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim Levels to map — trim_levels_to_map","text":"map data.frame. data.frame defining allowed combinations variables. combination level split present map removed data, variable split present data associated split parents .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_to_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim Levels to map — trim_levels_to_map","text":"fun","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_to_map.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trim Levels to map — trim_levels_to_map","text":"splitting occurs, map subset values previously performed splits. levels variable split pruned still present within subset map representing current hierarchical splitting context. Splitting performed via keep_split_levels split function. resulting element partition trimmed pruning values remaining variables specified map values allowed combination previous current split.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/trim_levels_to_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trim Levels to map — trim_levels_to_map","text":"","code":"map <- data.frame( LBCAT = c(\"CHEMISTRY\", \"CHEMISTRY\", \"CHEMISTRY\", \"IMMUNOLOGY\"), PARAMCD = c(\"ALT\", \"CRP\", \"CRP\", \"IGA\"), ANRIND = c(\"LOW\", \"LOW\", \"HIGH\", \"HIGH\"), stringsAsFactors = FALSE ) lyt <- basic_table() %>% split_rows_by(\"LBCAT\") %>% split_rows_by(\"PARAMCD\", split_fun = trim_levels_to_map(map = map)) %>% analyze(\"ANRIND\") tbl <- build_table(lyt, ex_adlb)"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_prune_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimming and Pruning Criteria — all_zero_or_na","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"Criteria functions (constructors thereof) trimming pruning tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_prune_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"","code":"all_zero_or_na(tr) all_zero(tr) content_all_zeros_nas(tt, criteria = all_zero_or_na) prune_empty_level(tt) prune_zeros_only(tt) low_obs_pruner(min, type = c(\"sum\", \"mean\"))"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_prune_funs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"tr TableRow (related class). TableRow object representing single row within populated table. tt TableTree (related class). TableTree object representing populated table. criteria function. Function takes TableRow object returns TRUE row removed. Defaults all_zero_or_na min numeric(1). (low_obs_pruner ). Minimum aggregate count value. Subtables whose combined/average count threshold pruned type character(1). count values aggregated. Must \"sum\" (default) \"mean\"","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_prune_funs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"logical value indicating whether tr included (TRUE) pruned (FALSE) pruning.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_prune_funs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"all_zero_or_na returns TRUE (thus indicates trimming/pruning) non-LabelRow TableRow contain mix NA (including NaN), 0, Inf -Inf values. all_zero returns TRUE non-Label row contains (non-missing) zero values. content_all_zeros_nas Prunes subtable ) content table exactly one row , b) all_zero_or_na returns TRUE single content row. practice, default summary/content function used, represents pruning subtable corresponds empty set input data (e.g., factor variable used split_rows_by levels present data). prune_empty_level combines all_zero_or_na behavior TableRow objects, content_all_zeros_nas content_table(tt) TableTree objects, additional check returns TRUE tt children. prune_zeros_only behaves prune_empty_level , except like all_zero prunes case non-missing zero values. low_obs_pruner constructor function , called, returns pruning criteria function prune content rows comparing sum mean (dictated type) count portions cell values (defined first value per cell regardless many values per cell ) min.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/trim_prune_funs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"","code":"adsl <- ex_adsl levels(adsl$SEX) <- c(levels(ex_adsl$SEX), \"OTHER\") adsl$AGE[adsl$SEX == \"UNDIFFERENTIATED\"] <- 0 adsl$BMRKR1 <- 0 tbl_to_prune <- basic_table() %>% analyze(\"BMRKR1\") %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") %>% build_table(adsl) tbl_to_prune %>% prune_table(all_zero_or_na) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 tbl_to_prune %>% prune_table(all_zero) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 0.00 #> B 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 0.00 NA 0.00 #> OTHER 0 (0.0%) 0 (0.0%) 0 (0.0%) #> A 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> B 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> C 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA tbl_to_prune %>% prune_table(content_all_zeros_nas) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> Mean 0.00 0.00 0.00 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 0.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 0.00 NA 0.00 tbl_to_prune %>% prune_table(prune_empty_level) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 tbl_to_prune %>% prune_table(prune_zeros_only) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 0.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 0.00 NA 0.00 min_prune <- low_obs_pruner(70, \"sum\") tbl_to_prune %>% prune_table(min_prune) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> Mean 0.00 0.00 0.00 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim rows from a populated table without regard for table structure — trim_rows","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"Trim rows populated table without regard table structure","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"","code":"trim_rows(tt, criteria = all_zero_or_na)"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"tt TableTree (related class). TableTree object representing populated table. criteria function. Function takes TableRow object returns TRUE row removed. Defaults all_zero_or_na","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"table rows NA 0 cell values removed","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"function deprecated future favor elegant versatile prune_table() function can perform function trim_rows() powerful takes table structure account.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"Visible LabelRows including trimming, can lead either label rows trimmed label rows remaining data rows trimmed, depending criteria returns called LabelRow object. avoid , use structurally-aware prune_table machinery instead.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/trim_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"","code":"adsl <- ex_adsl levels(adsl$SEX) <- c(levels(ex_adsl$SEX), \"OTHER\") tbl_to_trim <- basic_table() %>% analyze(\"BMRKR1\") %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") %>% build_table(adsl) tbl_to_trim %>% trim_rows() #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> Mean 5.97 5.70 5.62 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 44.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 28.00 NA 46.00 tbl_to_trim %>% trim_rows(all_zero) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> Mean 5.97 5.70 5.62 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 44.00 #> Mean NA NA NA #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 28.00 NA 46.00 #> Mean NA NA NA #> Mean NA NA NA #> Mean NA NA NA"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_zero_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim Zero Rows — trim_zero_rows","title":"Trim Zero Rows — trim_zero_rows","text":"Trim Zero Rows","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_zero_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim Zero Rows — trim_zero_rows","text":"","code":"trim_zero_rows(tbl)"},{"path":"https://insightsengineering.github.io/rtables/reference/trim_zero_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim Zero Rows — trim_zero_rows","text":"tbl table object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/trim_zero_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim Zero Rows — trim_zero_rows","text":"rtable object","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tsv_io.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Enriched flat value table with paths — export_as_tsv","title":"Create Enriched flat value table with paths — export_as_tsv","text":"function creates flat tabular file cell values corresponding paths via path_enriched_df(). writes data.frame tsv file. default (.e. value_func specified, List columns least one value length > 1 collapsed character vectors collapsing list element \"|\".","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tsv_io.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Enriched flat value table with paths — export_as_tsv","text":"","code":"export_as_tsv( tt, file = NULL, path_fun = collapse_path, value_fun = collapse_values ) import_from_tsv(file)"},{"path":"https://insightsengineering.github.io/rtables/reference/tsv_io.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Enriched flat value table with paths — export_as_tsv","text":"tt TableTree (related class). TableTree object representing populated table. file character(1). path file written read . path_fun function. Function transform paths single-string row/column names. value_fun function. Function transform cell values cells data.frame. Defaults collapse_values creates strings multi-valued cells collapsed together, separated |.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tsv_io.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Enriched flat value table with paths — export_as_tsv","text":"NULL silently export_as_tsv, data.frame re-constituted list values export_as_tsv.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tsv_io.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create Enriched flat value table with paths — export_as_tsv","text":"currently round-trip capability type export. can read values exported way back via import_from_tsv receive data.frame version back, TableTree.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/tt_to_flextable.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a FlexTable from an rtables table — tt_to_flextable","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"Principally used export (export_as_docx()), function produces flextable rtables table. theme = NULL, rtables-like style used. Otherwise, theme_docx_default() produce .docx-friendly table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tt_to_flextable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"","code":"tt_to_flextable( tt, theme = theme_docx_default(tt), border = flextable::fp_border_default(width = 0.5), indent_size = NULL, titles_as_header = TRUE, footers_as_text = FALSE, counts_in_newline = FALSE, paginate = FALSE, lpp = NULL, cpp = NULL, ..., colwidths = propose_column_widths(matrix_form(tt, indent_rownames = TRUE)), tf_wrap = !is.null(cpp), max_width = cpp, total_width = 10 ) theme_docx_default( tt = NULL, font = \"Arial\", font_size = 9, bold = c(\"header\", \"content_rows\", \"label_rows\"), bold_manual = NULL, border = flextable::fp_border_default(width = 0.5) )"},{"path":"https://insightsengineering.github.io/rtables/reference/tt_to_flextable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"tt TableTree (related class). TableTree object representing populated table. theme function(1). Defaults theme_docx_default(tt). expects theme function designed internally function flextable object changes layout style. set NULL, produce table similar rtables default. border officer border object. Defaults officer::fp_border(width = 0.5). indent_size integer(1). NULL, default indent size table (see matrix_form() indent_size) used. work docx, size multiplied 2 mm (5.67 pt) default. titles_as_header logical(1). Defaults TRUE tt_to_flextable(), table self-contained makes additional header rows main_title() string subtitles() character vector (one per element). FALSE suggested export_as_docx(). adds titles subtitles text paragraph table. style applied. footers_as_text logical(1). Defaults FALSE tt_to_flextable(), table self-contained flextable definition footnotes. TRUE used export_as_docx() add footers new paragraph table. style applied, smaller font. counts_in_newline logical(1). Defaults FALSE. rtables text printing (formatters::toString()), column counts, .e. (N=xx), always new line. noticed docx exports necessary line. paginate logical(1). need .docx export use export_as_docx, suggest relying word pagination system. Cooperation two mechanisms guaranteed. option splits tt different \"pages\" multiple flextables. Defaults FALSE. lpp numeric. Maximum lines per page including (re)printed header context rows cpp numeric(1) NULL. Width (characters) pages horizontal pagination. NA (default) indicates cpp inferred page size; NULL indicates horizontal pagination done regardless page size. ... Passed methods tabulation functions. colwidths numeric vector. Column widths use vertical pagination. tf_wrap logical(1). texts title, subtitle, footnotes wrapped? max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE. total_width numeric(1). Total width inches resulting flextable(s). Defaults 10. font character(1). Defaults \"Arial\". font vailable, flextable default used. font_size integer(1). Positive integerish value defaults 9. bold character vector. can combination c(\"header\", \"content_rows\", \"label_rows\"). first one renders column names bold (topleft content). Second third option use make_row_df() render content /label rows bold. bold_manual named list. List indexes lists. See example needed structure. Accepted groupings/names c(\"header\", \"body\").","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tt_to_flextable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"flextable object.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/tt_to_flextable.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"theme_docx_default(): main theme function export_as_docx()","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/tt_to_flextable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"Title says Whaaaat Oh, ok. : Drug X B: Placebo C: Combination row1 {1} 5 5 5 row2 1, 2 {2} 1, 2 {2} 1, 2 {2} {1} - row 1 - row footnote {2} - row 2 - cell footnote ha HA! Footer! Title says Whaaaat Oh, ok. : Drug X B: Placebo C: Combination row1 {1} 5 5 5 row2 1, 2 {2} 1, 2 {2} 1, 2 {2} {1} - row 1 - row footnote {2} - row 2 - cell footnote ha HA! Footer! Title says Whaaaat Oh, ok. : Drug X B: Placebo C: Combination row1 {1} 5 5 5 row2 1, 2 {2} 1, 2 {2} 1, 2 {2} {1} - row 1 - row footnote {2} - row 2 - cell footnote ha HA! Footer!","code":"analysisfun <- function(x, ...) { in_rows( row1 = 5, row2 = c(1, 2), .row_footnotes = list(row1 = \"row 1 - row footnote\"), .cell_footnotes = list(row2 = \"row 2 - cell footnote\") ) } lyt <- basic_table( title = \"Title says Whaaaat\", subtitles = \"Oh, ok.\", main_footer = \"ha HA! Footer!\" ) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = analysisfun) tbl <- build_table(lyt, ex_adsl) # rtables style tt_to_flextable(tbl, theme = NULL) .cl-17070c7a{table-layout:auto;}.cl-16ff1970{font-family:'DejaVu Sans';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1702b102{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1702b10c{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1702b116{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:0.3pt;padding-right:0.3pt;line-height: 1;background-color:transparent;}.cl-1702c9b2{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9bc{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9c6{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9c7{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9d0{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9d1{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9d2{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9da{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9db{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9dc{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9e4{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9e5{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9ee{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9ef{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9f8{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9f9{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9fa{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca02{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca03{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca0c{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca0d{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca0e{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca16{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca17{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}Title says WhaaaatOh, ok.A: Drug XB: PlaceboC: Combinationrow1 {1}555row21, 2 {2}1, 2 {2}1, 2 {2}{1} - row 1 - row footnote{2} - row 2 - cell footnoteha HA! Footer! tt_to_flextable(tbl, theme = theme_docx_default(tbl, font_size = 7)) .cl-17476806{table-layout:auto;}.cl-1740ed8c{font-family:'Arial';font-size:7pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1740ed96{font-family:'Arial';font-size:7pt;font-weight:bold;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1740eda0{font-family:'Arial';font-size:6pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1743ccaa{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1743ccb4{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:3pt;padding-top:0;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1743ccbe{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:1pt;padding-top:1pt;padding-left:0.3pt;padding-right:0.3pt;line-height: 1;background-color:transparent;}.cl-1743ccbf{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1743defc{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df06{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df07{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df10{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df1a{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df1b{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df24{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df25{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df2e{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0.5pt solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df2f{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df38{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df39{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0.5pt solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df42{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.5pt solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df43{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df4c{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df4d{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0.5pt solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df4e{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.5pt solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df56{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df60{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df61{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0.5pt solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df6a{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df6b{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df74{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df75{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}Title says WhaaaatOh, ok.A: Drug XB: PlaceboC: Combinationrow1 {1}555row21, 2 {2}1, 2 {2}1, 2 {2}{1} - row 1 - row footnote{2} - row 2 - cell footnoteha HA! Footer! # Custom theme special_bold <- list( \"header\" = list(\"i\" = 1, \"j\" = c(1, 3)), \"body\" = list(\"i\" = c(1, 2), \"j\" = 1) ) custom_theme <- theme_docx_default(tbl, font_size = 10, font = \"Brush Script MT\", border = flextable::fp_border_default(color = \"pink\", width = 2), bold = NULL, bold_manual = special_bold ) tt_to_flextable(tbl, border = flextable::fp_border_default(color = \"pink\", width = 2), theme = custom_theme ) .cl-177f04dc{table-layout:auto;}.cl-1776dad2{font-family:'Brush Script MT';font-size:10pt;font-weight:bold;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1776dae6{font-family:'Brush Script MT';font-size:10pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1776daf0{font-family:'Brush Script MT';font-size:9pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa54b{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa54c{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa554{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa555{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}Title says WhaaaatOh, ok.A: Drug XB: PlaceboC: Combinationrow1 {1}555row21, 2 {2}1, 2 {2}1, 2 {2}{1} - row 1 - row footnote{2} - row 2 - cell footnoteha HA! Footer!"},{"path":"https://insightsengineering.github.io/rtables/reference/ttap.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or set table elements at specified path — tt_at_path","title":"Get or set table elements at specified path — tt_at_path","text":"Get set table elements specified path","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ttap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or set table elements at specified path — tt_at_path","text":"","code":"tt_at_path(tt, path, ...) tt_at_path(tt, path, ...) <- value"},{"path":"https://insightsengineering.github.io/rtables/reference/ttap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or set table elements at specified path — tt_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. ... unused. value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ttap.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get or set table elements at specified path — tt_at_path","text":"Setting NULL defined path removes corresponding sub table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/ttap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get or set table elements at specified path — tt_at_path","text":"","code":"# Accessing sub table. lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% split_rows_by(\"BMRKR2\") %>% analyze(\"AGE\") tbl <- build_table(lyt, ex_adsl) %>% prune_table() sub_tbl <- tt_at_path(tbl, path = c(\"SEX\", \"F\", \"BMRKR2\")) # Removing sub table. tbl2 <- tbl tt_at_path(tbl2, path = c(\"SEX\", \"F\")) <- NULL tbl2 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> M #> LOW #> Mean 34.43 37.13 32.73 #> MEDIUM #> Mean 37.67 38.78 34.35 #> HIGH #> Mean 35.07 36.21 37.42 #> U #> LOW #> Mean 31.00 27.00 37.00 #> MEDIUM #> Mean 33.00 NA 33.00 #> HIGH #> Mean NA 35.00 38.00 #> UNDIFFERENTIATED #> LOW #> Mean 28.00 NA 45.00 # Setting sub table. lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(\"BMRKR2\") tbl3 <- build_table(lyt3, ex_adsl) %>% prune_table() tt_at_path(tbl3, path = c(\"SEX\", \"F\", \"BMRKR2\")) <- sub_tbl tbl3 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> LOW #> Mean 32.19 34.05 33.73 #> MEDIUM #> Mean 32.00 33.21 33.82 #> HIGH #> Mean 33.72 36.11 37.87 #> M #> LOW 21 23 11 #> MEDIUM 15 18 23 #> HIGH 15 14 26 #> U #> LOW 2 1 1 #> MEDIUM 1 0 2 #> HIGH 0 1 1 #> UNDIFFERENTIATED #> LOW 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/reference/update_ref_indexing.html","id":null,"dir":"Reference","previous_headings":"","what":"Update footnote indexes on a built table — update_ref_indexing","title":"Update footnote indexes on a built table — update_ref_indexing","text":"Re-indexes footnotes within built table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/update_ref_indexing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update footnote indexes on a built table — update_ref_indexing","text":"","code":"update_ref_indexing(tt)"},{"path":"https://insightsengineering.github.io/rtables/reference/update_ref_indexing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update footnote indexes on a built table — update_ref_indexing","text":"tt TableTree (related class). TableTree object representing populated table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/update_ref_indexing.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Update footnote indexes on a built table — update_ref_indexing","text":"adding removing referential footnotes manually, subsetting table, reference indexes (.e. number associated specific footnotes) may incorrect. function recalculates based full table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/update_ref_indexing.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Update footnote indexes on a built table — update_ref_indexing","text":"future generally need called manually.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/validate_table_struct.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate and Assert valid table structure (Experimental). — validate_table_struct","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"Validate Assert valid table structure (Experimental).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/validate_table_struct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"","code":"validate_table_struct(tt) assert_valid_table(tt, warn_only = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/reference/validate_table_struct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"tt TableTree TableTree (rtables-built table) considered degenerate contains subtables data rows (content rows count) contains subtable degenerate criterion validate_table_struct assesses whether tt valid (non-degenerate) structure. assert_valid_table asserts table must valid structure, throws informative error (default) warning (warn_only TRUE) table degenerate (invalid structure contains one invalid substructures warn_only logical(1). warning thrown instead error? Defaults FALSE","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/validate_table_struct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"validate_table_struct logical value indicating valid structure; assert_valid_table called side-effect throwing error warning degenerate tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/validate_table_struct.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"function experimental exact text warning/error subject change future releases.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/validate_table_struct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"","code":"validate_table_struct(rtable(\"hahaha\")) #> [1] FALSE if (FALSE) { assert_valid_table(rtable(\"oops\")) }"},{"path":"https://insightsengineering.github.io/rtables/reference/value_formats.html","id":null,"dir":"Reference","previous_headings":"","what":"Value Formats — value_formats","title":"Value Formats — value_formats","text":"Returns matrix formats cells table","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/value_formats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Value Formats — value_formats","text":"","code":"value_formats(obj, default = obj_format(obj)) # S4 method for ANY value_formats(obj, default = obj_format(obj)) # S4 method for TableRow value_formats(obj, default = obj_format(obj)) # S4 method for LabelRow value_formats(obj, default = obj_format(obj)) # S4 method for VTableTree value_formats(obj, default = obj_format(obj))"},{"path":"https://insightsengineering.github.io/rtables/reference/value_formats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Value Formats — value_formats","text":"obj table row object. default FormatSpec.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/value_formats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Value Formats — value_formats","text":"Matrix (storage mode list) containing effective format cell position table (including 'virtual' cells implied label rows, whose formats always NULL).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/reference/value_formats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Value Formats — value_formats","text":"","code":"lyt <- basic_table() %>% split_rows_by(\"RACE\", split_fun = keep_split_levels(c(\"ASIAN\", \"WHITE\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) value_formats(tbl) #> all obs #> ASIAN NULL #> Mean \"xx.xx\" #> WHITE NULL #> Mean \"xx.xx\""},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":null,"dir":"Reference","previous_headings":"","what":"Split on static or dynamic cuts of the data — split_cols_by_cuts","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"Create columns (row splits) based values (quartiles) var.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"","code":"split_cols_by_cuts( lyt, var, cuts, cutlabels = NULL, split_label = var, nested = TRUE, cumulative = FALSE ) split_rows_by_cuts( lyt, var, cuts, cutlabels = NULL, split_label = var, format = NULL, na_str = NA_character_, nested = TRUE, cumulative = FALSE, label_pos = \"hidden\", section_div = NA_character_ ) split_cols_by_cutfun( lyt, var, cutfun = qtile_cuts, cutlabelfun = function(x) NULL, split_label = var, nested = TRUE, extra_args = list(), cumulative = FALSE ) split_cols_by_quartiles( lyt, var, split_label = var, nested = TRUE, extra_args = list(), cumulative = FALSE ) split_rows_by_quartiles( lyt, var, split_label = var, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), cumulative = FALSE, indent_mod = 0L, label_pos = \"hidden\", section_div = NA_character_ ) split_rows_by_cutfun( lyt, var, cutfun = qtile_cuts, cutlabelfun = function(x) NULL, split_label = var, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), cumulative = FALSE, indent_mod = 0L, label_pos = \"hidden\", section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"lyt layout object pre-data used tabulation var string, variable name cuts numeric. Cuts use cutlabels character (NULL). Labels cuts split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. cumulative logical. cuts treated cumulative. Defaults FALSE format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. cutfun function. Function accepts full vector var values returns cut points passed cut. cutlabelfun function. Function returns either labels cuts NULL passed return value cutfun extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"dynamic cuts, cut transformed static cut build_table based full dataset, proceeding. Thus even nested within another split column/row space, resulting split reflect overall values (e.g., quartiles) dataset, values subset nested .","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/varcuts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"","code":"library(dplyr) # split_cols_by_cuts lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_cuts(\"AGE\", split_label = \"Age\", cuts = c(0, 25, 35, 1000), cutlabels = c(\"young\", \"medium\", \"old\") ) %>% analyze(c(\"BMRKR2\", \"STRATA2\")) %>% append_topleft(\"counts\") tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> counts young medium old young medium old young medium old #> ————————————————————————————————————————————————————————————————————————————— #> BMRKR2 #> LOW 4 30 16 4 17 24 5 19 16 #> MEDIUM 6 12 19 2 28 26 4 25 13 #> HIGH 4 24 19 2 17 14 1 21 28 #> STRATA2 #> S1 8 33 32 2 27 38 5 25 26 #> S2 6 33 22 6 35 26 5 40 31 # split_rows_by_cuts lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by_cuts(\"AGE\", split_label = \"Age\", cuts = c(0, 25, 35, 1000), cutlabels = c(\"young\", \"medium\", \"old\") ) %>% analyze(c(\"BMRKR2\", \"STRATA2\")) %>% append_topleft(\"counts\") tbl2 <- build_table(lyt2, ex_adsl) tbl2 #> counts A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————— #> young #> BMRKR2 #> LOW 4 4 5 #> MEDIUM 6 2 4 #> HIGH 4 2 1 #> STRATA2 #> S1 8 2 5 #> S2 6 6 5 #> medium #> BMRKR2 #> LOW 30 17 19 #> MEDIUM 12 28 25 #> HIGH 24 17 21 #> STRATA2 #> S1 33 27 25 #> S2 33 35 40 #> old #> BMRKR2 #> LOW 16 24 16 #> MEDIUM 19 26 13 #> HIGH 19 14 28 #> STRATA2 #> S1 32 38 26 #> S2 22 26 31 # split_cols_by_quartiles lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_quartiles(\"AGE\", split_label = \"Age\") %>% analyze(c(\"BMRKR2\", \"STRATA2\")) %>% append_topleft(\"counts\") tbl3 <- build_table(lyt3, ex_adsl) tbl3 #> A: Drug X B: Placebo C: Combination #> counts [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] #> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> BMRKR2 #> LOW 18 16 7 9 12 8 10 15 8 11 13 8 #> MEDIUM 11 7 9 10 14 15 14 13 12 13 7 10 #> HIGH 14 11 14 8 6 10 9 8 7 12 13 18 #> STRATA2 #> S1 22 18 18 15 15 11 22 19 11 14 12 19 #> S2 21 16 12 12 17 22 11 17 16 22 21 17 # split_rows_by_quartiles lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by_quartiles(\"AGE\", split_label = \"Age\") %>% analyze(\"BMRKR2\") %>% append_topleft(c(\"Age Quartiles\", \" Counts BMRKR2\")) tbl4 <- build_table(lyt4, ex_adsl) tbl4 #> Age Quartiles A: Drug X B: Placebo C: Combination #> Counts BMRKR2 (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————— #> Age #> [min, Q1] #> LOW 18 12 8 #> MEDIUM 11 14 12 #> HIGH 14 6 7 #> (Q1, Q2] #> LOW 16 8 11 #> MEDIUM 7 15 13 #> HIGH 11 10 12 #> (Q2, Q3] #> LOW 7 10 13 #> MEDIUM 9 14 7 #> HIGH 14 9 13 #> (Q3, max] #> LOW 9 15 8 #> MEDIUM 10 13 10 #> HIGH 8 8 18 # split_cols_by_cutfun cutfun <- function(x) { cutpoints <- c( min(x), mean(x), max(x) ) names(cutpoints) <- c(\"\", \"Younger\", \"Older\") cutpoints } lyt5 <- basic_table() %>% split_cols_by_cutfun(\"AGE\", cutfun = cutfun) %>% analyze(\"SEX\") tbl5 <- build_table(lyt5, ex_adsl) tbl5 #> Younger Older #> —————————————————————————————————— #> F 124 98 #> M 75 91 #> U 5 4 #> UNDIFFERENTIATED 1 2 # split_rows_by_cutfun lyt6 <- basic_table() %>% split_cols_by(\"SEX\") %>% split_rows_by_cutfun(\"AGE\", cutfun = cutfun) %>% analyze(\"BMRKR2\") tbl6 <- build_table(lyt6, ex_adsl) tbl6 #> F M U UNDIFFERENTIATED #> ——————————————————————————————————————————— #> AGE #> Younger #> LOW 43 26 3 1 #> MEDIUM 47 23 2 0 #> HIGH 34 26 0 0 #> Older #> LOW 30 29 1 2 #> MEDIUM 29 33 1 0 #> HIGH 39 29 2 0"},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":null,"dir":"Reference","previous_headings":"","what":"List Variables required by a pre-data table layout — vars_in_layout","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"List Variables required pre-data table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"","code":"vars_in_layout(lyt) # S4 method for PreDataTableLayouts vars_in_layout(lyt) # S4 method for PreDataAxisLayout vars_in_layout(lyt) # S4 method for SplitVector vars_in_layout(lyt) # S4 method for Split vars_in_layout(lyt) # S4 method for CompoundSplit vars_in_layout(lyt) # S4 method for ManualSplit vars_in_layout(lyt)"},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"lyt Layout (component thereof)","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"character vector containing unique variables explicitly used layout (see Notes).","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"walk layout declaration return vector names unique variables used following ways: Variable split (directly via cuts) Element Multi-variable column split Content variable Value-label variable","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"function detect dependencies implicit analysis summary functions accept x df rely existence particular variables split / analyzed. order variable names appear within return vector undefined relied upon.","code":""},{"path":"https://insightsengineering.github.io/rtables/reference/vil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% summarize_row_groups(label_fstr = \"Overall (N)\") %>% split_rows_by(\"RACE\", split_label = \"Ethnicity\", labels_var = \"ethn_lab\", split_fun = drop_split_levels ) %>% summarize_row_groups(\"RACE\", label_fstr = \"%s (n)\") %>% analyze(\"AGE\", var_labels = \"Age\", afun = mean, format = \"xx.xx\") vars_in_layout(lyt) #> [1] \"ARM\" \"SEX\" \"RACE\" \"ethn_lab\" \"AGE\""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"new-features-0-6-6-9008","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.6.9008","text":"Added top_level_section_div basic_table set section dividers top level rows. Added keep_label_rows as_result_df lines visible.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"bug-fixes-0-6-6-9008","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.6.9008","text":"Fixed rlistings decoration (e.g. titles footers) expansion new lines. Moved relevant handling rtables’ matrix_form function formatters’ dedicated mform_handle_newlines function. Fixed issue rtables_root removed using as_result_df. Fixed edge case bug as_result_df rows table \"root\" path index.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-066","dir":"Changelog","previous_headings":"","what":"rtables 0.6.6","title":"rtables 0.6.6","text":"CRAN release: 2023-12-08","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"new-features-0-6-6","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.6","text":"Removed ref_group reordering column splits change order. Added bold argument as_html bold specified elements, header_sep_line argument print horizontal line table header rendered HTML output. Duplicate referential footnotes consolidated tables rendered. Section divisors can set analysis rows. Added setter getter section dividers (section_div section_div<-). also accept split section structure assignment. Added header_section_div setters getters layout table objects along related basic_table parameter. Added na_str argument analyze_colvars set custom string print place missing values. Added flat data.frame outputs as_result_df() via flag parameters as_viewer, as_strings, expand_colnames. Migrated export_as_pdf function formatters.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"bug-fixes-0-6-6","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.6","text":"Fixed bug failing wrapping section dividers used time. Fixed bug as_result_df causing misalignment column names. Fixed bug allowing path indexing row_paths() giving different path due made named values. Fixed bug as_result_df called tables less 3 rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"miscellaneous-0-6-6","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.6","text":"Applied styler resolved package lint. Changed default indentation 4 spaces 2. Added Developer Guide pkgdown site Debugging, Split Machinery, Tabulation sections. Whitespace trimmed rendering tables as_html. Started deprecation cycle col_fnotes_here replaced col_footnotes. Exported section_div methods now dedicated documentation page visible users. tables exported txt, preserve horizontal separator table. Added imports stringi checkmate fundamental packages string handling argument checking. Updated introduction vignette split two. Section introspecting tables now located separate vignette.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"new-features-0-6-5","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.5","text":"Added support white spaces labels text redesigning wrapping functions formatters. Added support new line characters across rtables (titles, column names, row names, footers, na_str). Modified top left information vertical alignment stay bottom header.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"bug-fixes-0-6-5","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.5","text":"Fixed bug causing Viewer as_html fail new line characters added.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"miscellaneous-0-6-5","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.5","text":"Added slide decks advanced training internal files.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"new-features-0-6-4","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.4","text":"Added support .docx exports export_as_docx(). Expanded support flextable customization theme function specific word documents (theme_docx_default()).","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"bug-fixes-0-6-4","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.4","text":"Fixed bug causing -NA rows included every .df_row split.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"miscellaneous-0-6-4","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.4","text":"Specified minimal version package dependencies.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-063","dir":"Changelog","previous_headings":"","what":"rtables 0.6.3","title":"rtables 0.6.3","text":"CRAN release: 2023-08-30","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"new-features-0-6-3","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.3","text":"Analysis functions (cfun/afun) can use new parameters extend analysis calculations: .alt_df_row .alt_df give access alt_counts_df across columns, .all_col_exprs .all_col_counts contains global information columns. Binding objects via rbind retain titles/footer information identical objects present first object bound.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"enhancements-0-6-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"rtables 0.6.3","text":"Analysis functions (cfun/afun) information current column split; .spl_context access cur_col_id, cur_col_expr, cur_col_split, cur_col_split_val. Added vignette exploratory analysis qtable. Extracted qtable_layout qtable.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"bug-fixes-0-6-3","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.3","text":"Page-splits generate zero facets (thus tables zero pages rendered) now throw informative error table build time. Removed superfluous warning arose custom split functions reference group set (https://github.com/insightsengineering/rtables/issues/707#issuecomment-1678810598). Fixed qtable labeling via row_labels (#698). Error catching test coverage cases alt_counts_df presents different splits df.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"miscellaneous-0-6-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.3","text":"Cleaned spelling documentation (#685) Custom appearance vignette updated decimal alignment support. Alignment checks moved formatters: formatters::check_aligns superseded internal function chk_rtables_align formatters::list_valid_aligns superseded rtables_aligns.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-062","dir":"Changelog","previous_headings":"","what":"rtables 0.6.2","title":"rtables 0.6.2","text":"Fixed major regressions page_by machinery caused migration formatters 0.5.1 pagination framework. Fixed page_by labels become missing one level exist split_rows_by. Fixed bug dropping var levels lblvar levels. Added checks catch bad labels ({}) throw informative error. Added qtable function create table single top-level structure row column dimensions involving faceting 0 variables . Added as_result_df function flatten table dataframe. Added sanitize_table_struct, validate_table_struct, find_degen_struct support degenerative table rendering.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-061","dir":"Changelog","previous_headings":"","what":"rtables 0.6.1","title":"rtables 0.6.1","text":"CRAN release: 2023-05-25 Improved resilience pagination machinery (paginate_table) generalizing parameters’ defaults (cpp, lpp, font_size). Moved export_as_txt formatters. Added reexports. Migrated export_as_rtf formatters. re-exported. add r2rtf Suggests pagination logic migrated completely (excepting page_by splits) formatters now invoked . paginate_table remains convenience function. Removed warning str method called upon table objects. Provide str method VTableTree objects default max.level 3, infinite default base useful informative. default font_size value now 8 across pagination export machinery margins argument pagination export machinery now (correctly) interpreted inches. change inherited formatters lpp cpp now default NA_integer_, interpreted inferring value physical page size specified. Horizontal pagination now occurs default due (default page type - \"letter\". Pagination can still turned either direction setting l/cpp NULL explicitly. Referential footnotes now symbol index. Messages associated symbols appear per page footer materials regardless number elements referenced page symbol. Matches inherits changes formatters Started deprecation cycle trim_zero_rows. Fixed bug occurring extracting cell_values sorting. Removed deprecated function vpaginate_table. Added examples details sort_at_path. Added split_label function split_rows_by_multivar extra_args function split_cols_by_multivar. Added split_rows_by_multivar documentation.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-060","dir":"Changelog","previous_headings":"","what":"rtables 0.6.0","title":"rtables 0.6.0","text":"CRAN release: 2023-03-02 added make_split_fun function creation custom split functions basic_table now accepts colcount_format 2d formats now allowed column counts provided one element percent, automatically set 100% spl_context now includes root row row-split contexts. Added vignette format precedence Added vignette split functions Added custom appearance vignette Significant overhaul sorting vignette extended clarified documentation export_as_pdf now correctly takes margins account calculating lpp cpp page size. exporters now pass non-default colwidths values correctly nlines TableRow method (used rows column label extent pagination) now correctly handles column spanning pagination verbose = TRUE now includes original adjusted lines-per page information cont_n_allcols cont_n_onecol score functions now throw errors applied subtables content table, instead previously returned NA sort_at_path now emits informative error message score functions fail. paginate_table now accepts colwidths paginates assuming column label cell values wrapped widths. make_row_df now accepts colwidths calculates row extents assuming cell values wrapped widths nlines TableRow method now uses provided colwidths assume cell-value wrapping export_to_txt now automatically paginates form page dimension provided (previously default unconditionally paginating). Versioned dependency formatters increased >=0.4.0","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-053","dir":"Changelog","previous_headings":"","what":"rtables 0.5.3","title":"rtables 0.5.3","text":"[<- now treats character j values paths, [ always . [<- CellValue method now preserves CellValue attributes (e.g., format) detailed subsetting modification vignette nlines methods now accept colwidths max_width max_width now used pagination determine lines taken referential footnotes make_col_df now accepts colwidths argument, can called directly InstantiatedColumnInfo objects versioned dependency formatters increase >0.3.3.12 word wrapping title/footer materials longer fails presence \"\" values. versioned dependency formatters increase >0.3.3.11 paginate_table now accepts tf_wrap max_width respects title/footer word wrapping present export functions now accepts tf_wrap max_width use pagination (turned ) toString used (pdf, txt exporters). versioned dependency formatters increased >0.3.3.10 export_as_pdf now accepts standard page/font size parameters original parameters (width, height, fontsize soft deprecated (warning) fully deprecated removed future. toString method VTableTree now accepts tf_wrap max_width export_as_txt export_as_pdf now accept cpp, well tf_wrap max_width default tf_wrap max_width = cpp cpp non-NULL. basic_table now accepts inset argument declaring table inset Table Layout object classes now table_inset slot, accessor functions. matrix_form method VTableTree sets table_inset value Increase versioned dependency formatters >0.3.3.5 table_inset support Use exact=TRUE calls attr within access functions Increase versioned dependency formatters >0.3.3.4 layouting instructions now accept na_str argument, specifies na string inheritance rules formats (pre-data) Split (post tabulation) Table/row S4 classes now carry around na_str information Increase versioned dependency formatters >= 0.3.3.3 support na_strs NA_character_ values paginate_table now takes page dimension font information uses formatters::page_lcpp calculate lpp cpp automatically provided. Increase versioned dependency formatters >= 0.3.3.2 page_lcpp","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-052","dir":"Changelog","previous_headings":"","what":"rtables 0.5.2","title":"rtables 0.5.2","text":"paginate_table now accepts cpp perform vertical pagination non-null vpaginate_table now deprecated Increased versioned dependency formatters >=0.3.2.4","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0515","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.5","title":"rtables 0.5.1.5","text":"Support section dividers (section_div argument split_rows_by* function) Updated versioned dependency formatters >=0.3.2.3 Equivalent split functions different enclosing environments (e.g., 2 identical calls add_combo_levels #340) longer block rbinding Fixed various documentation bugs description section added header.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0514","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.4","title":"rtables 0.5.1.4","text":"empty level check splitting variables reinstated.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0513","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.3","title":"rtables 0.5.1.3","text":"Throw informative error messages custom analysis, content split functions fail (#329)","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0512","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.2","title":"rtables 0.5.1.2","text":"empty level check splitting variables temporarily removed. likely reinstated future release.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0511","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.1","title":"rtables 0.5.1.1","text":"col_counts getter setter now accept path argument. empty levels splitting variable now result informative error message (character factor cases). fixed bug handling column extra arguments preventing cbinding tables working correctly ([#324]](https://github.com/insightsengineering/rtables/issues/324))","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-051","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1","title":"rtables 0.5.1","text":"CRAN release: 2022-05-21 empty factor levels now dropped column splits ref_group set (#323) linesep argument toString related functions renamed hsep Increase versioned dependency formatters >=0.3.0 Default “line separator” header body now falls back “-” non-UTF charset locales. New hsep argument build_table sets horizontal separator constructed table (subtables thereof) New horizontal_sep horizontal_sep<- accessors constructed tables, latter mandatorily recursive. split_rows_by(var, child_labels=\"hidden\") longer removes structural subtable corresponding levels var (#314)","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-050","dir":"Changelog","previous_headings":"","what":"rtables 0.5.0","title":"rtables 0.5.0","text":"CRAN release: 2022-04-01 formatable dependency renamed formatters suitability release CRAN Update versioned dependency formatters (previously formatable) >=0.2.0","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0410004","dir":"Changelog","previous_headings":"","what":"rtables 0.4.1.0004","title":"rtables 0.4.1.0004","text":"Fix bug function format combined NULL cfun caused error (#307) Fix bug path_enriched_df (powers tsv export), related (#308)","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0410002","dir":"Changelog","previous_headings":"","what":"rtables 0.4.1.0002","title":"rtables 0.4.1.0002","text":"added table_shell display shell table formats","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0410001","dir":"Changelog","previous_headings":"","what":"rtables 0.4.1.0001","title":"rtables 0.4.1.0001","text":"added linesep argument toString specify character create line separator. Previously used en dash line separator character, now changed default em dash reducing gap dash line elements.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-040","dir":"Changelog","previous_headings":"","what":"rtables 0.4.0","title":"rtables 0.4.0","text":"CRAN release: 2021-10-06 Initializing layouts NULL now deprecated insert_rrow deprecated favor new insert_row_at_path label_at_path<- functions split analysis/content functions can now depend values splits nested inside accepting using new .spl_context optional argument new trim_levels_to_map split function dictating exact combinations values appear across splits value_formats function now exported returns/displays effective formats cells table compare_rtables now much faster tables many cells compare_rtables now accepts structure argument adds comparison structure (way row- column-path positions) new tt_to_flextable coercion function new export_as_pdf exporter function value_at cell_values functions now methods TableRow objects making usable sorting/pruning functions","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0389001","dir":"Changelog","previous_headings":"","what":"rtables 0.3.8.9001","title":"rtables 0.3.8.9001","text":"new trim_levels_to_map split function based [@wwojciech](https://github.com/wwojciech)’s work #203 support column referential footnotes support adding footnotes existing table via fnotes_at_path<- function trim_levels_in_group now trims empty levels outer (split) variable default value_at cell_values now work tablerow objects Fixed as_html bug multivar split columns case Fixed pagination --one error","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-038","dir":"Changelog","previous_headings":"","what":"rtables 0.3.8","title":"rtables 0.3.8","text":"CRAN release: 2021-07-13 Add experimental support newlines column names, row labels, cell values (supported top-left annotations) as_html refactored support newlines respecting table structure self_extent column df returned make_row_df now reflects extent lines, thus return larger values row-label cell values contain newlines. Fix bug tables output using as_html (viewed Viewer) missing table class attribute (#194) inserting DataRow incorrect number columns now error (#199) Referential footer machinery now works colspan case. Fix extraneous footnote attribute bug (#198) Fix max -Inf warning content rows appear positions whose children 0 visible rows (#200) Resync NEWS.md file Introduce titles footnotes Support automatic population top-left Introduce referential footnote support cells row labels Added vars_in_layout list (explicitly named ) variables used layout Fix column label ordering bug value label variable factor (#173)","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-037","dir":"Changelog","previous_headings":"","what":"rtables 0.3.7","title":"rtables 0.3.7","text":"Synchronize release GitHub commit sha.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-036","dir":"Changelog","previous_headings":"","what":"rtables 0.3.6","title":"rtables 0.3.6","text":"CRAN release: 2021-01-22 Documentation revisions requested CRAN. change package code.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-035","dir":"Changelog","previous_headings":"","what":"rtables 0.3.5","title":"rtables 0.3.5","text":"Documentation-text changes introduction vignette pass CRAN’s URL checks. package, example, test, vignette code fully identical tagged GitHub release 0.3.4","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-034","dir":"Changelog","previous_headings":"","what":"rtables 0.3.4","title":"rtables 0.3.4","text":"Minor changes 0.3.3 version order submit rtables CRAN.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-033","dir":"Changelog","previous_headings":"","what":"rtables 0.3.3","title":"rtables 0.3.3","text":"version completely refactors rtables package. provide backwards compatibility layer rtable, rcell, rrow, rheader, rtabulate family functions. However table data structure main tabulation framework changed. provide extensive documentation manuals help(package = \"rtables\") vignettes vignette(package = \"rtables\") package. changes rtables undertaken better meet requirements creating analyzing & reporting tables context clinical trials.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179046","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9046","title":"rtables 0.3.2.17.9046","text":"make_afun now force()s customization arguments immediately, prevents problems called within loop/lapply constructs.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179045","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9045","title":"rtables 0.3.2.17.9045","text":"Tabulation machinery longer removes NAs mandatorily cases, including multivar column splits analyze_colvars’s inclNAs argument now respected.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179044","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9044","title":"rtables 0.3.2.17.9044","text":"Fix indent modifier propagation tabulation Fix indent calculation make_pagdf Add significant testing ensure make_pagdf indent calculation remains correct","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179043","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9043","title":"rtables 0.3.2.17.9043","text":"Rework reference columns handled analyses use .in_ref_col .ref_group work correctly custom splitting used (including provided combination-levels mechanism)","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179042","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9042","title":"rtables 0.3.2.17.9042","text":"Fix naming/pathing columns multivar case (split now default name \"multivars\") Fix labeling bug variable appears multiple times MultiVarSplit different associated levels","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179041","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9041","title":"rtables 0.3.2.17.9041","text":"Allow single variable used within split_cols_by_multivar Various removal defunct","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179040","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9040","title":"rtables 0.3.2.17.9040","text":"Fix regression caused 0.3.2.17.9039 column split values displayed name rather label.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179039","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9039","title":"rtables 0.3.2.17.9039","text":"Fix bug display column information column structure symmetric, recursive cbinds.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179036","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9036","title":"rtables 0.3.2.17.9036","text":"Fixed bug row subsetting table content rows. Basic compare_rtables function now works previous versions, awareness row column structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179036-1","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9036","title":"rtables 0.3.2.17.9036","text":"summarize_row_groups can now accept list functions cfun argument analyze_colvars .","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179035","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9035","title":"rtables 0.3.2.17.9035","text":"Fix bug unearthed change 0.3.2.17.9034 cell formats retained column subsetting","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179034","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9034","title":"rtables 0.3.2.17.9034","text":"Fix internal value_formats accessor operates CellValues rather raw contained values (thus always returning NULL) rrow constructor longer interprets cell formats row format across cells. Fixes bug “correct way” code discussed #112","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179033","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9033","title":"rtables 0.3.2.17.9033","text":"Interpret .formats in_rows cell formats rather row formats.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179031","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9031","title":"rtables 0.3.2.17.9031","text":"cbind_rtables can now take 2 tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179029","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9029","title":"rtables 0.3.2.17.9029","text":"Fix issue underlying spurious length-mismatch warning cases using analyze_colvars","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179028","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9028","title":"rtables 0.3.2.17.9028","text":"analyze_colvars now takes adheres inclNAs argument","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-032179027","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9027","title":"rtables 0.3.2.17.9027","text":"issues news:","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-017","dir":"Changelog","previous_headings":"","what":"rtables 0.1.7","title":"rtables 0.1.7","text":"added format xx.xx (xx.xx - xx.xx) x.xxxx | (<0.0001)","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-016","dir":"Changelog","previous_headings":"","what":"rtables 0.1.6","title":"rtables 0.1.6","text":"Minor changes.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-015","dir":"Changelog","previous_headings":"","what":"rtables 0.1.5","title":"rtables 0.1.5","text":"Changed testing approach fit internal pipelines.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-014","dir":"Changelog","previous_headings":"","what":"rtables 0.1.4","title":"rtables 0.1.4","text":"Replaced dots underscore class checking functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-013","dir":"Changelog","previous_headings":"","what":"rtables 0.1.3","title":"rtables 0.1.3","text":"col_by_to_matrix, col_by_to_factor, by_factor_to_matrix. by_add_total, by_all, by_combine, by_quartile, by_compare_subset, by_hierarchical, by_drop_empty_cols. label, var_labels<-, var_labels, var_labels_remove, var_relabel, with_label. cbing_rtables. empty_rtables, is_empty_rtable, is_non_empty_rtable, is_rtable. header_indent, header_indent<-, header_row.names, header_row.names<-. insert_rrow.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-012","dir":"Changelog","previous_headings":"","what":"rtables 0.1.2","title":"rtables 0.1.2","text":"rbind.rtable now supports binding rtables rows, e.g. rbind(tbl1, rrow(), tbl2) rbind(tbl1, rrow(\"row name\"), tbl2). rbindl_rtables supports NULL objects list (except first element). Add indent function. header_add_N deals gracefully NULL objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-011","dir":"Changelog","previous_headings":"","what":"rtables 0.1.1","title":"rtables 0.1.1","text":"rtablulate family functions support row_*_data_args arguments anymore. Instead, col_wise_args argument introduced. Functions order_rrows, sort_rrows, order_rtables, sort_rtables introduced. Prevent rtables unlisted unlist.rtables.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0106","dir":"Changelog","previous_headings":"","what":"rtables 0.1.0.6","title":"rtables 0.1.0.6","text":"Viewer now also accepts objects class shiny.tag (defined package htmltools). .html accepts class.table, class.tr, class.th, class.td argument.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-0105","dir":"Changelog","previous_headings":"","what":"rtables 0.1.0.5","title":"rtables 0.1.0.5","text":"Added sprintf_format formatting rcells (thanks Doug Kelkhoff suggestion). Added \"(N=xx)\" \">999.9\" format labels. rtabulate now argument col_N function col_N().","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-010","dir":"Changelog","previous_headings":"","what":"rtables 0.1.0","title":"rtables 0.1.0","text":"Redesign: rtable now header argument instead col.names. header can created rheader collection rrows. header set c(\"\", \"B\") rtable create rheader single rrow setting row.name NULL. header header<- function added. Renamed get_rcell_formats list_rcell_format_labels. rcell format NULL cell content converted string paste(.character(x), collapse = ', '). Accessor [,] works now subset table. rbind method rtables. row.names<-.rtable method. rtabulate added creating tables. indented_row.names function added.","code":""},{"path":"https://insightsengineering.github.io/rtables/news/index.html","id":"rtables-001","dir":"Changelog","previous_headings":"","what":"rtables 0.0.1","title":"rtables 0.0.1","text":"Initial public release","code":""}] +[{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"interest fostering open welcoming environment, contributors maintainers pledge making participation project community harassment-free experience everyone, regardless age, body size, disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes creating positive environment include: Using welcoming inclusive language respectful differing viewpoints experiences Gracefully accepting constructive criticism Focusing best community Showing empathy towards community members Examples unacceptable behavior participants include: use sexualized language imagery unwelcome sexual attention advances Trolling, insulting/derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical electronic address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CODE_OF_CONDUCT.html","id":"our-responsibilities","dir":"","previous_headings":"","what":"Our Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Project maintainers responsible clarifying standards acceptable behavior expected take appropriate fair corrective action response instances unacceptable behavior. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, ban temporarily permanently contributor behaviors deem inappropriate, threatening, offensive, harmful.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within project spaces public spaces individual representing project community. Examples representing project community include using official project e-mail address, posting via official social media account, acting appointed representative online offline event. Representation project may defined clarified project maintainers.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported contacting project team support@github.com. complaints reviewed investigated result response deemed necessary appropriate circumstances. project team obligated maintain confidentiality regard reporter incident. details specific enforcement policies may posted separately. Project maintainers follow enforce Code Conduct good faith may face temporary permanent repercussions determined members project’s leadership.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 1.4, available https://www.contributor-covenant.org/version/1/4/code--conduct.html answers common questions code conduct, see https://www.contributor-covenant.org/faq","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to {rtables}","title":"Contributing to {rtables}","text":"welcome contributions big small ongoing development {rtables} package. , best way contribute package filing issues feature requests bugs encountered. interested contributing code package, contributions can made working current issues opening pull requests code changes. help able provide greatly appreciated! Contributions project released public project’s open source license.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"filing-issues","dir":"","previous_headings":"","what":"Filing Issues","title":"Contributing to {rtables}","text":"Issues used establish prioritized timeline track development progress within package. new feature feel enhance experience package users, please open Feature Request issue. notice bug existing code, please file Bug Fix issue description bug reprex (reproducible example). types issues (questions, typos ’ve noticed, improvements documentation, etc.) can filed well. Click file new issue, see list current issues. Please utilize labels wherever possible creating issues organization purposes narrow scope work required.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"creating-pull-requests","dir":"","previous_headings":"","what":"Creating Pull Requests","title":"Contributing to {rtables}","text":"Development {rtables} package relies Issue → Branch → PR → Code Review → Merge pipeline facilitated GitHub. experienced programmer interested contributing package code, please begin filing issue describing changes like make. may case idea already implemented way, package maintainers can help determine whether feature necessary begin development. Whether opening issue pull request, detailed description, easier package maintainers help ! make code changes package, please follow following process.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Creating Pull Requests","what":"Pull Request Process","title":"Contributing to {rtables}","text":"{rtables} package part NEST project utilizes staged.dependencies ensure simplify development process track upstream downstream package dependencies. highly recommend installing using package developing within {rtables}.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"id_1-create-a-branch","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process","what":"1. Create a branch","title":"Contributing to {rtables}","text":"order work new pull request, please first create branch main upon can work commit changes. comply staged.dependencies standards, {rtables} uses following branch naming convention: issue#_description_of_issue@target_merge_branch example, 443_refactor_splits@main. cases, target merge branch base (main) branch. cases, change {rtables} may first require upstream changes {formatters} package. Suppose branch 100_update_fmts@main {formatters} containing required upstream changes. branch created {rtables} named follows example: 443_refactor_splits@100_update_fmts@main. ensures correct branches checked running tests, etc. details staged.dependencies branch naming conventions, click .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"id_2-code","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process","what":"2. Code","title":"Contributing to {rtables}","text":"Work within {rtables} package apply code changes. Avoid combining issues single branch - ideally, branch associated single issue prefixed issue number. information basics {rtables} package, please read package vignettes, available . advanced development work within {rtables}, consider reading {rtables} Developer Guide. Developer Guide can accessed {rtables} site navigation bar, listed convenience: Developer Guide: Split Machinery Developer Guide: Tabulation Developer Guide: Debugging {rtables} Beyond Developer Guide: Sparse Notes {rtables} Internals","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"Code style","title":"Contributing to {rtables}","text":"{rtables} package follows tidyverse style guide please adhere guidelines submitted code. making changes file within package, can apply package styler automatically check lint running following two lines code within file:","code":"styler:::style_active_file() lintr:::addin_lint()"},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"documentation","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"Documentation","title":"Contributing to {rtables}","text":"Package documentation uses roxygen2. contribution requires updates documentation, ensure roxygen comments updated within source code file. updating roxygen documentation, run devtools::document() update accompanying .Rd files (update files hand!).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"tests","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"Tests","title":"Contributing to {rtables}","text":"ensure high code coverage, create tests using testthat package. cases, changes package code necessitate addition one tests ensure added features working expected existing features broken.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"news","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process > 2. Code","what":"NEWS","title":"Contributing to {rtables}","text":"making updates package, please add descriptive entry NEWS file reflects changes. See tidyverse style guide guidelines creating NEWS entry.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"id_3-make-a-pull-request","dir":"","previous_headings":"Creating Pull Requests > Pull Request Process","what":"3. Make a Pull Request","title":"Contributing to {rtables}","text":"previous two steps complete, can create pull request. Indicate description issue addressed pull request, utilize labels help reviewers identify category changes contained within pull request. pull request created, series checks automatically triggered, including R CMD check, tests/code coverage, auto-documentation, . checks must passing order eventually merge pull request, changes may required order resolve status checks. pull requests must also reviewed approved least one package maintainers can merged. review automatically requested several {rtables} maintainers upon creating pull request. maintainer reviews pull request, please try address comments short order - {rtables} package updated regular basis leaving pull request open long likely result merge conflicts create work developer.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to {rtables}","text":"Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/ISSUE_TEMPLATE.html","id":null,"dir":"","previous_headings":"","what":"Reporting an Issue with rtables","title":"Reporting an Issue with rtables","text":"Please briefly describe problem , relevant, output expect. Please also provide output utils::sessionInfo() devtools::session_info() end post. possible, please include minimal, reproducible example. rtables team much likely resolve issue able reproduce locally. Please delete preamble read . brief description problem","code":"library(rtables) # your reproducible example here"},{"path":"https://insightsengineering.github.io/rtables/main/articles/advanced_usage.html","id":"note","dir":"Articles","previous_headings":"","what":"NOTE","title":"{rtables} Advanced Usage","text":"vignette currently development. code prose appears version vignette main branch repository work/correct, likely final form. Initialization","code":"library(rtables)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/advanced_usage.html","id":"control-splitting-with-provided-function-limited-customization","dir":"Articles","previous_headings":"","what":"Control splitting with provided function (limited customization)","title":"{rtables} Advanced Usage","text":"rtables provides array functions control splitting logic without creating entirely new split functions. default split_*_by facets data based categorical variable. continuous variables, split_*_by_cutfun can leveraged create categories corresponding faceting, break points dependent data. Alternatively, split_*_by_cuts can used breakpoints predefined split_*_by_quartiles data faceted quantile.","code":"d1 <- subset(ex_adsl, AGE < 25) d1$AGE <- as.factor(d1$AGE) lyt1 <- basic_table() %>% split_cols_by(\"AGE\") %>% analyze(\"SEX\") build_table(lyt1, d1) ## 20 21 23 24 ## ———————————————————————————————————— ## F 0 2 4 5 ## M 1 1 2 3 ## U 0 0 0 0 ## UNDIFFERENTIATED 0 0 0 0 sd_cutfun <- function(x) { cutpoints <- c( min(x), mean(x) - sd(x), mean(x) + sd(x), max(x) ) names(cutpoints) <- c(\"\", \"Low\", \"Medium\", \"High\") cutpoints } lyt1 <- basic_table() %>% split_cols_by_cutfun(\"AGE\", cutfun = sd_cutfun) %>% analyze(\"SEX\") build_table(lyt1, ex_adsl) ## Low Medium High ## —————————————————————————————————————— ## F 36 165 21 ## M 21 115 30 ## U 1 8 0 ## UNDIFFERENTIATED 0 1 2 lyt1 <- basic_table() %>% split_cols_by_cuts( \"AGE\", cuts = c(0, 30, 60, 100), cutlabels = c(\"0-30 y.o.\", \"30-60 y.o.\", \"60-100 y.o.\") ) %>% analyze(\"SEX\") build_table(lyt1, ex_adsl) ## 0-30 y.o. 30-60 y.o. 60-100 y.o. ## ——————————————————————————————————————————————————————— ## F 71 150 1 ## M 48 116 2 ## U 2 7 0 ## UNDIFFERENTIATED 1 2 0"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/advanced_usage.html","id":"adding-an-overall-column-only-when-the-split-would-already-define-2-facets","dir":"Articles","previous_headings":"Custom Split Functions","what":"Adding an Overall Column Only When The Split Would Already Define 2+ Facets","title":"{rtables} Advanced Usage","text":"custom split functions can anything, including conditionally applying one existing custom split functions. define function constructor accepts variable name want check, return custom split function behavior want using functions provided rtables cases: gives us desired behavior one column corner case: standard multi-column case: Notice use add_overall_level function constructor, immediately call constructed function --one-columns case.","code":"picky_splitter <- function(var) { function(df, spl, vals, labels, trim) { orig_vals <- vals if (is.null(vals)) { vec <- df[[var]] vals <- if (is.factor(vec)) levels(vec) else unique(vec) } if (length(vals) == 1) { do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim) } else { add_overall_level( \"Overall\", label = \"All Obs\", first = FALSE )(df = df, spl = spl, vals = orig_vals, trim = trim) } } } d1 <- subset(ex_adsl, ARM == \"A: Drug X\") d1$ARM <- factor(d1$ARM) lyt1 <- basic_table() %>% split_cols_by(\"ARM\", split_fun = picky_splitter(\"ARM\")) %>% analyze(\"AGE\") build_table(lyt1, d1) ## A: Drug X ## ———————————————— ## Mean 33.77 build_table(lyt1, ex_adsl) ## A: Drug X B: Placebo C: Combination All Obs ## ———————————————————————————————————————————————————————— ## Mean 33.77 35.43 35.43 34.88"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/advanced_usage.html","id":"what-is--spl_context","dir":"Articles","previous_headings":"Leveraging .spl_context","what":"What Is .spl_context?","title":"{rtables} Advanced Usage","text":".spl_context (see ?spl_context) mechanism rtables tabulation machinery gives custom split, analysis content (row-group summary) functions information overarching facet-structure splits cells generate reside . particular .spl_context ensures functions know (thus computations based ) following types information:","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/advanced_usage.html","id":"different-formats-for-different-values-within-a-row-split","dir":"Articles","previous_headings":"Leveraging .spl_context","what":"Different Formats For Different Values Within A Row-Split","title":"{rtables} Advanced Usage","text":"","code":"dta_test <- data.frame( USUBJID = rep(1:6, each = 3), PARAMCD = rep(\"lab\", 6 * 3), AVISIT = rep(paste0(\"V\", 1:3), 6), ARM = rep(LETTERS[1:3], rep(6, 3)), AVAL = c(9:1, rep(NA, 9)), CHG = c(1:9, rep(NA, 9)) ) my_afun <- function(x, .spl_context) { n <- sum(!is.na(x)) meanval <- mean(x, na.rm = TRUE) sdval <- sd(x, na.rm = TRUE) ## get the split value of the most recent parent ## (row) split above this analyze val <- .spl_context[nrow(.spl_context), \"value\"] ## do a silly thing to decide the different format precisiosn ## your real logic would go here valnum <- min(2L, as.integer(gsub(\"[^[:digit:]]*\", \"\", val))) fstringpt <- paste0(\"xx.\", strrep(\"x\", valnum)) fmt_mnsd <- sprintf(\"%s (%s)\", fstringpt, fstringpt) in_rows( n = n, \"Mean, SD\" = c(meanval, sdval), .formats = c(n = \"xx\", \"Mean, SD\" = fmt_mnsd) ) } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% analyze_colvars(my_afun) build_table(lyt, dta_test) ## A B C ## AVAL CHG AVAL CHG AVAL CHG ## ——————————————————————————————————————————————————————————————————————————— ## V1 ## n 2 2 1 1 0 0 ## Mean, SD 7.5 (2.1) 2.5 (2.1) 3.0 (NA) 7.0 (NA) NA NA ## V2 ## n 2 2 1 1 0 0 ## Mean, SD 6.50 (2.12) 3.50 (2.12) 2.00 (NA) 8.00 (NA) NA NA ## V3 ## n 2 2 1 1 0 0 ## Mean, SD 5.50 (2.12) 4.50 (2.12) 1.00 (NA) 9.00 (NA) NA NA"},{"path":"https://insightsengineering.github.io/rtables/main/articles/advanced_usage.html","id":"simulating-baseline-comparison-in-row-space","dir":"Articles","previous_headings":"Leveraging .spl_context","what":"Simulating ‘Baseline Comparison’ In Row Space","title":"{rtables} Advanced Usage","text":"can simulate formal modeling reference row(s) using extra_args machinery","code":"my_afun <- function(x, .var, .spl_context) { n <- sum(!is.na(x)) meanval <- mean(x, na.rm = TRUE) sdval <- sd(x, na.rm = TRUE) ## get the split value of the most recent parent ## (row) split above this analyze val <- .spl_context[nrow(.spl_context), \"value\"] ## we show it if its not a CHG within V1 show_it <- val != \"V1\" || .var != \"CHG\" ## do a silly thing to decide the different format precisiosn ## your real logic would go here valnum <- min(2L, as.integer(gsub(\"[^[:digit:]]*\", \"\", val))) fstringpt <- paste0(\"xx.\", strrep(\"x\", valnum)) fmt_mnsd <- if (show_it) sprintf(\"%s (%s)\", fstringpt, fstringpt) else \"xx\" in_rows( n = if (show_it) n, ## NULL otherwise \"Mean, SD\" = if (show_it) c(meanval, sdval), ## NULL otherwise .formats = c(n = \"xx\", \"Mean, SD\" = fmt_mnsd) ) } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% analyze_colvars(my_afun) build_table(lyt, dta_test) ## A B C ## AVAL CHG AVAL CHG AVAL CHG ## ——————————————————————————————————————————————————————————————————————————— ## V1 ## n 2 1 0 ## Mean, SD 7.5 (2.1) 3.0 (NA) NA ## V2 ## n 2 2 1 1 0 0 ## Mean, SD 6.50 (2.12) 3.50 (2.12) 2.00 (NA) 8.00 (NA) NA NA ## V3 ## n 2 2 1 1 0 0 ## Mean, SD 5.50 (2.12) 4.50 (2.12) 1.00 (NA) 9.00 (NA) NA NA my_afun <- function(x, .var, ref_rowgroup, .spl_context) { n <- sum(!is.na(x)) meanval <- mean(x, na.rm = TRUE) sdval <- sd(x, na.rm = TRUE) ## get the split value of the most recent parent ## (row) split above this analyze val <- .spl_context[nrow(.spl_context), \"value\"] ## we show it if its not a CHG within V1 show_it <- val != ref_rowgroup || .var != \"CHG\" fmt_mnsd <- if (show_it) \"xx.x (xx.x)\" else \"xx\" in_rows( n = if (show_it) n, ## NULL otherwise \"Mean, SD\" = if (show_it) c(meanval, sdval), ## NULL otherwise .formats = c(n = \"xx\", \"Mean, SD\" = fmt_mnsd) ) } lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AVISIT\") %>% split_cols_by_multivar(vars = c(\"AVAL\", \"CHG\")) %>% analyze_colvars(my_afun, extra_args = list(ref_rowgroup = \"V1\")) build_table(lyt2, dta_test) ## A B C ## AVAL CHG AVAL CHG AVAL CHG ## ————————————————————————————————————————————————————————————————————— ## V1 ## n 2 1 0 ## Mean, SD 7.5 (2.1) 3.0 (NA) NA ## V2 ## n 2 2 1 1 0 0 ## Mean, SD 6.5 (2.1) 3.5 (2.1) 2.0 (NA) 8.0 (NA) NA NA ## V3 ## n 2 2 1 1 0 0 ## Mean, SD 5.5 (2.1) 4.5 (2.1) 1.0 (NA) 9.0 (NA) NA NA"},{"path":"https://insightsengineering.github.io/rtables/main/articles/baseline.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Comparing Against Baselines or Control","text":"Often data one column considered reference/baseline/comparison group compared data columns. example, lets calculate average age: difference average AGE placebo arm arms: Note column order changed reference group displayed first column. cases want cells blank reference column, (e.g., “B: Placebo”) use non_ref_rcell() instead rcell(), pass .in_ref_col second argument: can see arguments available afun manual analyze().","code":"library(rtables) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 34.91 33.02 34.57 lyt2 <- basic_table() %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% analyze(\"AGE\", afun = function(x, .ref_group) { in_rows( \"Difference of Averages\" = rcell(mean(x) - mean(.ref_group), format = \"xx.xx\") ) }) tbl2 <- build_table(lyt2, DM) tbl2 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————— # Difference of Averages 1.89 0.00 1.55 lyt3 <- basic_table() %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { in_rows( \"Difference of Averages\" = non_ref_rcell(mean(x) - mean(.ref_group), is_ref = .in_ref_col, format = \"xx.xx\") ) } ) tbl3 <- build_table(lyt3, DM) tbl3 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————— # Difference of Averages 1.89 1.55 lyt4 <- basic_table() %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% analyze( \"AGE\", afun = function(x, .ref_group, .in_ref_col) { in_rows( \"Difference of Averages\" = non_ref_rcell(mean(x) - mean(.ref_group), is_ref = .in_ref_col, format = \"xx.xx\"), \"another row\" = non_ref_rcell(\"aaa\", .in_ref_col) ) } ) tbl4 <- build_table(lyt4, DM) tbl4 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————— # Difference of Averages 1.89 1.55 # another row aaa aaa"},{"path":"https://insightsengineering.github.io/rtables/main/articles/baseline.html","id":"row-splitting","dir":"Articles","previous_headings":"","what":"Row Splitting","title":"Comparing Against Baselines or Control","text":"adding row-splitting reference data may represented column without row splitting. example: data assigned .ref_full full data reference column whereas data assigned .ref_group respects subsetting defined row-splitting hence subset argument x df afun.","code":"lyt5 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", ref_group = \"B: Placebo\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"AGE\", afun = function(x, .ref_group, .ref_full, .in_ref_col) { in_rows( \"is reference (.in_ref_col)\" = rcell(.in_ref_col), \"ref cell N (.ref_group)\" = rcell(length(.ref_group)), \"ref column N (.ref_full)\" = rcell(length(.ref_full)) ) }) tbl5 <- build_table(lyt5, subset(DM, SEX %in% c(\"M\", \"F\"))) tbl5 # A: Drug X B: Placebo C: Combination # (N=121) (N=106) (N=129) # —————————————————————————————————————————————————————————————————————— # F # is reference (.in_ref_col) FALSE TRUE FALSE # ref cell N (.ref_group) 56 56 56 # ref column N (.ref_full) 106 106 106 # M # is reference (.in_ref_col) FALSE TRUE FALSE # ref cell N (.ref_group) 50 50 50 # ref column N (.ref_full) 106 106 106"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Example Clinical Trials Tables","text":"vignette create demographic table adverse event table response table time--event analysis table using rtables layout facility. , demonstrate layout based tabulation framework can specify structure relations commonly found analyzing clinical trials data. Note data created using random number generators. ex_* data currently attached rtables package provided formatters package created using publicly available random.cdisc.data R package. packages used vignette :","code":"library(rtables) library(tibble) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"demographic-table","dir":"Articles","previous_headings":"","what":"Demographic Table","title":"Example Clinical Trials Tables","text":"Demographic tables summarize variables content different population subsets (encoded columns). One feature analyze() introduced previous vignette analysis function afun can specify multiple rows in_rows() function: Multiple variables can analyzed one analyze() call: Hence, afun can process different data vector types (.e. variables selected data) fairly close standard demographic table. function either creates count table number summary argument x factor numeric, respectively: Note use rcell wrap results order add formatting instructions rtables. can use s_summary outside context tabulation: can now create commonly used variant demographic table: Note analyze() can also called multiple times sequence: leads table identical summary_tbl: clinical trials analyses number patients per column often referred N (rather overall population outside clinical trials commonly referred N). Column Ns added setting show_colcounts argument basic_table() TRUE:","code":"ADSL <- ex_adsl # Example ADSL dataset lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx.xx - xx.xx\") ) }) tbl <- build_table(lyt, ADSL) tbl # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = c(\"AGE\", \"BMRKR1\"), afun = function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx.xx - xx.xx\") ) }) tbl2 <- build_table(lyt2, ADSL) tbl2 # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————— # AGE # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR1 # Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) # Range 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 s_summary <- function(x) { if (is.numeric(x)) { in_rows( \"n\" = rcell(sum(!is.na(x)), format = \"xx\"), \"Mean (sd)\" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = \"xx.xx (xx.xx)\"), \"IQR\" = rcell(IQR(x, na.rm = TRUE), format = \"xx.xx\"), \"min - max\" = rcell(range(x, na.rm = TRUE), format = \"xx.xx - xx.xx\") ) } else if (is.factor(x)) { vs <- as.list(table(x)) do.call(in_rows, lapply(vs, rcell, format = \"xx\")) } else { stop(\"type not supported\") } } s_summary(ADSL$AGE) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod row_label # 1 n 400 0 n # 2 Mean (sd) 34.88 (7.44) 0 Mean (sd) # 3 IQR 10.00 0 IQR # 4 min - max 20.00 - 69.00 0 min - max s_summary(ADSL$SEX) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod row_label # 1 F 222 0 F # 2 M 166 0 M # 3 U 9 0 U # 4 UNDIFFERENTIATED 3 0 UNDIFFERENTIATED summary_lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze(c(\"AGE\", \"SEX\"), afun = s_summary) summary_tbl <- build_table(summary_lyt, ADSL) summary_tbl # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 summary_lyt2 <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze(\"AGE\", s_summary) %>% analyze(\"SEX\", s_summary) summary_tbl2 <- build_table(summary_lyt2, ADSL) summary_tbl2 # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 identical(summary_tbl, summary_tbl2) # [1] TRUE summary_lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARMCD\") %>% analyze(c(\"AGE\", \"SEX\"), s_summary) summary_tbl3 <- build_table(summary_lyt3, ADSL) summary_tbl3 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"variations-on-the-demographic-table","dir":"Articles","previous_headings":"Demographic Table","what":"Variations on the Demographic Table","title":"Example Clinical Trials Tables","text":"now show couple variations demographic table developed . variations structure analysis, hence don’t require modification s_summary function. start standard table analyzing variables AGE BMRKR2 variables: Assume like analysis carried per gender encoded row space: now subset ADSL include males females analysis order reduce number rows table: Note UNDIFFERENTIATED U levels still show table. tabulation respects factor levels level order, exactly split table function . empty levels dropped rtables needs know splitting time via split_fun argument split_rows_by(). number predefined functions. example drop_split_levels() required drop empty levels splitting time. Splitting big topic eventually addressed specific package vignette. table labels M F descriptive. can add full labels follows: next table variation stratify gender AGE analysis. nested argument set FALSE analyze() call: split rows groups (Male Female ) one might want summarize groups: usually showing count column percentages. especially important missing data. example, create table add missing data AGE variable: easy see many females males arm n represents number non-missing data elements variables. Groups within rows defined splitting can summarized summarize_row_groups(), example: couple things note : Group summaries produce “content” rows. Visually, ’s impossible distinguish data rows content rows. difference justified (’s important design decision) paginate tables content rows default repeated group gets divided via pagination. Conceptually content rows summarize patient population analyzed hence often count & group percentages (default behavior summarize_row_groups()). can recreate default behavior (count percentage) defining cfun illustrative purposes results table : Note cfun, like afun (used analyze()), can operate either variables, passed via x argument, data.frames tibbles, passed via df argument (afun can optionally request df ). Unlike afun, cfun must accept labelstr second argument gives default group label (factor level splitting) hence modified:","code":"lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl <- build_table(lyt, ADSL) tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 50 45 40 # MEDIUM 37 56 42 # HIGH 47 33 50 lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl <- build_table(lyt, ADSL) tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 # U # AGE # n 3 2 4 # Mean (sd) 31.67 (3.21) 31.00 (5.66) 35.25 (3.10) # IQR 3.00 4.00 3.25 # min - max 28.00 - 34.00 27.00 - 35.00 31.00 - 38.00 # BMRKR2 # LOW 2 1 1 # MEDIUM 1 0 2 # HIGH 0 1 1 # UNDIFFERENTIATED # AGE # n 1 0 2 # Mean (sd) 28.00 (NA) NA 45.00 (1.41) # IQR 0.00 NA 1.00 # min - max 28.00 - 28.00 Inf - -Inf 44.00 - 46.00 # BMRKR2 # LOW 1 0 2 # MEDIUM 0 0 0 # HIGH 0 0 0 ADSL_M_F <- filter(ADSL, SEX %in% c(\"M\", \"F\")) lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl2 <- build_table(lyt2, ADSL_M_F) tbl2 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ————————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 # U # AGE # n 0 0 0 # Mean (sd) NA NA NA # IQR NA NA NA # min - max Inf - -Inf Inf - -Inf Inf - -Inf # BMRKR2 # LOW 0 0 0 # MEDIUM 0 0 0 # HIGH 0 0 0 # UNDIFFERENTIATED # AGE # n 0 0 0 # Mean (sd) NA NA NA # IQR NA NA NA # min - max Inf - -Inf Inf - -Inf Inf - -Inf # BMRKR2 # LOW 0 0 0 # MEDIUM 0 0 0 # HIGH 0 0 0 lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, child_labels = \"visible\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl3 <- build_table(lyt3, ADSL_M_F) tbl3 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # —————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 ADSL_M_F_l <- ADSL_M_F %>% mutate(lbl_sex = case_when( SEX == \"M\" ~ \"Male\", SEX == \"F\" ~ \"Female\", SEX == \"U\" ~ \"Unknown\", SEX == \"UNDIFFERENTIATED\" ~ \"Undifferentiated\" )) lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"visible\") %>% analyze(c(\"AGE\", \"BMRKR2\"), s_summary) tbl4 <- build_table(lyt4, ADSL_M_F_l) tbl4 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # —————————————————————————————————————————————————————————————— # Female # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # Male # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 lyt5 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"visible\") %>% analyze(\"AGE\", s_summary, show_labels = \"visible\") %>% analyze(\"BMRKR2\", s_summary, nested = FALSE, show_labels = \"visible\") tbl5 <- build_table(lyt5, ADSL_M_F_l) tbl5 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # —————————————————————————————————————————————————————————————— # Female # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # Male # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 47 44 37 # MEDIUM 36 56 40 # HIGH 47 32 49 insert_NAs <- function(x) { x[sample(c(TRUE, FALSE), length(x), TRUE, prob = c(0.2, 0.8))] <- NA x } set.seed(1) ADSL_NA <- ADSL_M_F_l %>% mutate(AGE = insert_NAs(AGE)) lyt6 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by( \"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"visible\" ) %>% analyze(\"AGE\", s_summary) %>% analyze(\"BMRKR2\", s_summary, nested = FALSE, show_labels = \"visible\") tbl6 <- build_table(lyt6, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl6 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ———————————————————————————————————————————————————————————— # Female # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BMRKR2 # LOW 47 44 37 # MEDIUM 36 56 40 # HIGH 47 32 49 lyt7 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% analyze(\"AGE\", s_summary) %>% analyze(\"BMRKR2\", afun = s_summary, nested = FALSE, show_labels = \"visible\") tbl7 <- build_table(lyt7, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl7 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ———————————————————————————————————————————————————————————— # Female 79 (60.8%) 77 (58.3%) 66 (52.4%) # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male 51 (39.2%) 55 (41.7%) 60 (47.6%) # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BMRKR2 # LOW 47 44 37 # MEDIUM 36 56 40 # HIGH 47 32 49 lyt8 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels) %>% summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) { in_rows( rcell(nrow(df) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\"), .labels = labelstr ) }) %>% analyze(\"AGE\", s_summary) %>% analyze(\"BEP01FL\", afun = s_summary, nested = FALSE, show_labels = \"visible\") tbl8 <- build_table(lyt8, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl8 # A: Drug X B: Placebo C: Combination # (N=130) (N=132) (N=126) # ———————————————————————————————————————————————————————————— # Female 79 (60.77%) 77 (58.33%) 66 (52.38%) # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male 51 (39.23%) 55 (41.67%) 60 (47.62%) # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BEP01FL # Y 67 63 65 # N 63 69 61 lyt9 <- basic_table() %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", labels_var = \"lbl_sex\", split_fun = drop_split_levels, child_labels = \"hidden\") %>% summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) { in_rows( rcell(nrow(df) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\"), .labels = paste0(labelstr, \": count (perc.)\") ) }) %>% analyze(\"AGE\", s_summary) %>% analyze(\"BEP01FL\", s_summary, nested = FALSE, show_labels = \"visible\") tbl9 <- build_table(lyt9, filter(ADSL_NA, SEX %in% c(\"M\", \"F\"))) tbl9 # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————————————————— # Female: count (perc.) 79 (60.77%) 77 (58.33%) 66 (52.38%) # n 65 61 54 # Mean (sd) 32.71 (6.07) 34.33 (7.31) 34.61 (6.78) # IQR 9.00 10.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 54.00 # Male: count (perc.) 51 (39.23%) 55 (41.67%) 60 (47.62%) # n 44 44 50 # Mean (sd) 35.66 (6.78) 36.93 (8.18) 35.64 (8.42) # IQR 10.50 8.25 10.75 # min - max 24.00 - 48.00 21.00 - 58.00 20.00 - 69.00 # BEP01FL # Y 67 63 65 # N 63 69 61"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"using-layouts","dir":"Articles","previous_headings":"Demographic Table","what":"Using Layouts","title":"Example Clinical Trials Tables","text":"Layouts couple advantages tabulating tables directly: .e. separate analyses description actual data referencing variable names happens via strings (non-standard evaluation (NSE) needed, though arguably either feature shortcoming) layouts can reused example demonstrates reusability layouts: can now build table ADSL patients older 18:","code":"adsl_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"SEX\"), afun = s_summary) adsl_lyt # A Pre-data Table Layout # # Column-Split Structure: # ARM (lvls) # # Row-Split Structure: # AGE:SEX (** multivar analysis **) adsl_tbl <- build_table(adsl_lyt, ADSL) adsl_tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ——————————————————————————————————————————————————————————————————— # AGE # n 134 134 132 # Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) # IQR 11.00 10.00 10.00 # min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # SEX # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 adsl_f_tbl <- build_table(lyt, ADSL %>% filter(AGE > 18)) # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf adsl_f_tbl # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————————————— # F # AGE # n 79 77 66 # Mean (sd) 32.76 (6.09) 34.12 (7.06) 35.20 (7.43) # IQR 9.00 8.00 6.75 # min - max 21.00 - 47.00 23.00 - 58.00 21.00 - 64.00 # BMRKR2 # LOW 26 21 26 # MEDIUM 21 38 17 # HIGH 32 18 23 # M # AGE # n 51 55 60 # Mean (sd) 35.57 (7.08) 37.44 (8.69) 35.38 (8.24) # IQR 11.00 9.00 11.00 # min - max 23.00 - 50.00 21.00 - 62.00 20.00 - 69.00 # BMRKR2 # LOW 21 23 11 # MEDIUM 15 18 23 # HIGH 15 14 26 # U # AGE # n 3 2 4 # Mean (sd) 31.67 (3.21) 31.00 (5.66) 35.25 (3.10) # IQR 3.00 4.00 3.25 # min - max 28.00 - 34.00 27.00 - 35.00 31.00 - 38.00 # BMRKR2 # LOW 2 1 1 # MEDIUM 1 0 2 # HIGH 0 1 1 # UNDIFFERENTIATED # AGE # n 1 0 2 # Mean (sd) 28.00 (NA) NA 45.00 (1.41) # IQR 0.00 NA 1.00 # min - max 28.00 - 28.00 Inf - -Inf 44.00 - 46.00 # BMRKR2 # LOW 1 0 2 # MEDIUM 0 0 0 # HIGH 0 0 0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"adverse-events","dir":"Articles","previous_headings":"","what":"Adverse Events","title":"Example Clinical Trials Tables","text":"number different adverse event tables. now present two tables show adverse events ID grade ID. time won’t use ADAE dataset random.cdisc.data rather generate dataset fly (see Adrian’s 2016 Phuse paper):","code":"set.seed(1) lookup <- tribble( ~AEDECOD, ~AEBODSYS, ~AETOXGR, \"HEADACHE\", \"NERVOUS SYSTEM DISORDERS\", \"5\", \"BACK PAIN\", \"MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS\", \"2\", \"GINGIVAL BLEEDING\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"HYPOTENSION\", \"VASCULAR DISORDERS\", \"3\", \"FAECES SOFT\", \"GASTROINTESTINAL DISORDERS\", \"2\", \"ABDOMINAL DISCOMFORT\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"DIARRHEA\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"ABDOMINAL FULLNESS DUE TO GAS\", \"GASTROINTESTINAL DISORDERS\", \"1\", \"NAUSEA (INTERMITTENT)\", \"GASTROINTESTINAL DISORDERS\", \"2\", \"WEAKNESS\", \"MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS\", \"3\", \"ORTHOSTATIC HYPOTENSION\", \"VASCULAR DISORDERS\", \"4\" ) normalize <- function(x) x / sum(x) weightsA <- normalize(c(0.1, dlnorm(seq(0, 5, length.out = 25), meanlog = 3))) weightsB <- normalize(c(0.2, dlnorm(seq(0, 5, length.out = 25)))) N_pop <- 300 ADSL2 <- data.frame( USUBJID = seq(1, N_pop, by = 1), ARM = sample(c(\"ARM A\", \"ARM B\"), N_pop, TRUE), SEX = sample(c(\"F\", \"M\"), N_pop, TRUE), AGE = 20 + rbinom(N_pop, size = 40, prob = 0.7) ) l.adae <- mapply( ADSL2$USUBJID, ADSL2$ARM, ADSL2$SEX, ADSL2$AGE, FUN = function(id, arm, sex, age) { n_ae <- sample(0:25, 1, prob = if (arm == \"ARM A\") weightsA else weightsB) i <- sample(seq_len(nrow(lookup)), size = n_ae, replace = TRUE, prob = c(6, rep(1, 10)) / 16) lookup[i, ] %>% mutate( AESEQ = seq_len(n()), USUBJID = id, ARM = arm, SEX = sex, AGE = age ) }, SIMPLIFY = FALSE ) ADAE2 <- do.call(rbind, l.adae) ADAE2 <- ADAE2 %>% mutate( ARM = factor(ARM, levels = c(\"ARM A\", \"ARM B\")), AEDECOD = as.factor(AEDECOD), AEBODSYS = as.factor(AEBODSYS), AETOXGR = factor(AETOXGR, levels = as.character(1:5)) ) %>% select(USUBJID, ARM, AGE, SEX, AESEQ, AEDECOD, AEBODSYS, AETOXGR) ADAE2 # # A tibble: 3,118 × 8 # USUBJID ARM AGE SEX AESEQ AEDECOD AEBODSYS AETOXGR # # 1 1 ARM A 45 F 1 NAUSEA (INTERMITTENT) GASTROINTESTIN… 2 # 2 1 ARM A 45 F 2 HEADACHE NERVOUS SYSTEM… 5 # 3 1 ARM A 45 F 3 HEADACHE NERVOUS SYSTEM… 5 # 4 1 ARM A 45 F 4 HEADACHE NERVOUS SYSTEM… 5 # 5 1 ARM A 45 F 5 HEADACHE NERVOUS SYSTEM… 5 # 6 1 ARM A 45 F 6 HEADACHE NERVOUS SYSTEM… 5 # 7 1 ARM A 45 F 7 HEADACHE NERVOUS SYSTEM… 5 # 8 1 ARM A 45 F 8 HEADACHE NERVOUS SYSTEM… 5 # 9 1 ARM A 45 F 9 HEADACHE NERVOUS SYSTEM… 5 # 10 1 ARM A 45 F 10 FAECES SOFT GASTROINTESTIN… 2 # # ℹ 3,108 more rows"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"adverse-events-by-id","dir":"Articles","previous_headings":"Adverse Events","what":"Adverse Events By ID","title":"Example Clinical Trials Tables","text":"start defining events summary function: , population 5 patients one patient 2 AEs one patient 1 AE three patients AEs get following summary: .N_col argument special keyword argument build_table() passes population size respective column. list keyword arguments functions passed afun analyze(), refer documentation ?analyze. now use s_events_patients summary function tabulation: Note column Ns wrong default set number rows per group (.e. number AEs per arm ). also affects percentages. table interested number patients per column/arm usually taken ADSL (var ADSL2 ). rtables handles allowing us override column counts computed. can specify alt_counts_df build_table(). , rtables calculates column counts applying column faceting alt_counts_df primary data tabulation: Alternatively, desired column counts already calculated, can specified directly via col_counts argument build_table(), though specifying alt_counts_df preferred mechanism. next calculate information per system organ class: now add count table AEDECOD AEBODSYS. default analyze() behavior factor create count table per level (using rtab_inner): indent_mod argument enables relative indenting changes tree structure table result desired indentation default. table far however usual adverse event table counts total number events number subjects one events particular term. get correct table need write custom analysis function: desired AE table : Note missing overall summary first two rows. can added initial analyze() call. Finally, wanted prune 0 count rows can trim_rows() function: Pruning larger topic separate rtables package vignette.","code":"s_events_patients <- function(x, labelstr, .N_col) { in_rows( \"Total number of patients with at least one event\" = rcell(length(unique(x)) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\"), \"Total number of events\" = rcell(length(x), format = \"xx\") ) } s_events_patients(x = c(\"id 1\", \"id 1\", \"id 2\"), .N_col = 5) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 Total number of patients with at least one event 2 (40.00%) 0 # 2 Total number of events 3 0 # row_label # 1 Total number of patients with at least one event # 2 Total number of events adae_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"USUBJID\", s_events_patients) adae_tbl <- build_table(adae_lyt, ADAE2) adae_tbl # ARM A ARM B # (N=2060) (N=1058) # ————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (5.53%) 150 (14.18%) # Total number of events 2060 1058 adae_adsl_tbl <- build_table(adae_lyt, ADAE2, alt_counts_df = ADSL2) adae_adsl_tbl # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 adae_soc_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"USUBJID\", s_events_patients) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", nested = FALSE) %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) adae_soc_tbl <- build_table(adae_soc_lyt, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl # ARM A ARM B # (N=146) (N=154) # ———————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 adae_soc_lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1) %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) %>% analyze(\"AEDECOD\", indent_mod = -1) adae_soc_tbl2 <- build_table(adae_soc_lyt2, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl2 # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 113 65 # ABDOMINAL FULLNESS DUE TO GAS 119 65 # BACK PAIN 0 0 # DIARRHEA 107 53 # FAECES SOFT 122 58 # GINGIVAL BLEEDING 147 71 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 152 62 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 135 75 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 138 67 # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 787 420 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 104 58 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 136 64 # WEAKNESS 0 0 table_count_once_per_id <- function(df, termvar = \"AEDECOD\", idvar = \"USUBJID\") { x <- df[[termvar]] id <- df[[idvar]] counts <- table(x[!duplicated(id)]) in_rows( .list = as.vector(counts), .labels = names(counts) ) } table_count_once_per_id(ADAE2) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 ABDOMINAL DISCOMFORT 23 0 # 2 ABDOMINAL FULLNESS DUE TO GAS 21 0 # 3 BACK PAIN 20 0 # 4 DIARRHEA 7 0 # 5 FAECES SOFT 11 0 # 6 GINGIVAL BLEEDING 15 0 # 7 HEADACHE 100 0 # 8 HYPOTENSION 16 0 # 9 NAUSEA (INTERMITTENT) 21 0 # 10 ORTHOSTATIC HYPOTENSION 14 0 # 11 WEAKNESS 16 0 # row_label # 1 ABDOMINAL DISCOMFORT # 2 ABDOMINAL FULLNESS DUE TO GAS # 3 BACK PAIN # 4 DIARRHEA # 5 FAECES SOFT # 6 GINGIVAL BLEEDING # 7 HEADACHE # 8 HYPOTENSION # 9 NAUSEA (INTERMITTENT) # 10 ORTHOSTATIC HYPOTENSION # 11 WEAKNESS adae_soc_lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1) %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) %>% analyze(\"AEDECOD\", afun = table_count_once_per_id, show_labels = \"hidden\", indent_mod = -1) adae_soc_tbl3 <- build_table(adae_soc_lyt3, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl3 # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 24 28 # ABDOMINAL FULLNESS DUE TO GAS 18 26 # BACK PAIN 0 0 # DIARRHEA 17 17 # FAECES SOFT 17 14 # GINGIVAL BLEEDING 18 25 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 20 20 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 58 45 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 40 36 # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 113 133 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 44 31 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 49 44 # WEAKNESS 0 0 adae_soc_lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"USUBJID\", afun = s_events_patients) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1, section_div = \"\") %>% summarize_row_groups(\"USUBJID\", cfun = s_events_patients) %>% analyze(\"AEDECOD\", table_count_once_per_id, show_labels = \"hidden\", indent_mod = -1) adae_soc_tbl4 <- build_table(adae_soc_lyt4, ADAE2, alt_counts_df = ADSL2) adae_soc_tbl4 # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 24 28 # ABDOMINAL FULLNESS DUE TO GAS 18 26 # BACK PAIN 0 0 # DIARRHEA 17 17 # FAECES SOFT 17 14 # GINGIVAL BLEEDING 18 25 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 20 20 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 58 45 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 40 36 # # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 113 133 # HYPOTENSION 0 0 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 0 0 # WEAKNESS 0 0 # # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # ABDOMINAL DISCOMFORT 0 0 # ABDOMINAL FULLNESS DUE TO GAS 0 0 # BACK PAIN 0 0 # DIARRHEA 0 0 # FAECES SOFT 0 0 # GINGIVAL BLEEDING 0 0 # HEADACHE 0 0 # HYPOTENSION 44 31 # NAUSEA (INTERMITTENT) 0 0 # ORTHOSTATIC HYPOTENSION 49 44 # WEAKNESS 0 0 trim_rows(adae_soc_tbl4) # ARM A ARM B # (N=146) (N=154) # —————————————————————————————————————————————————————————————————————————————————— # Total number of patients with at least one event 114 (78.08%) 150 (97.40%) # Total number of events 2060 1058 # GASTROINTESTINAL DISORDERS # Total number of patients with at least one event 114 (78.08%) 130 (84.42%) # Total number of events 760 374 # ABDOMINAL DISCOMFORT 24 28 # ABDOMINAL FULLNESS DUE TO GAS 18 26 # DIARRHEA 17 17 # FAECES SOFT 17 14 # GINGIVAL BLEEDING 18 25 # NAUSEA (INTERMITTENT) 20 20 # # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # Total number of patients with at least one event 98 (67.12%) 81 (52.60%) # Total number of events 273 142 # BACK PAIN 58 45 # WEAKNESS 40 36 # # NERVOUS SYSTEM DISORDERS # Total number of patients with at least one event 113 (77.40%) 133 (86.36%) # Total number of events 787 420 # HEADACHE 113 133 # # VASCULAR DISORDERS # Total number of patients with at least one event 93 (63.70%) 75 (48.70%) # Total number of events 240 122 # HYPOTENSION 44 31 # ORTHOSTATIC HYPOTENSION 49 44"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"adverse-events-by-id-and-by-grade","dir":"Articles","previous_headings":"Adverse Events","what":"Adverse Events By ID and By Grade","title":"Example Clinical Trials Tables","text":"adverse events table ID grade shows many patients least one adverse event per grade different subsets data (e.g. defined system organ class). table show zero count grades. Note add “overall” groups custom split function. layouting concepts needed create table already introduced far:","code":"table_count_grade_once_per_id <- function(df, labelstr = \"\", gradevar = \"AETOXGR\", idvar = \"USUBJID\", grade_levels = NULL) { id <- df[[idvar]] grade <- df[[gradevar]] if (!is.null(grade_levels)) { stopifnot(all(grade %in% grade_levels)) grade <- factor(grade, levels = grade_levels) } id_sel <- !duplicated(id) in_rows( \"--Any Grade--\" = sum(id_sel), .list = as.list(table(grade[id_sel])) ) } table_count_grade_once_per_id(ex_adae, grade_levels = 1:5) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod row_label # 1 --Any Grade-- 365 0 --Any Grade-- # 2 1 131 0 1 # 3 2 70 0 2 # 4 3 74 0 3 # 5 4 25 0 4 # 6 5 65 0 5 adae_grade_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze( \"AETOXGR\", afun = table_count_grade_once_per_id, extra_args = list(grade_levels = 1:5), var_labels = \"- Any adverse events -\", show_labels = \"visible\" ) %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\", indent_mod = 1) %>% summarize_row_groups(cfun = table_count_grade_once_per_id, format = \"xx\", indent_mod = 1) %>% split_rows_by(\"AEDECOD\", child_labels = \"visible\", indent_mod = -2) %>% analyze( \"AETOXGR\", afun = table_count_grade_once_per_id, extra_args = list(grade_levels = 1:5), show_labels = \"hidden\" ) adae_grade_tbl <- build_table(adae_grade_lyt, ADAE2, alt_counts_df = ADSL2) adae_grade_tbl # ARM A ARM B # (N=146) (N=154) # ————————————————————————————————————————————————————————————————————— # - Any adverse events - # --Any Grade-- 114 150 # 1 32 34 # 2 22 30 # 3 11 21 # 4 8 6 # 5 41 59 # GASTROINTESTINAL DISORDERS # --Any Grade-- 114 130 # 1 77 96 # 2 37 34 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL DISCOMFORT # --Any Grade-- 68 49 # 1 68 49 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 73 51 # 1 73 51 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 68 40 # 1 68 40 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 76 44 # 1 0 0 # 2 76 44 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 80 52 # 1 80 52 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 83 50 # 1 0 0 # 2 83 50 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # WEAKNESS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS # --Any Grade-- 98 81 # 1 0 0 # 2 58 45 # 3 40 36 # 4 0 0 # 5 0 0 # ABDOMINAL DISCOMFORT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 79 62 # 1 0 0 # 2 79 62 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # WEAKNESS # --Any Grade-- 73 43 # 1 0 0 # 2 0 0 # 3 73 43 # 4 0 0 # 5 0 0 # NERVOUS SYSTEM DISORDERS # --Any Grade-- 113 133 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 113 133 # ABDOMINAL DISCOMFORT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 113 133 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 113 133 # HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # WEAKNESS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # VASCULAR DISORDERS # --Any Grade-- 93 75 # 1 0 0 # 2 0 0 # 3 44 31 # 4 49 44 # 5 0 0 # ABDOMINAL DISCOMFORT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ABDOMINAL FULLNESS DUE TO GAS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # BACK PAIN # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # DIARRHEA # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # FAECES SOFT # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # GINGIVAL BLEEDING # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HEADACHE # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # HYPOTENSION # --Any Grade-- 66 43 # 1 0 0 # 2 0 0 # 3 66 43 # 4 0 0 # 5 0 0 # NAUSEA (INTERMITTENT) # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0 # ORTHOSTATIC HYPOTENSION # --Any Grade-- 70 54 # 1 0 0 # 2 0 0 # 3 0 0 # 4 70 54 # 5 0 0 # WEAKNESS # --Any Grade-- 0 0 # 1 0 0 # 2 0 0 # 3 0 0 # 4 0 0 # 5 0 0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"response-table","dir":"Articles","previous_headings":"","what":"Response Table","title":"Example Clinical Trials Tables","text":"response table create composed 3 parts: Binary response table Unstratified analysis comparison vs. control group Multinomial response table Let’s start first part fairly simple derive: Note set ref_group argument split_cols_by() current table effect use cell data responder non-responder counts. ref_group argument needed part 2 3 table. now look implementation part 2: unstratified analysis comparison vs. control group. Let’s start analysis function: Hence can now add next vignette table: Next add part 3: multinomial response table. , adding row-split response level, thing binary response table . can now create final response table three parts: case wanted rename levels AVALC remove CI NE follows: Note table missing rows gaps make readable. row spacing feature rtables roadmap implemented future.","code":"ADRS_BESRSPI <- ex_adrs %>% filter(PARAMCD == \"BESRSPI\") %>% mutate( rsp = factor(AVALC %in% c(\"CR\", \"PR\"), levels = c(TRUE, FALSE), labels = c(\"Responders\", \"Non-Responders\")), is_rsp = (rsp == \"Responders\") ) s_proportion <- function(x, .N_col) { in_rows( .list = lapply( as.list(table(x)), function(xi) rcell(xi * c(1, 1 / .N_col), format = \"xx.xx (xx.xx%)\") ) ) } rsp_lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") rsp_tbl <- build_table(rsp_lyt, ADRS_BESRSPI) rsp_tbl # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # ——————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) s_unstrat_resp <- function(x, .ref_group, .in_ref_col) { if (.in_ref_col) { return(in_rows( \"Difference in Response Rates (%)\" = rcell(numeric(0)), \"95% CI (Wald, with correction)\" = rcell(numeric(0)), \"p-value (Chi-Squared Test)\" = rcell(numeric(0)), \"Odds Ratio (95% CI)\" = rcell(numeric(0)) )) } fit <- stats::prop.test( x = c(sum(x), sum(.ref_group)), n = c(length(x), length(.ref_group)), correct = FALSE ) fit_glm <- stats::glm( formula = rsp ~ group, data = data.frame( rsp = c(.ref_group, x), group = factor(rep(c(\"ref\", \"x\"), times = c(length(.ref_group), length(x))), levels = c(\"ref\", \"x\")) ), family = binomial(link = \"logit\") ) in_rows( \"Difference in Response Rates (%)\" = non_ref_rcell( (mean(x) - mean(.ref_group)) * 100, .in_ref_col, format = \"xx.xx\" ), \"95% CI (Wald, with correction)\" = non_ref_rcell( fit$conf.int * 100, .in_ref_col, format = \"(xx.xx, xx.xx)\" ), \"p-value (Chi-Squared Test)\" = non_ref_rcell( fit$p.value, .in_ref_col, format = \"x.xxxx | (<0.0001)\" ), \"Odds Ratio (95% CI)\" = non_ref_rcell( c( exp(stats::coef(fit_glm)[-1]), exp(stats::confint.default(fit_glm, level = .95)[-1, , drop = FALSE]) ), .in_ref_col, format = \"xx.xx (xx.xx - xx.xx)\" ) ) } s_unstrat_resp( x = ADRS_BESRSPI %>% filter(ARM == \"A: Drug X\") %>% pull(is_rsp), .ref_group = ADRS_BESRSPI %>% filter(ARM == \"B: Placebo\") %>% pull(is_rsp), .in_ref_col = FALSE ) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 Difference in Response Rates (%) 17.91 0 # 2 95% CI (Wald, with correction) (7.93, 27.89) 0 # 3 p-value (Chi-Squared Test) 0.0006 0 # 4 Odds Ratio (95% CI) 2.79 (1.53 - 5.06) 0 # row_label # 1 Difference in Response Rates (%) # 2 95% CI (Wald, with correction) # 3 p-value (Chi-Squared Test) # 4 Odds Ratio (95% CI) rsp_lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") %>% analyze( \"is_rsp\", s_unstrat_resp, show_labels = \"visible\", var_labels = \"Unstratified Response Analysis\" ) rsp_tbl2 <- build_table(rsp_lyt2, ADRS_BESRSPI) rsp_tbl2 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) # Unstratified Response Analysis # Difference in Response Rates (%) -17.91 5.83 # 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61) # p-value (Chi-Squared Test) 0.0006 0.1436 # Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75) s_prop <- function(df, .N_col) { in_rows( \"95% CI (Wald, with correction)\" = rcell(binom.test(nrow(df), .N_col)$conf.int * 100, format = \"(xx.xx, xx.xx)\") ) } s_prop( df = ADRS_BESRSPI %>% filter(ARM == \"A: Drug X\", AVALC == \"CR\"), .N_col = sum(ADRS_BESRSPI$ARM == \"A: Drug X\") ) # RowsVerticalSection (in_rows) object print method: # ---------------------------- # row_name formatted_cell indent_mod # 1 95% CI (Wald, with correction) (49.38, 66.67) 0 # row_label # 1 95% CI (Wald, with correction) rsp_lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") %>% analyze( \"is_rsp\", s_unstrat_resp, show_labels = \"visible\", var_labels = \"Unstratified Response Analysis\" ) %>% split_rows_by( var = \"AVALC\", split_fun = reorder_split_levels(neworder = c(\"CR\", \"PR\", \"SD\", \"NON CR/PD\", \"PD\", \"NE\"), drlevels = TRUE), nested = FALSE ) %>% summarize_row_groups() %>% analyze(\"AVALC\", afun = s_prop) rsp_tbl3 <- build_table(rsp_lyt3, ADRS_BESRSPI) rsp_tbl3 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) # Unstratified Response Analysis # Difference in Response Rates (%) -17.91 5.83 # 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61) # p-value (Chi-Squared Test) 0.0006 0.1436 # Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75) # CR 78 (58.2%) 55 (41.0%) 97 (73.5%) # 95% CI (Wald, with correction) (49.38, 66.67) (32.63, 49.87) (65.10, 80.79) # PR 36 (26.9%) 35 (26.1%) 23 (17.4%) # 95% CI (Wald, with correction) (19.58, 35.20) (18.92, 34.41) (11.38, 24.99) # SD 20 (14.9%) 44 (32.8%) 12 (9.1%) # 95% CI (Wald, with correction) (9.36, 22.11) (24.97, 41.47) (4.79, 15.34) rsp_label <- function(x) { rsp_full_label <- c( CR = \"Complete Response (CR)\", PR = \"Partial Response (PR)\", SD = \"Stable Disease (SD)\", `NON CR/PD` = \"Non-CR or Non-PD (NON CR/PD)\", PD = \"Progressive Disease (PD)\", NE = \"Not Evaluable (NE)\", Missing = \"Missing\", `NE/Missing` = \"Missing or unevaluable\" ) stopifnot(all(x %in% names(rsp_full_label))) rsp_full_label[x] } rsp_lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\", ref_group = \"ARM A\") %>% analyze(\"rsp\", s_proportion, show_labels = \"hidden\") %>% analyze( \"is_rsp\", s_unstrat_resp, show_labels = \"visible\", var_labels = \"Unstratified Response Analysis\" ) %>% split_rows_by( var = \"AVALC\", split_fun = keep_split_levels(c(\"CR\", \"PR\", \"SD\", \"PD\"), reorder = TRUE), nested = FALSE ) %>% summarize_row_groups(cfun = function(df, labelstr, .N_col) { in_rows(nrow(df) * c(1, 1 / .N_col), .formats = \"xx (xx.xx%)\", .labels = rsp_label(labelstr)) }) %>% analyze(\"AVALC\", afun = s_prop) %>% analyze(\"AVALC\", afun = function(x, .N_col) { in_rows(rcell(sum(x == \"NE\") * c(1, 1 / .N_col), format = \"xx.xx (xx.xx%)\"), .labels = rsp_label(\"NE\")) }, nested = FALSE) rsp_tbl4 <- build_table(rsp_lyt4, ADRS_BESRSPI) rsp_tbl4 # ARM A ARM B ARM C # (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————————————————————————————————————————— # Responders 114.00 (85.07%) 90.00 (67.16%) 120.00 (90.91%) # Non-Responders 20.00 (14.93%) 44.00 (32.84%) 12.00 (9.09%) # Unstratified Response Analysis # Difference in Response Rates (%) -17.91 5.83 # 95% CI (Wald, with correction) (-27.89, -7.93) (-1.94, 13.61) # p-value (Chi-Squared Test) 0.0006 0.1436 # Odds Ratio (95% CI) 0.36 (0.20 - 0.65) 1.75 (0.82 - 3.75) # Complete Response (CR) 78 (58.21%) 55 (41.04%) 97 (73.48%) # 95% CI (Wald, with correction) (49.38, 66.67) (32.63, 49.87) (65.10, 80.79) # Partial Response (PR) 36 (26.87%) 35 (26.12%) 23 (17.42%) # 95% CI (Wald, with correction) (19.58, 35.20) (18.92, 34.41) (11.38, 24.99) # Stable Disease (SD) 20 (14.93%) 44 (32.84%) 12 (9.09%) # 95% CI (Wald, with correction) (9.36, 22.11) (24.97, 41.47) (4.79, 15.34) # Progressive Disease (PD) 0 (0.00%) 0 (0.00%) 0 (0.00%) # 95% CI (Wald, with correction) (0.00, 2.72) (0.00, 2.72) (0.00, 2.76) # Not Evaluable (NE) 0.00 (0.00%) 0.00 (0.00%) 0.00 (0.00%)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/clinical_trials.html","id":"time-to-event-analysis-table","dir":"Articles","previous_headings":"","what":"Time to Event Analysis Table","title":"Example Clinical Trials Tables","text":"time event analysis table constructed consists four parts: Overall subject counts Censored subjects summary Cox proportional-hazards analysis Time--event analysis table constructed sequential use analyze() function, four custom analysis functions corresponding four parts listed . addition table includes referential footnotes relevant table contents. table faceted column-wise arm. First start loading necessary packages preparing data used construction table. adtte dataset used preparing models adtte2 dataset handles missing values “Censor Date Description” column used produce final table. add censoring data example purposes. Next create basic analysis function, a_count_subjs prints overall unique subject counts percentages within data. analysis function created generate counts censored subjects level factor variable dataset. case cnsr_counter function applied CNSDTDSC variable contains censor date description censored subject. function generates counts fractions unique subjects corresponding factor level, excluding missing values (uncensored patients). Cox proportional-hazards (Cox P-H) analysis generated next third custom analysis function, a_cph. Prior creating analysis function, Cox P-H model fit data using coxph() Surv() functions survival package. model used input a_cph analysis function returns hazard ratios, 95% confidence intervals, p-values comparing reference group - case leftmost column. fourth final analysis function, a_tte, generates time first adverse event table three rows corresponding Median, 95% Confidence Interval, Min Max respectively. First survival table constructed summary table survival model using survfit() Surv() functions survival package. table given input a_tte produces table time first adverse event consisting previously mentioned summary statistics. Additionally, a_tte function creates referential footnote within table indicate censoring occurred data. Now able use four analysis functions build time event analysis table. set show_colcounts argument basic_table() TRUE first print total subject counts column. Next use split_cols_by() split table three columns corresponding three different levels ARM, specify first arm, \": Drug X\" act reference group compared - reference group used Cox P-H analysis. call analyze() sequentially using four custom analysis functions argument afun specifying additional arguments necessary. use build_table() construct rtable using adtte2 dataset. Finally, annotate table using fnotes_at_path() function specify product-limit estimates used calculate statistics listed “Time first adverse event” heading within table. referential footnote created earlier time--event analysis function (a_tte) also displayed.","code":"library(survival) adtte <- ex_adaette %>% dplyr::filter(PARAMCD == \"AETTE2\", SAFFL == \"Y\") # Add censoring to data for example adtte[adtte$AVAL > 1.0, ] <- adtte[adtte$AVAL > 1.0, ] %>% mutate(AVAL = 1.0, CNSR = 1) adtte2 <- adtte %>% mutate(CNSDTDSC = ifelse(CNSDTDSC == \"\", \"__none__\", CNSDTDSC)) a_count_subjs <- function(x, .N_col) { in_rows( \"Subjects with Adverse Events n (%)\" = rcell(length(unique(x)) * c(1, 1 / .N_col), format = \"xx (xx.xx%)\") ) } cnsr_counter <- function(df, .var, .N_col) { x <- df[!duplicated(df$USUBJID), .var] x <- x[x != \"__none__\"] lapply(table(x), function(xi) rcell(xi * c(1, 1 / .N_col), format = \"xx (xx.xx%)\")) } cph <- coxph(Surv(AVAL, CNSR == 0) ~ ACTARM + STRATA1, ties = \"exact\", data = adtte) a_cph <- function(df, .var, .in_ref_col, .ref_full, full_cox_fit) { if (.in_ref_col) { ret <- replicate(3, list(rcell(NULL))) } else { curtrt <- df[[.var]][1] coefs <- coef(full_cox_fit) sel_pos <- grep(curtrt, names(coefs), fixed = TRUE) hrval <- exp(coefs[sel_pos]) sdf <- survdiff(Surv(AVAL, CNSR == 0) ~ ACTARM + STRATA1, data = rbind(df, .ref_full)) pval <- (1 - pchisq(sdf$chisq, length(sdf$n) - 1)) / 2 ci_val <- exp(unlist(confint(full_cox_fit)[sel_pos, ])) ret <- list( rcell(hrval, format = \"xx.x\"), rcell(ci_val, format = \"(xx.x, xx.x)\"), rcell(pval, format = \"x.xxxx | (<0.0001)\") ) } in_rows( .list = ret, .names = c(\"Hazard ratio\", \"95% confidence interval\", \"p-value (one-sided stratified log rank)\") ) } surv_tbl <- as.data.frame( summary(survfit(Surv(AVAL, CNSR == 0) ~ ACTARM, data = adtte, conf.type = \"log-log\"))$table ) %>% dplyr::mutate( ACTARM = factor(gsub(\"ACTARM=\", \"\", row.names(.)), levels = levels(adtte$ACTARM)), ind = FALSE ) a_tte <- function(df, .var, kp_table) { ind <- grep(df[[.var]][1], row.names(kp_table), fixed = TRUE) minmax <- range(df[[\"AVAL\"]]) mm_val_str <- format_value(minmax, format = \"xx.x, xx.x\") rowfn <- list() if (all(df$CNSR[df$AVAL == minmax[2]])) { mm_val_str <- paste0(mm_val_str, \"*\") rowfn <- \"* indicates censoring\" } in_rows( Median = kp_table[ind, \"median\", drop = TRUE], \"95% confidence interval\" = unlist(kp_table[ind, c(\"0.95LCL\", \"0.95UCL\")]), \"Min Max\" = mm_val_str, .formats = c(\"xx.xx\", \"xx.xx - xx.xx\", \"xx\"), .row_footnotes = list(NULL, NULL, rowfn) ) } lyt <- basic_table(show_colcounts = TRUE) %>% ## Column faceting split_cols_by(\"ARM\", ref_group = \"A: Drug X\") %>% ## Overall count analyze(\"USUBJID\", a_count_subjs, show_labels = \"hidden\") %>% ## Censored subjects summary analyze(\"CNSDTDSC\", cnsr_counter, var_labels = \"Censored Subjects\", show_labels = \"visible\") %>% ## Cox P-H analysis analyze(\"ARM\", a_cph, extra_args = list(full_cox_fit = cph), show_labels = \"hidden\") %>% ## Time-to-event analysis analyze( \"ARM\", a_tte, var_labels = \"Time to first adverse event\", show_labels = \"visible\", extra_args = list(kp_table = surv_tbl), table_names = \"kapmeier\" ) tbl_tte <- build_table(lyt, adtte2) fnotes_at_path( tbl_tte, c(\"ma_USUBJID_CNSDTDSC_ARM_kapmeier\", \"kapmeier\") ) <- \"Product-limit (Kaplan-Meier) estimates.\" tbl_tte # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————————————————————————————————————— # Subjects with Adverse Events n (%) 134 (100.00%) 134 (100.00%) 132 (100.00%) # Censored Subjects # Clinical Cut Off 6 (4.48%) 3 (2.24%) 14 (10.61%) # Completion or Discontinuation 9 (6.72%) 5 (3.73%) 9 (6.82%) # End of AE Reporting Period 14 (10.45%) 7 (5.22%) 14 (10.61%) # Preferred Term 11 (8.21%) 5 (3.73%) 13 (9.85%) # Hazard ratio 0.7 1.0 # 95% confidence interval (0.5, 0.9) (0.8, 1.4) # p-value (one-sided stratified log rank) 0.1070 0.4880 # Time to first adverse event {1} # Median 0.23 0.39 0.29 # 95% confidence interval 0.18 - 0.33 0.29 - 0.49 0.22 - 0.35 # Min Max {2} 0.0, 1.0* 0.0, 1.0* 0.0, 1.0* # ———————————————————————————————————————————————————————————————————————————————————————— # # {1} - Product-limit (Kaplan-Meier) estimates. # {2} - * indicates censoring # ————————————————————————————————————————————————————————————————————————————————————————"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"customizing-appearance","dir":"Articles","previous_headings":"","what":"Customizing Appearance","title":"Customizing Appearance","text":"vignette, describe various ways can modify customize appearance rtables. Loading package:","code":"library(rtables) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"rows-and-cell-values-alignments","dir":"Articles","previous_headings":"Customizing Appearance","what":"Rows and cell values alignments","title":"Customizing Appearance","text":"possible align content assigning \"left\", \"center\" (default), \"right\" .aligns align arguments in_rows() rcell(), respectively. also possible use decimal, dec_right, dec_left decimal alignments. first takes numerical values aligns decimal character . every value column align = \"decimal\". Also numberic without decimal values aligned according imaginary . specified . dec_left dec_right behave similarly, difference column present empty spaces left right, pushes values towards left right taking one value decimal characters, right, non-decimal values left. details, please read related documentation page help(\"decimal_align\"). Please consider using ?in_rows ?rcell clarifications two arguments, use formatters::list_valid_aligns() see available alignment options. following show two simplified examples use align .aligns, respectively. concepts can well applied clinical table shown following, complex, example.","code":"# In rcell we use align. lyt <- basic_table() %>% analyze(\"AGE\", function(x) { in_rows( left = rcell(\"l\", align = \"left\"), right = rcell(\"r\", align = \"right\"), center = rcell(\"c\", align = \"center\") ) }) tbl <- build_table(lyt, DM) tbl # all obs # ———————————————— # left l # right r # center c # In in_rows, we use .aligns. This can either set the general value or the # single values (see NB). lyt2 <- basic_table() %>% analyze(\"AGE\", function(x) { in_rows( left = rcell(\"l\"), right = rcell(\"r\"), center = rcell(\"c\"), .aligns = c(\"right\") ) # NB: .aligns = c(\"right\", \"left\", \"center\") }) tbl2 <- build_table(lyt2, DM) tbl2 # all obs # ———————————————— # left l # right r # center c lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"STRATA1\"), function(x) { if (is.numeric(x)) { in_rows( \"mean\" = rcell(mean(x)), \"sd\" = rcell(sd(x)), .formats = c(\"xx.x\"), .aligns = \"left\" ) } else if (is.factor(x)) { rcell(length(unique(x)), align = \"right\") } else { stop(\"Unsupported type\") } }, show_labels = \"visible\", na_str = \"NE\") tbl3 <- build_table(lyt3, ex_adsl) tbl3 # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # AGE # mean 32.8 34.1 35.2 # sd 6.1 7.1 7.4 # STRATA1 # STRATA1 3 3 3 # M # AGE # mean 35.6 37.4 35.4 # sd 7.1 8.7 8.2 # STRATA1 # STRATA1 3 3 3 # U # AGE # mean 31.7 31.0 35.2 # sd 3.2 5.7 3.1 # STRATA1 # STRATA1 3 2 3 # UNDIFFERENTIATED # AGE # mean 28.0 NE 45.0 # sd NE NE 1.4 # STRATA1 # STRATA1 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"top-left-materials","dir":"Articles","previous_headings":"Customizing Appearance","what":"Top-left Materials","title":"Customizing Appearance","text":"sequence strings printed area column header display first row label can modified pre-processing using label position argument row splits split_rows_by, append_topleft function, post-processing using top_left() function. Note: Indenting automatically added label_pos = \"topleft\". Within layout initializer: Specify label position using split_rows function. Notice position STRATA1 SEX. Post-processing using top_left() function:","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\") %>% append_topleft(\"New top_left material here\") build_table(lyt, DM) # New top_left material here A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # A # Mean 32.53 32.30 35.76 # B # Mean 35.46 32.42 34.39 # C # Mean 36.34 34.45 33.54 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\", label_pos = \"topleft\") %>% split_rows_by(\"SEX\", label_pos = \"topleft\") %>% analyze(\"AGE\") build_table(lyt, DM) # STRATA1 # SEX A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————— # A # F # Mean 30.91 32.91 35.95 # M # Mean 35.07 31.09 35.60 # U # Mean NA NA NA # UNDIFFERENTIATED # Mean NA NA NA # B # F # Mean 34.85 32.88 34.42 # M # Mean 36.64 32.09 34.37 # U # Mean NA NA NA # UNDIFFERENTIATED # Mean NA NA NA # C # F # Mean 35.19 36.00 34.32 # M # Mean 37.39 32.81 32.83 # U # Mean NA NA NA # UNDIFFERENTIATED # Mean NA NA NA lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\", \"STRATA1\"), function(x) { if (is.numeric(x)) { in_rows( \"mean\" = rcell(mean(x)), \"sd\" = rcell(sd(x)), .formats = c(\"xx.x\"), .aligns = \"left\" ) } else if (is.factor(x)) { rcell(length(unique(x)), align = \"right\") } else { stop(\"Unsupported type\") } }, show_labels = \"visible\", na_str = \"NE\") %>% build_table(ex_adsl) # Adding top-left material top_left(lyt) <- \"New top-left material here\" lyt # New top-left material here A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # F # AGE # mean 32.8 34.1 35.2 # sd 6.1 7.1 7.4 # STRATA1 # STRATA1 3 3 3 # M # AGE # mean 35.6 37.4 35.4 # sd 7.1 8.7 8.2 # STRATA1 # STRATA1 3 3 3 # U # AGE # mean 31.7 31.0 35.2 # sd 3.2 5.7 3.1 # STRATA1 # STRATA1 3 2 3 # UNDIFFERENTIATED # AGE # mean 28.0 NE 45.0 # sd NE NE 1.4 # STRATA1 # STRATA1 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"table-inset","dir":"Articles","previous_headings":"Customizing Appearance","what":"Table Inset","title":"Customizing Appearance","text":"Table title, table body, referential footnotes main footers can inset left alignment titles provenance footer materials. can modified within layout initializer basic_table() using inset argument post-processing table_inset(). Using layout initializer: Using post-processing function: Without inset - inset 5 characters - example table produced clinical data. Compare inset table main footer two tables. Without inset - inset - Notice, inset apply title materials (main title, subtitles, page titles), provenance footer materials. Inset settings applied top-left materials, referential footnotes main footer materials horizontal dividers.","code":"lyt <- basic_table(inset = 5) %>% analyze(\"AGE\") build_table(lyt, DM) # all obs # —————————————— # Mean 34.22 lyt <- basic_table() %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl # all obs # —————————————— # Mean 34.22 table_inset(tbl) <- 5 tbl # all obs # —————————————— # Mean 34.22 analysisfun <- function(x, ...) { in_rows( row1 = 5, row2 = c(1, 2), .row_footnotes = list(row1 = \"row 1 rfn\"), .cell_footnotes = list(row2 = \"row 2 cfn\") ) } lyt <- basic_table( title = \"Title says Whaaaat\", subtitles = \"Oh, ok.\", main_footer = \"ha HA! Footer!\", prov_footer = \"provenaaaaance\" ) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = analysisfun) result <- build_table(lyt, ex_adsl) result # Title says Whaaaat # Oh, ok. # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # row1 {1} 5 5 5 # row2 1, 2 {2} 1, 2 {2} 1, 2 {2} # —————————————————————————————————————————————————— # # {1} - row 1 rfn # {2} - row 2 cfn # —————————————————————————————————————————————————— # # ha HA! Footer! # # provenaaaaance table_inset(result) <- 5 result # Title says Whaaaat # Oh, ok. # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # row1 {1} 5 5 5 # row2 1, 2 {2} 1, 2 {2} 1, 2 {2} # —————————————————————————————————————————————————— # # {1} - row 1 rfn # {2} - row 2 cfn # —————————————————————————————————————————————————— # # ha HA! Footer! # # provenaaaaance"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"horizontal-separation","dir":"Articles","previous_headings":"Customizing Appearance","what":"Horizontal Separation","title":"Customizing Appearance","text":"character value can specified modify horizontal separation column headers table. Horizontal separation applies : separating title + subtitles column labels + top left materials, column labels + top left material row labels + cells, row labels + cells footer content, Referential footnotes main + provenance content something sides divider. , replace default line “=”.","code":"tbl <- basic_table() %>% split_cols_by(\"Species\") %>% add_colcounts() %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), function(x) { in_rows( mean_sd = c(mean(x), sd(x)), var = var(x), min_max = range(x), .formats = c(\"xx.xx (xx.xx)\", \"xx.xxx\", \"xx.x - xx.x\"), .labels = c(\"Mean (sd)\", \"Variance\", \"Min - Max\") ) }) %>% build_table(iris, hsep = \"=\") tbl # setosa versicolor virginica # (N=50) (N=50) (N=50) # ====================================================== # Sepal.Length # Mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) # Variance 0.124 0.266 0.404 # Min - Max 4.3 - 5.8 4.9 - 7.0 4.9 - 7.9 # Petal.Width # Mean (sd) 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) # Variance 0.011 0.039 0.075 # Min - Max 0.1 - 0.6 1.0 - 1.8 1.4 - 2.5"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"section-dividers","dir":"Articles","previous_headings":"Customizing Appearance","what":"Section Dividers","title":"Customizing Appearance","text":"character value can specified section divider succeed every group defined split instruction. Note, trailing divider end table never printed. , “+” repeated used section divider. Section dividers can set ” ” create blank line. Separation characters can specified different row splits. However, one printed “pile ” next .","code":"lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(head(names(iris), -1), afun = function(x) { list( \"mean / sd\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = rcell(diff(range(x)), format = \"xx.xx\") ) }, section_div = \"+\") build_table(lyt, iris) # setosa versicolor virginica # —————————————————————————————————————————————————————— # Sepal.Length # mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 1.50 2.10 3.00 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Sepal.Width # mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 2.10 1.40 1.60 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Petal.Length # mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 0.90 2.10 2.40 # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # Petal.Width # mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) # ++++++++++++++++++++++++++++++++++++++++++++++++++++++ # range 0.50 0.80 1.10 lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(head(names(iris), -1), afun = function(x) { list( \"mean / sd\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = rcell(diff(range(x)), format = \"xx.xx\") ) }, section_div = \" \") build_table(lyt, iris) # setosa versicolor virginica # —————————————————————————————————————————————————————— # Sepal.Length # mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) # # range 1.50 2.10 3.00 # # Sepal.Width # mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32) # # range 2.10 1.40 1.60 # # Petal.Length # mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55) # # range 0.90 2.10 2.40 # # Petal.Width # mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) # # range 0.50 0.80 1.10 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", section_div = \"=\") %>% split_rows_by(\"STRATA1\", section_div = \"~\") %>% analyze(\"AGE\", mean, var_labels = \"Age\", format = \"xx.xx\") build_table(lyt, DM) # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————————————————————————————————————— # ASIAN # A # mean 32.19 33.90 36.81 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean 34.12 31.62 34.73 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean 36.21 33.00 32.39 # =================================================================================== # BLACK OR AFRICAN AMERICAN # A # mean 31.50 28.57 33.62 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean 35.60 30.83 33.67 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean 35.50 34.18 35.00 # =================================================================================== # WHITE # A # mean 37.67 31.33 33.17 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean 39.86 39.00 34.75 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean 39.75 44.67 36.75 # =================================================================================== # AMERICAN INDIAN OR ALASKA NATIVE # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # MULTIPLE # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # OTHER # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA # =================================================================================== # UNKNOWN # A # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # B # mean NA NA NA # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # C # mean NA NA NA"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"indent-modifier","dir":"Articles","previous_headings":"Customizing Appearance","what":"Indent Modifier","title":"Customizing Appearance","text":"Tables default indenting level splitting. custom indent value can supplied indent_mod argument within split function modify default. Compare indenting tables : Default Indent - Modified indent -","code":"basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # U # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # UNDIFFERENTIATED # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # —————————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", indent_mod = 3) %>% split_rows_by(\"STRATA1\", indent_mod = 5) %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # U # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # UNDIFFERENTIATED # A # mean NA NA NA # B # mean NA NA NA # C # mean NA NA NA # —————————————————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"variable-label-visibility","dir":"Articles","previous_headings":"Customizing Appearance","what":"Variable Label Visibility","title":"Customizing Appearance","text":"split instructions, visibility label variable split can modified visible, hidden topleft show_labels argument, label_pos argument, child_labels argument applicable. Note: name levels contained variable. analyze calls, indicates variable visible multiple variables analyzed level nesting. Visibility labels groups generated split can also modified using child_label argument split call. child_label argument can force labels visible addition content rows hide move content rows. Notice placement “AGE” label example: set default, label AGE repeated since one variable analyzed level nesting. Override setting show_labels argument “visible”. example using label_pos argument modifying label visibility: Label order mirror order split_rows_by calls. labels subgroups hidden, label_pos argument set hidden. “SEX” label position hidden - “SEX” label position top-left materials -","code":"lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, child_labels = \"visible\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, show_labels = \"default\") build_table(lyt, DM) # A: Drug X B: Placebo C: Combination # (N=121) (N=106) (N=129) # ————————————————————————————————————————————————————————————————— # F # A # mean 30.9090909090909 32.9090909090909 35.95 # B # mean 34.8518518518519 32.8823529411765 34.4210526315789 # C # mean 35.1904761904762 36 34.3181818181818 # M # A # mean 35.0714285714286 31.0909090909091 35.6 # B # mean 36.6428571428571 32.0869565217391 34.3684210526316 # C # mean 37.3913043478261 32.8125 32.8333333333333 lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(var = \"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, child_labels = \"hidden\") %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, show_labels = \"visible\") build_table(lyt2, DM) # A: Drug X B: Placebo C: Combination # (N=121) (N=106) (N=129) # ————————————————————————————————————————————————————————————————— # A # AGE # mean 30.9090909090909 32.9090909090909 35.95 # B # AGE # mean 34.8518518518519 32.8823529411765 34.4210526315789 # C # AGE # mean 35.1904761904762 36 34.3181818181818 # A # AGE # mean 35.0714285714286 31.0909090909091 35.6 # B # AGE # mean 36.6428571428571 32.0869565217391 34.3684210526316 # C # AGE # mean 37.3913043478261 32.8125 32.8333333333333 basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, label_pos = \"visible\") %>% split_rows_by(\"STRATA1\", label_pos = \"hidden\") %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # ———————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————— # SEX # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # ———————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels, label_pos = \"topleft\") %>% split_rows_by(\"STRATA1\", label_pos = \"hidden\") %>% analyze(\"AGE\", mean, format = \"xx.x\") %>% build_table(DM) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————— # SEX A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"cell-label-and-annotation-wrapping","dir":"Articles","previous_headings":"Customizing Appearance","what":"Cell, Label, and Annotation Wrapping","title":"Customizing Appearance","text":"rtable can rendered customized width setting custom rendering widths cell contents, row labels, titles/footers. demonstrated using sample data table . section aim render table reduced width since table wide contents several cells, labels, titles/footers. following sections use toString() function render table string form. resulting string representation ready printed written plain text file, use strsplit() function combination matrix() function preview rendered wrapped table matrix form within vignette.","code":"trimmed_data <- ex_adsl %>% filter(SEX %in% c(\"M\", \"F\")) %>% filter(RACE %in% levels(RACE)[1:2]) levels(trimmed_data$ARM)[1] <- \"Incredibly long column name to be wrapped\" levels(trimmed_data$ARM)[2] <- \"This_column_name_should_be_split_somewhere\" wide_tbl <- basic_table( title = \"Title that is too long and also needs to be wrapped to a smaller width\", subtitles = \"Subtitle that is also long and also needs to be wrapped to a smaller width\", main_footer = \"Footnote that is wider than expected for this table.\", prov_footer = \"Provenance footer material that is also wider than expected for this table.\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze( c(\"AGE\", \"EOSDY\"), na_str = \"Very long cell contents to_be_wrapped_and_splitted\", inclNAs = TRUE ) %>% build_table(trimmed_data) wide_tbl # Title that is too long and also needs to be wrapped to a smaller width # Subtitle that is also long and also needs to be wrapped to a smaller width # # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # Incredibly long column name to be wrapped This_column_name_should_be_split_somewhere C: Combination # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN # AGE # Mean 32.50 36.68 36.99 # EOSDY # Mean Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted # BLACK OR AFRICAN AMERICAN # AGE # Mean 34.27 34.93 33.71 # EOSDY # Mean Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted Very long cell contents to_be_wrapped_and_splitted # ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # # Footnote that is wider than expected for this table. # # Provenance footer material that is also wider than expected for this table."},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"cell-label-wrapping","dir":"Articles","previous_headings":"Customizing Appearance > Cell, Label, and Annotation Wrapping","what":"Cell & Label Wrapping","title":"Customizing Appearance","text":"width rendered table can customized wrapping column widths. done setting custom width values via widths argument toString() function. length vector passed widths argument must equal total number columns table, including row labels column, value vector corresponding maximum width (characters) allowed column, left right. Similarly, wrapping can applied exporting table via one four export_as_* functions implementing pagination via paginate_table() function rtables package. cases, rendered column widths set using colwidths argument takes input format widths argument toString(). example, wide_tbl four columns (1 row label column 3 content columns) set widths use rendered table. set width row label column 10 characters widths 3 content columns 8 characters. words longer specified width broken continued following line. default 3 spaces separating columns rendered table can customized via col_gap argument toString() width customization desired. resulting output can see table correctly rendered using wrapping total width 43 characters, titles footers remain wider rendered table.","code":"result_wrap_cells <- toString(wide_tbl, widths = c(10, 8, 8, 8)) matrix_wrap_cells <- matrix(strsplit(result_wrap_cells, \"\\n\")[[1]], ncol = 1) matrix_wrap_cells # [,1] # [1,] \"Title that is too long and also needs to be wrapped to a smaller width\" # [2,] \"Subtitle that is also long and also needs to be wrapped to a smaller width\" # [3,] \"\" # [4,] \"———————————————————————————————————————————\" # [5,] \" Incredib This_col \" # [6,] \" ly long umn_name \" # [7,] \" column _should_ \" # [8,] \" name be_split \" # [9,] \" to be _somewhe C: Combi\" # [10,] \" wrapped re nation \" # [11,] \"———————————————————————————————————————————\" # [12,] \"ASIAN \" # [13,] \" AGE \" # [14,] \" Mean 32.50 36.68 36.99 \" # [15,] \" EOSDY \" # [16,] \" Mean Very Very Very \" # [17,] \" long long long \" # [18,] \" cell cell cell \" # [19,] \" contents contents contents\" # [20,] \" to_be_wr to_be_wr to_be_wr\" # [21,] \" apped_an apped_an apped_an\" # [22,] \" d_splitt d_splitt d_splitt\" # [23,] \" ed ed ed \" # [24,] \"BLACK OR \" # [25,] \"AFRICAN \" # [26,] \"AMERICAN \" # [27,] \" AGE \" # [28,] \" Mean 34.27 34.93 33.71 \" # [29,] \" EOSDY \" # [30,] \" Mean Very Very Very \" # [31,] \" long long long \" # [32,] \" cell cell cell \" # [33,] \" contents contents contents\" # [34,] \" to_be_wr to_be_wr to_be_wr\" # [35,] \" apped_an apped_an apped_an\" # [36,] \" d_splitt d_splitt d_splitt\" # [37,] \" ed ed ed \" # [38,] \"———————————————————————————————————————————\" # [39,] \"\" # [40,] \"Footnote that is wider than expected for this table.\" # [41,] \"\" # [42,] \"Provenance footer material that is also wider than expected for this table.\""},{"path":"https://insightsengineering.github.io/rtables/main/articles/custom_appearance.html","id":"title-footer-wrapping","dir":"Articles","previous_headings":"Customizing Appearance > Cell, Label, and Annotation Wrapping","what":"Title & Footer Wrapping","title":"Customizing Appearance","text":"addition wrapping column widths, titles footers can wrapped setting tf_wrap = TRUE toString() setting max_width argument toString() maximum width (characters) allowed titles/footers. four export_as_* functions paginate_table() can also wrap titles/footers setting two arguments. following code, set max_width = 43 rendered table annotations maximum width 43 characters.","code":"result_wrap_cells_tf <- toString( wide_tbl, widths = c(10, 8, 8, 8), tf_wrap = TRUE, max_width = 43 ) matrix_wrap_cells_tf <- matrix(strsplit(result_wrap_cells_tf, \"\\n\")[[1]], ncol = 1) matrix_wrap_cells_tf # [,1] # [1,] \"Title that is too long and also needs to be\" # [2,] \"wrapped to a smaller width\" # [3,] \"Subtitle that is also long and also needs\" # [4,] \"to be wrapped to a smaller width\" # [5,] \"\" # [6,] \"———————————————————————————————————————————\" # [7,] \" Incredib This_col \" # [8,] \" ly long umn_name \" # [9,] \" column _should_ \" # [10,] \" name be_split \" # [11,] \" to be _somewhe C: Combi\" # [12,] \" wrapped re nation \" # [13,] \"———————————————————————————————————————————\" # [14,] \"ASIAN \" # [15,] \" AGE \" # [16,] \" Mean 32.50 36.68 36.99 \" # [17,] \" EOSDY \" # [18,] \" Mean Very Very Very \" # [19,] \" long long long \" # [20,] \" cell cell cell \" # [21,] \" contents contents contents\" # [22,] \" to_be_wr to_be_wr to_be_wr\" # [23,] \" apped_an apped_an apped_an\" # [24,] \" d_splitt d_splitt d_splitt\" # [25,] \" ed ed ed \" # [26,] \"BLACK OR \" # [27,] \"AFRICAN \" # [28,] \"AMERICAN \" # [29,] \" AGE \" # [30,] \" Mean 34.27 34.93 33.71 \" # [31,] \" EOSDY \" # [32,] \" Mean Very Very Very \" # [33,] \" long long long \" # [34,] \" cell cell cell \" # [35,] \" contents contents contents\" # [36,] \" to_be_wr to_be_wr to_be_wr\" # [37,] \" apped_an apped_an apped_an\" # [38,] \" d_splitt d_splitt d_splitt\" # [39,] \" ed ed ed \" # [40,] \"———————————————————————————————————————————\" # [41,] \"\" # [42,] \"Footnote that is wider than expected for\" # [43,] \"this table.\" # [44,] \"\" # [45,] \"Provenance footer material that is also\" # [46,] \"wider than expected for this table.\""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"debugging","dir":"Articles > Dev-guide","previous_headings":"","what":"Debugging","title":"Debugging in {rtables} and Beyond","text":"short non-comprehensive guide debugging rtables. Regardless, considered valid personal use discretion.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"coding-in-practice","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Coding in Practice","title":"Debugging in {rtables} and Beyond","text":"easy read find problems clever impossible debug","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"some-definitions","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Some Definitions","title":"Debugging in {rtables} and Beyond","text":"Coding Error - Code intended -> Bug punch card Unexpected Input - Defensive programming FAIL FAST FAIL LOUD (FFFL) -> useful time consuming Bug Dependency -> never use dependencies can!","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"considerations-about-fffl","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Considerations About FFFL","title":"Debugging in {rtables} and Beyond","text":"Errors close possible source. example, bad inputs found early. worst possible example software silently giving incorrect results. Common things can catch early missing values, column length == 0, length > 1.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"general-suggestions","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"General Suggestions","title":"Debugging in {rtables} and Beyond","text":"Robust code base attempt possibly problematic operations. Read Error Messages debugcall can add signature (formals) trace powerful can add reaction tracer good precise find happens options(error = recover) one best tools debug core tool developing allows step point function call sequence. dump.frames debugger: saves file object call debugger step recover.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"warn-global-option","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"warn Global Option","title":"Debugging in {rtables} and Beyond","text":"<0 ignored 0 top level function call 1 immediately occur >=2 throws errors <<- recover debugger gives global environment","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"lo-fi-debugging","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"lo-fi debugging","title":"Debugging in {rtables} and Beyond","text":"PRINT / CAT always low level debugging can used. helpful server jobs maybe terminal console output available browser() can used. example, can print position state function certain point untill find break point. comment blocks -> work pipes (can use identity() step nothing break pipes) browser() bombing","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"regression-tests","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Regression Tests","title":"Debugging in {rtables} and Beyond","text":"Almost every bug become regression test.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"debugging-with-pipes","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Debugging with Pipes","title":"Debugging in {rtables} and Beyond","text":"Pipes better write code horrible debug T pipe %T>% print midway debug_pipe() -> like T pipe going browser()","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"shiny-debugging","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"Shiny Debugging","title":"Debugging in {rtables} and Beyond","text":"difficult due reactivity.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"general-suggestion","dir":"Articles > Dev-guide","previous_headings":"Debugging","what":"General Suggestion","title":"Debugging in {rtables} and Beyond","text":"CLEVER CODE - , CLEVER ALSO SUBJECTIVE CHANGE TIME.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_debug_rtables.html","id":"debugging-in-rtables","dir":"Articles > Dev-guide","previous_headings":"","what":"Debugging in rtables","title":"Debugging in {rtables} and Beyond","text":"invite smart developer use provided examples way get “interactive” dynamic view internal algorithms routinely executed constructing tables rtables. achieved using browser() debugonce() internal exported functions (rtables::: rtables::), see moment. invite continuously autonomously explore multiple S3 S4 objects constitute complexity power rtables. , use following functions: methods(generic_function): function lists methods available generic function. Specifically S4 generic functions, showMethods(generic_function) gives detailed information method (e.g. inheritance). class(object): function returns class object. class one built-classes R, can use information search documentation examples. help(class) may informative call documentation specific class. Similarly, ? operator bring documentation page different S4 methods. S3 methods necessary postfix class name dot (e.g. ?summary.lm). getClass(class): describes type class compact way, slots , relationships may classes may inherit inherited . getClass(object) can see values slots object assigned. possible use str(object, max.level = 2) see less formal compact descriptions slots, may problematic one objects class slots. Hence, maximum number levels always limited 2 3 (max.level = 2). Similarly, attributes() can used retrieve information, need remember storing important variables way encouraged. Information regarding type class can retrieved mode() indirectly summary() .S4(). *getAnywhere(function) useful get source code internal functions specific generics. works well S3 methods, display relevant namespace methods found. Similarly, getMethod(S4_generic, S4_class) can retrieve source code class-specific S4 methods. eval(debugcall(generic_function(obj))): useful way browse S4 method, specifically defined object, without manually insert browser() code. also possible similarly R > 3.4.0 debug*() calls can triggering signature (class) specified. modern simplified wrappers tracing function trace().","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_notes.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Sparse Notes on {rtables} Internals","text":"collection notes divided issues working document end dev vignette one day.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_notes.html","id":"section_div-notes","dir":"Articles > Dev-guide","previous_headings":"","what":"section_div notes","title":"Sparse Notes on {rtables} Internals","text":"Everything layout built split objects, reside 00_tabletrees.R. section_div defined internally split object child_section_div assigned NA_character default. needs split objects need separator divisor. Object-wise, virtual class Split contains section_div following subclasses. tagged “X” constructor allows section_div assigned value different NA_character, “NX” otherwise. can updated related layout functions. important, covered tests analyze split_rows_by. Now relevant understand information saved table object built build_table. need see present assigned. Let’s go back 00tabletree.Rand look trailing_section_div. classes definitions goes, notice search trailing_section_div present virtual classes TableRow VTableTree. following class hierarchy makes `trailing_section_div: Always check constructors finding classes. case example, DataRow ContentRow share constructor, need add identical getter setters two classes virtual class TableRow. Different story LabelRow needs handle differently. Now, understand two feature, lets see structure table built section dividers: , show trailing_section_div methods TableRow virtual object, LabelRow, VTableTree. three make whole section_div structure VTableTree present TableTree ElementaryTable two main table objects. NA_character_ section_div printed split divisions. LabelRow TableRow different assignment allows row-wise modification separators. special case ContentRow, represented content_table(obj) one-line ElementaryTable, label row turned . Please take moment check following setter: only_sep_sections parameter used change separators (splits) data rows. happening forcefully set TRUE, automatically activated section_div(tbl) <- char_v character vector length < nrow(tbl). Notice exception ContentRow activated switcher is_content_table. content rows visible label row. see main table structure change two blocks depending only_sep_sections. TRUE VTableTree modified leading split section separators modified. Also consider looking section_div getter tests test-accessors.R insights structure. Also understand exactly bound output, please check result make_row_df() column trailing_sep. Indeed, alternative iterative method used make_row_df retrieve information separators table row. trailing separator definition, added header_section_div function parameter basic_table, possibly add empty line header (e.g. header_section_div(tbl) = \" \"). trailing separator, separator added header. close circle, please check trailing_sep header_section_div propagated printed/used formatters::toString.","code":"library(rtables) ## Loading required package: formatters ## Loading required package: magrittr ## ## Attaching package: 'rtables' ## The following object is masked from 'package:utils': ## ## str getClass(\"Split\") ## Virtual Class \"Split\" [package \"rtables\"] ## ## Slots: ## ## Name: payload name split_label ## Class: ANY character character ## ## Name: split_format split_na_str split_label_position ## Class: FormatSpec character character ## ## Name: content_fun content_format content_na_str ## Class: listOrNULL FormatSpec character ## ## Name: content_var label_children extra_args ## Class: character logical list ## ## Name: indent_modifier content_indent_modifier content_extra_args ## Class: integer integer list ## ## Name: page_title_prefix child_section_div ## Class: character character ## ## Known Subclasses: ## Class \"CustomizableSplit\", directly ## Class \"AllSplit\", directly ## Class \"VarStaticCutSplit\", directly ## Class \"VarDynCutSplit\", directly ## Class \"VAnalyzeSplit\", directly ## Class \"CompoundSplit\", directly ## Class \"VarLevelSplit\", by class \"CustomizableSplit\", distance 2 ## Class \"MultiVarSplit\", by class \"CustomizableSplit\", distance 2 ## Class \"RootSplit\", by class \"AllSplit\", distance 2 ## Class \"ManualSplit\", by class \"AllSplit\", distance 2 ## Class \"CumulativeCutSplit\", by class \"VarStaticCutSplit\", distance 2 ## Class \"AnalyzeVarSplit\", by class \"VAnalyzeSplit\", distance 2 ## Class \"AnalyzeColVarSplit\", by class \"VAnalyzeSplit\", distance 2 ## Class \"AnalyzeMultiVars\", by class \"CompoundSplit\", distance 2 ## Class \"VarLevWBaselineSplit\", by class \"VarLevelSplit\", distance 3 # Known Subclasses: #? Class \"CustomizableSplit\", directly # vclass used for grouping different split types (I guess) # Class \"AllSplit\", directly # NX # Class \"VarStaticCutSplit\", directly # X via make_static_cut_split # Class \"VarDynCutSplit\", directly # X # Class \"VAnalyzeSplit\", directly # X #? Class \"CompoundSplit\", directly # Used only for AnalyzeMultiVars (maybe not needed?) # Class \"VarLevelSplit\", by class \"CustomizableSplit\", distance 2 # X # Class \"MultiVarSplit\", by class \"CustomizableSplit\", distance 2 # X # Class \"RootSplit\", by class \"AllSplit\", distance 2 # NX # Class \"ManualSplit\", by class \"AllSplit\", distance 2 # X # Class \"CumulativeCutSplit\", by class \"VarStaticCutSplit\", distance 2 # X via make_static_cut_split # Class \"AnalyzeVarSplit\", by class \"VAnalyzeSplit\", distance 2 # Virtual # Class \"AnalyzeColVarSplit\", by class \"VAnalyzeSplit\", distance 2 # X # Class \"AnalyzeMultiVars\", by class \"CompoundSplit\", distance 2 # X # Class \"VarLevWBaselineSplit\", by class \"VarLevelSplit\", distance 3 # NX getClass(\"TableRow\") ## Virtual Class \"TableRow\" [package \"rtables\"] ## ## Slots: ## ## Name: leaf_value var_analyzed label ## Class: ANY character character ## ## Name: row_footnotes trailing_section_div level ## Class: list character integer ## ## Name: name col_info format ## Class: character InstantiatedColumnInfo FormatSpec ## ## Name: na_str indent_modifier table_inset ## Class: character integer integer ## ## Extends: ## Class \"VLeaf\", directly ## Class \"VTableNodeInfo\", directly ## Class \"VNodeInfo\", by class \"VLeaf\", distance 2 ## ## Known Subclasses: \"DataRow\", \"ContentRow\", \"LabelRow\" # Extends: # Class \"VLeaf\", directly # Class \"VTableNodeInfo\", directly # Class \"VNodeInfo\", by class \"VLeaf\", distance 2 # # Known Subclasses: \"DataRow\", \"ContentRow\", \"LabelRow\" getClass(\"VTableTree\") ## Virtual Class \"VTableTree\" [package \"rtables\"] ## ## Slots: ## ## Name: children rowspans labelrow ## Class: list data.frame LabelRow ## ## Name: page_titles horizontal_sep header_section_div ## Class: character character character ## ## Name: trailing_section_div col_info format ## Class: character InstantiatedColumnInfo FormatSpec ## ## Name: na_str indent_modifier table_inset ## Class: character integer integer ## ## Name: level name main_title ## Class: integer character character ## ## Name: subtitles main_footer provenance_footer ## Class: character character character ## ## Extends: ## Class \"VTableNodeInfo\", directly ## Class \"VTree\", directly ## Class \"VTitleFooter\", directly ## Class \"VNodeInfo\", by class \"VTableNodeInfo\", distance 2 ## ## Known Subclasses: \"ElementaryTable\", \"TableTree\" # Extends: # Class \"VTableNodeInfo\", directly # Class \"VTree\", directly # Class \"VTitleFooter\", directly # Class \"VNodeInfo\", by class \"VTableNodeInfo\", distance 2 # # Known Subclasses: \"ElementaryTable\", \"TableTree\" lyt <- basic_table() %>% split_rows_by(\"ARM\", section_div = \"+\") %>% split_rows_by(\"STRATA1\", section_div = \"\") %>% analyze(\"AGE\", afun = function(x) list(\"Mean\" = mean(x), \"Standard deviation\" = sd(x)), format = list(\"Mean\" = \"xx.\", \"Standard deviation\" = \"xx.\"), section_div = \"~\") tbl <- build_table(lyt, DM) print(tbl) ## all obs ## ———————————————————————————————— ## A: Drug X ## A ## Mean 33 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ## B ## Mean 35 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ## C ## Mean 36 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 9 ## ++++++++++++++++++++++++++++++++ ## B: Placebo ## A ## Mean 32 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 ## ## B ## Mean 32 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 ## ## C ## Mean 34 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ++++++++++++++++++++++++++++++++ ## C: Combination ## A ## Mean 36 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 7 ## ## B ## Mean 34 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 ## ## C ## Mean 34 ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Standard deviation 6 print(class(tbl)) # TableTree ## [1] \"TableTree\" ## attr(,\"package\") ## [1] \"rtables\" # methods(\"trailing_section_div\") # to see this please do devtools::load_all() # [1] trailing_section_div,LabelRow-method # trailing_section_div,TableRow-method # trailing_section_div,VTableTree-method setMethod(\"section_div<-\", \"VTableTree\", function(obj, value, only_sep_sections = FALSE) { char_v <- as.character(value) tree_depths <- unname(vapply(collect_leaves(obj), tt_level, numeric(1))) max_tree_depth <- max(tree_depths) stopifnot(is.logical(only_sep_sections)) .check_char_vector_for_section_div(char_v, max_tree_depth, nrow(obj)) # Automatic establishment of intent if (length(char_v) < nrow(obj)) { only_sep_sections <- TRUE } # Case where only separators or splits need to change externally if (only_sep_sections && length(char_v) < nrow(obj)) { if (length(char_v) == 1) { char_v <- rep(char_v, max_tree_depth - 1) # -1 is the data row } # Case where char_v is longer than the max depth char_v <- char_v[seq_len(min(max_tree_depth, length(char_v)))] # Filling up with NAs the rest of the tree depth section div chr vector missing_char_v_len <- max_tree_depth - length(char_v) char_v <- c(char_v, rep(NA_character_, missing_char_v_len)) # char_v <- unlist( # lapply(tree_depths, function(tree_depth_i) char_v[seq_len(tree_depth_i)]), # use.names = FALSE # ) } # Retrieving if it is a contentRow (no need for labelrow to be visible in this case) content_row_tbl <- content_table(obj) is_content_table <- isS4(content_row_tbl) && nrow(content_row_tbl) > 0 # Main table structure change if (labelrow_visible(obj) || is_content_table) { if (only_sep_sections) { # Only tables are modified trailing_section_div(tt_labelrow(obj)) <- NA_character_ trailing_section_div(obj) <- char_v[1] section_div(tree_children(obj), only_sep_sections = only_sep_sections) <- char_v[-1] } else { # All leaves are modified trailing_section_div(tt_labelrow(obj)) <- char_v[1] trailing_section_div(obj) <- NA_character_ section_div(tree_children(obj), only_sep_sections = only_sep_sections) <- char_v[-1] } } else { section_div(tree_children(obj), only_sep_sections = only_sep_sections) <- char_v } obj })"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Split Machinery","text":"article intended use developers contain low-level explanations topics covered. user-friendly vignettes, please see Articles page rtables website. code prose appears version article main branch repository may reflect specific state things can less recent. guide describes important pieces split machinery unlikely change. Regardless, invite reader keep mind current repository code may drifted following material document, always best practice read code directly main. Please keep mind rtables still active development, seen efforts multiple contributors across different years. Therefore, may legacy mechanisms ongoing transformations look different future. working document may subjected deprecation updates, keep xxx comments indicate placeholders warnings -’s need work.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"introduction","dir":"Articles > Dev-guide","previous_headings":"","what":"Introduction","title":"Split Machinery","text":"scope article understanding rtables creates facets splitting incoming data hierarchical groups go root node singular rcells. latter level, also called leaf-level, contains final partition subjected analysis functions. details user perspective can found Split Functions vignette function documentation like ?split_rows_by ?split_funcs. following article describe split machinery works row domain. information split machinery works column domain covered separate article.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"process-and-methods","dir":"Articles > Dev-guide","previous_headings":"","what":"Process and Methods","title":"Split Machinery","text":"Beforehand, encourage reader familiarize Debugging {rtables} article rtables Developers Guide. document generally valid R programming, tailored study understand complex packages rely heavily S3 S4 object programming like rtables. , explore study split machinery growing amount complexity, following relevant functions methods throughout execution. going basic complex discussing important special cases, hope able give good understanding split machinery works. practice, majority split engine resides source file R/split_funs.R, occasional incursion R/make_split_fun.R custom split function creation rarer references general tabulation files.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"do_split","dir":"Articles > Dev-guide","previous_headings":"","what":"do_split","title":"Split Machinery","text":"split machinery fundamental rtables relevant functions like do_split executed even split requested. following example shows can enter do_split start understanding class hierarchy main split engine. following code, copied do_split function code allow reader go general structure enhanced comments sections. section code reflects roughly one section article. see input parameters used. important parameters spl df - split objects input data.frame, respectively.","code":"library(rtables) # debugonce(rtables:::do_split) # Uncomment me to enter the function!!! basic_table() %>% build_table(DM) ## all obs ## —————————— # rtables 0.6.2 ### NB This is called at EACH level of recursive splitting do_split <- function(spl, df, vals = NULL, labels = NULL, trim = FALSE, spl_context) { # - CHECKS - # ## This will error if, e.g., df does not have columns ## required by spl, or generally any time the split (spl) ## can not be applied to df check_validsplit(spl, df) # - SPLIT FUNCTION - # ## In special cases, we need to partition data (split) ## in a very specific way, e.g. depending on the data or ## external values. These can be achieved by using a custom ## split function. ## note the <- here!!! if (!is.null(splfun <- split_fun(spl))) { ## Currently split functions take df, vals, labels and ## return list(values = ..., datasplit = ..., labels = ...), ## with an optional additional 'extras' element if (func_takes(splfun, \".spl_context\")) { ret <- tryCatch( splfun(df, spl, vals, labels, trim = trim, .spl_context = spl_context ), error = function(e) e ) ## rawvalues(spl_context)) } else { ret <- tryCatch(splfun(df, spl, vals, labels, trim = trim), error = function(e) e ) } if (is(ret, \"error\")) { stop( \"Error applying custom split function: \", ret$message, \"\\n\\tsplit: \", class(spl), \" (\", payloadmsg(spl), \")\\n\", \"\\toccured at path: \", spl_context_to_disp_path(spl_context), \"\\n\" ) } } else { # - .apply_split_inner - # ## This is called when no split function is provided. Please note that this function ## will also probably be called when the split function is provided, as long as the ## main splitting method is not willingly modified by the split function. ret <- .apply_split_inner(df = df, spl = spl, vals = vals, labels = labels, trim = trim) } # - EXTRA - # ## this adds .ref_full and .in_ref_col if (is(spl, \"VarLevWBaselineSplit\")) { ret <- .add_ref_extras(spl, df, ret) } # - FIXUPVALS - # ## This: ## - guarantees that ret$values contains SplitValue objects ## - removes the extras element since its redundant after the above ## - ensures datasplit and values lists are named according to labels ## - ensures labels are character not factor ret <- .fixupvals(ret) # - RETURN - # ret }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"checks-and-classes","dir":"Articles > Dev-guide","previous_headings":"do_split","what":"Checks and Classes","title":"Split Machinery","text":"start looking first function called do_split. give us good overview split defined. function , course, check function (check_validsplit) used verify split valid data. following describe split-class hierarchy step--step, invite reader explore well. Let’s first search package check_validsplit. find defined generic R/split_funs.R, applied following “split” classes: VarLevelSplit, MultiVarSplit, VAnalyzeSplit, CompoundSplit, Split. Another way find information, useful spread complicated objects, using showMethods(check_validsplit). virtual class VAnalyzeSplit (convention virtual classes start “V”) defines main parent analysis split discuss detail related vignette vignette() (xxx). , can see analyze() calls actually mimic split objects create different results specific final split (node). Now, notice check_validsplit also called another location, main R/tt_dotabulation.R source file. something related making “analyze” rows mainly checks VAnalyzeSplit. See Tabulation article details. discuss classes appear examples. See class hierarchy Table Hierarchy article. moment, see class(spl) (main do_split function) dealing AllSplit object. calling showMethods(check_validsplit) produce following: means listed classes dedicated definition check_validsplit may largely differ others. class AllSplit function definition inherited Split class. Therefore, understand AllSplit parent class Split. one first definitions virtual class package one include “V” prefix. classes defined along constructors R/00tabletrees.R. Reading AllSplit structured can useful understanding split objects expected work. Please see comments following: can also print information calling getClass(\"AllSplit\") general slot definition, calling getClass(spl). Note first call give also lot information class hierarchy. information regarding class hierarchy, please refer relevant article . discuss majority slots end document. Now, let’s see can find values described constructor within object. , show compact representation given str. multiple hierarchical slots contain objects , calling str much less informative maximum level nesting set (e.g. max.level = 2). Details slots become necessary future examples, deal time. Now, gave hint complex class hierarchy makes rtables, explore autonomously. Let’s go forward do_split. case, AllSplit inherited Split, sure called function following (read comment!):","code":"# rtables 0.6.2 Function: check_validsplit (package rtables) spl=\"AllSplit\" (inherited from: spl=\"Split\") spl=\"CompoundSplit\" spl=\"MultiVarSplit\" spl=\"Split\" spl=\"VAnalyzeSplit\" spl=\"VarLevelSplit\" # rtables 0.6.2 setClass(\"AllSplit\", contains = \"Split\") AllSplit <- function(split_label = \"\", cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = NULL, extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), ...) { if (is.null(split_name)) { # If the split has no name if (nzchar(split_label)) { # (std is \"\") split_name <- split_label } else { split_name <- \"all obs\" # No label, a standard split with all # observations is assigned. } } new(\"AllSplit\", split_label = split_label, content_fun = cfun, content_format = cformat, content_na_str = cna_str, split_format = split_format, split_na_str = split_na_str, name = split_name, label_children = FALSE, extra_args = extra_args, indent_modifier = as.integer(indent_mod), content_indent_modifier = as.integer(cindent_mod), content_var = cvar, split_label_position = \"hidden\", content_extra_args = cextra_args, page_title_prefix = NA_character_, child_section_div = NA_character_ ) } # rtables 0.6.2 Browse[2]> str(spl, max.level = 2) Formal class 'AllSplit' [package \"rtables\"] with 17 slots ..@ payload : NULL ..@ name : chr \"all obs\" ..@ split_label : chr \"\" ..@ split_format : NULL ..@ split_na_str : chr NA ..@ split_label_position : chr \"hidden\" ..@ content_fun : NULL ..@ content_format : NULL ..@ content_na_str : chr NA ..@ content_var : chr \"\" ..@ label_children : logi FALSE ..@ extra_args : list() ..@ indent_modifier : int 0 ..@ content_indent_modifier: int 0 ..@ content_extra_args : list() ..@ page_title_prefix : chr NA ..@ child_section_div : chr NA # rtables 0.6.2 ## Default does nothing, add methods as they become required setMethod( \"check_validsplit\", \"Split\", function(spl, df) invisible(NULL) )"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"split-functions-and--apply_split_inner","dir":"Articles > Dev-guide","previous_headings":"do_split","what":"Split Functions and .apply_split_inner","title":"Split Machinery","text":"diving custom split functions, need take moment analyze .apply_split_inner works. function routinely called whether split function. Let’s see case entering debugonce(.apply_split_inner). course, still currently browsing within do_split debug mode first example. print comment function following: reading .apply_split_inner, see fundamental functions - defined strictly internal use (convention start “.”) - generics depend kind split input. R/split_funs.R kind groups generic definitions beginning file. functions main dispatchers majority split machinery. clear example shows using S4 logic enables better clarity flexibility programming, allowing easy extension program. compactness also show showMethods result generic. Now, know .applysplit_extras function called first. specify vals therefore NULL. S4 generic function can seen showMethod(.applysplit_extras), definition can seen following: .applysplit_extras, simply extracts extra arguments split objects assigns relative split values. function covered detail later section. still split values available, function exit empty split. Otherwise, data divided different splits data subsets (facets) .applysplit_datapart. current example, resulting list comprises whole input dataset (getMethod(\".applysplit_datapart\", \"AllSplit\") list evident: function (spl, df, vals) list(df)). Next, split labels checked. present, split values (vals) used .applysplit_partlabels, transformed .character(vals) applied Split object. Otherwise, inserted labels checked names split values. Lastly, split values ordered according spl_child_order. case, concerns general AllSplit, sorting happen, .e. dependent simply number split values (seq_along(vals)).","code":"# rtables 0.6.2 .apply_split_inner <- function(spl, df, vals = NULL, labels = NULL, trim = FALSE) { # - INPUTS - # # In this case .applysplit_rawvals will attempt to find the split values if vals is NULL. # Please notice that there may be a non-mutually exclusive set or subset of elements that # will constitute the split. # - SPLIT VALS - # ## Try to calculate values first - most of the time we can if (is.null(vals)) { vals <- .applysplit_rawvals(spl, df) } # - EXTRA PARAMETERS - # # This call extracts extra parameters from the split, according to the split values extr <- .applysplit_extras(spl, df, vals) # If there are no values to do the split upon, we return an empty final split if (is.null(vals)) { return(list( values = list(), datasplit = list(), labels = list(), extras = list() )) } # - DATA SUBSETTING - # dpart <- .applysplit_datapart(spl, df, vals) # - LABEL RETRIEVAL - # if (is.null(labels)) { labels <- .applysplit_partlabels(spl, df, vals, labels) } else { stopifnot(names(labels) == names(vals)) } # - TRIM - # ## Get rid of columns that would not have any observations, ## but only if there were any rows to start with - if not ## we're in a manually constructed table column tree if (trim) { hasdata <- sapply(dpart, function(x) nrow(x) > 0) if (nrow(df) > 0 && length(dpart) > sum(hasdata)) { # some empties dpart <- dpart[hasdata] vals <- vals[hasdata] extr <- extr[hasdata] labels <- labels[hasdata] } } # - ORDER RESULTS - # # Finds relevant order depending on spl_child_order() if (is.null(spl_child_order(spl)) || is(spl, \"AllSplit\")) { vord <- seq_along(vals) } else { vord <- match( spl_child_order(spl), vals ) vord <- vord[!is.na(vord)] } ## FIXME: should be an S4 object, not a list ret <- list( values = vals[vord], datasplit = dpart[vord], labels = labels[vord], extras = extr[vord] ) ret } # rtables 0.6.2 # Retrieves the values that will constitute the splits (facets), not necessarily a unique list. # They could come from the data cuts for example -> it can be anything that produces a set of strings. setGeneric( \".applysplit_rawvals\", function(spl, df) standardGeneric(\".applysplit_rawvals\") ) # Browse[2]> showMethods(.applysplit_rawvals) # Function: .applysplit_rawvals (package rtables) # spl=\"AllSplit\" # spl=\"ManualSplit\" # spl=\"MultiVarSplit\" # spl=\"VAnalyzeSplit\" # spl=\"VarLevelSplit\" # spl=\"VarStaticCutSplit\" # Nothing here is inherited from the virtual class Split!!! # Contains the subset of the data (default, but these can overlap and can also NOT be mutually exclusive). setGeneric( \".applysplit_datapart\", function(spl, df, vals) standardGeneric(\".applysplit_datapart\") ) # Same as .applysplit_rawvals # Extract the extra parameter for the split setGeneric( \".applysplit_extras\", function(spl, df, vals) standardGeneric(\".applysplit_extras\") ) # Browse[2]> showMethods(.applysplit_extras) # Function: .applysplit_extras (package rtables) # spl=\"AllSplit\" # (inherited from: spl=\"Split\") # spl=\"Split\" # This means there is only a function for the virtual class Split. # So all splits behave the same!!! # Split label retrieval and assignment if visible. setGeneric( \".applysplit_partlabels\", function(spl, df, vals, labels) standardGeneric(\".applysplit_partlabels\") ) # Browse[2]> showMethods(.applysplit_partlabels) # Function: .applysplit_partlabels (package rtables) # spl=\"AllSplit\" # (inherited from: spl=\"Split\") # spl=\"MultiVarSplit\" # spl=\"Split\" # spl=\"VarLevelSplit\" setGeneric( \"check_validsplit\", # our friend function(spl, df) standardGeneric(\"check_validsplit\") ) # Note: check_validsplit is an internal function but may one day be exported. # This is why it does not have the \".\" prefix. setGeneric( \".applysplit_ref_vals\", function(spl, df, vals) standardGeneric(\".applysplit_ref_vals\") ) # Browse[2]> showMethods(.applysplit_ref_vals) # Function: .applysplit_ref_vals (package rtables) # spl=\"Split\" # spl=\"VarLevWBaselineSplit\" # rtables 0.6.2 Browse[3]> getMethod(\".applysplit_rawvals\", \"AllSplit\") Method Definition: function (spl, df) obj_name(spl) Signatures: spl target \"AllSplit\" defined \"AllSplit\" # What is obj_name -> slot in spl Browse[3]> obj_name(spl) [1] \"all obs\" # coming from Browse[3]> getMethod(\"obj_name\", \"Split\") Method Definition: function (obj) obj@name ##### Slot that we could see from str(spl, max.level = 2) Signatures: obj target \"Split\" defined \"Split\""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"a-simple-split","dir":"Articles > Dev-guide","previous_headings":"","what":"A Simple Split","title":"Split Machinery","text":"following, demonstrate row splits work using features already described. add two splits see behavior do_split changes. Note add analyze call split behave , giving empty table observations. default, calling analyze variable calculate mean data subset generated splits. want go beyond first call do_split design applied observations, purpose generating root split contains data splits (indeed AllSplit). achieve use debug(rtables:::do_split) instead debugonce(rtables:::do_split) need step splits. Alternatively, possible use powerful trace function enter cases input specific class. , following can used: trace(\"do_split\", quote((!(spl, \"AllSplit\")) browser()), = asNamespace(\"rtables\")). Note specify namespace . Multiple tracer elements can added expression(E1, E2), c(quote(E1), quote(E2)). Specific steps can specified parameter. Remember call untrace(\"do_split\", quote((!(spl, \"AllSplit\")) browser()), = asNamespace(\"rtables\")) finished remove trace. continuing, want check formal class spl. , can directly infer class different now (VarLevelSplit) understand split label hidden (split_label_position slot). Moreover, see specific value order specific split values. VarLevelSplit also seems three slots AllSplit. precisely? Remember always check constructor class definition R/00tabletrees.R exploratory tools suffice. Now, check_validsplit(spl, df) use different method (getMethod(\"check_validsplit\", \"VarLevelSplit\")). uses internal utility function .checkvarsok check vars, .e. payload, actually present names(df). next relevant function .apply_split_inner, exactly changes using debugonce(.apply_split_inner). course, function called directly custom split function provided. Since parameter vals specified (NULL), split values retrieved df using split payload select specific columns (varvec <- df[[spl_payload(spl)]]). Whenever split values specified retrieved selected column unique values (character) levels (factor). Next, .applysplit_datapart creates named list facets data subsets. case, result actually mutually exclusive partition data. specify split values column content retrieved via unique (case character vector) levels (case factors). .applysplit_partlabels bit less linear take account possibility specified labels payload. Instead looking function source code getMethod(\".applysplit_partlabels\", \"VarLevelSplit\"), can enter S4 generic function debugging mode follows: case, final labels vals explicitly assigned. order retrieved split object (spl_child_order(spl)) matched current split values. returned list processed . continue next call do_split, procedure followed second ARM split. applied partition created first split. main df now constituted subset (facet) total data, determined first split. repeated iteratively many data splits requested. concluding iteration, take moment discuss detail .fixupvals(partinfo) works. generic function source code can easily accessed. suggest running debugonce(.fixupvals) understand practice. fundamental aspects .fixupvals(partinfo) follows: Ensures labels character factor. Ensures splits data list values named according labels. Guarantees ret$values contains SplitValue objects. Removes list element extra since now included SplitValue. Note function can occasionally called return object (named list now). course, first call checks applied.","code":"# rtables 0.6.2 library(rtables) library(dplyr) # This filter is added to avoid having too many calls to do_split DM_tmp <- DM %>% filter(ARM %in% names(table(DM$ARM)[1:2])) %>% # limit to two filter(SEX %in% c(\"M\", \"F\")) %>% # limit to two mutate(SEX = factor(SEX), ARM = factor(ARM)) # to drop unused levels # debug(rtables:::do_split) lyt <- basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(\"BMRKR1\") # analyze() is needed for the table to have non-label rows lyt %>% build_table(DM_tmp) ## all obs ## ———————————————————— ## A: Drug X ## F ## Mean 6.06 ## M ## Mean 5.42 ## B: Placebo ## F ## Mean 6.24 ## M ## Mean 5.97 # undebug(rtables:::do_split) # rtables 0.6.2 Browse[2]> str(spl, max.level = 2) Formal class 'VarLevelSplit' [package \"rtables\"] with 20 slots ..@ value_label_var : chr \"ARM\" ..@ value_order : chr [1:2] \"A: Drug X\" \"B: Placebo\" ..@ split_fun : NULL ..@ payload : chr \"ARM\" ..@ name : chr \"ARM\" ..@ split_label : chr \"ARM\" ..@ split_format : NULL ..@ split_na_str : chr NA ..@ split_label_position : chr \"hidden\" ..@ content_fun : NULL ..@ content_format : NULL ..@ content_na_str : chr NA ..@ content_var : chr \"\" ..@ label_children : logi NA ..@ extra_args : list() ..@ indent_modifier : int 0 ..@ content_indent_modifier: int 0 ..@ content_extra_args : list() ..@ page_title_prefix : chr NA ..@ child_section_div : chr NA # rtables 0.6.2 slots_as <- getSlots(\"AllSplit\") # inherits virtual class Split and is general class for all splits # getClass(\"CustomizableSplit\") # -> Extends: \"Split\", Known Subclasses: Class \"VarLevelSplit\", directly slots_cs <- getSlots(\"CustomizableSplit\") # Adds split function slots_vls <- getSlots(\"VarLevelSplit\") slots_cs[!(names(slots_cs) %in% names(slots_as))] # split_fun # \"functionOrNULL\" slots_vls[!(names(slots_vls) %in% names(slots_cs))] # value_label_var value_order # \"character\" \"ANY\" # rtables 0.6.2 eval(debugcall(.applysplit_partlabels(spl, df, vals, labels))) # We leave to the smart developer to see how the labels are assigned # Remember to undebugcall() similarly! # rtables 0.6.2 # Can find the following core function: # vals <- make_splvalue_vec(vals, extr, labels = labels) # ---> Main list of SplitValue objects: iterative call of # new(\"SplitValue\", value = val, extra = extr, label = label) # Structure of ret before calling .fixupvals Browse[2]> str(ret, max.level = 2) List of 4 $ values : chr [1:2] \"A: Drug X\" \"B: Placebo\" $ datasplit:List of 2 ..$ A: Drug X : tibble [121 × 8] (S3: tbl_df/tbl/data.frame) ..$ B: Placebo: tibble [106 × 8] (S3: tbl_df/tbl/data.frame) $ labels : Named chr [1:2] \"A: Drug X\" \"B: Placebo\" ..- attr(*, \"names\")= chr [1:2] \"A: Drug X\" \"B: Placebo\" $ extras :List of 2 ..$ : list() ..$ : list() # Structure of ret after the function call Browse[2]> str(.fixupvals(ret), max.level = 2) List of 3 $ values :List of 2 ..$ A: Drug X :Formal class 'SplitValue' [package \"rtables\"] with 3 slots ..$ B: Placebo:Formal class 'SplitValue' [package \"rtables\"] with 3 slots $ datasplit:List of 2 ..$ A: Drug X : tibble [121 × 8] (S3: tbl_df/tbl/data.frame) ..$ B: Placebo: tibble [106 × 8] (S3: tbl_df/tbl/data.frame) $ labels : Named chr [1:2] \"A: Drug X\" \"B: Placebo\" ..- attr(*, \"names\")= chr [1:2] \"A: Drug X\" \"B: Placebo\" # The SplitValue object is fundamental Browse[2]> str(ret$values) List of 2 $ A: Drug X :Formal class 'SplitValue' [package \"rtables\"] with 3 slots .. ..@ extra: list() .. ..@ value: chr \"A: Drug X\" .. ..@ label: chr \"A: Drug X\" $ B: Placebo:Formal class 'SplitValue' [package \"rtables\"] with 3 slots .. ..@ extra: list() .. ..@ value: chr \"B: Placebo\" .. ..@ label: chr \"B: Placebo\""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"pre-made-split-functions","dir":"Articles > Dev-guide","previous_headings":"A Simple Split","what":"Pre-Made Split Functions","title":"Split Machinery","text":"start examining split function already defined rtables. scope filtering specific values follows: root split, enter split based SEX. specified split function, can retrieve split function using splfun <- split_fun(spl) enter -else statement two possible cases: whether split context . cases, error catching framework used give informative errors case failure. Later see depth works. invite reader always keep eye spl_context, fundamental sophisticated splits, e.g. cases split depends mainly preceding splits values. split function called, please take moment look drop_split_levels defined. see function fundamentally wrapper .apply_split_inner drops empty factor levels, therefore avoiding empty splits. many pre-made split functions included rtables. list functions can found Split Functions vignette, via ?split_funcs. leave developer look split functions work, particular trim_levels_to_map may interest.","code":"library(rtables) # debug(rtables:::do_split) # uncomment to see into the main split function basic_table() %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"BMRKR1\") %>% build_table(DM) ## all obs ## ———————————————— ## F ## Mean 6.04 ## M ## Mean 5.64 # undebug(rtables:::do_split) # This produces the same output as before (when filters were used) # rtables 0.6.2 > drop_split_levels function(df, spl, vals = NULL, labels = NULL, trim = FALSE) { # Retrieve split column var <- spl_payload(spl) df2 <- df ## This call is exactly the one we used when filtering to get rid of empty levels df2[[var]] <- factor(df[[var]]) ## Our main function! .apply_split_inner(spl, df2, vals = vals, labels = labels, trim = trim ) }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"creating-custom-split-functions","dir":"Articles > Dev-guide","previous_headings":"A Simple Split","what":"Creating Custom Split Functions","title":"Split Machinery","text":"Now create custom split function. Firstly, see system manages error messages. general understanding custom split functions created, please read Custom Split Functions section Advanced Usage vignette see ?custom_split_funs. following code use browser() enter custom split functions. invite reader activate options(error = recover) investigate cases encounter error. Note can revert default behavior restarting R session, caching default option value, using callr retrieve default follows: default_opts <- callr::r(function(){options()}); options(error = default_opts$error). commented debugging lines allow inspect error. Alternatively, using recover option allow possibility select frame number, .e. trace level, enter. Selecting last frame number (10 case) allow see value ret rtables:::do_split causes error informative error message follows created. previous split function fails exploratory_split_fun given arguments accepts. simple way avoid add ... function call. Now let’s construct interesting split function (error): Now take moment dwell machinery included rtables create custom split functions. , please read relevant documentation ?make_split_fun. pre-made split functions included rtables written make_split_fun stable constructor functions previously used. invite reader take look make_split_fun.R. majority functions understandable knowledge gained guide far. important note core split function specified, commonly case, make_split_fun calls do_base_split directly, minimal wrapper well-known do_split. drop_facet_levels, example, pre-processing function core simply removes empty factor levels split “column”, thus avoiding showing empty lines. also possible provide list functions, can seen examples ?make_split_fun. Note pre- post-processing requires list input support possibility combining multiple functions. contrast, core splitting function must single function call expected stacked features. rarely needs modified majority included split functions work pre- post-processing. Included post-processing functions interesting interact split object, e.g. reordering facets adding overall facet (add_overall_facet). attentive reader noticed core function relies do_split many post-processing functions rely make_split_result, best way get correct split return structure. Note modifying core split works row space moment.","code":"# rtables 0.6.2 # Table call with only the function changing simple_table <- function(DM, f) { lyt <- basic_table() %>% split_rows_by(\"ARM\", split_fun = f) %>% analyze(\"BMRKR1\") lyt %>% build_table(DM) } # First round will fail because there are unused arguments exploratory_split_fun <- function(df, spl) NULL # debug(rtables:::do_split) err_msg <- tryCatch(simple_table(DM, exploratory_split_fun), error = function(e) e) # undebug(rtables:::do_split) message(err_msg$message) ## Error applying custom split function: unused arguments (vals, labels, trim = trim) ## split: VarLevelSplit (ARM) ## occured at path: root # rtables 0.6.2 # Debugging level 10: tt_dotabulation.R#627: do_split(spl, df, spl_context = spl_context) # Original call and final error > simple_table(DM, exploratory_split_fun) Error in do_split(spl, df, spl_context = spl_context) : Error applying custom split function: unused arguments (vals, labels, trim = trim) # This is main error split: VarLevelSplit (ARM) # Split reference occured at path: root # Path level (where it occurred) # rtables 0.6.2 f_brakes_if <- function(split_col = NULL, error = FALSE) { function(df, spl, ...) { # order matters! more than naming # browser() # To check how it works if (is.null(split_col)) { # Retrieves the default split_col <- spl_variable(spl) # Internal accessor to split obj } my_payload <- split_col # Changing split column value vals <- levels(df[[my_payload]]) # Extracting values to split datasplit <- lapply(seq_along(vals), function(i) { df[df[[my_payload]] == vals[[i]], ] }) names(datasplit) <- as.character(vals) # Error if (isTRUE(error)) { # browser() # If you need to check how it works mystery_error_values <- sapply(datasplit, function(x) mean(x$BMRKR1)) if (any(mystery_error_values > 6)) { stop( \"It should not be more than 6! Should it be? Found in split values: \", names(datasplit)[which(mystery_error_values > 6)] ) } } # Handy function to return a split result!! make_split_result(vals, datasplit, vals) } } simple_table(DM, f_brakes_if()) # works! ## all obs ## ———————————————————————— ## A: Drug X ## Mean 5.79 ## B: Placebo ## Mean 6.11 ## C: Combination ## Mean 5.69 simple_table(DM, f_brakes_if(split_col = \"STRATA1\")) # works! ## all obs ## ———————————————— ## A ## Mean 5.95 ## B ## Mean 5.90 ## C ## Mean 5.71 # simple_table(DM, f_brakes_if(error = TRUE)) # does not work, but returns an informative message # Error in do_split(spl, df, spl_context = spl_context) : # Error applying custom split function: It should not be more than 6! Should it be? Found in split values: B: Placebo # split: VarLevelSplit (ARM) # occurred at path: root"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"spl_context---adding-context-to-our-splits","dir":"Articles > Dev-guide","previous_headings":"A Simple Split > Creating Custom Split Functions","what":".spl_context - Adding Context to Our Splits","title":"Split Machinery","text":"best way understand split context , use , read Leveraging .spl_context section Advanced Usage vignette, use browser() within split function see structured. .spl_context needed rewriting core functions, propose wrapper do_base_split , handy redirection standard do_split without split function part (.e. wrapper .apply_split_inner, real core splitting machinery). curiosity, set trim = TRUE . trimming works mixed table (values 0s content), trim 0s. rarely case, encourage using replacement functions trim_levels_to_group trim_levels_to_map trimming. Nowadays, even impossible set differently trim = FALSE. (write issue informative error list xxx). can see split column variable (split, first column) level splitting procedure. value current split value dealt . next column, let’s see number rows data frames: sapply(.spl_context$full_parent_df, nrow) # [1] 356 121 36 36. Indeed, root level contains full input data frame, levels subgroups full data according split value. all_cols_n shows exactly numbers just described. obs current filter applied columns. Applying root data (row subgroup data) reveals current column-wise facet (row-wise row split). also possible use information make complex splits column space using full data frame value splits select interested values. something change simplify within rtables need becomes apparent.","code":"# rtables 0.6.2 browsing_f <- function(df, spl, .spl_context, ...) { # browser() # do_base_split(df, spl, ...) # order matters!! This would fail if done do_base_split(spl = spl, df = df, vals = NULL, labels = NULL, trim = TRUE) } fnc_tmp <- function(innervar) { # Exploring trim_levels_in_facets (check its form) function(ret, ...) { # browser() for (var in innervar) { # of course AGE is not here, so nothing is dropped!! ret$datasplit <- lapply(ret$datasplit, function(df) { df[[var]] <- factor(df[[var]]) df }) } ret } } basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"STRATA1\") %>% split_rows_by_cuts(\"AGE\", cuts = c(0, 50, 100), cutlabels = c(\"young\", \"old\") ) %>% split_rows_by(\"SEX\", split_fun = make_split_fun( pre = list(drop_facet_levels), # This is dropping the SEX levels (AGE is upper level) core_split = browsing_f, post = list(fnc_tmp(\"AGE\")) # To drop these we should use a split_fun in the above level )) %>% summarize_row_groups() %>% build_table(DM) # The following is the .spl_contest printout: Browse[1]> .spl_context split value full_parent_df all_cols_n all obs 1 root root c(\"S1\", .... 356 TRUE, TR.... 2 ARM A: Drug X c(\"S6\", .... 121 TRUE, TR.... 3 STRATA1 A c(\"S14\",.... 36 TRUE, TR.... 4 AGE young c(\"S14\",.... 36 TRUE, TR.... # NOTE: make_split_fun(pre = list(drop_facet_levels)) and drop_split_levels # do the same thing in this case"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"extra-arguments-extra_args","dir":"Articles > Dev-guide","previous_headings":"A Simple Split","what":"Extra Arguments: extra_args","title":"Split Machinery","text":"functionality well-known used setting analysis functions (somewhat complicated example can found Example Complex Analysis Function vignette), show can also apply splits. demonstrated, seem like impossible cases considered vestigial deprecated.","code":"# rtables 0.6.2 # Let's use the tracer!! my_tracer <- quote(if (length(spl@extra_args) > 0) browser()) trace( what = \"do_split\", tracer = my_tracer, where = asNamespace(\"rtables\") ) custom_mean_var <- function(var) { function(df, labelstr, na.rm = FALSE, ...) { # browser() mean(df[[var]], na.rm = na.rm) } } DM_ageNA <- DM DM_ageNA$AGE[1] <- NA basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% summarize_row_groups( cfun = custom_mean_var(\"AGE\"), extra_args = list(na.rm = TRUE), format = \"xx.x\", label_fstr = \"label %s\" ) %>% # content_extra_args, c_extra_args are different slots!! (xxx) split_rows_by(\"STRATA1\", split_fun = keep_split_levels(\"A\")) %>% analyze(\"AGE\") %>% # check with the extra_args (xxx) build_table(DM_ageNA) # You can pass extra_args down to other splits. It is possible this will not not # work. Should it? That is why extra_args lives only in splits (xxx) check if it works # as is. Difficult to find an use case for this. Maybe it could work for the ref_group # info. That does not work with nesting already (fairly sure that it will break stuff). # Does it make sense to have more than one ref_group at any point of the analysis? No docs, # send a warning if users try to nest things with ref_group (that is passed around via # extra_args) # As we can see that was not possible. What if we now force it a bit? my_split_fun <- function(df, spl, .spl_context, ...) { spl@extra_args <- list(na.rm = TRUE) # does not work because do_split is not changing the object # the split does not do anything with it drop_split_levels(df, spl) } # does not work basic_table() %>% split_rows_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = my_split_fun) %>% analyze(\"AGE\", inclNAs = TRUE, afun = mean) %>% # include_NAs is set FALSE build_table(DM_ageNA) # extra_args is in available in cols but not in rows, because different columns # may need it for different col space. Row-wise it seems not necessary. # The only thing that works is adding it to analyze (xxx) check if it is worth adding # We invite the developer now to test all the test files of this package with the tracer on # therefore -> extra_args is not currently used in splits (xxx could be wrong) # could be not being hooked up untrace(what = \"do_split\", where = asNamespace(\"rtables\")) # Let's try with the other variables identically my_tracer <- quote(if (!is.null(vals) || !is.null(labels) || isTRUE(trim)) { print(\"A LOT TO SAY\") message(\"CANT BLOCK US ALL\") stop(\"NOW FOR SURE\") browser() }) trace( what = \"do_split\", tracer = my_tracer, where = asNamespace(\"rtables\") ) # Run tests by copying the above in setup-fakedata.R (then devtools::test()) untrace( what = \"do_split\", where = asNamespace(\"rtables\") )"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_split_machinery.html","id":"multivarsplit-compoundsplit-examples","dir":"Articles > Dev-guide","previous_headings":"","what":"MultiVarSplit & CompoundSplit Examples","title":"Split Machinery","text":"final part article still construction, hence non-specific mentions list. xxx CompoundSplit generates facets one variable (e.g. cumulative distributions) MultiVarSplit uses different variables split. See AnalyzeMultiVars, inherits CompoundSplit details analyzes facets multiple times. MultiVarColSplit works analyze_colvars, scope article. .set_kids_sect_sep adds things children (can set split). First, want see MultiVarSplit class behaves example case taken ?split_rows_by_multivar. print output, notice two groups (one called “SEX” “STRATA1”) identical along columns. subgroup actually created. interesting way personalize splits help custom split functions split context, widely different subgroups table. invite reader try understand split_rows_by_multivar can row splits (see xxx comment previous code), split_cols_by_multivar . known bug moment, work towards fix . Known issues often linked source code GitHub issue number (e.g. #690). Lastly, briefly show example split cut function replace solve empty age groups problem . propose simplified situation: row split cases (*_cuts *_cutfun), empty levels dropped. expected can avoided using dedicated split function. Intentionally looking future split possible order determine element present . moment possible add spl_fun dedicated split functions like split_rows_by_cuts. Note previous table used summarize_row_groups, analyze calls. rendered table nicely, standard method use summarize_row_groups intended decorate row groups, .e. rows labels. Internally, rows called content rows analysis functions summarize_row_groups called cfun instead afun. Indeed, tabulation machinery also presents two differently described Tabulation Row Structure section Tabulation vignette. can try construct split function cuts manually make_split_fun: Alternatively, choose prune rows prune_table! add pre-proc z-scoring","code":"# rtables 0.6.2 my_tracer <- quote(if (is(spl, \"MultiVarSplit\")) browser()) trace( what = \"do_split\", tracer = my_tracer, where = asNamespace(\"rtables\") ) # We want also to take a look at the following: debugonce(rtables:::.apply_split_inner) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by_multivar(c(\"BMRKR1\", \"BMRKR1\"), varlabels = c(\"SD\", \"MEAN\") ) %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(\"PAK\") ) %>% # xxx for #690 #691 summarize_row_groups() %>% analyze(c(\"AGE\", \"SEX\")) build_table(lyt, DM) # xxx check empty space on top -> check if it is a bug, file it untrace( what = \"do_split\", where = asNamespace(\"rtables\") ) # rtables 0.6.2 cutfun <- function(x) { # browser() cutpoints <- c(0, 50, 100) names(cutpoints) <- c(\"\", \"Younger\", \"Older\") cutpoints } tbl <- basic_table(show_colcounts = TRUE) %>% split_rows_by(\"ARM\", split_fun = drop_and_remove_levels(c(\"B: Placebo\", \"C: Combination\"))) %>% split_rows_by(\"STRATA1\") %>% split_rows_by_cutfun(\"AGE\", cutfun = cutfun) %>% # split_rows_by_cuts(\"AGE\", cuts = c(0, 50, 100), # cutlabels = c(\"young\", \"old\")) %>% # Works the same split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% # This is degenerate!!! build_table(DM) tbl ## all obs ## (N=356) ## ————————————————————————— ## A: Drug X ## A ## AGE ## Younger ## F 22 (6.2%) ## M 14 (3.9%) ## Older ## B ## AGE ## Younger ## F 26 (7.3%) ## M 14 (3.9%) ## Older ## F 1 (0.3%) ## C ## AGE ## Younger ## F 19 (5.3%) ## M 21 (5.9%) ## Older ## F 2 (0.6%) ## M 2 (0.6%) my_count_afun <- function(x, .N_col, .spl_context, ...) { # browser() out <- list(c(length(x), length(x) / .N_col)) names(out) <- .spl_context$value[nrow(.spl_context)] # workaround (xxx #689) in_rows( .list = out, .formats = c(\"xx (xx.x%)\") ) } # ?make_split_fun # To check for docs/examples # Core split cuts_core <- function(spl, df, vals, labels, .spl_context) { # browser() # file an issue xxx # variables that are split on are converted to factor during the original clean-up # cut split are not doing it but it is an exception. xxx # young_v <- as.numeric(df[[\"AGE\"]]) < 50 # current solution: young_v <- as.numeric(as.character(df[[\"AGE\"]])) < 50 make_split_result(c(\"young\", \"old\"), datasplit = list(df[young_v, ], df[!young_v, ]), labels = c(\"Younger\", \"Older\") ) } drop_empties <- function(splret, spl, fulldf, ...) { # browser() nrows_data_split <- vapply(splret$datasplit, nrow, numeric(1)) to_keep <- nrows_data_split > 0 make_split_result( splret$values[to_keep], splret$datasplit[to_keep], splret$labels[to_keep] ) } gen_split <- make_split_fun( core_split = cuts_core, post = list(drop_empties) ) tbl <- basic_table(show_colcounts = TRUE) %>% split_rows_by(\"ARM\", split_fun = keep_split_levels(c(\"A: Drug X\"))) %>% split_rows_by(\"STRATA1\") %>% split_rows_by(\"AGE\", split_fun = gen_split) %>% analyze(\"SEX\") %>% # It is the last step!! No need of BMRKR1 right? # split_rows_by(\"SEX\", split_fun = drop_split_levels, # child_labels = \"hidden\") %>% # close issue #689. would it work for # analyze_colvars? probably (xxx) # analyze(\"BMRKR1\", afun = my_count_afun) %>% # This is NOT degenerate!!! BMRKR1 is only placeholder build_table(DM) tbl # rtables 0.6.2 tbl <- basic_table(show_colcounts = TRUE) %>% split_rows_by(\"ARM\", split_fun = keep_split_levels(c(\"A: Drug X\"))) %>% split_rows_by(\"STRATA1\") %>% split_rows_by_cuts( \"AGE\", cuts = c(0, 50, 100), cutlabels = c(\"young\", \"old\") ) %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% # This is degenerate!!! # we keep it until #689 build_table(DM) tbl ## all obs ## (N=356) ## ————————————————————— ## A: Drug X ## A ## young ## F 22 (6.2%) ## M 14 (3.9%) ## old ## B ## young ## F 26 (7.3%) ## M 14 (3.9%) ## old ## F 1 (0.3%) ## C ## young ## F 19 (5.3%) ## M 21 (5.9%) ## old ## F 2 (0.6%) ## M 2 (0.6%) # Trying with pruning prune_table(tbl) # (xxx) what is going on here? it is degenerate so it has no real leaves ## NULL # It is degenerate -> what to do? # The same mechanism is applied in the case of NULL leaves, they are rolled up in the # table tree"},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Table Hierarchy","text":"article intended use developers contain low-level explanations topics covered. user-friendly vignettes, please see Articles page rtables website. code prose appears version article main branch repository may reflect specific state things can less recent. guide describes important aspects table hierarchy unlikely change. Regardless, invite reader keep mind current repository code may drifted following material document, always best practice read code directly main. Please keep mind rtables still active development, seen efforts multiple contributors across different years. Therefore, may legacy mechanisms ongoing transformations look different future.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"introduction","dir":"Articles > Dev-guide","previous_headings":"","what":"Introduction","title":"Table Hierarchy","text":"scope vignette understand structure rtable objects, class hierarchy exploration tree structures S4 objects. Exploring table structure enables better understanding rtables concepts split machinery, tabulation, pagination export. details user’s perspective table structure can found relevant vignettes. isS4 getclass - class structure","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"process-and-methods","dir":"Articles > Dev-guide","previous_headings":"","what":"Process and Methods","title":"Table Hierarchy","text":"invite developers use provided examples interactively explore rtables hierarchy. helpful command ‘getClass’ list slots associated class, addition related classes relative distances.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"table-representation","dir":"Articles > Dev-guide","previous_headings":"","what":"Table Representation","title":"Table Hierarchy","text":"“PredataAxisLayout” class used define data subset instructions tabulation. 2 subclasses (one axis): PredataColLayout, PredataRowLayout","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"content-summary-row-groups","dir":"Articles > Dev-guide","previous_headings":"","what":"Content (summary row groups)","title":"Table Hierarchy","text":"Splits core functionality rtables tabulation calculations often required subsets data.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"split-machinery","dir":"Articles > Dev-guide","previous_headings":"","what":"Split Machinery","title":"Table Hierarchy","text":"“TreePos” class contains split information list splits, split label values, subsets data generated split. AllSplit RootSplit MultiVarSplit VarStaticCutSplit CumulativeCutSplit VarDynCutSplit CompoundSplit VarLevWBaselineSplit highest level table hierarchy belong “TableTree”. code identifies slots associated class. S4 object, slots can accessed using “@” (similar use “$” list objects). ’ll notice classes fall “Extends”. classes contained relationship TableTree object “virtual” classes. avoid repetition slots carrying data (set slots example) multiple classes may need, rtables extensively uses virtual classes. virtual class instantiated, purpose classes inherit information .","code":"library(rtables) getClass(\"TreePos\") ## Class \"TreePos\" [package \"rtables\"] ## ## Slots: ## ## Name: splits s_values sval_labels subset ## Class: list list character SubsetDef getClass(\"TableTree\") ## Class \"TableTree\" [package \"rtables\"] ## ## Slots: ## ## Name: content page_title_prefix children ## Class: ElementaryTable character list ## ## Name: rowspans labelrow page_titles ## Class: data.frame LabelRow character ## ## Name: horizontal_sep header_section_div trailing_section_div ## Class: character character character ## ## Name: col_info format na_str ## Class: InstantiatedColumnInfo FormatSpec character ## ## Name: indent_modifier table_inset level ## Class: integer integer integer ## ## Name: name main_title subtitles ## Class: character character character ## ## Name: main_footer provenance_footer ## Class: character character ## ## Extends: ## Class \"VTableTree\", directly ## Class \"VTableNodeInfo\", by class \"VTableTree\", distance 2 ## Class \"VTree\", by class \"VTableTree\", distance 2 ## Class \"VTitleFooter\", by class \"VTableTree\", distance 2 ## Class \"VNodeInfo\", by class \"VTableTree\", distance 3 lyt <- basic_table(title = \"big title\") %>% split_rows_by(\"SEX\", page_by = TRUE) %>% analyze(\"AGE\") tt <- build_table(lyt, DM) # Though we don't recommend using str for studying rtable objects, # we do find it useful in this instance to visualize the parent/child relationships. str(tt, max.level=2) ## Formal class 'TableTree' [package \"rtables\"] with 20 slots ## ..@ content :Formal class 'ElementaryTable' [package \"rtables\"] with 19 slots ## ..@ page_title_prefix : chr \"SEX\" ## ..@ children :List of 4 ## ..@ rowspans :'data.frame': 0 obs. of 0 variables ## ..@ labelrow :Formal class 'LabelRow' [package \"rtables\"] with 13 slots ## ..@ page_titles : chr(0) ## ..@ horizontal_sep : chr \"—\" ## ..@ header_section_div : chr NA ## ..@ trailing_section_div: chr NA ## ..@ col_info :Formal class 'InstantiatedColumnInfo' [package \"rtables\"] with 9 slots ## ..@ format : NULL ## ..@ na_str : chr NA ## ..@ indent_modifier : int 0 ## ..@ table_inset : int 0 ## ..@ level : int 1 ## ..@ name : chr \"SEX\" ## ..@ main_title : chr \"big title\" ## ..@ subtitles : chr(0) ## ..@ main_footer : chr(0) ## ..@ provenance_footer : chr(0) ## Warning: str provides a low level, implementation-detail-specific description ## of the TableTree object structure. See table_structure(.) for a summary of ## table struture intended for end users."},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"tree-paths","dir":"Articles > Dev-guide","previous_headings":"","what":"Tree Paths","title":"Table Hierarchy","text":"Root Leaves, vectors vectors Tables tree, nodes tree can summaries associated . Tables trees nested structure. also benefit keeping repeating necessary information trying paginate table. Children ElementaryTables row objects. TableTree can children either row objects table objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_table_hierarchy.html","id":"todo","dir":"Articles > Dev-guide","previous_headings":"Tree Paths","what":"TODO:","title":"Table Hierarchy","text":"Create Tree Diagram showing class hierarchy.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_tabulation.html","id":"disclaimer","dir":"Articles > Dev-guide","previous_headings":"","what":"Disclaimer","title":"Tabulation","text":"article intended use developers contain low-level explanations topics covered. user-friendly vignettes, please see Articles page rtables website. code prose appears version article main branch repository may reflect specific state things can less recent. guide describes important aspects tabulation process unlikely change. Regardless, invite reader keep mind current repository code may drifted following material document, always best practice read code directly main. Please keep mind rtables still active development, seen efforts multiple contributors across different years. Therefore, may legacy mechanisms ongoing transformations look different future. working document may subjected deprecation updates, keep xxx comments indicate placeholders warnings -’s need work.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_tabulation.html","id":"introduction","dir":"Articles > Dev-guide","previous_headings":"","what":"Introduction","title":"Tabulation","text":"Tabulation rtables process takes pre-defined layout applies data. layout object, splits analyzes, can applied different data produce valid tables. process happens principally within tt_dotabulation.R file user-facing function build_table resides . occasionally use functions methods present files, like colby_construction.R make_subset_expr.R. assume reader already familiar documentation build_table. suggest reading Split Machinery article prior one, instrumental understanding layout object, essentially built splits, tabulated data supplied.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/dev-guide/dg_tabulation.html","id":"tabulation","dir":"Articles > Dev-guide","previous_headings":"","what":"Tabulation","title":"Tabulation","text":"enter build_table using debugonce see works. Now let’s look within build_table call. initial check layout pre-data table layout, checks column layout defined (clayout accessor), .e. column split. case, obs column added automatically observations. , couple defensive programming calls checks transformations finally data. can divided two categories: mainly concern layout, defined generics, concern data, instead function dependent layout class. Indeed, layout structured can divided clayout rlayout (column row layout). first one used create cinfo, general object container column splits information. second one contains obligatory data split, .e. root split (accessible root_spl), row splits’ vectors iterative splits row space. following, consider initial checks defensive programming. Along various checks defensive programming, find PreDataAxisLayout virtual class row column layouts inherit . Virtual classes handy group classes need share things like labels functions need applicable relative classes. See information rtables class hierarchy dedicated article . Now, continue build_table. checks, notice TreePos() constructor object retains representation tree position along split values labels. mainly used create_colinfo, enter now debugonce(create_colinfo). function creates object represents column splits everything else may related columns. particular, column counts calculated function. parameter inputs follows: create_colinfo make_subset_expr.R. , see topleft present build_table, override one lyt. Entering create_colinfo, see following calls: Next function determination column counts. Currently, happens leaf level, can certainly calculated independently levels (open issue rtables, .e. print levels’ totals). Precedence column counts may documented (xxx todo). main use case analyzing participation-level dataset, multiple records per subject, like retain total numbers subjects per column, often taken subject-level dataset, use column counts. Originally, counts able added vector, often case users like possibility use alt_counts_df. cinfo object (InstantiatedColumnInfo) created information. continue inside build_table, see .make_ctab used make root split. general procedure generates initial root split content row. ctab applied content row, row contains label. ?summarize_row_groups, know rtables defines label rows, .e. content rows. .make_ctab similar function actual creates table rows, .make_tablerows. Note function uses parent_cfun .make_caller retrieve content function inserted levels. split structural handling table object row-creation engine, divided .make_tablerows call. search package, find function called twice, .make_ctab .make_analyzed_tab. two final elements table construction: creation rows. Going back build_table, see row layout actually list split vectors. fundamental line, kids <- lapply(seq_along(rlyt), function() {, allows us appreciate . Going forward see recursive_applysplit applied split vector. may worthwhile check vector looks like test case. last print informative. can see layout construction object built 2 VarLevelSplits rows one final AnalyzeMultiVars, leaf analysis split final level rows. second split vector following AnalyzeVarSplit. xxx get multiple split vectors, need escape nesting nest = FALSE adding split_rows_by call analyze call. Continuing recursive_applysplit, made two main calls: one .make_ctab makes content row calculates counts specified, .make_split_kids. eventually contains recursive_applysplit applied split vector built Splits analyze splits. generic handy switch different downstream processes. case (rlyt[[1]]) call method getMethod(\".make_split_kids\", \"Split\") twice getting analysis split. , (xxx) multi-variable split applies .make_split_kids elements, turn calling main getMethod(\".make_split_kids\", \"VAnalyzeSplit\") turn go .make_analyzed_tab. interesting edge cases different split cases, like split_by_multivars one splits reference group. internal code , called baseline. follow variable across function layers, see split (do_split) happens (getMethod(\".make_split_kids\", \"Split\")) second split reference group. done make available row calculate, example, differences reference group. Now move towards .make_tablerows, analysis functions become key place applied analyzed. First, external tryCatch used cache errors higher level, differentiate two major blocks. function parameters quite intuitive, exception spl_context. fundamental parameter keeps information splits can visible analysis functions. look value, see carried updated everywhere split happens, except columns. Column-related information added last, gen_onerv, lowest level one result value produced. .make_tablerows go gen_rowvalues, aside row referential footers handling. gen_rowvalues unpacks cinfo object crosses arriving row split information generate rows. particular, rawvals <- mapply(gen_onerv, maps columns generate list values corresponding table row. Looking final gen_onerv see (!(val, \"RowsVerticalSection\")) function in_rows called. invite reader explore building blocks in_rows , .make_tablerows constructs data row (DataRow) content row (ContentRow) depending whether called .make_ctab .make_analyzed_tab. .make_tablerows either makes content table “analysis table”. gen_rowvalues generates list stacks (RowsVerticalSection, one rows potentially!) column. add: conceptual part -> calculating things column putting side side slicing rows putting together -> rtables row dominant.","code":"# rtables 0.6.2 library(rtables) debugonce(build_table) # A very simple layout lyt <- basic_table() %>% split_rows_by(\"STRATA1\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_cols_by(\"ARM\") %>% analyze(\"BMRKR1\") # lyt must be a PreDataTableLayouts object is(lyt, \"PreDataTableLayouts\") lyt %>% build_table(DM) ## do checks and defensive programming now that we have the data lyt <- fix_dyncuts(lyt, df) # Create the splits that depends on data lyt <- set_def_child_ord(lyt, df) # With the data I set the same order for all splits lyt <- fix_analyze_vis(lyt) # Checks if the analyze last split should be visible # If there is only one you will not get the variable name, otherwise you get it if you # have multivar. Default is NA. You can do it now only because you are sure to # have the whole layout. df <- fix_split_vars(lyt, df, char_ok = is.null(col_counts)) # checks if split vars are present lyt[] # preserve names - warning if names longer, repeats the name value if only one lyt@.Data # might not preserve the names # it works only when it is another class that inherits from lists # We suggest doing extensive testing about these behaviors in order to do choose the appropriate one cinfo <- create_colinfo( lyt, # Main layout with col split info df, # df used for splits and col counts if no alt_counts_df is present rtpos, # TreePos (does not change out of this function) counts = col_counts, # If we want to overwrite the calculations with df/alt_counts_df alt_counts_df = alt_counts_df, # alternative data for col counts total = col_total, # calculated from build_table inputs (nrow of df or alt_counts_df) topleft # topleft information added into build_table ) clayout <- clayout(lyt) # Extracts column split and info if (is.null(topleft)) { topleft <- top_left(lyt) # If top_left is not present in build_table, it is taken from lyt } ctree <- coltree(clayout, df = df, rtpos = rtpos) # Main constructor of LayoutColTree # The above is referenced as generic and principally represented as # setMethod(\"coltree\", \"PreDataColLayout\", (located in `tree_accessor.R`). # This is a call that restructures information from clayout, df, and rtpos # to get a more compact column tree layout. Part of this design is related # to past implementations. cexprs <- make_col_subsets(ctree, df) # extracts expressions in a compact fashion. # WARNING: removing NAs at this step is automatic. This should # be coupled with a warning for NAs in the split (xxx) colextras <- col_extra_args(ctree) # retrieves extra_args from the tree. It may not be used # rtables 0.6.2 # A very simple layout lyt <- basic_table() %>% split_rows_by(\"STRATA1\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_cols_by(\"ARM\") %>% analyze(\"BMRKR1\") rlyt <- rtables:::rlayout(lyt) str(rlyt, max.level = 2) Formal class 'PreDataRowLayout' [package \"rtables\"] with 2 slots ..@ .Data :List of 2 # rlyt is a rtables object (PreDataRowLayout) that is also a list! ..@ root_split:Formal class 'RootSplit' [package \"rtables\"] with 17 slots # another object! # If you do summarize_row_groups before anything you act on the root split. We need this to # have a place for the content that is valid for the whole table. str(rtables:::root_spl(rlyt), max.level = 2) # it is still a split str(rlyt[[1]], max.level = 3) # still a rtables object (SplitVector) that is a list Formal class 'SplitVector' [package \"rtables\"] with 1 slot ..@ .Data:List of 3 .. ..$ :Formal class 'VarLevelSplit' [package \"rtables\"] with 20 slots .. ..$ :Formal class 'VarLevelSplit' [package \"rtables\"] with 20 slots .. ..$ :Formal class 'AnalyzeMultiVars' [package \"rtables\"] with 17 slots # rtables 0.6.2 str(rlyt[[2]], max.level = 5) Formal class 'SplitVector' [package \"rtables\"] with 1 slot ..@ .Data:List of 1 .. ..$ :Formal class 'AnalyzeVarSplit' [package \"rtables\"] with 21 slots .. .. .. ..@ analysis_fun :function (x, ...) .. .. .. .. ..- attr(*, \"srcref\")= 'srcref' int [1:8] 1723 5 1732 5 5 5 4198 4207 .. .. .. .. .. ..- attr(*, \"srcfile\")=Classes 'srcfilealias', 'srcfile' .. .. .. ..@ default_rowlabel : chr \"Var3 Counts\" .. .. .. ..@ include_NAs : logi FALSE .. .. .. ..@ var_label_position : chr \"default\" .. .. .. ..@ payload : chr \"VAR3\" .. .. .. ..@ name : chr \"VAR3\" .. .. .. ..@ split_label : chr \"Var3 Counts\" .. .. .. ..@ split_format : NULL .. .. .. ..@ split_na_str : chr NA .. .. .. ..@ split_label_position : chr(0) .. .. .. ..@ content_fun : NULL .. .. .. ..@ content_format : NULL .. .. .. ..@ content_na_str : chr(0) .. .. .. ..@ content_var : chr \"\" .. .. .. ..@ label_children : logi FALSE .. .. .. ..@ extra_args : list() .. .. .. ..@ indent_modifier : int 0 .. .. .. ..@ content_indent_modifier: int 0 .. .. .. ..@ content_extra_args : list() .. .. .. ..@ page_title_prefix : chr NA .. .. .. ..@ child_section_div : chr NA"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"vignette demonstrate complex analysis function can constructed order build highly-customized tables rtables. example detail steps creating analysis function calculate basic univariable Cox regression summary table analyze treatment effect ARM variable covariate/interaction effects survival analysis. Cox regression analysis function customization options capability fitting multivariable Cox regression models, see summarize_coxreg() function tern package, builds upon concepts used construction example. packages used vignette :","code":"library(rtables) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"data-pre-processing","dir":"Articles","previous_headings":"","what":"Data Pre-Processing","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"First, prepare data used generate table example. use example ADTTE (Time--Event Analysis) dataset ex_adtte formatters package, contains treatment variable ARM, several variables can chosen covariates, censor variable CNSR derive event variable EVENT required model. purpose example, use age (AGE) race (RACE) covariates. prepare data needed observe desired effects summary table. PARAMCD filtered records overall survival (OS) included, filter mutate include levels interest covariates. ARM variable mutated indicate \"B: Placebo\" used reference level treatment variable, EVENT variable derived CNSR.","code":"adtte <- ex_adtte anl <- adtte %>% dplyr::filter(PARAMCD == \"OS\") %>% dplyr::filter(ARM %in% c(\"A: Drug X\", \"B: Placebo\")) %>% dplyr::filter(RACE %in% c(\"ASIAN\", \"BLACK OR AFRICAN AMERICAN\", \"WHITE\")) %>% dplyr::mutate(RACE = droplevels(RACE)) %>% dplyr::mutate(ARM = droplevels(stats::relevel(ARM, \"B: Placebo\"))) %>% dplyr::mutate(EVENT = 1 - CNSR)"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"tidy-method-for-summary-coxph-objects-tidy-summary-coxph","dir":"Articles","previous_headings":"Creating Helper Functions: Cox Regression Model Calculations","what":"tidy Method for summary.coxph Objects: tidy.summary.coxph","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"method allows tidy function broom package operate summary.coxph output, extracting values interest analysis returning tidied tibble::tibble() object.","code":"tidy.summary.coxph <- function(x, ...) { is(x, \"summary.coxph\") pval <- x$coefficients confint <- x$conf.int levels <- rownames(pval) pval <- tibble::as_tibble(pval) confint <- tibble::as_tibble(confint) ret <- cbind(pval[, grepl(\"Pr\", names(pval))], confint) ret$level <- levels ret$n <- x[[\"n\"]] ret }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"function-to-estimate-interaction-effects-h_coxreg_inter_effect","dir":"Articles","previous_headings":"Creating Helper Functions: Cox Regression Model Calculations","what":"Function to Estimate Interaction Effects: h_coxreg_inter_effect","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"h_coxreg_inter_effect helper function used within following helper function, h_coxreg_extract_interaction, estimate interaction effects given model given covariate. function calculates desired statistics given model returns data.frame label information row well statistics n, hr (hazard ratio), lcl (CI lower bound), ucl (CI upper bound), pval (effect p-value), pval_inter (interaction p-value). numeric covariate selected, median value used sole “level” interaction effect calculated. non-numeric covariates, interaction effect calculated level covariate, result returned separate row.","code":"h_coxreg_inter_effect <- function(x, effect, covar, mod, label, control, data) { if (is.numeric(x)) { betas <- stats::coef(mod) attrs <- attr(stats::terms(mod), \"term.labels\") term_indices <- grep(pattern = effect, x = attrs[!grepl(\"strata\\\\(\", attrs)]) betas <- betas[term_indices] betas_var <- diag(stats::vcov(mod))[term_indices] betas_cov <- stats::vcov(mod)[term_indices[1], term_indices[2]] xval <- stats::median(x) effect_index <- !grepl(covar, names(betas)) coef_hat <- betas[effect_index] + xval * betas[!effect_index] coef_se <- sqrt(betas_var[effect_index] + xval^2 * betas_var[!effect_index] + 2 * xval * betas_cov) q_norm <- stats::qnorm((1 + control$conf_level) / 2) } else { var_lvl <- paste0(effect, levels(data[[effect]])[-1]) # [-1]: reference level giv_lvl <- paste0(covar, levels(data[[covar]])) design_mat <- expand.grid(effect = var_lvl, covar = giv_lvl) design_mat <- design_mat[order(design_mat$effect, design_mat$covar), ] design_mat <- within(data = design_mat, expr = { inter <- paste0(effect, \":\", covar) rev_inter <- paste0(covar, \":\", effect) }) split_by_variable <- design_mat$effect interaction_names <- paste(design_mat$effect, design_mat$covar, sep = \"/\") mmat <- stats::model.matrix(mod)[1, ] mmat[!mmat == 0] <- 0 design_mat <- apply(X = design_mat, MARGIN = 1, FUN = function(x) { mmat[names(mmat) %in% x[-which(names(x) == \"covar\")]] <- 1 mmat }) colnames(design_mat) <- interaction_names coef <- stats::coef(mod) vcov <- stats::vcov(mod) betas <- as.matrix(coef) coef_hat <- t(design_mat) %*% betas dimnames(coef_hat)[2] <- \"coef\" coef_se <- apply(design_mat, 2, function(x) { vcov_el <- as.logical(x) y <- vcov[vcov_el, vcov_el] y <- sum(y) y <- sqrt(y) y }) q_norm <- stats::qnorm((1 + control$conf_level) / 2) y <- cbind(coef_hat, `se(coef)` = coef_se) y <- apply(y, 1, function(x) { x[\"hr\"] <- exp(x[\"coef\"]) x[\"lcl\"] <- exp(x[\"coef\"] - q_norm * x[\"se(coef)\"]) x[\"ucl\"] <- exp(x[\"coef\"] + q_norm * x[\"se(coef)\"]) x }) y <- t(y) y <- by(y, split_by_variable, identity) y <- lapply(y, as.matrix) attr(y, \"details\") <- paste0( \"Estimations of \", effect, \" hazard ratio given the level of \", covar, \" compared to \", effect, \" level \", levels(data[[effect]])[1], \".\" ) xval <- levels(data[[covar]]) } data.frame( effect = \"Covariate:\", term = rep(covar, length(xval)), term_label = as.character(paste0(\" \", xval)), level = as.character(xval), n = NA, hr = if (is.numeric(x)) exp(coef_hat) else y[[1]][, \"hr\"], lcl = if (is.numeric(x)) exp(coef_hat - q_norm * coef_se) else y[[1]][, \"lcl\"], ucl = if (is.numeric(x)) exp(coef_hat + q_norm * coef_se) else y[[1]][, \"ucl\"], pval = NA, pval_inter = NA, stringsAsFactors = FALSE ) }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"function-to-extract-effect-information-h_coxreg_extract_interaction","dir":"Articles","previous_headings":"Creating Helper Functions: Cox Regression Model Calculations","what":"Function to Extract Effect Information: h_coxreg_extract_interaction","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"Using previous two helper functions, h_coxreg_extract_interaction uses ANOVA extract information given model given covariate. function extract different information depending whether effect interest treatment/main effect interaction effect, returns data.frame label information row (corresponding effect) well statistics n, hr, lcl, ucl, pval, pval_inter (interaction effects ). helper function used directly within analysis function analyze Cox regression model extract relevant information processed displayed within output table.","code":"h_coxreg_extract_interaction <- function(effect, covar, mod, data) { control <- list(pval_method = \"wald\", ties = \"exact\", conf_level = 0.95, interaction = FALSE) test_statistic <- c(wald = \"Wald\", likelihood = \"LR\")[control$pval_method] mod_aov <- withCallingHandlers( expr = car::Anova(mod, test.statistic = test_statistic, type = \"III\"), message = function(m) invokeRestart(\"muffleMessage\") ) msum <- if (!any(attr(stats::terms(mod), \"order\") == 2)) summary(mod, conf.int = control$conf_level) else mod_aov sum_anova <- broom::tidy(msum) if (!any(attr(stats::terms(mod), \"order\") == 2)) { effect_aov <- mod_aov[effect, , drop = TRUE] pval <- effect_aov[[grep(pattern = \"Pr\", x = names(effect_aov)), drop = TRUE]] sum_main <- sum_anova[grepl(effect, sum_anova$level), ] term_label <- if (effect == covar) { paste0(levels(data[[covar]])[2], \" vs control (\", levels(data[[covar]])[1], \")\") } else { unname(formatters::var_labels(data, fill = TRUE)[[covar]]) } y <- data.frame( effect = ifelse(covar == effect, \"Treatment:\", \"Covariate:\"), term = covar, term_label = term_label, level = levels(data[[effect]])[2], n = mod[[\"n\"]], hr = unname(sum_main[\"exp(coef)\"]), lcl = unname(sum_main[grep(\"lower\", names(sum_main))]), ucl = unname(sum_main[grep(\"upper\", names(sum_main))]), pval = pval, stringsAsFactors = FALSE ) y$pval_inter <- NA y } else { pval <- sum_anova[sum_anova$term == effect, ][[\"p.value\"]] ## Test the interaction effect pval_inter <- sum_anova[grep(\":\", sum_anova$term), ][[\"p.value\"]] covar_test <- data.frame( effect = \"Covariate:\", term = covar, term_label = unname(formatters::var_labels(data, fill = TRUE)[[covar]]), level = \"\", n = mod$n, hr = NA, lcl = NA, ucl = NA, pval = pval, pval_inter = pval_inter, stringsAsFactors = FALSE ) ## Estimate the interaction y <- h_coxreg_inter_effect( data[[covar]], covar = covar, effect = effect, mod = mod, label = unname(formatters::var_labels(data, fill = TRUE)[[covar]]), control = control, data = data ) rbind(covar_test, y) } }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"creating-a-helper-function-cached_model","dir":"Articles","previous_headings":"","what":"Creating a Helper Function: cached_model","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"Next, create helper function, cached_model, used within analysis function cache return fitted Cox regression model current covariate. df argument directly inherited df argument passed analysis function, contains full dataset analyzed. cov argument covariate analyzed depending current row context. treatment effect currently analyzed, value empty string. cache_env parameter environment object used store model current covariate, also passed analysis function. course, function can also run outside analysis function still cache return Cox regression model. Using arguments, cached_model function first checks model given covariate cov already stored caching environment cache_env. , model retrieved returned cached_model. , model must constructed. done first constructing model formula, model_form, starting treatment effect (ARM) adding covariate effect one currently analyzed. Cox regression model fit using df model formula, model returned stored caching environment object cache_env[[cov]].","code":"cached_model <- function(df, cov, cache_env) { ## Check if a model already exists for ## `cov` in the caching environment if (!is.null(cache_env[[cov]])) { ## If model already exists, retrieve it from cache_env model <- cache_env[[cov]] } else { ## Build model formula model_form <- paste0(\"survival::Surv(AVAL, EVENT) ~ ARM\") if (length(cov) > 0) { model_form <- paste(c(model_form, cov), collapse = \" * \") } else { cov <- \"ARM\" } ## Calculate Cox regression model model <- survival::coxph( formula = stats::as.formula(model_form), data = df, ties = \"exact\" ) ## Store model in the caching environment cache_env[[cov]] <- model } model }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"creating-the-analysis-function-a_cox_summary","dir":"Articles","previous_headings":"","what":"Creating the Analysis Function: a_cox_summary","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"data prepared helper function created, can proceed construct analysis function a_cox_summary, used populate rows table. order used generate data rows (interaction effects) content rows (main effects), must create function can used afun analyze cfun summarize_row_groups. Therefore, function must accept labelstr parameter. arguments analysis function follows: df - data.frame full dataset required fit Cox regression model. labelstr - string label variable analyzed current row/column split context. .spl_context - data.frame containing value column used analysis function determine name variable/covariate current split. details information stored .spl_context see ?analyze. stat format - strings indicate statistic column currently format applied print statistic. cache_env - environment object can used store cached models can prevent repeatedly fitting model. Instead, model generated per covariate reused. argument passed directly cached_model helper function defined previously. cov_main - logical value indicating whether current row summarizing covariate main effects. analysis function works within given row/column split context using current covariate (cov) cached_model function obtain desired Cox regression model. model, h_coxreg_extract_interaction function able extract information/statistics relevant analysis store data.frame. rows data.frame interest current row/column split context extracted statistic printed current column retrieved rows. Finally, formatted cells statistic returned VerticalRowsSection object. detail see commented function code , purpose line within a_cox_summary described.","code":"a_cox_summary <- function(df, labelstr = \"\", .spl_context, stat, format, cache_env, cov_main = FALSE) { ## Get current covariate (variable used in latest row split) cov <- tail(.spl_context$value, 1) ## If currently analyzing treatment effect (ARM) replace empty ## value of cov with \"ARM\" so the correct model row is analyzed if (length(cov) == 0) cov <- \"ARM\" ## Use cached_model to get the fitted Cox regression ## model for the current covariate model <- cached_model(df = df, cov = cov, cache_env = cache_env) ## Extract levels of cov to be used as row labels for interaction effects. ## If cov is numeric, the median value of cov is used as a row label instead cov_lvls <- if (is.factor(df[[cov]])) levels(df[[cov]]) else as.character(median(df[[cov]])) ## Use function to calculate and extract information relevant to cov from the model cov_rows <- h_coxreg_extract_interaction(effect = \"ARM\", covar = cov, mod = model, data = df) ## Effect p-value is only printed for treatment effect row if (!cov == \"ARM\") cov_rows[, \"pval\"] <- NA_real_ ## Extract rows containing statistics for cov from model information if (!cov_main) { ## Extract rows for main effect cov_rows <- cov_rows[cov_rows$level %in% cov_lvls, ] } else { ## Extract all non-main effect rows cov_rows <- cov_rows[nchar(cov_rows$level) == 0, ] } ## Extract value(s) of statistic for current column and variable/levels stat_vals <- as.list(apply(cov_rows[stat], 1, function(x) x, simplify = FALSE)) ## Assign labels: covariate name for main effect (content) rows, ARM comparison description ## for treatment effect (content) row, cov_lvls for interaction effect (data) rows nms <- if (cov_main) labelstr else if (cov == \"ARM\") cov_rows$term_label else cov_lvls ## Return formatted/labelled row in_rows( .list = stat_vals, .names = nms, .labels = nms, .formats = setNames(rep(format, length(nms)), nms), .format_na_strs = setNames(rep(\"\", length(nms)), nms) ) }"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"selecting-parameters","dir":"Articles","previous_headings":"","what":"Selecting Parameters","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"able customize Cox regression summary using analysis function selecting covariates (labels), statistics (labels), statistic formats use generating output table. also initialize new environment object used analysis function caching environment store models . purpose example, choose 5 possible statistics include table: n, hazard ratio, confidence interval, effect p-value, interaction p-value.","code":"my_covs <- c(\"AGE\", \"RACE\") ## Covariates my_cov_labs <- c(\"Age\", \"Race\") ## Covariate labels my_stats <- list(\"n\", \"hr\", c(\"lcl\", \"ucl\"), \"pval\", \"pval_inter\") ## Statistics my_stat_labs <- c(\"n\", \"Hazard Ratio\", \"95% CI\", \"p-value\\n(effect)\", \"p-value\\n(interaction)\") ## Statistic labels my_formats <- c( n = \"xx\", hr = \"xx.xx\", lcl = \"(xx.xx, xx.xx)\", pval = \"xx.xxxx\", pval_inter = \"xx.xxxx\" ## Statistic formats ) my_env <- new.env() ny_cache_env <- replicate(length(my_stats), list(my_env)) ## Caching environment"},{"path":"https://insightsengineering.github.io/rtables/main/articles/example_analysis_coxreg.html","id":"constructing-the-table","dir":"Articles","previous_headings":"","what":"Constructing the Table","title":"Example Complex Analysis Function: Modelling Cox Regression","text":"Finally, table layout can constructed used build desired table. first split basic_table using split_cols_by_multivar ensure statistic exists column. , choose variable (case STUDYID) shares value every row, use split variable every column full dataset used compute model every column. use extra_args argument list element’s element positions correspond children (columns generated ) split. arguments inherited following layout elements operating within split, use elements argument inputs. elaborate , three elements extra_args: stat, format, cache_env - arguments a_cox_summary length equal number columns (defined ). use analysis function following column split, depending current column context, corresponding element three list elements inherited extra_args used input. example, analyze_colvars called a_cox_summary afun performing calculations column 1, my_stats[1] (\"n\") given argument stat, my_formats[1] (\"xx\") argument format, my_cache_env[1] (my_env) cache_env. useful table since want column print values different statistic apply corresponding format. Next, can use summarize_row_groups generate content row treatment effect. first instance extra_args column split inherited used argument input cfun. generating treatment effect row, want add rows covariates. use split_rows_by_multivar split rows covariate apply appropriate labels. Following row split, use summarize_row_groups a_cox_summary cfun generate one content row covariate main effect. contents extra_args column split inherited input. specify cov_main = TRUE extra_args argument main effects rather interactions considered. Since split, instance extra_args inherited following layout elements. cov_main singular value, cov_main = TRUE used within every column context. last part table covariate interaction effects. use analyze_colvars a_cox_summary afun, inherit extra_args column split. Using rtables “analyze” function generates data rows, one row corresponding covariate level (median value, numeric covariates), nested content row (main effect) covariate. Using pre-processed anl dataset, can now build output final Cox regression summary table.","code":"lyt <- basic_table() %>% ## Column split: one column for each statistic split_cols_by_multivar( vars = rep(\"STUDYID\", length(my_stats)), varlabels = my_stat_labs, extra_args = list( stat = my_stats, format = my_formats, cache_env = ny_cache_env ) ) %>% ## Create content row for treatment effect summarize_row_groups(cfun = a_cox_summary) %>% ## Row split: one content row for each covariate split_rows_by_multivar( vars = my_covs, varlabels = my_cov_labs, split_label = \"Covariate:\", indent_mod = -1 ## Align split label left ) %>% ## Create content rows for covariate main effects summarize_row_groups( cfun = a_cox_summary, extra_args = list(cov_main = TRUE) ) %>% ## Create data rows for covariate interaction effects analyze_colvars(afun = a_cox_summary) cox_tbl <- build_table(lyt, anl) cox_tbl ## p-value p-value ## n Hazard Ratio 95% CI (effect) (interaction) ## ———————————————————————————————————————————————————————————————————————————————————————————————— ## A: Drug X vs control (B: Placebo) 247 0.97 (0.71, 1.32) 0.8243 ## Covariate: ## Age 247 0.7832 ## 34 0.92 (0.68, 1.26) ## Race 247 0.7441 ## ASIAN 1.03 (0.68, 1.57) ## BLACK OR AFRICAN AMERICAN 0.78 (0.41, 1.49) ## WHITE 1.06 (0.55, 2.04)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Exploratory Analysis","text":"vignette, like introduce qtable() can used easily create cross tabulations exploratory data analysis. qtable() extension table() base R can much beyond creating two-way contingency tables. function simple use interface internally builds layouts using rtables framework.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting Started","title":"Exploratory Analysis","text":"Load packages used vignette: Let’s start seeing table() can : can easily recreate cross-tables qtable() specifying data.frame variable(s) tabulate. col_vars row_vars arguments control split data across columns rows respectively. Aside display style, main difference qtable() add (N=xx) table header default. can removed show_colcounts. variables used row column facets empty strings (““). non empty values required labels generating table. code generate error.","code":"library(rtables) library(dplyr) table(ex_adsl$ARM) # # A: Drug X B: Placebo C: Combination # 134 134 132 table(ex_adsl$SEX, ex_adsl$ARM) # # A: Drug X B: Placebo C: Combination # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 qtable(ex_adsl, col_vars = \"ARM\") # A: Drug X B: Placebo C: Combination # (N=134) (N=134) (N=132) # ——————————————————————————————————————————————— # count 134 134 132 qtable(ex_adsl, col_vars = \"ARM\", row_vars = \"SEX\") # A: Drug X B: Placebo C: Combination # count (N=134) (N=134) (N=132) # —————————————————————————————————————————————————————————— # F 79 77 66 # M 51 55 60 # U 3 2 4 # UNDIFFERENTIATED 1 0 2 qtable(ex_adsl, \"ARM\", show_colcounts = FALSE) # count all obs # ———————————————————————— # A: Drug X 134 # B: Placebo 134 # C: Combination 132 tmp_adsl <- ex_adsl tmp_adsl$new <- rep_len(c(\"\", \"A\", \"B\"), nrow(tmp_adsl)) qtable(tmp_adsl, row_vars = \"new\")"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"nested-tables","dir":"Articles","previous_headings":"","what":"Nested Tables","title":"Exploratory Analysis","text":"Providing one variable name row column structure qtable() create nested table. Arbitrary nesting supported dimension. Note default, unobserved factor levels within facet included table. can modified drop_levels. code adds row 0s STRATA1 level “B” nested SEX level “UNDIFFERENTIATED”. contrast, table() return nested table. Rather produces list contingency tables two variables used inputs. help stats::ftable() nested structure can achieved two steps.","code":"qtable(ex_adsl, row_vars = c(\"SEX\", \"STRATA1\"), col_vars = c(\"ARM\", \"STRATA2\")) # A: Drug X B: Placebo C: Combination # S1 S2 S1 S2 S1 S2 # count (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) # ———————————————————————————————————————————————————————————————————————— # F # A 12 9 11 13 7 11 # B 14 11 12 15 9 12 # C 17 16 13 13 14 13 # M # A 5 11 10 9 6 14 # B 13 8 7 10 9 12 # C 8 6 13 6 8 11 # U # A 1 0 1 0 1 0 # B 1 0 0 1 0 1 # C 1 0 0 0 1 1 # UNDIFFERENTIATED # A 0 0 0 0 0 1 # C 1 0 0 0 1 0 qtable( ex_adsl, row_vars = c(\"SEX\", \"STRATA1\"), col_vars = c(\"ARM\", \"STRATA2\"), drop_levels = FALSE ) # A: Drug X B: Placebo C: Combination # S1 S2 S1 S2 S1 S2 # count (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) # ———————————————————————————————————————————————————————————————————————— # F # A 12 9 11 13 7 11 # B 14 11 12 15 9 12 # C 17 16 13 13 14 13 # M # A 5 11 10 9 6 14 # B 13 8 7 10 9 12 # C 8 6 13 6 8 11 # U # A 1 0 1 0 1 0 # B 1 0 0 1 0 1 # C 1 0 0 0 1 1 # UNDIFFERENTIATED # A 0 0 0 0 0 1 # B 0 0 0 0 0 0 # C 1 0 0 0 1 0 table(ex_adsl$SEX, ex_adsl$STRATA1, ex_adsl$ARM, ex_adsl$STRATA2) # , , = A: Drug X, = S1 # # # A B C # F 12 14 17 # M 5 13 8 # U 1 1 1 # UNDIFFERENTIATED 0 0 1 # # , , = B: Placebo, = S1 # # # A B C # F 11 12 13 # M 10 7 13 # U 1 0 0 # UNDIFFERENTIATED 0 0 0 # # , , = C: Combination, = S1 # # # A B C # F 7 9 14 # M 6 9 8 # U 1 0 1 # UNDIFFERENTIATED 0 0 1 # # , , = A: Drug X, = S2 # # # A B C # F 9 11 16 # M 11 8 6 # U 0 0 0 # UNDIFFERENTIATED 0 0 0 # # , , = B: Placebo, = S2 # # # A B C # F 13 15 13 # M 9 10 6 # U 0 1 0 # UNDIFFERENTIATED 0 0 0 # # , , = C: Combination, = S2 # # # A B C # F 11 12 13 # M 14 12 11 # U 0 1 1 # UNDIFFERENTIATED 1 0 0 t1 <- ftable(ex_adsl[, c(\"SEX\", \"STRATA1\", \"ARM\", \"STRATA2\")]) ftable(t1, row.vars = c(\"SEX\", \"STRATA1\")) # ARM A: Drug X B: Placebo C: Combination # STRATA2 S1 S2 S1 S2 S1 S2 # SEX STRATA1 # F A 12 9 11 13 7 11 # B 14 11 12 15 9 12 # C 17 16 13 13 14 13 # M A 5 11 10 9 6 14 # B 13 8 7 10 9 12 # C 8 6 13 6 8 11 # U A 1 0 1 0 1 0 # B 1 0 0 1 0 1 # C 1 0 0 0 1 1 # UNDIFFERENTIATED A 0 0 0 0 0 1 # B 0 0 0 0 0 0 # C 1 0 0 0 1 0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"na-values","dir":"Articles","previous_headings":"","what":"NA Values","title":"Exploratory Analysis","text":"far examples seen, used counts summarize data table cell default analysis used qtable(). Internally, single analysis variable specified avar used generate counts table. default analysis variable first variable data. case ex_adsl “STUDYID”. Let’s see happens introduce NA values analysis variable: resulting table showing 0’s across cells values analysis variable NA. Keep behavior mind quick exploratory analysis using default counts aggregate function qtable. suit purpose, can either pre-process data recode NA values use another analysis function. see latter done Custom Aggregation section. addition, row column variables NA levels explicitly labelled . done, columns /rows reflect full data. Explicitly labeling NA levels column facet adds column table:","code":"tmp_adsl <- ex_adsl tmp_adsl[[1]] <- NA_character_ qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"SEX\") # F M U UNDIFFERENTIATED # count (N=222) (N=166) (N=9) (N=3) # ————————————————————————————————————————————————————————————— # A: Drug X 0 0 0 0 # B: Placebo 0 0 0 0 # C: Combination 0 0 0 0 # Recode NA values tmp_adsl[[1]] <- addNA(tmp_adsl[[1]]) qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"SEX\") # F M U UNDIFFERENTIATED # count (N=222) (N=166) (N=9) (N=3) # ————————————————————————————————————————————————————————————— # A: Drug X 79 51 3 1 # B: Placebo 77 55 2 0 # C: Combination 66 60 4 2 tmp_adsl$new1 <- factor(NA_character_, levels = c(\"X\", \"Y\", \"Z\")) qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"new1\") # X Y Z # count (N=0) (N=0) (N=0) # —————————————————————————————————————— # A: Drug X 0 0 0 # B: Placebo 0 0 0 # C: Combination 0 0 0 tmp_adsl$new2 <- addNA(tmp_adsl$new1) levels(tmp_adsl$new2)[4] <- \"\" # NA needs to be a recognizible string qtable(tmp_adsl, row_vars = \"ARM\", col_vars = \"new2\") # X Y Z # count (N=0) (N=0) (N=0) (N=400) # ———————————————————————————————————————————————— # A: Drug X 0 0 0 134 # B: Placebo 0 0 0 134 # C: Combination 0 0 0 132"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"custom-aggregation","dir":"Articles","previous_headings":"","what":"Custom Aggregation","title":"Exploratory Analysis","text":"powerful feature qtable() user can define type function used summarize data facet. can specify type analysis summary using afun argument: Note analysis variable AGE analysis function name included top right header table. analysis function returns vector 2 3 elements, result displayed multi-valued single cells. want use analysis function 3 summary elements, can use list. case, values displayed table multiple stacked cells within facet. list elements named, names used row labels. advanced formatting can controlled in_rows(). See function documentation details.","code":"qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = mean) # A: Drug X B: Placebo C: Combination # AGE - mean (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————— # S1 34.10 36.46 35.70 # S2 33.38 34.40 35.24 qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = range) # A: Drug X B: Placebo C: Combination # AGE - range (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————— # S1 23.0 / 48.0 24.0 / 62.0 20.0 / 69.0 # S2 21.0 / 50.0 21.0 / 58.0 23.0 / 64.0 fivenum2 <- function(x) { setNames(as.list(fivenum(x)), c(\"min\", \"Q1\", \"MED\", \"Q3\", \"max\")) } qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = fivenum2) # A: Drug X B: Placebo C: Combination # AGE - fivenum2 (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————————— # S1 # min 23.00 24.00 20.00 # Q1 28.00 30.00 30.50 # MED 34.00 36.00 35.00 # Q3 39.00 40.50 40.00 # max 48.00 62.00 69.00 # S2 # min 21.00 21.00 23.00 # Q1 29.00 29.50 30.00 # MED 32.00 32.00 34.50 # Q3 38.00 39.50 38.00 # max 50.00 58.00 64.00 meansd_range <- function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx - xx\") ) } qtable(ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", avar = \"AGE\", afun = meansd_range) # A: Drug X B: Placebo C: Combination # AGE - meansd_range (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————————————— # S1 # Mean (sd) 34.10 (6.71) 36.46 (7.72) 35.70 (8.22) # Range 23 - 48 24 - 62 20 - 69 # S2 # Mean (sd) 33.38 (6.40) 34.40 (7.99) 35.24 (7.39) # Range 21 - 50 21 - 58 23 - 64"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"marginal-summaries","dir":"Articles","previous_headings":"","what":"Marginal Summaries","title":"Exploratory Analysis","text":"Another feature qtable() ability quickly add marginal summary rows summarize_groups argument. summary add table count non-NA records analysis variable level nesting. example, compare two tables: second table, marginal summary rows level two row facet variables: STRATA1 STRATA2. number 18 second row gives count observations part ARM level “: Drug X”, STRATA1 level “”, STRATA2 level “S1”. percent calculated cell count divided column count given table header. can see mean AGE 31.61 subgroup based 18 subjects correspond 13.4% subjects arm “: Drug X”. See ?summarize_row_groups add marginal summary rows using core rtables framework.","code":"qtable( ex_adsl, row_vars = c(\"STRATA1\", \"STRATA2\"), col_vars = \"ARM\", avar = \"AGE\", afun = mean ) # A: Drug X B: Placebo C: Combination # AGE - mean (N=134) (N=134) (N=132) # ———————————————————————————————————————————————————— # A # S1 31.61 36.68 34.00 # S2 34.40 33.55 34.35 # B # S1 34.57 37.68 35.83 # S2 32.79 34.77 36.68 # C # S1 35.26 35.38 36.58 # S2 32.95 34.89 34.72 qtable( ex_adsl, row_vars = c(\"STRATA1\", \"STRATA2\"), col_vars = \"ARM\", summarize_groups = TRUE, avar = \"AGE\", afun = mean ) # A: Drug X B: Placebo C: Combination # AGE - mean (N=134) (N=134) (N=132) # ————————————————————————————————————————————————————————— # A 38 (28.4%) 44 (32.8%) 40 (30.3%) # S1 18 (13.4%) 22 (16.4%) 14 (10.6%) # AGE - mean 31.61 36.68 34.00 # S2 20 (14.9%) 22 (16.4%) 26 (19.7%) # AGE - mean 34.40 33.55 34.35 # B 47 (35.1%) 45 (33.6%) 43 (32.6%) # S1 28 (20.9%) 19 (14.2%) 18 (13.6%) # AGE - mean 34.57 37.68 35.83 # S2 19 (14.2%) 26 (19.4%) 25 (18.9%) # AGE - mean 32.79 34.77 36.68 # C 49 (36.6%) 45 (33.6%) 49 (37.1%) # S1 27 (20.1%) 26 (19.4%) 24 (18.2%) # AGE - mean 35.26 35.38 36.58 # S2 22 (16.4%) 19 (14.2%) 25 (18.9%) # AGE - mean 32.95 34.89 34.72"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"table-decorations","dir":"Articles","previous_headings":"","what":"Table Decorations","title":"Exploratory Analysis","text":"Tables generated qtable() can include annotations titles, subtitles footnotes like :","code":"qtable( ex_adsl, row_vars = \"STRATA2\", col_vars = \"ARM\", title = \"Strata 2 Summary\", subtitle = paste0(\"STUDY \", ex_adsl$STUDYID[1]), main_footer = paste0(\"Date: \", as.character(Sys.Date())) ) # Strata 2 Summary # STUDY AB12345 # # ——————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # count (N=134) (N=134) (N=132) # ——————————————————————————————————————————————— # S1 73 67 56 # S2 61 67 76 # ——————————————————————————————————————————————— # # Date: 2024-02-22"},{"path":"https://insightsengineering.github.io/rtables/main/articles/exploratory_analysis.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Exploratory Analysis","text":"learned vignette: qtable() can replace extend uses table() stats::ftable() qtable() useful exploratory data analysis intended use qtable() exploratory data analysis, limited functionality building complex tables. details get started core rtables layout functionality see introduction vignette.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"formats-precedence","dir":"Articles","previous_headings":"","what":"Formats Precedence","title":"Format Precedence and NA Handling","text":"Users rtables package can specify format numbers reporting tables printed. Formatting functionality provided formatters R package. See formatters::list_valid_format_labels() list available formats. format can specified user different places. may happen , single table layout, format specified one place. case, final format applied depends format precedence rules defined rtables. vignette, describe basic rules rtables format precedence. examples shown vignette utilize example ADSL dataset, demographic table summarizes variables content different population subsets (encoded columns). Note ex_* data currently attached rtables package provided formatters package created using publicly available random.cdisc.data R package.","code":"library(rtables) ADSL <- ex_adsl"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"format-precedence-and-inheritance-rules","dir":"Articles","previous_headings":"Formats Precedence","what":"Format Precedence and Inheritance Rules","title":"Format Precedence and NA Handling","text":"format numbers printed can specified user different places. context precedence, important level split hierarchy formats specified . general, two levels: cell level -called parent table level. concept cell parent table results way rtables package stores resulting tables. models resulting tables hierarchical, tree-like objects cells (leaves) containing multiple values. Particularly noteworthy context fact actual table splitting occurs row-dominant way (even column splitting present layout). rtables provides user-end function table_structure() prints structure given table object. simple illustration, consider following example: table, 4 sub-tables SEX table. : F, M, U, UNDIFFERENTIATED. sub-tables one sub-table AGE. example, first AGE sub-table, parent table F. concept hierarchical, tree-like representations resulting tables translates directly format precedence inheritance rules. general principle, format finally applied cell one specific, , one closest cell given path tree. Hence, precedence-inheritance chain looks like following: chain, outermost parent_table least specific place specify format, cell specific one. cases format specified user one place, one specific applied cell. specific format selected user split, default format applied. default format \"xx\" yields formatting .character() function. following sections vignette, illustrate format precedence rules examples.","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = mean) adsl_analyzed <- build_table(lyt, ADSL) adsl_analyzed # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————————————————— # F # mean 32.7594936708861 34.1168831168831 35.1969696969697 # M # mean 35.5686274509804 37.4363636363636 35.3833333333333 # U # mean 31.6666666666667 31 35.25 # UNDIFFERENTIATED # mean 28 NA 45 table_structure(adsl_analyzed) # [TableTree] SEX # [TableTree] F # [ElementaryTable] AGE (1 x 3) # [TableTree] M # [ElementaryTable] AGE (1 x 3) # [TableTree] U # [ElementaryTable] AGE (1 x 3) # [TableTree] UNDIFFERENTIATED # [ElementaryTable] AGE (1 x 3) parent_table -> parent_table -> ... -> parent_table -> cell"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"standard-format","dir":"Articles","previous_headings":"Formats Precedence","what":"Standard Format","title":"Format Precedence and NA Handling","text":"simple layout explicitly set format output analysis function. case, default format applied.","code":"lyt0 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = mean) build_table(lyt0, ADSL) # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————— # mean 33.7686567164179 35.4328358208955 35.4318181818182"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"cell-format","dir":"Articles","previous_headings":"Formats Precedence","what":"Cell Format","title":"Format Precedence and NA Handling","text":"format cell can explicitly specified via rcell() in_rows() functions. former essentially collection data objects latter collection rcell() objects. previously mentioned, specific place format can specified user. format specified places time, one specified via in_rows() takes highest precedence. Technically, case, format defined rcell() simply overwritten one defined in_rows(). format specified in_rows() applied cells rows (overriding previously specified cell-specific values), indicates precedence rules described still place.","code":"lyt1 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\") }) build_table(lyt1, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 lyt1a <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x)), .formats = \"xx.xx\" ) }) build_table(lyt1a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xxx\"), .formats = \"xx.xx\" ) }) build_table(lyt2, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"parent-table-format-and-inheritance","dir":"Articles","previous_headings":"Formats Precedence","what":"Parent Table Format and Inheritance","title":"Format Precedence and NA Handling","text":"addition cell level, format can specified parent table level. format set user cell, specific format cell one defined innermost parent table split (). cell format also specified cell, parent table format ignored cell since cell format specific therefore takes precedence. following, slightly complicated, example, can observe partial inheritance. , SD cells inherit parent table’s format Mean cells .","code":"lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(vars = \"AGE\", mean, format = \"xx.x\") build_table(lyt3, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # mean 33.8 35.4 35.4 lyt4 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze( vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\") }, format = \"xx.x\" ) build_table(lyt4, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 lyt4a <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x)), \"SD\" = rcell(sd(x)), .formats = \"xx.xx\" ) }, format = \"xx.x\" ) build_table(lyt4a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 # SD 6.55 7.90 7.72 lyt5 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xx\"), \"SD\" = rcell(sd(x)) ) }, format = \"xx.x\" ) build_table(lyt5, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————— # Mean 33.77 35.43 35.43 # SD 6.6 7.9 7.7"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"na-handling","dir":"Articles","previous_headings":"","what":"NA Handling","title":"Format Precedence and NA Handling","text":"Consider following layout resulting table created: output cell corresponding UNDIFFERENTIATED level SEX B: Placebo level ARM displayed NA. occurs non-NA values facet used compute mean. rtables allows user specify string display cell values NA. Similar formats numbers, user can specify string replace NA parameter format_na_str .format_na_str. can specified cell parent table level. NA string precedence inheritance rules number format precedence, described previous section vignette. illustrate examples.","code":"lyt6 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = mean, format = \"xx.xx\") build_table(lyt6, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # mean 32.76 34.12 35.20 # M # mean 35.57 37.44 35.38 # U # mean 31.67 31.00 35.25 # UNDIFFERENTIATED # mean 28.00 NA 45.00"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"replacing-na-values-at-the-cell-level","dir":"Articles","previous_headings":"NA Handling","what":"Replacing NA Values at the Cell Level","title":"Format Precedence and NA Handling","text":"cell level, possible replace NA values custom string means format_na_str parameter rcell() .format_na_str parameter in_rows(). NA string specified places time, one specified in_rows() takes precedence. Technically, case NA replacement string defined rcell() simply overwritten one defined in_rows(). NA string specified in_rows() applied cells, rows (overriding previously specified cell specific values), means precedence rules described still place.","code":"lyt7 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\", format_na_str = \"\") }) build_table(lyt7, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00 lyt7a <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xx\"), .format_na_strs = \"\" ) }) build_table(lyt7a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00 lyt8 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format = \"xx.xx\", format_na_str = \"\"), .format_na_strs = \"\" ) }) build_table(lyt8, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00"},{"path":"https://insightsengineering.github.io/rtables/main/articles/format_precedence.html","id":"parent-table-replacement-of-na-values-and-inheritance-principles","dir":"Articles","previous_headings":"NA Handling","what":"Parent Table Replacement of NA Values and Inheritance Principles","title":"Format Precedence and NA Handling","text":"addition cell level, string replacement NA values can specified parent table level. replacement string specified user cell, specific NA string cell one defined innermost parent table split (). NA value replacement string also specified cell level, one set parent table level ignored cell cell level format specific therefore takes precedence. following, slightly complicated example, can observe partial inheritance NA strings. , SD cells inherit parent table’s NA string, Mean cells .","code":"lyt9 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(vars = \"AGE\", mean, format = \"xx.xx\", na_str = \"not available\") build_table(lyt9, ADSL) # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————— # F # mean 32.76 34.12 35.20 # M # mean 35.57 37.44 35.38 # U # mean 31.67 31.00 35.25 # UNDIFFERENTIATED # mean 28.00 not available 45.00 lyt10 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze( vars = \"AGE\", afun = function(x) { rcell(mean(x), format = \"xx.xx\", label = \"Mean\", format_na_str = \"\") }, na_str = \"not available\" ) build_table(lyt10, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # M # Mean 35.57 37.44 35.38 # U # Mean 31.67 31.00 35.25 # UNDIFFERENTIATED # Mean 28.00 45.00 lyt10a <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x)), \"SD\" = rcell(sd(x)), .formats = \"xx.xx\", .format_na_strs = \"\" ) }, na_str = \"not available\" ) build_table(lyt10a, ADSL) # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # SD 6.09 7.06 7.43 # M # Mean 35.57 37.44 35.38 # SD 7.08 8.69 8.24 # U # Mean 31.67 31.00 35.25 # SD 3.21 5.66 3.10 # UNDIFFERENTIATED # Mean 28.00 45.00 # SD 1.41 lyt11 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze( vars = \"AGE\", afun = function(x) { in_rows( \"Mean\" = rcell(mean(x), format_na_str = \"\"), \"SD\" = rcell(sd(x)) ) }, format = \"xx.xx\", na_str = \"not available\" ) build_table(lyt11, ADSL) # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————————————————————— # F # Mean 32.76 34.12 35.20 # SD 6.09 7.06 7.43 # M # Mean 35.57 37.44 35.38 # SD 7.08 8.69 8.24 # U # Mean 31.67 31.00 35.25 # SD 3.21 5.66 3.10 # UNDIFFERENTIATED # Mean 28.00 45.00 # SD not available not available 1.41"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introduction to {rtables}","text":"rtables package provides framework create, tabulate, output tables R. design requirements rtables origin studying tables commonly used report analyses clinical trials; however, careful keep rtables general purpose toolkit. vignette, give short introduction rtables tabulating table. content vignette based following two resources: rtables useR 2020 presentation Gabriel Becker rtables - Framework Creating Complex Structured Reporting Tables Via Multi-Level Faceted Computations. packages used vignette rtables dplyr:","code":"library(rtables) library(dplyr)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Introduction to {rtables}","text":"build table using rtables two components required: layout constructed using rtables functions, data.frame unaggregated data. two elements combined build table object. Table objects contain information content structure table, well instructions information processed construct table. obtaining table object, formatted table can printed ASCII format, exported variety formats (.txt, .pdf, .docx, etc.).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"data","dir":"Articles","previous_headings":"","what":"Data","title":"Introduction to {rtables}","text":"data used vignette made using random number generators. data content relatively simple: one row per imaginary person one column per measurement: study arm, country origin, gender, handedness, age, weight. Note use factor variables level order represented row column order tabulate information df .","code":"n <- 400 set.seed(1) df <- tibble( arm = factor(sample(c(\"Arm A\", \"Arm B\"), n, replace = TRUE), levels = c(\"Arm A\", \"Arm B\")), country = factor(sample(c(\"CAN\", \"USA\"), n, replace = TRUE, prob = c(.55, .45)), levels = c(\"CAN\", \"USA\")), gender = factor(sample(c(\"Female\", \"Male\"), n, replace = TRUE), levels = c(\"Female\", \"Male\")), handed = factor(sample(c(\"Left\", \"Right\"), n, prob = c(.6, .4), replace = TRUE), levels = c(\"Left\", \"Right\")), age = rchisq(n, 30) + 10 ) %>% mutate( weight = 35 * rnorm(n, sd = .5) + ifelse(gender == \"Female\", 140, 180) ) head(df) # # A tibble: 6 × 6 # arm country gender handed age weight # # 1 Arm A USA Female Left 31.3 139. # 2 Arm B CAN Female Right 50.5 116. # 3 Arm A USA Male Right 32.4 186. # 4 Arm A USA Male Right 34.6 169. # 5 Arm B USA Female Right 43.0 160. # 6 Arm A USA Female Right 43.2 126."},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"building-a-table","dir":"Articles","previous_headings":"","what":"Building a Table","title":"Introduction to {rtables}","text":"aim vignette build following table step step:","code":"# Arm A Arm B # Female Male Female Male # (N=96) (N=105) (N=92) (N=107) # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.87 40.43 40.33 37.68 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.64 40.19 40.16 40.65 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.36 39.68 39.21 40.07 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.94 39.80 38.53 39.02"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"quick-start","dir":"Articles","previous_headings":"","what":"Quick Start","title":"Introduction to {rtables}","text":"table can achieved via qtable() function. new tabulation rtables layout framework, can use convenience wrapper create many types two-way frequency tables. purpose qtable enable quick exploratory data analysis. See exploratory_analysis vignette details. code recreate table : qtable function arguments can see many key concepts underlying rtables layout framework. user needs define: variables used facets row /column space? variable used summary analysis? function used summary? table include marginal summaries? labels needed clarify table content? sections look translating questions set features part rtables layout framework. Now let’s take look building example table layout.","code":"qtable(df, row_vars = c(\"country\", \"handed\"), col_vars = c(\"arm\", \"gender\"), avar = \"age\", afun = mean, summarize_groups = TRUE, row_labels = \"mean\" ) # Arm A Arm B # Female Male Female Male # age - mean (N=96) (N=105) (N=92) (N=107) # —————————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.87 40.43 40.33 37.68 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.64 40.19 40.16 40.65 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.36 39.68 39.21 40.07 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.94 39.80 38.53 39.02"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"layout-instructions","dir":"Articles","previous_headings":"","what":"Layout Instructions","title":"Introduction to {rtables}","text":"rtables basic table defined 0 rows one column representing data. Analyzing variable one way adding row: code first described table assigned description variable lyt. built table using actual data build_table(). description table called table layout. basic_table() start every table layout contains information one column representing data. analyze() instruction adds layout age variable analyzed mean() analysis function result rounded 1 decimal place. Hence, layout “pre-data”, , ’s description build table get data. can look layout isolated: general layouting instructions summarized : basic_table() layout representing table zero rows one column row space: split_rows_by(), split_rows_by_multivar(), split_rows_by_cuts(), split_rows_by_cutfun(), split_rows_by_quartiles() column space: split_cols_by(), split_cols_by_multivar(), split_cols_by_cuts(), split_cols_by_cutfun(), split_cols_by_quartiles() Summarizing Groups: summarize_row_groups() Analyzing Variables: analyze(), analyze_colvars() Using functions, possible create wide variety tables show document.","code":"lyt <- basic_table() %>% analyze(\"age\", mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # all obs # —————————————— # mean 39.4 lyt # A Pre-data Table Layout # # Column-Split Structure: # () # # Row-Split Structure: # age (** analysis **)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"adding-column-structure","dir":"Articles","previous_headings":"","what":"Adding Column Structure","title":"Introduction to {rtables}","text":"now add structure columns adding column split based factor variable arm: resulting table one column per factor level arm. data represented first column df[df$arm == \"ARM \", ]. Hence, split_cols_by() partitions data among columns default. Column splitting can done recursive/nested manner adding sequential split_cols_by() layout instruction. ’s also possible add non-nested split. splitting arm gender: first column represents data df df$arm == \"\" & df$gender == \"Female\" second column data df df$arm == \"\" & df$gender == \"Male\", .","code":"lyt <- basic_table() %>% split_cols_by(\"arm\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # ———————————————————— # mean 39.5 39.4 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————— # mean 38.8 40.1 39.6 39.2"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"adding-row-structure","dir":"Articles","previous_headings":"","what":"Adding Row Structure","title":"Introduction to {rtables}","text":"far, created layouts analysis column splitting instructions, .e. analyze() split_cols_by(), respectively. resulted table multiple columns one data row. add row structure stratifying mean analysis country (.e. adding split row space): table data used derive first data cell (average age female Canadians Arm ) df$country == \"CAN\" & df$arm == \"Arm \" & df$gender == \"Female\". cell value can also calculated manually: Row structure can also used group table titled groups pages rendering. via ‘page splits’, declared via page_by = TRUE within call split_rows_by: go detail page-splits control page-group specific titles Title footer vignette. Note print render table without pagination, page_by splits currently rendered normal row splits. may change future releases.","code":"lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # —————————————————————————————————————— # CAN # mean 38.2 40.3 40.3 38.9 # USA # mean 39.2 39.7 38.9 39.6 mean(df$age[df$country == \"CAN\" & df$arm == \"Arm A\" & df$gender == \"Female\"]) # [1] 38.22447 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\", page_by = TRUE) %>% split_rows_by(\"handed\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) cat(export_as_txt(tbl, page_type = \"letter\", page_break = \"\\n\\n~~~~~~ Page Break ~~~~~~\\n\\n\")) # # country: CAN # # ———————————————————————————————————————— # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————— # Left # mean 38.9 40.4 40.3 37.7 # Right # mean 36.6 40.2 40.2 40.6 # # # ~~~~~~ Page Break ~~~~~~ # # # country: USA # # ———————————————————————————————————————— # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————— # Left # mean 40.4 39.7 39.2 40.1 # Right # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"adding-group-information","dir":"Articles","previous_headings":"","what":"Adding Group Information","title":"Introduction to {rtables}","text":"adding row splits, get default label rows split level, example CAN USA table . Besides column space subsetting, now subsetted data cell. often useful defining row splitting display information row group. rtables referred content information, .e. mean() row 2 descendant CAN (visible via indenting, though table underlying tree structure importance vignette). order add content information turn CAN label row content row, summarize_row_groups() function required. default, count (nrows()) percentage data relative column associated data calculated: relative percentage average age female Canadians calculated follows: group percentages per row split sum 1 column. can split row space dividing country handedness: Next, add count percentage summary handedness within country:","code":"lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # —————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # mean 38.2 40.3 40.3 38.9 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # mean 39.2 39.7 38.9 39.6 df_cell <- subset(df, df$country == \"CAN\" & df$arm == \"Arm A\" & df$gender == \"Female\") df_col_1 <- subset(df, df$arm == \"Arm A\" & df$gender == \"Female\") c(count = nrow(df_cell), percentage = nrow(df_cell) / nrow(df_col_1)) # count percentage # 45.00000 0.46875 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left # mean 38.9 40.4 40.3 37.7 # Right # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left # mean 40.4 39.7 39.2 40.1 # Right # mean 36.9 39.8 38.5 39.0 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.9 40.4 40.3 37.7 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.4 39.7 39.2 40.1 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"comparing-with-other-tabulation-frameworks","dir":"Articles","previous_headings":"","what":"Comparing with Other Tabulation Frameworks","title":"Introduction to {rtables}","text":"number table frameworks available R, including: gt xtable tableone tables number reasons choose rtables (yet another tables R package): Output tables ASCII text files. Table rendering (ASCII, HTML, etc.) separate data model. Hence, one always access non-rounded/non-formatted numbers. Pagination horizontal vertical directions meet health authority submission requirements. Cell, row, column, table reference system. Titles, footers, referential footnotes. Path based access cell content useful automated content generation. depth comparisons various tabulation frameworks can found Overview table R packages chapter Tables Clinical Trials R book compiled R Consortium Tables Working Group.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/introduction.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to {rtables}","text":"vignette learned: Every cell associated subset data - means much tabulation splitting/subsetting data. Tables can described pre-data using layouts. Tables form visualization data. vignettes rtables package provide detailed information rtables package. recommend continue tabulation_dplyr vignette compares information derived table vignette using dplyr.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/introspecting_tables.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Introspecting Tables","text":"First, let’s set simple table.","code":"lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARMCD\") %>% split_cols_by(\"STRATA2\") %>% split_rows_by(\"STRATA1\") %>% add_overall_col(\"All\") %>% summarize_row_groups() %>% analyze(\"AGE\", afun = max, format = \"xx.x\") tbl <- build_table(lyt, ex_adsl) tbl # ARM A ARM B ARM C # S1 S2 S1 S2 S1 S2 All # (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) (N=400) # ————————————————————————————————————————————————————————————————————————————————————————————————— # A 18 (24.7%) 20 (32.8%) 22 (32.8%) 22 (32.8%) 14 (25.0%) 26 (34.2%) 122 (30.5%) # max 40.0 46.0 62.0 50.0 47.0 45.0 62.0 # B 28 (38.4%) 19 (31.1%) 19 (28.4%) 26 (38.8%) 18 (32.1%) 25 (32.9%) 135 (33.8%) # max 48.0 47.0 58.0 58.0 46.0 64.0 64.0 # C 27 (37.0%) 22 (36.1%) 26 (38.8%) 19 (28.4%) 24 (42.9%) 25 (32.9%) 143 (35.8%) # max 48.0 50.0 48.0 51.0 69.0 50.0 69.0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introspecting_tables.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting Started","title":"Introspecting Tables","text":"can get basic table dimensions, number rows, number columns following code:","code":"dim(tbl) # [1] 6 7 nrow(tbl) # [1] 6 ncol(tbl) # [1] 7"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introspecting_tables.html","id":"detailed-table-structure","dir":"Articles","previous_headings":"","what":"Detailed Table Structure","title":"Introspecting Tables","text":"table_structure() function prints summary table’s row structure one two levels detail. default, summarizes structure subtable level. detail argument set \"row\", however, provides detailed row-level summary acts useful alternative might normally use str() function interrogate compound nested lists. make_row_df() make_col_df() functions create data.frame variety information table’s structure. useful introspection purposes label, name, abs_rownumber, path node_class columns (remainder information returned data.frame used pagination) also wrapper function, row_paths() available make_row_df display row path structure: default make_row_df() summarizes visible rows, setting visible_only FALSE gives us structural summary table full hierarchy subtables, including represented directly visible rows: make_col_df() similarly accepts visible_only, though meaning slightly different, indicating whether leaf columns summarized (defaults TRUE) whether higher level groups columns - analogous subtables row space - summarized well. Similarly, wrapper function col_paths() available, displays column structure: row_paths_summary() col_paths_summary() functions wrap respective make_*_df functions, printing name, node_class, path information (row case), label path information (column case), indented illustrate table structure:","code":"table_structure(tbl) # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 7] # [ElementaryTable] AGE (1 x 7) # [TableTree] B [cont: 1 x 7] # [ElementaryTable] AGE (1 x 7) # [TableTree] C [cont: 1 x 7] # [ElementaryTable] AGE (1 x 7) table_structure(tbl, detail = \"row\") # TableTree: [STRATA1] (STRATA1) # labelrow: [STRATA1] (STRATA1) - # children: # TableTree: [A] (A) # labelrow: [A] (A) - # content: # ElementaryTable: [A@content] () # labelrow: [] () - # children: # ContentRow: [A] (A) # children: # ElementaryTable: [AGE] (AGE) # labelrow: [AGE] (AGE) - # children: # DataRow: [max] (max) # TableTree: [B] (B) # labelrow: [B] (B) - # content: # ElementaryTable: [B@content] () # labelrow: [] () - # children: # ContentRow: [B] (B) # children: # ElementaryTable: [AGE] (AGE) # labelrow: [AGE] (AGE) - # children: # DataRow: [max] (max) # TableTree: [C] (C) # labelrow: [C] (C) - # content: # ElementaryTable: [C@content] () # labelrow: [] () - # children: # ContentRow: [C] (C) # children: # ElementaryTable: [AGE] (AGE) # labelrow: [AGE] (AGE) - # children: # DataRow: [max] (max) make_row_df(tbl)[, c(\"label\", \"name\", \"abs_rownumber\", \"path\", \"node_class\")] # label name abs_rownumber path node_class # 1 A A 1 STRATA1,.... ContentRow # 2 max max 2 STRATA1,.... DataRow # 3 B B 3 STRATA1,.... ContentRow # 4 max max 4 STRATA1,.... DataRow # 5 C C 5 STRATA1,.... ContentRow # 6 max max 6 STRATA1,.... DataRow row_paths(tbl) # [[1]] # [1] \"STRATA1\" \"A\" \"@content\" \"A\" # # [[2]] # [1] \"STRATA1\" \"A\" \"AGE\" \"max\" # # [[3]] # [1] \"STRATA1\" \"B\" \"@content\" \"B\" # # [[4]] # [1] \"STRATA1\" \"B\" \"AGE\" \"max\" # # [[5]] # [1] \"STRATA1\" \"C\" \"@content\" \"C\" # # [[6]] # [1] \"STRATA1\" \"C\" \"AGE\" \"max\" make_row_df(tbl, visible_only = FALSE)[, c(\"label\", \"name\", \"abs_rownumber\", \"path\", \"node_class\")] # label name abs_rownumber path node_class # 1 STRATA1 NA STRATA1 TableTree # 2 A NA STRATA1, A TableTree # 3 A@content NA STRATA1,.... ElementaryTable # 4 A A 1 STRATA1,.... ContentRow # 5 AGE NA STRATA1,.... ElementaryTable # 6 max max 2 STRATA1,.... DataRow # 7 B NA STRATA1, B TableTree # 8 B@content NA STRATA1,.... ElementaryTable # 9 B B 3 STRATA1,.... ContentRow # 10 AGE NA STRATA1,.... ElementaryTable # 11 max max 4 STRATA1,.... DataRow # 12 C NA STRATA1, C TableTree # 13 C@content NA STRATA1,.... ElementaryTable # 14 C C 5 STRATA1,.... ContentRow # 15 AGE NA STRATA1,.... ElementaryTable # 16 max max 6 STRATA1,.... DataRow make_col_df(tbl)[, c(\"label\", \"name\", \"abs_pos\", \"path\", \"leaf_indices\")] # label name abs_pos path leaf_indices # 1 S1 S1 1 ARMCD, A.... 1 # 2 S2 S2 2 ARMCD, A.... 2 # 3 S1 S1 3 ARMCD, A.... 3 # 4 S2 S2 4 ARMCD, A.... 4 # 5 S1 S1 5 ARMCD, A.... 5 # 6 S2 S2 6 ARMCD, A.... 6 # 7 All All 7 All, All 7 make_col_df(tbl, visible_only = FALSE)[, c(\"label\", \"name\", \"abs_pos\", \"path\", \"leaf_indices\")] # label name abs_pos path leaf_indices # 1 ARM A ARM A NA ARMCD, ARM A 1, 2 # 2 S1 S1 1 ARMCD, A.... 1 # 3 S2 S2 2 ARMCD, A.... 2 # 4 ARM B ARM B NA ARMCD, ARM B 3, 4 # 5 S1 S1 3 ARMCD, A.... 3 # 6 S2 S2 4 ARMCD, A.... 4 # 7 ARM C ARM C NA ARMCD, ARM C 5, 6 # 8 S1 S1 5 ARMCD, A.... 5 # 9 S2 S2 6 ARMCD, A.... 6 # 10 All All 7 All, All 7 col_paths(tbl) # [[1]] # [1] \"ARMCD\" \"ARM A\" \"STRATA2\" \"S1\" # # [[2]] # [1] \"ARMCD\" \"ARM A\" \"STRATA2\" \"S2\" # # [[3]] # [1] \"ARMCD\" \"ARM B\" \"STRATA2\" \"S1\" # # [[4]] # [1] \"ARMCD\" \"ARM B\" \"STRATA2\" \"S2\" # # [[5]] # [1] \"ARMCD\" \"ARM C\" \"STRATA2\" \"S1\" # # [[6]] # [1] \"ARMCD\" \"ARM C\" \"STRATA2\" \"S2\" # # [[7]] # [1] \"All\" \"All\" row_paths_summary(tbl) # rowname node_class path # ———————————————————————————————————————————————— # A ContentRow STRATA1, A, @content, A # max DataRow STRATA1, A, AGE, max # B ContentRow STRATA1, B, @content, B # max DataRow STRATA1, B, AGE, max # C ContentRow STRATA1, C, @content, C # max DataRow STRATA1, C, AGE, max col_paths_summary(tbl) # label path # —————————————————————————————————— # ARM A ARMCD, ARM A # S1 ARMCD, ARM A, STRATA2, S1 # S2 ARMCD, ARM A, STRATA2, S2 # ARM B ARMCD, ARM B # S1 ARMCD, ARM B, STRATA2, S1 # S2 ARMCD, ARM B, STRATA2, S2 # ARM C ARMCD, ARM C # S1 ARMCD, ARM C, STRATA2, S1 # S2 ARMCD, ARM C, STRATA2, S2 # All All, All"},{"path":"https://insightsengineering.github.io/rtables/main/articles/introspecting_tables.html","id":"insights-on-value-format-structure","dir":"Articles","previous_headings":"","what":"Insights on Value Format Structure","title":"Introspecting Tables","text":"can gain insight value formatting structure table using table_shell(), returns table output print() cell values replaced underlying format strings (e.g. instead 40.0, xx.x displayed, ). useful understanding structure table, debugging purposes. Another useful tool value_formats() function instead table returns matrix format strings cell value table. See printout examples:","code":"table_shell(tbl) # ARM A ARM B ARM C # S1 S2 S1 S2 S1 S2 All # (N=73) (N=61) (N=67) (N=67) (N=56) (N=76) (N=400) # ———————————————————————————————————————————————————————————————————————————————————————————————— # A xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) # max xx.x xx.x xx.x xx.x xx.x xx.x xx.x # B xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) # max xx.x xx.x xx.x xx.x xx.x xx.x xx.x # C xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) xx (xx.x%) # max xx.x xx.x xx.x xx.x xx.x xx.x xx.x value_formats(tbl) # ARM A.S1 ARM A.S2 ARM B.S1 ARM B.S2 ARM C.S1 # A \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" # B \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" # C \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" \"xx.x\" # ARM C.S2 All # A \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" # B \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\" # C \"xx (xx.x%)\" \"xx (xx.x%)\" # max \"xx.x\" \"xx.x\""},{"path":"https://insightsengineering.github.io/rtables/main/articles/introspecting_tables.html","id":"applications","dir":"Articles","previous_headings":"","what":"Applications","title":"Introspecting Tables","text":"Knowing structure rtable object helpful retrieving specific values table. examples, see Path Based Cell Value Accessing section Subsetting Manipulating Table Contents vignette. Understanding table structure also important post-processing processes sorting pruning. details covered Pruning Sorting Tables vignette vignette.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/introspecting_tables.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introspecting Tables","text":"vignette learned number utility functions available examining underlying structure rtable objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/manual_table_construction.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Constructing rtables Manually","text":"main functions currently associated rtables Tables rtables can constructed via layout rtabulate tabulation frameworks also manually. Currently manual table construction way define column spans. main functions manual table constructions : rtable(): collection rrow() objects, column header default format rrow(): collection rcell() objects default format rcell(): collection data objects cell format","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/manual_table_construction.html","id":"simple-example","dir":"Articles","previous_headings":"","what":"Simple Example","title":"Constructing rtables Manually","text":"go explaining individual components used create table continue html conversion rtable() object: Next, [ operator lets access cell content. format cell run format_rcell(tbl[1,1])=. Note tbl[6, 1] tbl[6, 2] display rcell colspan.","code":"library(rtables) tbl <- rtable( header = c(\"Treatement\\nN=100\", \"Comparison\\nN=300\"), format = \"xx (xx.xx%)\", rrow(\"A\", c(104, .2), c(100, .4)), rrow(\"B\", c(23, .4), c(43, .5)), rrow(), rrow(\"this is a very long section header\"), rrow(\"estimate\", rcell(55.23, \"xx.xx\", colspan = 2)), rrow(\"95% CI\", indent = 1, rcell(c(44.8, 67.4), format = \"(xx.x, xx.x)\", colspan = 2)) ) as_html(tbl, width = \"80%\") tbl[1, 1] # Treatement # N=100 # ———————————————— # A 104 (20.00%)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Pruning and Sorting Tables","text":"Often want filter reorder elements table ways take account table structure. example: Sorting subtables corresponding factor levels commonly observed levels occur first table. Sorting rows within single subtable Removing subtables represent 0 observations filtering contain 0 rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"a-table-in-need-of-attention","dir":"Articles","previous_headings":"","what":"A Table In Need of Attention","title":"Pruning and Sorting Tables","text":"","code":"library(rtables) library(dplyr) raw_lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% split_rows_by(\"RACE\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") raw_tbl <- build_table(raw_lyt, DM) raw_tbl # A: Drug X B: Placebo C: Combination # F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED # —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 0 (NA%) 0 (NA%) 37 (66.1%) 31 (62.0%) 0 (NA%) 0 (NA%) 40 (65.6%) 44 (64.7%) 0 (NA%) 0 (NA%) # A 15 (21.4%) 12 (23.5%) 0 (NA%) 0 (NA%) 14 (25.0%) 6 (12.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 30.40 34.42 NA NA 35.43 30.33 NA NA 37.40 36.25 NA NA # B 16 (22.9%) 8 (15.7%) 0 (NA%) 0 (NA%) 13 (23.2%) 16 (32.0%) 0 (NA%) 0 (NA%) 10 (16.4%) 12 (17.6%) 0 (NA%) 0 (NA%) # Mean 33.75 34.88 NA NA 32.46 30.94 NA NA 33.30 35.92 NA NA # C 13 (18.6%) 15 (29.4%) 0 (NA%) 0 (NA%) 10 (17.9%) 9 (18.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 36.92 35.60 NA NA 34.00 31.89 NA NA 33.47 31.38 NA NA # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 0 (NA%) 0 (NA%) 12 (21.4%) 12 (24.0%) 0 (NA%) 0 (NA%) 13 (21.3%) 14 (20.6%) 0 (NA%) 0 (NA%) # A 5 (7.1%) 1 (2.0%) 0 (NA%) 0 (NA%) 5 (8.9%) 2 (4.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.20 33.00 NA NA 28.00 30.00 NA NA 30.75 36.50 NA NA # B 7 (10.0%) 3 (5.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 6 (9.8%) 6 (8.8%) 0 (NA%) 0 (NA%) # Mean 36.14 34.33 NA NA 29.67 32.00 NA NA 36.33 31.00 NA NA # C 6 (8.6%) 6 (11.8%) 0 (NA%) 0 (NA%) 4 (7.1%) 7 (14.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.33 39.67 NA NA 34.50 34.00 NA NA 33.00 36.50 NA NA # WHITE 8 (11.4%) 6 (11.8%) 0 (NA%) 0 (NA%) 7 (12.5%) 7 (14.0%) 0 (NA%) 0 (NA%) 8 (13.1%) 10 (14.7%) 0 (NA%) 0 (NA%) # A 2 (2.9%) 1 (2.0%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 1 (1.6%) 5 (7.4%) 0 (NA%) 0 (NA%) # Mean 34.00 45.00 NA NA 29.33 33.33 NA NA 35.00 32.80 NA NA # B 4 (5.7%) 3 (5.9%) 0 (NA%) 0 (NA%) 1 (1.8%) 4 (8.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 1 (1.5%) 0 (NA%) 0 (NA%) # Mean 37.00 43.67 NA NA 48.00 36.75 NA NA 34.33 36.00 NA NA # C 2 (2.9%) 2 (3.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 0 (0.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 35.50 44.00 NA NA 44.67 NA NA NA 38.50 35.00 NA NA # AMERICAN INDIAN OR ALASKA NATIVE 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # MULTIPLE 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # OTHER 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # UNKNOWN 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # A 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # B 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA # C 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) 0 (0.0%) 0 (0.0%) 0 (NA%) 0 (NA%) # Mean NA NA NA NA NA NA NA NA NA NA NA NA"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"trimming-rows","dir":"Articles","previous_headings":"Trimming","what":"Trimming Rows","title":"Pruning and Sorting Tables","text":"Trimming represents convenience wrapper around simple, direct subsetting rows TableTree. use trim_rows() function table criteria function. rows criteria function returns TRUE removed, others retained. NOTE: row kept removed completely independently, awareness surrounding structure. means, example, subtree analysis rows removed removed . structure-aware filtering table, use pruning described next section. trimming function accepts TableRow object returns TRUE row removed. default trimming function removes rows columns values , .e. NA values 0 values:","code":"trim_rows(raw_tbl) # A: Drug X B: Placebo C: Combination # F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED F M U UNDIFFERENTIATED # —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 0 (NA%) 0 (NA%) 37 (66.1%) 31 (62.0%) 0 (NA%) 0 (NA%) 40 (65.6%) 44 (64.7%) 0 (NA%) 0 (NA%) # A 15 (21.4%) 12 (23.5%) 0 (NA%) 0 (NA%) 14 (25.0%) 6 (12.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 30.40 34.42 NA NA 35.43 30.33 NA NA 37.40 36.25 NA NA # B 16 (22.9%) 8 (15.7%) 0 (NA%) 0 (NA%) 13 (23.2%) 16 (32.0%) 0 (NA%) 0 (NA%) 10 (16.4%) 12 (17.6%) 0 (NA%) 0 (NA%) # Mean 33.75 34.88 NA NA 32.46 30.94 NA NA 33.30 35.92 NA NA # C 13 (18.6%) 15 (29.4%) 0 (NA%) 0 (NA%) 10 (17.9%) 9 (18.0%) 0 (NA%) 0 (NA%) 15 (24.6%) 16 (23.5%) 0 (NA%) 0 (NA%) # Mean 36.92 35.60 NA NA 34.00 31.89 NA NA 33.47 31.38 NA NA # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 0 (NA%) 0 (NA%) 12 (21.4%) 12 (24.0%) 0 (NA%) 0 (NA%) 13 (21.3%) 14 (20.6%) 0 (NA%) 0 (NA%) # A 5 (7.1%) 1 (2.0%) 0 (NA%) 0 (NA%) 5 (8.9%) 2 (4.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.20 33.00 NA NA 28.00 30.00 NA NA 30.75 36.50 NA NA # B 7 (10.0%) 3 (5.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 6 (9.8%) 6 (8.8%) 0 (NA%) 0 (NA%) # Mean 36.14 34.33 NA NA 29.67 32.00 NA NA 36.33 31.00 NA NA # C 6 (8.6%) 6 (11.8%) 0 (NA%) 0 (NA%) 4 (7.1%) 7 (14.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 31.33 39.67 NA NA 34.50 34.00 NA NA 33.00 36.50 NA NA # WHITE 8 (11.4%) 6 (11.8%) 0 (NA%) 0 (NA%) 7 (12.5%) 7 (14.0%) 0 (NA%) 0 (NA%) 8 (13.1%) 10 (14.7%) 0 (NA%) 0 (NA%) # A 2 (2.9%) 1 (2.0%) 0 (NA%) 0 (NA%) 3 (5.4%) 3 (6.0%) 0 (NA%) 0 (NA%) 1 (1.6%) 5 (7.4%) 0 (NA%) 0 (NA%) # Mean 34.00 45.00 NA NA 29.33 33.33 NA NA 35.00 32.80 NA NA # B 4 (5.7%) 3 (5.9%) 0 (NA%) 0 (NA%) 1 (1.8%) 4 (8.0%) 0 (NA%) 0 (NA%) 3 (4.9%) 1 (1.5%) 0 (NA%) 0 (NA%) # Mean 37.00 43.67 NA NA 48.00 36.75 NA NA 34.33 36.00 NA NA # C 2 (2.9%) 2 (3.9%) 0 (NA%) 0 (NA%) 3 (5.4%) 0 (0.0%) 0 (NA%) 0 (NA%) 4 (6.6%) 4 (5.9%) 0 (NA%) 0 (NA%) # Mean 35.50 44.00 NA NA 44.67 NA NA NA 38.50 35.00 NA NA"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"trimming-columns","dir":"Articles","previous_headings":"Trimming","what":"Trimming Columns","title":"Pruning and Sorting Tables","text":"currently special utilities trimming columns can remove empty columns fairly straightforward column subsetting using col_counts() function: Now, interesting see table structured: deeper understanding fundamental structures rtables, suggest taking look slides 69-76 Slide deck. brief, important notice [TableTree] RACE root table split (split_rows_by(\"RACE\") %>%) two subtables: [TableTree] ASIAN [cont: 1 x 6] [TableTree] BLACK AFRICAN AMERICAN [cont: 1 x 6]. “described” summarize_row_groups() %>%, creates every split “content” table containing 1 row (1 cont: 1 x 6), rendered takes place LabelRow. two subtables contain STRATA1 table, representing split_rows_by(\"STRATA1\") layout, , similar RACE table, split subtables: one strata similar content tables; individual strata subtable, , contains ElementaryTable (whose children individual rows) generated analyze(\"AGE\") layout directive, .e. [ElementaryTable] AGE (1 x 6). subtable row structure important sorting pruning; values “content” (ContentRow) “value” (DataRow) rows use different access functions treated differently. Another interesting function can used understand connection row names representational path following:","code":"coltrimmed <- raw_tbl[, col_counts(raw_tbl) > 0] # Note: method with signature 'VTableTree#missing#ANY' chosen for function '[', # target signature 'TableTree#missing#logical'. # \"VTableTree#ANY#logical\" would also be valid h_coltrimmed <- head(coltrimmed, n = 14) h_coltrimmed # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 table_structure(h_coltrimmed) # [TableTree] RACE # [TableTree] ASIAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) row_paths_summary(h_coltrimmed) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN ContentRow RACE, ASIAN, @content, ASIAN # A ContentRow RACE, ASIAN, STRATA1, A, @content, A # Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean # B ContentRow RACE, ASIAN, STRATA1, B, @content, B # Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean # C ContentRow RACE, ASIAN, STRATA1, C, @content, C # Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean # BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN # A ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, @content, A # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean # B ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, @content, B # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean # C ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, @content, C # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"pruning","dir":"Articles","previous_headings":"","what":"Pruning","title":"Pruning and Sorting Tables","text":"Pruning similar outcome trimming, powerful complex, takes structure account. Pruning applied recursively, structural unit (subtable, row) applies pruning function level ’s children (user-specifiable maximum depth). default pruning function, example, determines subtree empty : Removing children contain single content row contains zeros NAs Removing rows contain either zeros NAs Removing full subtree unpruned children remain can also use low_obs_pruner() pruning function constructor create pruning function removes subtrees content summaries whose first entries column sum average specified number. (default summaries first entry per column count). Note pruning applied recursively, ASIAN subtree remains even though full BLACK AFRICAN AMERICAN subtree encompassed enough observations, strata within . can take care setting stop_depth pruning 1. can also see pruning lower number observations, say, total 16, stop_depth removes strata third race (WHITE).","code":"pruned <- prune_table(coltrimmed) pruned # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 pruned2 <- prune_table(coltrimmed, low_obs_pruner(10, \"mean\")) pruned2 # A: Drug X B: Placebo C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 pruned3 <- prune_table(coltrimmed, low_obs_pruner(10, \"sum\"), stop_depth = 1) pruned3 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 pruned4 <- prune_table(coltrimmed, low_obs_pruner(16, \"sum\")) pruned4 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"sorting-fundamentals","dir":"Articles","previous_headings":"Sorting","what":"Sorting Fundamentals","title":"Pruning and Sorting Tables","text":"Sorting rtables table done path, meaning sort operation occur particular location within table, direct children element path reordered. occurs whether children subtables , individual rows. Sorting done via sort_at_path() function, accepts (row) path scoring function. score function accepts subtree TableRow returns single orderable (typically numeric) value. Within subtable currently sorted, children reordered value score function. Importantly, “content” (ContentRow) “values” (DataRow) need treated differently scoring function retrieved: content subtable retrieved via content _table accessor. cont_n_allcols() scoring function provided rtables, works scoring subtables sum first elements first row subtable’s content table. Note function fails child scored content function (.e., summarize_row_groups() used corresponding point layout). can see ’s definition, : Therefore, fundamental difference pruning sorting sorting occurs particular places table, defined path. example, can sort strata values (ContentRow) observation counts within just ASIAN subtable:","code":"cont_n_allcols # function (tt) # { # ctab <- content_table(tt) # if (NROW(ctab) == 0) { # stop(\"cont_n_allcols score function used at subtable [\", # obj_name(tt), \"] that has no content table.\") # } # sum(sapply(row_values(tree_children(ctab)[[1]]), function(cv) cv[1])) # } # # sort_at_path(pruned, path = c(\"RACE\", \"ASIAN\", \"STRATA1\"), scorefun = cont_n_allcols) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # B and C are swapped as the global count (sum of all column counts) of strata C is higher than the one of strata B"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"wildcards-in-sort-paths","dir":"Articles","previous_headings":"Sorting","what":"Wildcards in Sort Paths","title":"Pruning and Sorting Tables","text":"Unlike uses pathing (currently), sorting path can contain “*“. indicates children subtable matching * element path sorted separately indicated remainder path * score function. Thus can extend sorting strata within ASIAN subtable race-specific subtables using wildcard: equivalent separately calling following: possible understand better pathing table_structure() highlights tree-like structure node names: row_paths_summary: Note latter see content rows paths following @content, e.g., ASIAN, @content, ASIAN. first given path (.e., , @content, <> rows used scoring functions begin cont_. can directly sort ethnicity observations increasing order: Within ethnicity separately, sort strata number females arm C (.e. column position 5):","code":"sort_at_path(pruned, path = c(\"RACE\", \"*\", \"STRATA1\"), scorefun = cont_n_allcols) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # All subtables, i.e. ASIAN, BLACK..., and WHITE, are reordered separately tmptbl <- sort_at_path(pruned, path = c(\"RACE\", \"ASIAN\", \"STRATA1\"), scorefun = cont_n_allcols) tmptbl <- sort_at_path(tmptbl, path = c(\"RACE\", \"BLACK OR AFRICAN AMERICAN\", \"STRATA1\"), scorefun = cont_n_allcols) tmptbl <- sort_at_path(tmptbl, path = c(\"RACE\", \"WHITE\", \"STRATA1\"), scorefun = cont_n_allcols) tmptbl # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 table_structure(pruned) # [TableTree] RACE # [TableTree] ASIAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] WHITE [cont: 1 x 6] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] B [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) # [TableTree] C [cont: 1 x 6] # [ElementaryTable] AGE (1 x 6) row_paths_summary(pruned) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN ContentRow RACE, ASIAN, @content, ASIAN # A ContentRow RACE, ASIAN, STRATA1, A, @content, A # Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean # B ContentRow RACE, ASIAN, STRATA1, B, @content, B # Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean # C ContentRow RACE, ASIAN, STRATA1, C, @content, C # Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean # BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN # A ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, @content, A # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean # B ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, @content, B # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean # C ContentRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, @content, C # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean # WHITE ContentRow RACE, WHITE, @content, WHITE # A ContentRow RACE, WHITE, STRATA1, A, @content, A # Mean DataRow RACE, WHITE, STRATA1, A, AGE, Mean # B ContentRow RACE, WHITE, STRATA1, B, @content, B # Mean DataRow RACE, WHITE, STRATA1, B, AGE, Mean # C ContentRow RACE, WHITE, STRATA1, C, @content, C # Mean DataRow RACE, WHITE, STRATA1, C, AGE, Mean ethsort <- sort_at_path(pruned, path = c(\"RACE\"), scorefun = cont_n_allcols, decreasing = FALSE) ethsort # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 sort_at_path(pruned, path = c(\"RACE\", \"*\", \"STRATA1\"), cont_n_onecol(5)) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"sorting-within-an-analysis-subtable","dir":"Articles","previous_headings":"Sorting","what":"Sorting Within an Analysis Subtable","title":"Pruning and Sorting Tables","text":"sorting within analysis subtable (e.g., subtable generated analysis function generates one row per group data), name subtable (generally name variable analyzed) must appear path, even variable label displayed table printed. show differences sorting analysis subtable (DataRow), content subtable (ContentRow), modify prune () similar raw table : now want sort median mean strata variables? need write custom score function ready-made ones moment work content nodes (content_table() access function cont_n_allcols() cont_n_onecol(), talk moment). , need think ordering, .e. need specify right path. suggest looking structure first table_structure() row_paths_summary(). see order AGE nodes need get something like : RACE, ASIAN, STRATA1, , AGE next level need sort. see now path sort first group. need wildcards: RACE, *, STRATA1, *, AGE. Now, found way select relevant paths want sort. want construct scoring function works median mean sort . , may want enter scoring function browser() see fed try retrieve single value returned sorting. allow user experiment , show possible solution considers summing column values retrieved row_values(tt) subtable fed function . Note score function defined subtable tt unique input parameter single numeric value output. help user visualize happening score function show example exploration debugging: can see powerful pragmatic might change sorting principles within custom scoring function. show selecting specific column sort. Looking pre-defined function cont_n_onecol() gives us insight proceed. see similar function cont_n_allcols() wrapped one allows parameter j used select specific column. selecting column want sort. table see mean median rows reordered values first column, compared raw table, desired. function can also columns nested within larger splits:","code":"more_analysis_fnc <- function(x) { in_rows( \"median\" = median(x), \"mean\" = mean(x), .formats = \"xx.x\" ) } raw_lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by( \"RACE\", split_fun = drop_and_remove_levels(\"WHITE\") # dropping WHITE levels ) %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\", afun = more_analysis_fnc) tbl <- build_table(raw_lyt, DM) %>% prune_table() %>% print() # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) # A 27 (22.3%) 20 (18.9%) 31 (24.0%) # median 30.0 33.0 36.0 # mean 32.2 33.9 36.8 # B 24 (19.8%) 29 (27.4%) 22 (17.1%) # median 32.5 32.0 34.0 # mean 34.1 31.6 34.7 # C 28 (23.1%) 19 (17.9%) 31 (24.0%) # median 36.5 34.0 33.0 # mean 36.2 33.0 32.4 # BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) # A 6 (5.0%) 7 (6.6%) 8 (6.2%) # median 32.0 29.0 32.5 # mean 31.5 28.6 33.6 # B 10 (8.3%) 6 (5.7%) 12 (9.3%) # median 33.0 30.0 33.5 # mean 35.6 30.8 33.7 # C 12 (9.9%) 11 (10.4%) 7 (5.4%) # median 33.0 36.0 32.0 # mean 35.5 34.2 35.0 table_structure(tbl) # Direct inspection into the tree-like structure of rtables # [TableTree] RACE # [TableTree] ASIAN [cont: 1 x 3] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] B [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] C [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 3] # [TableTree] STRATA1 # [TableTree] A [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] B [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) # [TableTree] C [cont: 1 x 3] # [ElementaryTable] AGE (2 x 3) scorefun <- function(tt) { # Here we could use browser() sum(unlist(row_values(tt))) } sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun) # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) # A 27 (22.3%) 20 (18.9%) 31 (24.0%) # mean 32.2 33.9 36.8 # median 30.0 33.0 36.0 # B 24 (19.8%) 29 (27.4%) 22 (17.1%) # mean 34.1 31.6 34.7 # median 32.5 32.0 34.0 # C 28 (23.1%) 19 (17.9%) 31 (24.0%) # median 36.5 34.0 33.0 # mean 36.2 33.0 32.4 # BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) # A 6 (5.0%) 7 (6.6%) 8 (6.2%) # mean 31.5 28.6 33.6 # median 32.0 29.0 32.5 # B 10 (8.3%) 6 (5.7%) 12 (9.3%) # mean 35.6 30.8 33.7 # median 33.0 30.0 33.5 # C 12 (9.9%) 11 (10.4%) 7 (5.4%) # mean 35.5 34.2 35.0 # median 33.0 36.0 32.0 > sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun) Called from: scorefun(x) Browse[1]> tt ### THIS IS THE LEAF LEVEL -> DataRow ### [DataRow indent_mod 0]: median 30.0 33.0 36.0 Browse[1]> row_values(tt) ### Extraction of values -> It will be a named list! ### $`A: Drug X` [1] 30 $`B: Placebo` [1] 33 $`C: Combination` [1] 36 Browse[1]> sum(unlist(row_values(tt))) ### Final value we want to give back to sort_at_path ### [1] 99 cont_n_onecol # function (j) # { # function(tt) { # ctab <- content_table(tt) # if (NROW(ctab) == 0) { # stop(\"cont_n_allcols score function used at subtable [\", # obj_name(tt), \"] that has no content table.\") # } # row_values(tree_children(ctab)[[1]])[[j]][1] # } # } # # scorefun_onecol <- function(colpath) { function(tt) { # Here we could use browser() unlist(cell_values(tt, colpath = colpath), use.names = FALSE)[1] # Modified to lose the list names } } sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun_onecol(colpath = c(\"ARM\", \"A: Drug X\"))) # A: Drug X B: Placebo C: Combination # ———————————————————————————————————————————————————————————————————— # ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) # A 27 (22.3%) 20 (18.9%) 31 (24.0%) # mean 32.2 33.9 36.8 # median 30.0 33.0 36.0 # B 24 (19.8%) 29 (27.4%) 22 (17.1%) # mean 34.1 31.6 34.7 # median 32.5 32.0 34.0 # C 28 (23.1%) 19 (17.9%) 31 (24.0%) # median 36.5 34.0 33.0 # mean 36.2 33.0 32.4 # BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) # A 6 (5.0%) 7 (6.6%) 8 (6.2%) # median 32.0 29.0 32.5 # mean 31.5 28.6 33.6 # B 10 (8.3%) 6 (5.7%) 12 (9.3%) # mean 35.6 30.8 33.7 # median 33.0 30.0 33.5 # C 12 (9.9%) 11 (10.4%) 7 (5.4%) # mean 35.5 34.2 35.0 # median 33.0 36.0 32.0 # Simpler table tbl <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_and_remove_levels(c(\"U\", \"UNDIFFERENTIATED\")) ) %>% analyze(\"AGE\", afun = more_analysis_fnc) %>% build_table(DM) %>% prune_table() %>% print() # A: Drug X B: Placebo C: Combination # F M F M F M # ————————————————————————————————————————————————————————— # median 32.0 35.0 33.0 31.0 35.0 32.0 # mean 33.7 36.5 33.8 32.1 34.9 34.3 sort_at_path(tbl, c(\"AGE\"), scorefun_onecol(colpath = c(\"ARM\", \"B: Placebo\", \"SEX\", \"F\"))) # A: Drug X B: Placebo C: Combination # F M F M F M # ————————————————————————————————————————————————————————— # mean 33.7 36.5 33.8 32.1 34.9 34.3 # median 32.0 35.0 33.0 31.0 35.0 32.0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"writing-custom-pruning-criteria-and-scoring-functions","dir":"Articles","previous_headings":"","what":"Writing Custom Pruning Criteria and Scoring Functions","title":"Pruning and Sorting Tables","text":"Pruning criteria scoring functions map TableTree TableRow objects Boolean value (pruning criteria) sortable scalar value (scoring functions). currently need interact structure objects usual. Indeed, showed already sorting can complicated concept tree-like structure pathing well understood. important though mind following functions can used pruning sorting function retrieve relevant information table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"useful-functions-and-accessors","dir":"Articles","previous_headings":"Writing Custom Pruning Criteria and Scoring Functions","what":"Useful Functions and Accessors","title":"Pruning and Sorting Tables","text":"accepts rowpath colpath restrict cell values returned obj_name() - Retrieves name object. Note can differ label displayed () printing. match element path. obj_label() - Retrieves display label object. Note can differ name appears path. content_table() - Retrieves TableTree object’s content table (contains summary rows). tree_children() - Retrieves TableTree object’s direct children (either subtables, rows possibly mix thereof, though happen practice)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"sort-by-a-character-score","dir":"Articles","previous_headings":"Writing Custom Pruning Criteria and Scoring Functions > Example Custom Scoring Functions","what":"Sort by a character “score”","title":"Pruning and Sorting Tables","text":"case, convenience/simplicity, use name table element logic returns single string used . sort ethnicity alphabetical order (practice undoing previous sorting ethnicity ). NOTE: Generally appropriately done using reorder_split_levels() function within layout rather sort post-processing step, character scorers may may map easily layouting directives.","code":"silly_name_scorer <- function(tt) { nm <- obj_name(tt) print(nm) nm } sort_at_path(ethsort, \"RACE\", silly_name_scorer) # Now, it is sorted alphabetically! # [1] \"WHITE\" # [1] \"BLACK OR AFRICAN AMERICAN\" # [1] \"ASIAN\" # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00"},{"path":"https://insightsengineering.github.io/rtables/main/articles/sorting_pruning.html","id":"sort-by-the-percent-difference-in-counts-between-genders-in-arm-c","dir":"Articles","previous_headings":"Writing Custom Pruning Criteria and Scoring Functions > Example Custom Scoring Functions","what":"Sort by the Percent Difference in Counts Between Genders in Arm C","title":"Pruning and Sorting Tables","text":"need F M percents, Arm C (.e. columns 5 6), differenced. sort strata within ethnicity percent difference counts males females arm C. Note: statistically meaningful , fact terrible idea reorders strata seemingly () random within race, illustrates various things need inside custom sorting functions.","code":"silly_gender_diffcount <- function(tt) { ## (1st) content row has same name as object (STRATA1 level) rpath <- c(obj_name(tt), \"@content\", obj_name(tt)) ## the [1] below is cause these are count (pct%) cells ## and we only want the count part! mcount <- unlist(cell_values( tt, rowpath = rpath, colpath = c(\"ARM\", \"C: Combination\", \"SEX\", \"M\") ))[1] fcount <- unlist(cell_values( tt, rowpath = rpath, colpath = c(\"ARM\", \"C: Combination\", \"SEX\", \"F\") ))[1] (mcount - fcount) / fcount } sort_at_path(pruned, c(\"RACE\", \"*\", \"STRATA1\"), silly_gender_diffcount) # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) # B 16 (22.9%) 8 (15.7%) 13 (23.2%) 16 (32.0%) 10 (16.4%) 12 (17.6%) # Mean 33.75 34.88 32.46 30.94 33.30 35.92 # A 15 (21.4%) 12 (23.5%) 14 (25.0%) 6 (12.0%) 15 (24.6%) 16 (23.5%) # Mean 30.40 34.42 35.43 30.33 37.40 36.25 # C 13 (18.6%) 15 (29.4%) 10 (17.9%) 9 (18.0%) 15 (24.6%) 16 (23.5%) # Mean 36.92 35.60 34.00 31.89 33.47 31.38 # BLACK OR AFRICAN AMERICAN 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) # C 6 (8.6%) 6 (11.8%) 4 (7.1%) 7 (14.0%) 3 (4.9%) 4 (5.9%) # Mean 31.33 39.67 34.50 34.00 33.00 36.50 # A 5 (7.1%) 1 (2.0%) 5 (8.9%) 2 (4.0%) 4 (6.6%) 4 (5.9%) # Mean 31.20 33.00 28.00 30.00 30.75 36.50 # B 7 (10.0%) 3 (5.9%) 3 (5.4%) 3 (6.0%) 6 (9.8%) 6 (8.8%) # Mean 36.14 34.33 29.67 32.00 36.33 31.00 # WHITE 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) # A 2 (2.9%) 1 (2.0%) 3 (5.4%) 3 (6.0%) 1 (1.6%) 5 (7.4%) # Mean 34.00 45.00 29.33 33.33 35.00 32.80 # C 2 (2.9%) 2 (3.9%) 3 (5.4%) 0 (0.0%) 4 (6.6%) 4 (5.9%) # Mean 35.50 44.00 44.67 NA 38.50 35.00 # B 4 (5.7%) 3 (5.9%) 1 (1.8%) 4 (8.0%) 3 (4.9%) 1 (1.5%) # Mean 37.00 43.67 48.00 36.75 34.33 36.00"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"provided-functions","dir":"Articles","previous_headings":"Controlling Facet Levels","what":"Provided Functions","title":"Controlling Splitting Behavior","text":"default, split_*_by(varname, ...) generates facet level variable varname takes data - including unobserved ones factor case. behavior can customized various ways. straightforward way customize facets generated split one split functions split function families provided rtables. predefined split functions function factories implement commonly desired customization patterns splitting behavior (.e., faceting behavior). include: remove_split_levels - remove specified levels data facet generation. keep_split_levels - keep specified levels data facet generation (removing others). drop_split_levels - drop levels unobserved within data split, .e., associated parent facet. reorder_split_levels - reorder levels (thus generated facets) specified order. trim_levels_in_group - drop unobserved levels another variable independently within data associated facet generated current split. add_overall_level, add_combo_levels - add additional “virtual” levels combine two levels variable split. See following section. trim_levels_to_map - trim levels multiple variables pre-specified set value combinations. See following section. first four fairly self-describing brevity, refer readers ?split_funcs details including working examples.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"controlling-combinations-of-levels-across-multiple-variables","dir":"Articles","previous_headings":"Controlling Facet Levels","what":"Controlling Combinations of Levels Across Multiple Variables","title":"Controlling Splitting Behavior","text":"Often nested splitting involving multiple variables, values variables question logically nested; meaning certain values inner variable coherent combination specific value values outer variable. example, suppose variable vehicle_class, can take values \"automobile\", \"boat\", variable vehicle_type, can take values \"car\", \"truck\", \"suv\",\"sailboat\", \"cruiseliner\". combination (\"automobile\", \"cruiseliner\") make sense never occur (correctly cleaned) data set; combination (\"boat\", \"truck\"). showcase strategies deal next sections using following artificial data:","code":"set.seed(0) levs_type <- c(\"car\", \"truck\", \"suv\", \"sailboat\", \"cruiseliner\") vclass <- sample(c(\"auto\", \"boat\"), 1000, replace = TRUE) auto_inds <- which(vclass == \"auto\") vtype <- rep(NA_character_, 1000) vtype[auto_inds] <- sample( c(\"car\", \"truck\"), ## suv missing on purpose length(auto_inds), replace = TRUE ) vtype[-auto_inds] <- sample( c(\"sailboat\", \"cruiseliner\"), 1000 - length(auto_inds), replace = TRUE ) vehic_data <- data.frame( vehicle_class = factor(vclass), vehicle_type = factor(vtype, levels = levs_type), color = sample( c(\"white\", \"black\", \"red\"), 1000, prob = c(1, 2, 1), replace = TRUE ), cost = ifelse( vclass == \"boat\", rnorm(1000, 100000, sd = 5000), rnorm(1000, 40000, sd = 5000) ) ) head(vehic_data) ## vehicle_class vehicle_type color cost ## 1 boat sailboat black 100393.81 ## 2 auto car white 38150.17 ## 3 boat sailboat white 98696.13 ## 4 auto truck white 37677.16 ## 5 auto truck black 38489.27 ## 6 boat cruiseliner black 108709.72"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"trim_levels_in_group","dir":"Articles","previous_headings":"Controlling Facet Levels > Controlling Combinations of Levels Across Multiple Variables","what":"trim_levels_in_group","title":"Controlling Splitting Behavior","text":"trim_levels_in_group split function factory creates split functions deal issue empirically; combination observed data tabulated appear nested facets within table, , . use default level-based faceting, get several logically incoherent cells within table: obviously table want, majority space taken meaningless combinations. use trim_levels_in_group trim levels vehicle_type separately within level vehicle_class, get table meaningful combinations: Note, however, contain meaningful combinations, actually observed data; happens include perfectly valid \"auto\", \"suv\" combination. restrict level combinations valid regardless whether combination observed, must use trim_levels_to_map() instead.","code":"library(rtables) lyt <- basic_table() %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\") %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt, vehic_data) ## black white red ## ———————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 ## truck ## Mean 40061.70 40635.74 40024.41 ## suv ## Mean NA NA NA ## sailboat ## Mean NA NA NA ## cruiseliner ## Mean NA NA NA ## boat ## car ## Mean NA NA NA ## truck ## Mean NA NA NA ## suv ## Mean NA NA NA ## sailboat ## Mean 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100212.00 99340.25 100363.52 lyt2 <- basic_table() %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_in_group(\"vehicle_type\")) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt2, vehic_data) ## black white red ## ———————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 ## truck ## Mean 40061.70 40635.74 40024.41 ## boat ## sailboat ## Mean 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100212.00 99340.25 100363.52"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"trim_levels_to_map","dir":"Articles","previous_headings":"Controlling Facet Levels > Controlling Combinations of Levels Across Multiple Variables","what":"trim_levels_to_map","title":"Controlling Splitting Behavior","text":"trim_levels_to_map similar trim_levels_in_group purpose avoid combinatorial explosion nesting splitting logically nested variables. Unlike sibling function, however, trim_levels_to_map define exact set allowed combinations priori, exact set combinations produced resulting table, regardless whether observed . Now see \"auto\", \"suv\" combination present, even though populated NAs (data category), logically invalid combinations still absent.","code":"library(tibble) map <- tribble( ~vehicle_class, ~vehicle_type, \"auto\", \"truck\", \"auto\", \"suv\", \"auto\", \"car\", \"boat\", \"sailboat\", \"boat\", \"cruiseliner\" ) lyt3 <- basic_table() %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt3, vehic_data) ## black white red ## ———————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 ## truck ## Mean 40061.70 40635.74 40024.41 ## suv ## Mean NA NA NA ## boat ## sailboat ## Mean 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100212.00 99340.25 100363.52"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"combining-levels","dir":"Articles","previous_headings":"Controlling Facet Levels","what":"Combining Levels","title":"Controlling Splitting Behavior","text":"Another common manipulation faceting table context introduction combination levels explicitly modeled data. often, involves addition “overall” category, principle practice can involve arbitrary combination levels. rtables explicitly supports via add_overall_level (case) add_combo_levels split function factories.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"add_overall_level","dir":"Articles","previous_headings":"Controlling Facet Levels > Combining Levels","what":"add_overall_level","title":"Controlling Splitting Behavior","text":"add_overall_level accepts valname name new level, well label, first (whether come first, TRUE, last, FALSE, ordering). Building arbitrary vehicles table, can use create “colors” category: column counts turned , can see “Colors” column encompasses full 1000 (completely fake) vehicles data set. add arbitrary combinations, use add_combo_levels.","code":"lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\", split_fun = add_overall_level(\"allcolors\", label = \"All Colors\")) %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt4, vehic_data) ## All Colors black white red ## (N=1000) (N=521) (N=251) (N=228) ## ————————————————————————————————————————————————————————————— ## auto ## car ## Mean 40095.49 40431.92 40518.92 38713.14 ## truck ## Mean 40194.68 40061.70 40635.74 40024.41 ## suv ## Mean NA NA NA NA ## boat ## sailboat ## Mean 100133.22 99349.69 99996.54 101865.73 ## cruiseliner ## Mean 100036.76 100212.00 99340.25 100363.52"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"add_combo_levels","dir":"Articles","previous_headings":"Controlling Facet Levels > Combining Levels","what":"add_combo_levels","title":"Controlling Splitting Behavior","text":"add_combo_levels allows us add one arbitrary combination levels faceting structure table. defining combination data.frame describes levels want add. combination data.frame following columns one row combination add: valname - string indicating name value, appear paths. label - string indicating label displayed rendering. levelcombo - character vector individual levels combined combination level. exargs - list (usually list()) extra arguments passed analysis content functions tabulated within column row. Suppose wanted combinations levels non-white colors, white black colors. like :","code":"combodf <- tribble( ~valname, ~label, ~levelcombo, ~exargs, \"non-white\", \"Non-White\", c(\"black\", \"red\"), list(), \"blackwhite\", \"Black or White\", c(\"black\", \"white\"), list() ) lyt5 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\", split_fun = add_combo_levels(combodf)) %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt5, vehic_data) ## black white red Non-White Black or White ## (N=521) (N=251) (N=228) (N=749) (N=772) ## ————————————————————————————————————————————————————————————————————————————— ## auto ## car ## Mean 40431.92 40518.92 38713.14 39944.93 40460.77 ## truck ## Mean 40061.70 40635.74 40024.41 40050.66 40243.57 ## suv ## Mean NA NA NA NA NA ## boat ## sailboat ## Mean 99349.69 99996.54 101865.73 100179.72 99567.50 ## cruiseliner ## Mean 100212.00 99340.25 100363.52 100258.56 99937.47"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"fully-customizing-split-facet-behavior","dir":"Articles","previous_headings":"","what":"Fully Customizing Split (Facet) Behavior","title":"Controlling Splitting Behavior","text":"Beyond ability select common splitting customizations split functions split function factories rtables provides, can also fully customize every aspect splitting behavior creating split functions. possible hand, primary way via make_split_fun() function, accepts functions implementing different component behaviors combines split function can used layout. Splitting, faceting done rtables, can thought combination 3 steps: preprocessing - transformation incoming data faceted e.g., dropping unused factor levels, etc. splitting - mapping incoming data set 1 subsets representing individual facets. postprocessing - operations facets - e.g., combining , removing , etc. make_split_fun() function allows us specify custom behaviors steps independently defining custom splitting behavior via pre, core_split, post arguments, dictate steps, respectively. pre argument accepts zero pre-processing functions, must accept: df, spl, vals, labels, can optionally accept .spl_context. manipulate df (incoming data split) return modified data.frame. modified data.frame must contain columns present incoming data.frame, can add columns necessary. Although, note new columns used layout split analysis variables, present validity checking done. pre-processing component useful things manipulating factor levels, e.g., trim unobserved ones reorder levels based observed counts, etc. detailed discussion custom split functions , example custom split function implemented via make_split_fun(), see ?custom_split_funs.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"an-example-custom-split-function","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior","what":"An Example Custom Split Function","title":"Controlling Splitting Behavior","text":"implement arbitrary, custom split function specify pre- post-processing instructions. unusual users need override core splitting logic - , fact, supported row space currently - leave example provide another narrow example usage .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"an-illustrative-example-of-a-custom-split-function","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior > An Example Custom Split Function","what":"An Illustrative Example of A Custom Split Function","title":"Controlling Splitting Behavior","text":"First, define two aspects ‘pre-processing step’ behavior: function reverses order levels variable (retaining level associated observation), function factory creates function removes level data associated . Finally implement post-processing function. reorder facets based amount data represents. Finally, construct custom split function use create table:","code":"## reverse order of levels rev_lev <- function(df, spl, vals, labels, ...) { ## in the split_rows_by() and split_cols_by() cases, ## spl_variable() gives us the variable var <- spl_variable(spl) vec <- df[[var]] levs <- if (is.character(vec)) unique(vec) else levels(vec) df[[var]] <- factor(vec, levels = rev(levs)) df } rem_lev_facet <- function(torem) { function(df, spl, vals, labels, ...) { var <- spl_variable(spl) vec <- df[[var]] bad <- vec == torem df <- df[!bad, ] levs <- if (is.character(vec)) unique(vec) else levels(vec) df[[var]] <- factor(as.character(vec[!bad]), levels = setdiff(levs, torem)) df } } sort_them_facets <- function(splret, spl, fulldf, ...) { ord <- order(sapply(splret$datasplit, nrow)) make_split_result( splret$values[ord], splret$datasplit[ord], splret$labels[ord] ) } silly_splfun1 <- make_split_fun( pre = list( rev_lev, rem_lev_facet(\"white\") ), post = list(sort_them_facets) ) lyt6 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\", split_fun = silly_splfun1) %>% split_rows_by(\"vehicle_class\", split_fun = trim_levels_to_map(map)) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt6, vehic_data) ## red black ## (N=228) (N=521) ## ————————————————————————————————————— ## auto ## car ## Mean 38713.14 40431.92 ## truck ## Mean 40024.41 40061.70 ## suv ## Mean NA NA ## boat ## sailboat ## Mean 101865.73 99349.69 ## cruiseliner ## Mean 100363.52 100212.00"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"overriding-the-core-split-function","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior > An Example Custom Split Function","what":"Overriding the Core Split Function","title":"Controlling Splitting Behavior","text":"Currently, overriding core split behavior supported functions used row splits. Next, write custom core-splitting function divides observations 4 groups: first 100, observations 101-500, observations 501-900, last hundred. claim test structural bias first last observations, really simply illustrate overriding core splitting machinery meaningful statistical purpose. can use construct splitting function. can combined pre- post-processing functions, stages performed independently, case, won’t, core splitting behavior pre- post-processing make much sense.","code":"silly_core_split <- function(spl, df, vals, labels, .spl_context) { make_split_result( c(\"first\", \"lowmid\", \"highmid\", \"last\"), datasplit = list( df[1:100, ], df[101:500, ], df[501:900, ], df[901:1000, ] ), labels = c( \"first 100\", \"obs 101-500\", \"obs 501-900\", \"last 100\" ) ) } even_sillier_splfun <- make_split_fun(core_split = silly_core_split) lyt7 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"color\") %>% split_rows_by(\"vehicle_class\", split_fun = even_sillier_splfun) %>% split_rows_by(\"vehicle_type\") %>% analyze(\"cost\") build_table(lyt7, vehic_data) ## black white red ## (N=521) (N=251) (N=228) ## ————————————————————————————————————————————————— ## first 100 ## car ## Mean 40496.05 37785.41 37623.17 ## truck ## Mean 41094.17 40437.29 37866.81 ## suv ## Mean NA NA NA ## sailboat ## Mean 100560.80 102017.05 101185.96 ## cruiseliner ## Mean 100838.12 96952.27 100610.71 ## obs 101-500 ## car ## Mean 39350.88 41185.98 37978.72 ## truck ## Mean 40166.87 41385.32 39885.72 ## suv ## Mean NA NA NA ## sailboat ## Mean 98845.47 99563.02 101462.79 ## cruiseliner ## Mean 101558.62 99039.91 97335.05 ## obs 501-900 ## car ## Mean 40721.82 40379.48 38681.26 ## truck ## Mean 39951.92 39846.89 39840.39 ## suv ## Mean NA NA NA ## sailboat ## Mean 99533.20 100347.18 102732.12 ## cruiseliner ## Mean 99140.43 100074.43 101994.99 ## last 100 ## car ## Mean 45204.44 40626.95 41214.33 ## truck ## Mean 38920.70 40620.47 42899.14 ## suv ## Mean NA NA NA ## sailboat ## Mean 99380.21 97644.77 101691.92 ## cruiseliner ## Mean 100017.53 99581.94 100751.30"},{"path":"https://insightsengineering.github.io/rtables/main/articles/split_functions.html","id":"design-of-pre--and-post-processing-functions-for-use-in-make_split_fun","dir":"Articles","previous_headings":"Fully Customizing Split (Facet) Behavior > An Example Custom Split Function","what":"Design of Pre- and Post-Processing Functions For Use in make_split_fun","title":"Controlling Splitting Behavior","text":"Pre-processing post-processing functions custom-splitting context best thought (implemented ) independent, atomic building blocks desired overall behavior. allows reused flexible mix--match way. rtables provides several behavior components implemented either functions function factories: drop_facet_levels - drop unobserved levels variable split trim_levels_in_facets - provides trim_levels_in_group behavior add_overall_facet - add combination facet full data add_combo_facet - add single combination facet (can used single make_split_fun call)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/subsetting_tables.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Subsetting and Manipulating Table Contents","text":"TableTree objects based tree data structure name indicates. package written user need walk trees many basic table manipulations. Walking trees still necessary certain manipulation subject different vignette. vignette show methods subset tables extract cell values. use following table illustrative purposes:","code":"library(rtables) library(dplyr) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(c(\"AGE\", \"STRATA1\")) tbl <- build_table(lyt, ex_adsl %>% filter(SEX %in% c(\"M\", \"F\"))) tbl # A: Drug X B: Placebo C: Combination # ——————————————————————————————————————————————————— # F # AGE # Mean 32.76 34.12 35.20 # STRATA1 # A 21 24 18 # B 25 27 21 # C 33 26 27 # M # AGE # Mean 35.57 37.44 35.38 # STRATA1 # A 16 19 20 # B 21 17 21 # C 14 19 19"},{"path":"https://insightsengineering.github.io/rtables/main/articles/subsetting_tables.html","id":"traditional-subsetting-and-modification-with","dir":"Articles","previous_headings":"","what":"Traditional Subsetting and modification with [","title":"Subsetting and Manipulating Table Contents","text":"[ [<- accessor functions operate largely data.frame cousins: particular means label rows treated rows empty cell values, rather rows without cells multiple values can specified row column position negative numeric positions supported, though like [.data.frame mixed positive ones [ always returns class object subset unless drop = TRUE [ , drop = TRUE returns raw (possibly multi-element) value associated cell. Known Differences [.data.frame - absolute position currently used reorder columns rows. Note general result ordering unlikely structurally valid. change order values, please read sorting pruning vignette relevant function (sort_at_path()). - character indices treated paths, vectors names [ [<- [ accessor function always returns TableTree object drop=TRUE set. first argument row indices second argument column indices. Alternatively logical subsetting can used. indices based visible rows tree structure. : table empty cell first row label row. need access cell actual cell data: retrieve value, use drop = TRUE: One can access multiple rows columns: Note repeat label rows descending children, e.g. show first row derived AGE. order repeat content/label information, one use pagination feature. Please read related vignette. Character indices interpreted paths (see ), elements matched names(tbl):","code":"tbl[1, 1] # A: Drug X # ————————————— # F tbl[3, 1] # A: Drug X # ———————————————— # Mean 32.76 tbl[3, 1, drop = TRUE] # [1] 32.75949 tbl[1:3, 1:2] # A: Drug X B: Placebo # ————————————————————————————————— # F # AGE # Mean 32.76 34.12 tbl[2:4, ] # A: Drug X B: Placebo C: Combination # ————————————————————————————————————————————————— # AGE # Mean 32.76 34.12 35.20 # STRATA1 tbl[, c(\"ARM\", \"A: Drug X\")] # Note: method with signature 'VTableTree#missing#ANY' chosen for function '[', # target signature 'TableTree#missing#character'. # \"VTableTree#ANY#character\" would also be valid # A: Drug X # ————————————————————— # F # AGE # Mean 32.76 # STRATA1 # A 21 # B 25 # C 33 # M # AGE # Mean 35.57 # STRATA1 # A 16 # B 21 # C 14"},{"path":"https://insightsengineering.github.io/rtables/main/articles/subsetting_tables.html","id":"dealing-with-titles-foot-notes-and-top-left-information","dir":"Articles","previous_headings":"Traditional Subsetting and modification with [","what":"Dealing with titles, foot notes, and top left information","title":"Subsetting and Manipulating Table Contents","text":"standard additional information kept subsetting. , show complete table still possible keep (possibly) relevant information. Normal subsetting loses information showed . rows kept, top left information also kept. can also imposed adding keep_topleft = TRUE subsetting follows: referenced entry present subsetting, also referential footnote appear. Please consider reading relevant vignette referential footnotes. case subsetting, referential footnotes default indexed , produced table new one. Similar used keep top left information, can specify keep information original table. standard foot notes always present titles kept.","code":"top_left(tbl) <- \"SEX\" main_title(tbl) <- \"Table 1\" subtitles(tbl) <- c(\"Authors:\", \" - Abcd Zabcd\", \" - Cde Zbcd\") main_footer(tbl) <- \"Please regard this table as an example of smart subsetting\" prov_footer(tbl) <- \"Do remember where you read this though\" fnotes_at_path(tbl, rowpath = c(\"M\", \"AGE\", \"Mean\"), colpath = c(\"ARM\", \"A: Drug X\")) <- \"Very important mean\" tbl[3, 3] # C: Combination # ————————————————————— # Mean 35.20 tbl[, 2:3] # SEX B: Placebo C: Combination # ——————————————————————————————————————— # F # AGE # Mean 34.12 35.20 # STRATA1 # A 24 18 # B 27 21 # C 26 27 # M # AGE # Mean 37.44 35.38 # STRATA1 # A 19 20 # B 17 21 # C 19 19 tbl[1:3, 3, keep_topleft = TRUE] # SEX C: Combination # ————————————————————————— # F # AGE # Mean 35.20 tbl[10, 1] # A: Drug X # ———————————————— # Mean 35.57 {1} # ———————————————— # # {1} - Very important mean # ———————————————— col_paths_summary(tbl) # Use these to find the right path to value or label # label path # ————————————————————————————————————— # A: Drug X ARM, A: Drug X # B: Placebo ARM, B: Placebo # C: Combination ARM, C: Combination row_paths_summary(tbl) # # rowname node_class path # ————————————————————————————————————————————— # F LabelRow SEX, F # AGE LabelRow SEX, F, AGE # Mean DataRow SEX, F, AGE, Mean # STRATA1 LabelRow SEX, F, STRATA1 # A DataRow SEX, F, STRATA1, A # B DataRow SEX, F, STRATA1, B # C DataRow SEX, F, STRATA1, C # M LabelRow SEX, M # AGE LabelRow SEX, M, AGE # Mean DataRow SEX, M, AGE, Mean # STRATA1 LabelRow SEX, M, STRATA1 # A DataRow SEX, M, STRATA1, A # B DataRow SEX, M, STRATA1, B # C DataRow SEX, M, STRATA1, C # To select column value, use `NULL` for `rowpath` fnotes_at_path(tbl, rowpath = NULL, colpath = c(\"ARM\", \"A: Drug X\")) <- \"Interesting\" tbl[3, 1] # A: Drug X {1} # ———————————————————— # Mean 32.76 # ———————————————————— # # {1} - Interesting # ———————————————————— # reindexing of {2} as {1} fnotes_at_path(tbl, rowpath = c(\"M\", \"AGE\", \"Mean\"), colpath = NULL) <- \"THIS mean\" tbl # {1}, {2}, and {3} are present # Table 1 # Authors: # - Abcd Zabcd # - Cde Zbcd # # —————————————————————————————————————————————————————————— # SEX A: Drug X {1} B: Placebo C: Combination # —————————————————————————————————————————————————————————— # F # AGE # Mean 32.76 34.12 35.20 # STRATA1 # A 21 24 18 # B 25 27 21 # C 33 26 27 # M # AGE # Mean {2} 35.57 {3} 37.44 35.38 # STRATA1 # A 16 19 20 # B 21 17 21 # C 14 19 19 # —————————————————————————————————————————————————————————— # # {1} - Interesting # {2} - THIS mean # {3} - Very important mean # —————————————————————————————————————————————————————————— # # Please regard this table as an example of smart subsetting # # Do remember where you read this though tbl[10, 2] # only {1} which was previously {2} # B: Placebo # ————————————————————— # Mean {1} 37.44 # ————————————————————— # # {1} - THIS mean # ————————————————————— tbl[1:3, 2:3, keep_titles = TRUE] # Table 1 # Authors: # - Abcd Zabcd # - Cde Zbcd # # —————————————————————————————————————— # B: Placebo C: Combination # —————————————————————————————————————— # F # AGE # Mean 34.12 35.20 # —————————————————————————————————————— # # Please regard this table as an example of smart subsetting # # Do remember where you read this though tbl[1:3, 2:3, keep_titles = FALSE, keep_footers = TRUE] # B: Placebo C: Combination # —————————————————————————————————————— # F # AGE # Mean 34.12 35.20 # —————————————————————————————————————— # # Please regard this table as an example of smart subsetting # # Do remember where you read this though # Referential footnotes are not influenced by `keep_footers = FALSE` tbl[1:3, keep_titles = TRUE, keep_footers = FALSE] # Table 1 # Authors: # - Abcd Zabcd # - Cde Zbcd # # —————————————————————————————————————————————————————— # A: Drug X {1} B: Placebo C: Combination # —————————————————————————————————————————————————————— # F # AGE # Mean 32.76 34.12 35.20 # —————————————————————————————————————————————————————— # # {1} - Interesting # ——————————————————————————————————————————————————————"},{"path":"https://insightsengineering.github.io/rtables/main/articles/subsetting_tables.html","id":"path-based-cell-value-accessing","dir":"Articles","previous_headings":"","what":"Path Based Cell Value Accessing:","title":"Subsetting and Manipulating Table Contents","text":"Tables can subset modified structurally aware manner via pathing. Paths define semantically meaningful positions within constructed table correspond logic layout used create . path ordered set split names, names subgroups generated split, @content directive, steps position’s content (row group summary) table. can see row column paths existing table via row_paths(), col_paths(), row_paths_summary(), col_paths_summary(), functions, portion general make_row_df() function output. column paths follows: row paths follows: get semantically meaningful subset table, , can use [ (tt_at_path() underlies ) can also retrieve individual cell-values via value_at() convenience function, takes pair row column paths resolve together individual cell, e.g. average age Asian female patients arm : can also request information non-cell specific paths cell_values() function: Note return value cell_values() always list even specify path cell:","code":"lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% analyze(c(\"AGE\", \"STRATA1\")) tbl2 <- build_table(lyt2, ex_adsl %>% filter(SEX %in% c(\"M\", \"F\") & RACE %in% (levels(RACE)[1:3]))) tbl2 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # BLACK OR AFRICAN AMERICAN 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # WHITE 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 col_paths_summary(tbl2) # label path # ————————————————————————————————————————————— # A: Drug X ARM, A: Drug X # F ARM, A: Drug X, SEX, F # M ARM, A: Drug X, SEX, M # B: Placebo ARM, B: Placebo # F ARM, B: Placebo, SEX, F # M ARM, B: Placebo, SEX, M # C: Combination ARM, C: Combination # F ARM, C: Combination, SEX, F # M ARM, C: Combination, SEX, M row_paths_summary(tbl2) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— # ASIAN ContentRow RACE, ASIAN, @content, ASIAN # AGE LabelRow RACE, ASIAN, AGE # Mean DataRow RACE, ASIAN, AGE, Mean # STRATA1 LabelRow RACE, ASIAN, STRATA1 # A DataRow RACE, ASIAN, STRATA1, A # B DataRow RACE, ASIAN, STRATA1, B # C DataRow RACE, ASIAN, STRATA1, C # BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN # AGE LabelRow RACE, BLACK OR AFRICAN AMERICAN, AGE # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean # STRATA1 LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1 # A DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A # B DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B # C DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C # WHITE ContentRow RACE, WHITE, @content, WHITE # AGE LabelRow RACE, WHITE, AGE # Mean DataRow RACE, WHITE, AGE, Mean # STRATA1 LabelRow RACE, WHITE, STRATA1 # A DataRow RACE, WHITE, STRATA1, A # B DataRow RACE, WHITE, STRATA1, B # C DataRow RACE, WHITE, STRATA1, C tbl2[c(\"RACE\", \"ASIAN\"), c(\"ARM\", \"C: Combination\")] # C: Combination # F M # ——————————————————————————————————— # ASIAN 39 (60.9%) 32 (57.1%) # AGE # Mean 36.44 37.66 # STRATA1 # A 11 7 # B 11 14 # C 17 11 value_at(tbl2, c(\"RACE\", \"ASIAN\", \"AGE\", \"Mean\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\")) # [1] 31.21951 cell_values(tbl2, c(\"RACE\", \"ASIAN\", \"AGE\", \"Mean\"), c(\"ARM\", \"A: Drug X\")) # $`A: Drug X.F` # [1] 31.21951 # # $`A: Drug X.M` # [1] 34.6 cell_values(tbl2, c(\"RACE\", \"ASIAN\", \"AGE\", \"Mean\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\")) # $`A: Drug X.F` # [1] 31.21951"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_concepts.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Tabulation Concepts","text":"vignette introduce theory behind using layouts table creation. Much theory also holds true using table packages. vignette use following packages: data use following, created random number generators:","code":"library(dplyr) library(tibble) library(rtables) add_subgroup <- function(x) paste0(tolower(x), sample(1:3, length(x), TRUE)) set.seed(1) df <- tibble( x = rnorm(100), c1 = factor(sample(c(\"A\", \"B\", \"C\"), 100, replace = TRUE), levels = c(\"A\", \"B\", \"C\")), r1 = factor(sample(c(\"U\", \"V\", \"W\"), 100, replace = TRUE), levels = c(\"U\", \"V\", \"W\")) ) %>% mutate( c2 = add_subgroup(c1), r2 = add_subgroup(r1), y = as.numeric(2 * as.numeric(c1) - 3 * as.numeric(r1)) ) %>% select(c1, c2, r1, r2, x, y) df # # A tibble: 100 × 6 # c1 c2 r1 r2 x y # # 1 B b2 U u3 -0.626 1 # 2 A a3 V v2 0.184 -4 # 3 B b1 V v2 -0.836 -2 # 4 B b3 V v2 1.60 -2 # 5 B b1 U u1 0.330 1 # 6 C c1 U u3 -0.820 3 # 7 A a3 U u3 0.487 -1 # 8 B b1 U u3 0.738 1 # 9 C c3 V v2 0.576 0 # 10 C c3 U u2 -0.305 3 # # ℹ 90 more rows"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_concepts.html","id":"building-a-table-row-by-row","dir":"Articles","previous_headings":"","what":"Building A Table Row By Row","title":"Tabulation Concepts","text":"Let’s look table 3 columns 3 rows. row represents different analysis (functions foo, bar, zoo return rcell() object): data passed analysis functions subset defined respective column : Let’s concrete data analyze(): wanted x variable instead data frame: : function passed afun evaluated using argument matching. afun argument x analysis variable specified vars analyze() passed function, afun argument df subset dataset passed afun: Note also possible function returns multiple rows in_rows(): recommend specify row names explicitly.","code":"A B C ------------------------------------------------ foo_label foo(df_A) foo(df_B) foo(df_C) bar_label bar(df_A) bar(df_B) bar(df_C) zoo_label zoo(df_A) zoo(df_B) zoo(df_C) df_A <- df %>% filter(c1 == \"A\") df_B <- df %>% filter(c1 == \"B\") df_C <- df %>% filter(c1 == \"C\") foo <- prod bar <- sum zoo <- mean lyt <- basic_table() %>% split_cols_by(\"c1\") %>% analyze(\"x\", function(df) foo(df$x), var_labels = \"foo label\", format = \"xx.xx\") %>% analyze(\"x\", function(df) bar(df$x), var_labels = \"bar label\", format = \"xx.xx\") %>% analyze(\"x\", function(df) zoo(df$x), var_labels = \"zoo label\", format = \"xx.xx\") tbl <- build_table(lyt, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: root tbl # A B C # —————————————————————————————————— # foo label # foo label 0.00 -0.00 -0.00 # bar label # bar label 1.87 4.37 4.64 # zoo label # zoo label 0.05 0.13 0.18 A B C ------------------------------------------------ foo_label foo(x_A) foo(x_B) foo(x_C) bar_label bar(x_A) bar(x_B) bar(x_C) zoo_label zoo(x_A) zoo(x_B) zoo(x_C) x_A <- df_A$x x_B <- df_B$x x_C <- df_C$x lyt2 <- basic_table() %>% split_cols_by(\"c1\") %>% analyze(\"x\", foo, var_labels = \"foo label\", format = \"xx.xx\") %>% analyze(\"x\", bar, var_labels = \"bar label\", format = \"xx.xx\") %>% analyze(\"x\", zoo, var_labels = \"zoo label\", format = \"xx.xx\") tbl2 <- build_table(lyt2, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: root tbl2 # A B C # ———————————————————————————————— # foo label # foo 0.00 -0.00 -0.00 # bar label # bar 1.87 4.37 4.64 # zoo label # zoo 0.05 0.13 0.18 lyt3 <- basic_table() %>% split_cols_by(\"c1\") %>% analyze(\"x\", function(x) { in_rows( \"row 1\" = rcell(mean(x), format = \"xx.xx\"), \"row 2\" = rcell(sd(x), format = \"xx.xxx\") ) }, var_labels = \"foo label\") %>% analyze(\"x\", function(x) { in_rows( \"more rows 1\" = rcell(median(x), format = \"xx.x\"), \"even more rows 1\" = rcell(IQR(x), format = \"xx.xx\") ) }, var_labels = \"bar label\", format = \"xx.xx\") tbl3 <- build_table(lyt3, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: root tbl3 # A B C # —————————————————————————————————————————— # foo label # row 1 0.05 0.13 0.18 # row 2 0.985 0.815 0.890 # bar label # more rows 1 -0.0 0.2 0.3 # even more rows 1 1.20 1.15 1.16"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_concepts.html","id":"tabulation-with-row-structure","dir":"Articles","previous_headings":"","what":"Tabulation With Row Structure","title":"Tabulation Concepts","text":"Let’s say like create following table: df_* subsets df follows: note df_* class df, .e. tibbles. Hence foo aggregates subset data cell value. Given function foo (ignore ... now): can start calculating cell values individually: Now still missing table structure: rtables type tabulation done layouts: want see foo label use: now row labels disappeared. cfun needs define row label. let’s redefine foo:","code":"A B C -------------------------------------- U foo(df_UA) foo(df_UB) foo(df_UC) V foo(df_VA) foo(df_VB) foo(df_VC) W foo(df_WA) foo(df_WB) foo(df_WC) df_UA <- df %>% filter(r1 == \"U\", c1 == \"A\") df_VA <- df %>% filter(r1 == \"V\", c1 == \"A\") df_WA <- df %>% filter(r1 == \"W\", c1 == \"A\") df_UB <- df %>% filter(r1 == \"U\", c1 == \"B\") df_VB <- df %>% filter(r1 == \"V\", c1 == \"B\") df_WB <- df %>% filter(r1 == \"W\", c1 == \"C\") df_UC <- df %>% filter(r1 == \"U\", c1 == \"C\") df_VC <- df %>% filter(r1 == \"V\", c1 == \"C\") df_WC <- df %>% filter(r1 == \"W\", c1 == \"C\") foo <- function(df, labelstr = \"\", ...) { paste(dim(df), collapse = \" x \") } foo(df_UA) # [1] \"17 x 6\" foo(df_VA) # [1] \"9 x 6\" foo(df_WA) # [1] \"14 x 6\" foo(df_UB) # [1] \"13 x 6\" foo(df_VB) # [1] \"15 x 6\" foo(df_WB) # [1] \"11 x 6\" foo(df_UC) # [1] \"10 x 6\" foo(df_VC) # [1] \"5 x 6\" foo(df_WC) # [1] \"11 x 6\" matrix( list( foo(df_UA), foo(df_VA), foo(df_WA), foo(df_UB), foo(df_VB), foo(df_WB), foo(df_UC), foo(df_VC), foo(df_WC) ), byrow = FALSE, ncol = 3 ) # [,1] [,2] [,3] # [1,] \"17 x 6\" \"13 x 6\" \"10 x 6\" # [2,] \"9 x 6\" \"15 x 6\" \"5 x 6\" # [3,] \"14 x 6\" \"11 x 6\" \"11 x 6\" lyt4 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"x\", foo) tbl4 <- build_table(lyt4, df) tbl4 # A B C # ———————————————————————————————— # U # foo 17 x 6 13 x 6 10 x 6 # V # foo 9 x 6 15 x 6 5 x 6 # W # foo 14 x 6 6 x 6 11 x 6 lyt5 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = foo, format = \"xx\") tbl5 <- build_table(lyt5, df) tbl5 # A B C # ——————————————————————————— # 17 x 6 13 x 6 10 x 6 # 9 x 6 15 x 6 5 x 6 # 14 x 6 6 x 6 11 x 6 foo <- function(df, labelstr) { rcell(paste(dim(df), collapse = \" x \"), format = \"xx\", label = labelstr) } lyt6 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = foo) tbl6 <- build_table(lyt6, df) tbl6 # A B C # ———————————————————————————— # U 17 x 6 13 x 6 10 x 6 # V 9 x 6 15 x 6 5 x 6 # W 14 x 6 6 x 6 11 x 6"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_concepts.html","id":"calculating-the-mean","dir":"Articles","previous_headings":"Tabulation With Row Structure","what":"Calculating the Mean","title":"Tabulation Concepts","text":"Now let’s calculate mean df$y pattern : Note foo variable information hard-encoded function body. Let’s try alternatives returning analyze(): Note subset y variable passed x argument mean(). also get data.frame instead variable: contrast : function receives subset y.","code":"foo <- function(df, labelstr) { rcell(mean(df$y), label = labelstr, format = \"xx.xx\") } lyt7 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = foo) tbl7 <- build_table(lyt7, df) tbl7 # A B C # ————————————————————————— # U -1.00 1.00 3.00 # V -4.00 -2.00 0.00 # W -7.00 -5.00 -3.00 lyt8 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"y\", afun = mean) tbl8 <- build_table(lyt8, df) tbl8 # A B C # ————————————————————— # U # mean -1 1 3 # V # mean -4 -2 0 # W # mean -7 -5 -3 lyt9 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"y\", afun = function(df) mean(df$y)) tbl9 <- build_table(lyt9, df) tbl9 # A B C # —————————————————— # U # y -1 1 3 # V # y -4 -2 0 # W # y -7 -5 -3 lyt10 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% analyze(\"y\", afun = function(x) mean(x)) tbl10 <- build_table(lyt10, df) tbl10 # A B C # —————————————————— # U # y -1 1 3 # V # y -4 -2 0 # W # y -7 -5 -3"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_concepts.html","id":"group-summaries","dir":"Articles","previous_headings":"Tabulation With Row Structure","what":"Group Summaries","title":"Tabulation Concepts","text":"Pattern interesting one can add row structure (splits). Consider following table: <> represents data represented cell. cell U > u1, subset: . can get table follows: , wanted calculate two summaries per row split: following structure: rows U, u1, u2, …, W, w1, w2, w3 label rows rows (mean_sd range) data rows. Currently content rows table. Content rows summarize data defined splitting (.e. V > v1, B). wanted add content rows r2 split level get: s_cfun_2 content function either returns one row via rcell() multiple rows via in_rows(). data represented <> content rows data ’s descendant, .e. U > u1, content row cell df %>% filter(r1 == \"U\", r2 == \"u1\", c1 == \"\"). Note content functions cfun operate data frames vectors/variables must take df argument. , cfun must also labelstr argument split level. way, cfun can define row name. order get table can use layout framework follows: manner, want content rows r1 split can follows: pagination, content rows label rows get repeated page split descendant content row. , example, split following table ***: get following two tables: ","code":"A B C -------------------------------------- U u1 foo(<>) foo(<>) foo(<>) u2 foo(<>) foo(<>) foo(<>) u3 foo(<>) foo(<>) foo(<>) V v1 foo(<>) foo(<>) foo(<>) v2 foo(<>) foo(<>) foo(<>) v3 foo(<>) foo(<>) foo(<>) W w1 foo(<>) foo(<>) foo(<>) w2 foo(<>) foo(<>) foo(<>) w3 foo(<>) foo(<>) foo(<>) df %>% filter(r1 == \"U\", r2 == \"u1\", c1 == \"A\") # # A tibble: 2 × 6 # c1 c2 r1 r2 x y # # 1 A a2 U u1 1.12 -1 # 2 A a1 U u1 0.594 -1 lyt11 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% split_rows_by(\"r2\") %>% summarize_row_groups(cfun = function(df, labelstr) { rcell(mean(df$x), format = \"xx.xx\", label = paste(\"mean x for\", labelstr)) }) tbl11 <- build_table(lyt11, df) tbl11 # A B C # ——————————————————————————————————————— # U # mean x for u3 -0.04 0.36 -0.25 # mean x for u1 0.86 0.32 NA # mean x for u2 -0.28 0.38 0.08 # V # mean x for v2 0.01 0.55 0.60 # mean x for v3 -0.03 -0.30 1.06 # mean x for v1 0.56 -0.27 -0.54 # W # mean x for w1 -0.58 0.42 0.67 # mean x for w3 0.56 0.69 -0.39 # mean x for w2 -1.99 -0.10 0.53 s_mean_sd <- function(x) { in_rows(\"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\")) } s_range <- function(x) { in_rows(\"range\" = rcell(range(x), format = \"xx.xx - xx.xx\")) } lyt12 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% split_rows_by(\"r2\") %>% analyze(\"x\", s_mean_sd, show_labels = \"hidden\") %>% analyze(\"x\", s_range, show_labels = \"hidden\") tbl12 <- build_table(lyt12, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u3] # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w2] tbl12 # A B C # ——————————————————————————————————————————————————————————— # U # u3 # mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72) # range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56 # u1 # mean (sd) 0.86 (0.38) 0.32 (0.51) NA # range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf # u2 # mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91) # range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51 # V # v2 # mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03) # range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62 # v3 # mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA) # range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06 # v1 # mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18) # range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29 # W # w1 # mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39) # range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21 # w3 # mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68) # range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10 # w2 # mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60) # range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16 A B C --------------------------------------------------------- U u1 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u2 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u3 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) V v1 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v2 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v3 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) W w1 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w2 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w3 mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) A B C --------------------------------------------------------- U u1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) u3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) V v1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) v3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) W w1 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w2 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) w3 s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) s_mean_sd <- function(x) { in_rows(\"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\")) } s_range <- function(x) { in_rows(\"range\" = rcell(range(x), format = \"xx.xx - xx.xx\")) } s_cfun_2 <- function(df, labelstr) { rcell(nrow(df), format = \"xx\", label = paste(labelstr, \"(n)\")) } lyt13 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% split_rows_by(\"r2\") %>% summarize_row_groups(cfun = s_cfun_2) %>% analyze(\"x\", s_mean_sd, show_labels = \"hidden\") %>% analyze(\"x\", s_range, show_labels = \"hidden\") tbl13 <- build_table(lyt13, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u3] # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w2] tbl13 # A B C # ——————————————————————————————————————————————————————————— # U # u3 (n) 6 5 3 # mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72) # range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56 # u1 (n) 2 5 0 # mean (sd) 0.86 (0.38) 0.32 (0.51) NA # range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf # u2 (n) 9 3 7 # mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91) # range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51 # V # v2 (n) 2 4 2 # mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03) # range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62 # v3 (n) 3 4 1 # mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA) # range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06 # v1 (n) 4 7 2 # mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18) # range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29 # W # w1 (n) 4 1 4 # mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39) # range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21 # w3 (n) 9 1 3 # mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68) # range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10 # w2 (n) 1 4 4 # mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60) # range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16 lyt14 <- basic_table() %>% split_cols_by(\"c1\") %>% split_rows_by(\"r1\") %>% summarize_row_groups(cfun = s_cfun_2) %>% split_rows_by(\"r2\") %>% summarize_row_groups(cfun = s_cfun_2) %>% analyze(\"x\", s_mean_sd, show_labels = \"hidden\") %>% analyze(\"x\", s_range, show_labels = \"hidden\") tbl14 <- build_table(lyt14, df) # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u3] # Warning in min(x): no non-missing arguments to min; returning Inf # Warning in max(x): no non-missing arguments to max; returning -Inf # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[U]->r2[u2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v2] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[V]->r2[v1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w1] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w3] # Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times. # occured at (row) path: r1[W]->r2[w2] tbl14 # A B C # ——————————————————————————————————————————————————————————— # U (n) 17 13 10 # u3 (n) 6 5 3 # mean (sd) -0.04 (1.18) 0.36 (1.41) -0.25 (0.72) # range -1.80 - 1.47 -1.28 - 2.40 -0.82 - 0.56 # u1 (n) 2 5 0 # mean (sd) 0.86 (0.38) 0.32 (0.51) NA # range 0.59 - 1.12 -0.48 - 0.94 Inf - -Inf # u2 (n) 9 3 7 # mean (sd) -0.28 (0.96) 0.38 (0.67) 0.08 (0.91) # range -1.52 - 1.43 -0.39 - 0.82 -0.93 - 1.51 # V (n) 9 15 5 # v2 (n) 2 4 2 # mean (sd) 0.01 (0.25) 0.55 (1.14) 0.60 (0.03) # range -0.16 - 0.18 -0.84 - 1.60 0.58 - 0.62 # v3 (n) 3 4 1 # mean (sd) -0.03 (0.37) -0.30 (0.36) 1.06 (NA) # range -0.41 - 0.33 -0.62 - 0.03 1.06 - 1.06 # v1 (n) 4 7 2 # mean (sd) 0.56 (1.10) -0.27 (0.73) -0.54 (1.18) # range -0.16 - 2.17 -1.22 - 0.59 -1.38 - 0.29 # W (n) 14 6 11 # w1 (n) 4 1 4 # mean (sd) -0.58 (0.85) 0.42 (NA) 0.67 (0.39) # range -1.25 - 0.61 0.42 - 0.42 0.37 - 1.21 # w3 (n) 9 1 3 # mean (sd) 0.56 (0.85) 0.69 (NA) -0.39 (1.68) # range -0.71 - 1.98 0.69 - 0.69 -2.21 - 1.10 # w2 (n) 1 4 4 # mean (sd) -1.99 (NA) -0.10 (0.47) 0.53 (0.60) # range -1.99 - -1.99 -0.61 - 0.39 -0.10 - 1.16 A B C --------------------------------------------------------- U u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) *** range s_range(<>) s_range(<>) s_range(<>) u2 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>) A B C --------------------------------------------------------- U u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) A B C --------------------------------------------------------- U u1 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) range s_range(<>) s_range(<>) s_range(<>) u2 (n) s_cfun_2(<>) s_cfun_2(<>) s_cfun_2(<>) mean_sd s_mean_sd(<>) s_mean_sd(<>) s_mean_sd(<>) range s_range(<>) s_range(<>) s_range(<>)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_concepts.html","id":"pattern-iii","dir":"Articles","previous_headings":"Tabulation With Row Structure","what":"Pattern III","title":"Tabulation Concepts","text":"Let’s consider following tabulation pattern: discuss future release rtables.","code":"A B C ------------------------------------------------ label 1 foo(x_A) bar(x_B) zoo(x_C) label 2 foo(x_A) bar(x_B) zoo(x_C) label 3 foo(x_A) bar(x_B) zoo(x_C)"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_dplyr.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Comparison with dplyr Tabulation","text":"vignette, like discuss similarities differences dplyr rtable. Much rtables framework focuses tabulation/summarizing data visualization table. vignette, focus summarizing data using dplyr contrast rtables. won’t pay attention table visualization/markup just derive cell content. Using dplyr summarize data gt visualize table good way tabulation certain nature complexity. However, tables table created introduction vignette take effort create dplyr. Part effort due fact using dplyr table data stored data.frames tibbles natural way represent table show vignette. know elegant way deriving table content dplyr please let us know update vignette. table data used introduction vignette:","code":"library(rtables) library(dplyr) n <- 400 set.seed(1) df <- tibble( arm = factor(sample(c(\"Arm A\", \"Arm B\"), n, replace = TRUE), levels = c(\"Arm A\", \"Arm B\")), country = factor(sample(c(\"CAN\", \"USA\"), n, replace = TRUE, prob = c(.55, .45)), levels = c(\"CAN\", \"USA\")), gender = factor(sample(c(\"Female\", \"Male\"), n, replace = TRUE), levels = c(\"Female\", \"Male\")), handed = factor(sample(c(\"Left\", \"Right\"), n, prob = c(.6, .4), replace = TRUE), levels = c(\"Left\", \"Right\")), age = rchisq(n, 30) + 10 ) %>% mutate( weight = 35 * rnorm(n, sd = .5) + ifelse(gender == \"Female\", 140, 180) ) lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # (N=96) (N=105) (N=92) (N=107) # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.9 40.4 40.3 37.7 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.4 39.7 39.2 40.1 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_dplyr.html","id":"getting-started","dir":"Articles","previous_headings":"","what":"Getting Started","title":"Comparison with dplyr Tabulation","text":"start deriving first data cell row 3 (note, row 1 2 content cells, see introduction vignette). Cell 3,1 contains mean age left handed & female Canadians “Arm ”: dplyr: , dplyr gives us verbs easily get average age left handed Canadians group defined 4 columns: can get average age cell values : rtable syntax, need following code get content: mentioned introduction vignette, please ignore difference arranging formatting data: ’s possible condense rtable possible make tibble look like reference table using gt R package. terms tabulation example arguably much added rtables dplyr.","code":"mean(df$age[df$country == \"CAN\" & df$arm == \"Arm A\" & df$gender == \"Female\" & df$handed == \"Left\"]) # [1] 38.86979 df %>% filter(country == \"CAN\", arm == \"Arm A\", gender == \"Female\", handed == \"Left\") %>% summarise(mean_age = mean(age)) # # A tibble: 1 × 1 # mean_age # # 1 38.9 df %>% group_by(arm, gender) %>% filter(country == \"CAN\", handed == \"Left\") %>% summarise(mean_age = mean(age)) # `summarise()` has grouped output by 'arm'. You can override using the `.groups` # argument. # # A tibble: 4 × 3 # # Groups: arm [2] # arm gender mean_age # # 1 Arm A Female 38.9 # 2 Arm A Male 40.4 # 3 Arm B Female 40.3 # 4 Arm B Male 37.7 average_age <- df %>% group_by(arm, gender, country, handed) %>% summarise(mean_age = mean(age)) # `summarise()` has grouped output by 'arm', 'gender', 'country'. You can # override using the `.groups` argument. average_age # # A tibble: 16 × 5 # # Groups: arm, gender, country [8] # arm gender country handed mean_age # # 1 Arm A Female CAN Left 38.9 # 2 Arm A Female CAN Right 36.6 # 3 Arm A Female USA Left 40.4 # 4 Arm A Female USA Right 36.9 # 5 Arm A Male CAN Left 40.4 # 6 Arm A Male CAN Right 40.2 # 7 Arm A Male USA Left 39.7 # 8 Arm A Male USA Right 39.8 # 9 Arm B Female CAN Left 40.3 # 10 Arm B Female CAN Right 40.2 # 11 Arm B Female USA Left 39.2 # 12 Arm B Female USA Right 38.5 # 13 Arm B Male CAN Left 37.7 # 14 Arm B Male CAN Right 40.6 # 15 Arm B Male USA Left 40.1 # 16 Arm B Male USA Right 39.0 lyt <- basic_table() %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% split_rows_by(\"handed\") %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # ———————————————————————————————————————— # CAN # Left # mean 38.9 40.4 40.3 37.7 # Right # mean 36.6 40.2 40.2 40.6 # USA # Left # mean 40.4 39.7 39.2 40.1 # Right # mean 36.9 39.8 38.5 39.0"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_dplyr.html","id":"content-information","dir":"Articles","previous_headings":"","what":"Content Information","title":"Comparison with dplyr Tabulation","text":"Unlike rtables different levels summarization discrete computations dplyr need combine first focus count percentage information handedness within country (arm-gender pair), along analysis row mean values: 16 rows (cells) like average_age data frame defined . Next, derive group information countries: Finally, left_join() two levels summary get data.frame containing full set values make body table (note, however, order): Alternatively, calculate counts c_h_df, use mutate() left_join() divide counts n_col values naturally calculated within c_df. simplify c_h_df’s creation somewhat requiring explicit ungroup(), prevents level summarization self-contained set computations. rtables call contrast : can now spot check values , rtable syntax hopefully also become bit straightforward derive cell values dplyr particular table.","code":"c_h_df <- df %>% group_by(arm, gender, country, handed) %>% summarize(mean = mean(age), c_h_count = n()) %>% ## we need the sum below to *not* be by country, so that we're dividing by the column counts ungroup(country) %>% # now the `handed` grouping has been removed, therefore we can calculate percent now: mutate(n_col = sum(c_h_count), c_h_percent = c_h_count / n_col) # `summarise()` has grouped output by 'arm', 'gender', 'country'. You can # override using the `.groups` argument. c_h_df # # A tibble: 16 × 8 # # Groups: arm, gender [4] # arm gender country handed mean c_h_count n_col c_h_percent # # 1 Arm A Female CAN Left 38.9 32 96 0.333 # 2 Arm A Female CAN Right 36.6 13 96 0.135 # 3 Arm A Female USA Left 40.4 34 96 0.354 # 4 Arm A Female USA Right 36.9 17 96 0.177 # 5 Arm A Male CAN Left 40.4 42 105 0.4 # 6 Arm A Male CAN Right 40.2 22 105 0.210 # 7 Arm A Male USA Left 39.7 19 105 0.181 # 8 Arm A Male USA Right 39.8 22 105 0.210 # 9 Arm B Female CAN Left 40.3 26 92 0.283 # 10 Arm B Female CAN Right 40.2 20 92 0.217 # 11 Arm B Female USA Left 39.2 25 92 0.272 # 12 Arm B Female USA Right 38.5 21 92 0.228 # 13 Arm B Male CAN Left 37.7 37 107 0.346 # 14 Arm B Male CAN Right 40.6 25 107 0.234 # 15 Arm B Male USA Left 40.1 25 107 0.234 # 16 Arm B Male USA Right 39.0 20 107 0.187 c_df <- df %>% group_by(arm, gender, country) %>% summarize(c_count = n()) %>% # now the `handed` grouping has been removed, therefore we can calculate percent now: mutate(n_col = sum(c_count), c_percent = c_count / n_col) # `summarise()` has grouped output by 'arm', 'gender'. You can override using the # `.groups` argument. c_df # # A tibble: 8 × 6 # # Groups: arm, gender [4] # arm gender country c_count n_col c_percent # # 1 Arm A Female CAN 45 96 0.469 # 2 Arm A Female USA 51 96 0.531 # 3 Arm A Male CAN 64 105 0.610 # 4 Arm A Male USA 41 105 0.390 # 5 Arm B Female CAN 46 92 0.5 # 6 Arm B Female USA 46 92 0.5 # 7 Arm B Male CAN 62 107 0.579 # 8 Arm B Male USA 45 107 0.421 full_dplyr <- left_join(c_h_df, c_df) %>% ungroup() # Joining with `by = join_by(arm, gender, country, n_col)` lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"arm\") %>% split_cols_by(\"gender\") %>% split_rows_by(\"country\") %>% summarize_row_groups() %>% split_rows_by(\"handed\") %>% summarize_row_groups() %>% analyze(\"age\", afun = mean, format = \"xx.x\") tbl <- build_table(lyt, df) tbl # Arm A Arm B # Female Male Female Male # (N=96) (N=105) (N=92) (N=107) # ———————————————————————————————————————————————————————————— # CAN 45 (46.9%) 64 (61.0%) 46 (50.0%) 62 (57.9%) # Left 32 (33.3%) 42 (40.0%) 26 (28.3%) 37 (34.6%) # mean 38.9 40.4 40.3 37.7 # Right 13 (13.5%) 22 (21.0%) 20 (21.7%) 25 (23.4%) # mean 36.6 40.2 40.2 40.6 # USA 51 (53.1%) 41 (39.0%) 46 (50.0%) 45 (42.1%) # Left 34 (35.4%) 19 (18.1%) 25 (27.2%) 25 (23.4%) # mean 40.4 39.7 39.2 40.1 # Right 17 (17.7%) 22 (21.0%) 21 (22.8%) 20 (18.7%) # mean 36.9 39.8 38.5 39.0 frm_rtables_h <- cell_values( tbl, rowpath = c(\"country\", \"CAN\", \"handed\", \"Right\", \"@content\"), colpath = c(\"arm\", \"Arm B\", \"gender\", \"Female\") )[[1]] frm_rtables_h # [1] 20.0000000 0.2173913 frm_dplyr_h <- full_dplyr %>% filter(country == \"CAN\" & handed == \"Right\" & arm == \"Arm B\" & gender == \"Female\") %>% select(c_h_count, c_h_percent) frm_dplyr_h # # A tibble: 1 × 2 # c_h_count c_h_percent # # 1 20 0.217 frm_rtables_c <- cell_values( tbl, rowpath = c(\"country\", \"CAN\", \"@content\"), colpath = c(\"arm\", \"Arm A\", \"gender\", \"Male\") )[[1]] frm_rtables_c # [1] 64.0000000 0.6095238 frm_dplyr_c <- full_dplyr %>% filter(country == \"CAN\" & arm == \"Arm A\" & gender == \"Male\") %>% select(c_count, c_percent) frm_dplyr_c # # A tibble: 2 × 2 # c_count c_percent # # 1 64 0.610 # 2 64 0.610"},{"path":"https://insightsengineering.github.io/rtables/main/articles/tabulation_dplyr.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Comparison with dplyr Tabulation","text":"vignette learned : dplyr keeps simple things simple tables group summaries repeating information required rtables streamlines construction complex tables recommend continue reading clinical_trials vignette create number advanced tables using layouts.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/title_footer.html","id":"titles-and-non-referential-footer-materials","dir":"Articles","previous_headings":"","what":"Titles and Non-Referential Footer Materials","title":"Titles, Footers, and Referential Footnotes","text":"rtables table can annotated three types header (title) information, well three types footer information. Header information comes two forms specified directly (main title subtitles), well one populated automatically necessary (page title, see next section). Similarly, footer materials come two directly specified components: main footer provenance footer, addition one computed necessary: referential footnotes. basic_table() accepts values static title footer element layout construction:","code":"library(rtables) library(dplyr) lyt <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, format = \"xx.x\") tbl <- build_table(lyt, DM) cat(export_as_txt(tbl, paginate = TRUE, page_break = \"\\n\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # F # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # M # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/main/articles/title_footer.html","id":"page-by-splitting","dir":"Articles","previous_headings":"","what":"Page-by splitting","title":"Titles, Footers, and Referential Footnotes","text":"often want split tables based values one variables (e.g., lab measurement) paginate separately within table subsections. rtables via page row splits. Row splits can declared page splits setting page_by = TRUE split_rows_by*() call, . page splits present, page titles generated automatically appending split value (typically factor level, though need ), page_prefix, separated :. default, page_prefix name variable split. Page row splits can nested, within page_by splits, nested within traditional row splits. case, page title page split present every resulting page, seen :","code":"lyt2 <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", page_by = TRUE, page_prefix = \"Patient Subset - Gender\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", mean, format = \"xx.x\") tbl2 <- build_table(lyt2, DM) cat(export_as_txt(tbl2, paginate = TRUE, page_break = \"\\n\\n~~~~ Page Break ~~~~\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 # C # mean 35.2 36.0 34.3 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 # C # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a lyt3 <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", page_by = TRUE, page_prefix = \"Patient Subset - Gender\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\", page_by = TRUE, page_prefix = \"Stratification - Strata\") %>% analyze(\"AGE\", mean, format = \"xx.x\") tbl3 <- build_table(lyt3, DM) cat(export_as_txt(tbl3, paginate = TRUE, page_break = \"\\n\\n~~~~ Page Break ~~~~\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # Stratification - Strata: A # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 30.9 32.9 36.0 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # Stratification - Strata: B # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 34.9 32.9 34.4 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # Stratification - Strata: C # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 35.2 36.0 34.3 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # Stratification - Strata: A # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 35.1 31.1 35.6 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # Stratification - Strata: B # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 36.6 32.1 34.4 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # ~~~~ Page Break ~~~~ # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # Stratification - Strata: C # # —————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————— # mean 37.4 32.8 32.8 # —————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/main/articles/title_footer.html","id":"referential-footnotes","dir":"Articles","previous_headings":"","what":"Referential Footnotes","title":"Titles, Footers, and Referential Footnotes","text":"Referential footnotes footnotes associated particular component table: column, row, cell. can added tabulation via analysis functions, can also added post-hoc table created. rendered number within curly braces within table body, row, column labels, followed message associated number printed table rendering.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/articles/title_footer.html","id":"adding-cell--and-analysis-row-referential-footnotes-at-tabulation-time","dir":"Articles","previous_headings":"Referential Footnotes","what":"Adding Cell- and Analysis-row Referential Footnotes At Tabulation Time","title":"Titles, Footers, and Referential Footnotes","text":"note typically type footnote added within analysis function dependent computations done calculate cell value(s), e.g., model converging. simply use context information illustrative proxy . procedure adding footnotes content (summary row) rows cells identical , done within content function.","code":"afun <- function(df, .var, .spl_context) { val <- .spl_context$value[NROW(.spl_context)] rw_fnotes <- if (val == \"C\") list(\"This is strata level C for these patients\") else list() cl_fnotes <- if (val == \"B\" && df[1, \"ARM\", drop = TRUE] == \"C: Combination\") { list(\"these Strata B patients got the drug combination\") } else { list() } in_rows( mean = mean(df[[.var]]), .row_footnotes = rw_fnotes, .cell_footnotes = cl_fnotes, .formats = c(mean = \"xx.x\") ) } lyt <- basic_table( title = \"Study XXXXXXXX\", subtitles = c(\"subtitle YYYYYYYYYY\", \"subtitle2 ZZZZZZZZZ\"), main_footer = \"Analysis was done using cool methods that are correct\", prov_footer = \"file: /path/to/stuff/that/lives/there HASH:1ac41b242a\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", page_by = TRUE, page_prefix = \"Patient Subset - Gender\", split_fun = drop_split_levels) %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\", afun, format = \"xx.x\") tbl <- build_table(lyt, DM) cat(export_as_txt(tbl, paginate = TRUE, page_break = \"\\n\\n\\n\")) # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: F # # —————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————— # A # mean 30.9 32.9 36.0 # B # mean 34.9 32.9 34.4 {1} # C # mean {2} 35.2 36.0 34.3 # —————————————————————————————————————————————————————— # # {1} - these Strata B patients got the drug combination # {2} - This is strata level C for these patients # —————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a # # # # Study XXXXXXXX # subtitle YYYYYYYYYY # subtitle2 ZZZZZZZZZ # Patient Subset - Gender: M # # —————————————————————————————————————————————————————— # A: Drug X B: Placebo C: Combination # —————————————————————————————————————————————————————— # A # mean 35.1 31.1 35.6 # B # mean 36.6 32.1 34.4 {1} # C # mean {2} 37.4 32.8 32.8 # —————————————————————————————————————————————————————— # # {1} - these Strata B patients got the drug combination # {2} - This is strata level C for these patients # —————————————————————————————————————————————————————— # # Analysis was done using cool methods that are correct # # file: /path/to/stuff/that/lives/there HASH:1ac41b242a"},{"path":"https://insightsengineering.github.io/rtables/main/articles/title_footer.html","id":"annotating-an-existing-table-with-referential-footnotes","dir":"Articles","previous_headings":"Referential Footnotes","what":"Annotating an Existing Table with Referential Footnotes","title":"Titles, Footers, and Referential Footnotes","text":"addition inserting referential footnotes tabulation time within analysis functions, can also annotate tables post-hoc. also way add footnotes column labels, controlled within analysis content function. fnotes_at_path<- function accepts row path, column path, value full set footnotes defined locations (NULL character vector). non-NULL row path NULL column path specifies footnote(s) attached row, NULL row path non-NULL column path indicates go column. non-NULL indicates cell (must resolve individual cell). Note step content row must add path, even though didn’t need put footnote full row. Currently, content rows default named label rather name corresponding facet. reflected output , e.g., row_paths_summary. can add footnotes cell like :","code":"## from ?tolower example slightly modified .simpleCap <- function(x) { if (length(x) > 1) { return(sapply(x, .simpleCap)) } s <- strsplit(tolower(x), \" \")[[1]] paste(toupper(substring(s, 1, 1)), substring(s, 2), sep = \"\", collapse = \" \") } adsl2 <- ex_adsl %>% filter(SEX %in% c(\"M\", \"F\") & RACE %in% (levels(RACE)[1:3])) %>% ## we trim the level names here solely due to space considerations mutate(ethnicity = .simpleCap(gsub(\"(.*)OR.*\", \"\\\\1\", RACE)), RACE = factor(RACE)) lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"RACE\", labels_var = \"ethnicity\", split_fun = drop_split_levels) %>% summarize_row_groups() %>% analyze(c(\"AGE\", \"STRATA1\")) tbl2 <- build_table(lyt2, adsl2) tbl2 # A: Drug X B: Placebo C: Combination # F M F M F M # ——————————————————————————————————————————————————————————————————————————————————————— # Asian 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 fnotes_at_path(tbl2, c(\"RACE\", \"ASIAN\")) <- c(\"hi\", \"there\") tbl2 # A: Drug X B: Placebo C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————————— # Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 # —————————————————————————————————————————————————————————————————————————————————————————— # # {1} - hi # {2} - there # —————————————————————————————————————————————————————————————————————————————————————————— fnotes_at_path(tbl2, rowpath = NULL, c(\"ARM\", \"B: Placebo\")) <- c(\"this is a placebo\") tbl2 # A: Drug X B: Placebo {NA} C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————————— # Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 # —————————————————————————————————————————————————————————————————————————————————————————— # # {1} - hi # {2} - there # {NA} - this is a placebo # —————————————————————————————————————————————————————————————————————————————————————————— row_paths_summary(tbl2) # rowname node_class path # ——————————————————————————————————————————————————————————————————————————— # Asian ContentRow RACE, ASIAN, @content, Asian # AGE LabelRow RACE, ASIAN, AGE # Mean DataRow RACE, ASIAN, AGE, Mean # STRATA1 LabelRow RACE, ASIAN, STRATA1 # A DataRow RACE, ASIAN, STRATA1, A # B DataRow RACE, ASIAN, STRATA1, B # C DataRow RACE, ASIAN, STRATA1, C # Black ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, Black # AGE LabelRow RACE, BLACK OR AFRICAN AMERICAN, AGE # Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, AGE, Mean # STRATA1 LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1 # A DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A # B DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B # C DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C # White ContentRow RACE, WHITE, @content, White # AGE LabelRow RACE, WHITE, AGE # Mean DataRow RACE, WHITE, AGE, Mean # STRATA1 LabelRow RACE, WHITE, STRATA1 # A DataRow RACE, WHITE, STRATA1, A # B DataRow RACE, WHITE, STRATA1, B # C DataRow RACE, WHITE, STRATA1, C fnotes_at_path( tbl2, rowpath = c(\"RACE\", \"ASIAN\", \"@content\", \"Asian\"), colpath = c(\"ARM\", \"B: Placebo\", \"SEX\", \"F\") ) <- \"These asian women got placebo treatments\" tbl2 # A: Drug X B: Placebo {NA} C: Combination # F M F M F M # —————————————————————————————————————————————————————————————————————————————————————————————— # Asian {1, 2} 41 (53.9%) 25 (54.3%) 36 (52.2%) {3} 30 (60.0%) 39 (60.9%) 32 (57.1%) # AGE # Mean 31.22 34.60 35.06 38.63 36.44 37.66 # STRATA1 # A 11 10 14 10 11 7 # B 11 9 15 7 11 14 # C 19 6 7 13 17 11 # Black 18 (23.7%) 12 (26.1%) 16 (23.2%) 12 (24.0%) 14 (21.9%) 14 (25.0%) # AGE # Mean 34.06 34.58 33.88 36.33 33.21 34.21 # STRATA1 # A 5 2 5 6 3 7 # B 6 5 3 4 4 4 # C 7 5 8 2 7 3 # White 17 (22.4%) 9 (19.6%) 17 (24.6%) 8 (16.0%) 11 (17.2%) 10 (17.9%) # AGE # Mean 34.12 40.00 32.41 34.62 33.00 30.80 # STRATA1 # A 5 3 3 3 3 5 # B 5 4 8 4 5 2 # C 7 2 6 1 3 3 # —————————————————————————————————————————————————————————————————————————————————————————————— # # {1} - hi # {2} - there # {3} - These asian women got placebo treatments # {NA} - this is a placebo # ——————————————————————————————————————————————————————————————————————————————————————————————"},{"path":"https://insightsengineering.github.io/rtables/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"define authors actively maintaining code base, contributors made significant contribution past. acknowledgements, see eponymous section Home Page. Gabriel Becker. Author. Original creator package Adrian Waddell. Author. Daniel Sabanés Bové. Contributor. Maximilian Mordig. Contributor. Davide Garolini. Contributor. Emily de la Rua. Contributor. Abinaya Yogasekaram. Contributor. Joe Zhu. Contributor, maintainer. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Becker G, Waddell (2024). rtables: Reporting Tables. R package version 0.6.6.9008, https://insightsengineering.github.io/rtables/main/, https://github.com/insightsengineering/rtables.","code":"@Manual{, title = {rtables: Reporting Tables}, author = {Gabriel Becker and Adrian Waddell}, year = {2024}, note = {R package version 0.6.6.9008, https://insightsengineering.github.io/rtables/main/}, url = {https://github.com/insightsengineering/rtables}, }"},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"reporting-tables-with-r","dir":"","previous_headings":"","what":"Reporting tables with R","title":"Reporting Tables","text":"rtables R package designed create display complex tables R. cells rtable may contain high-dimensional data structure can displayed cell-specific formatting instructions. Currently, rtables can outputted ascii html, pdf, well Power Point (via conversion flextable objects). rtf support development future release. rtables developed copy written F. Hoffmann-La Roche released open source Apache License Version 2. rtables development driven need create regulatory ready tables health authority review. key requirements undertaking listed : values need programmatically accessible non-rounded state cross-checking multiple values displayed within cell flexible tabulation framework flexible formatting (cell spans, rounding, alignment, etc.) multiple output formats (html, ascii, latex, pdf, xml) flexible pagination horizontal vertical directions distinguish name label data structure work CDISC standards title, footnotes, cell cell/row/column references rtables currently covers virtually requirements, advances remain active development.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Reporting Tables","text":"rtables available CRAN can install latest released version : can install latest development version directly GitHub : Note might need set GITHUB_PAT environment variable order able install GitHub. Packaged releases (CRAN official CRAN releases) can found releases list.","code":"install.packages(\"rtables\") remotes::install_github(\"insightsengineering/formatters\") remotes::install_github(\"insightsengineering/rtables\")"},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Reporting Tables","text":"first demonstrate demographic table-like example show creation complex table.","code":"library(rtables) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR1\", \"BMRKR2\"), function(x, ...) { if (is.numeric(x)) { in_rows( \"Mean (sd)\" = c(mean(x), sd(x)), \"Median\" = median(x), \"Min - Max\" = range(x), .formats = c(\"xx.xx (xx.xx)\", \"xx.xx\", \"xx.xx - xx.xx\") ) } else if (is.factor(x) || is.character(x)) { in_rows(.list = list_wrap_x(table)(x)) } else { stop(\"type not supported\") } }) build_table(lyt, ex_adsl) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————— #> AGE #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> Median 33.00 35.00 35.00 #> Min - Max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 #> BMRKR1 #> Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) #> Median 5.39 4.81 4.61 #> Min - Max 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 library(rtables) library(dplyr) ## for simplicity grab non-sparse subset ADSL <- ex_adsl %>% filter(RACE %in% levels(RACE)[1:3]) biomarker_ave <- function(x, ...) { val <- if(length(x) > 0) round(mean(x), 2) else \"no data\" in_rows( \"Biomarker 1 (mean)\" = rcell(val) ) } basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_cols_by(\"BMRKR2\") %>% split_rows_by(\"RACE\", split_fun = trim_levels_in_group(\"SEX\")) %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% analyze(\"BMRKR1\", biomarker_ave) %>% build_table(ADSL) #> A: Drug X B: Placebo C: Combination #> LOW MEDIUM HIGH LOW MEDIUM HIGH LOW MEDIUM HIGH #> (N=45) (N=35) (N=46) (N=42) (N=48) (N=31) (N=40) (N=39) (N=47) #> ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN #> F 13 (28.9%) 9 (25.7%) 19 (41.3%) 9 (21.4%) 18 (37.5%) 9 (29.0%) 13 (32.5%) 9 (23.1%) 17 (36.2%) #> Biomarker 1 (mean) 5.23 6.17 5.38 5.64 5.55 4.33 5.46 5.48 5.19 #> M 8 (17.8%) 7 (20.0%) 10 (21.7%) 12 (28.6%) 10 (20.8%) 8 (25.8%) 5 (12.5%) 11 (28.2%) 16 (34.0%) #> Biomarker 1 (mean) 6.77 6.06 5.54 4.9 4.98 6.81 6.53 5.47 4.98 #> U 1 (2.2%) 1 (2.9%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (3.2%) 0 (0.0%) 1 (2.6%) 1 (2.1%) #> Biomarker 1 (mean) 4.68 7.7 no data no data no data 6.97 no data 11.93 9.01 #> BLACK OR AFRICAN AMERICAN #> F 6 (13.3%) 3 (8.6%) 9 (19.6%) 6 (14.3%) 8 (16.7%) 2 (6.5%) 7 (17.5%) 4 (10.3%) 3 (6.4%) #> Biomarker 1 (mean) 5.01 7.2 6.79 6.15 5.26 8.57 5.72 5.76 4.58 #> M 5 (11.1%) 5 (14.3%) 2 (4.3%) 3 (7.1%) 5 (10.4%) 4 (12.9%) 4 (10.0%) 5 (12.8%) 5 (10.6%) #> Biomarker 1 (mean) 6.92 5.82 11.66 4.46 6.14 8.47 6.16 5.25 4.83 #> U 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 1 (2.5%) 1 (2.6%) 0 (0.0%) #> Biomarker 1 (mean) no data no data no data no data no data no data 2.79 9.82 no data #> UNDIFFERENTIATED 1 (2.2%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 2 (5.0%) 0 (0.0%) 0 (0.0%) #> Biomarker 1 (mean) 9.48 no data no data no data no data no data 6.46 no data no data #> WHITE #> F 6 (13.3%) 7 (20.0%) 4 (8.7%) 5 (11.9%) 6 (12.5%) 6 (19.4%) 6 (15.0%) 3 (7.7%) 2 (4.3%) #> Biomarker 1 (mean) 4.43 7.83 4.52 6.42 5.07 7.83 6.71 5.87 10.7 #> M 4 (8.9%) 3 (8.6%) 2 (4.3%) 6 (14.3%) 1 (2.1%) 1 (3.2%) 2 (5.0%) 5 (12.8%) 3 (6.4%) #> Biomarker 1 (mean) 5.81 7.23 1.39 4.72 4.58 12.87 2.3 5.1 5.98 #> U 1 (2.2%) 0 (0.0%) 0 (0.0%) 1 (2.4%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Biomarker 1 (mean) 3.94 no data no data 3.77 no data no data no data no data no data"},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"acknowledgments","dir":"","previous_headings":"","what":"Acknowledgments","title":"Reporting Tables","text":"like thank everyone made rtables better project providing feedback improving examples & vignettes. following list contributors alphabetical: Maximo Carreras, Francois Collins, Saibah Chohan, Tadeusz Lewandowski, Nick Paszty, Nina Qi, Jana Stoilova, Heng Wang, Godwin Yung","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"advanced-rtables-training","dir":"","previous_headings":"Presentations","what":"Advanced rtables Training","title":"Reporting Tables","text":"Part 1 Slides Part 2 Slides","code":""},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"rinpharma-workshop-creating-submission-quality-clinical-trial-reporting-tables-in-r-with-rtables","dir":"","previous_headings":"Presentations","what":"RinPharma Workshop: Creating Submission-Quality Clinical Trial Reporting Tables in R with rtables","title":"Reporting Tables","text":"Slides","code":""},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"r-adoption-series","dir":"","previous_headings":"Presentations","what":"R Adoption Series","title":"Reporting Tables","text":"R Adoption Series presentation 2022 Slides","code":""},{"path":"https://insightsengineering.github.io/rtables/main/index.html","id":"new-current-layouting-and-tabulation-framework-v03","dir":"","previous_headings":"Presentations","what":"New (Current) Layouting and Tabulation Framework (v.0.3+)","title":"Reporting Tables","text":"useR!2020 Presentation (v0.3.1.1) July 2020","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/CellValue.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell Value constructor — CellValue","title":"Cell Value constructor — CellValue","text":"Cell Value constructor","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/CellValue.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell Value constructor — CellValue","text":"","code":"CellValue( val, format = NULL, colspan = 1L, label = NULL, indent_mod = NULL, footnotes = NULL, align = NULL, format_na_str = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/CellValue.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell Value constructor — CellValue","text":"val . value cell exactly passed formatter returned extracted format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. colspan integer(1). Column span value. label character(1). label (confused name) object/structure. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. footnotes list NULL. Referential footnote messages cell. align character(1) NULL. Alignment value rendered . defaults \"center\" NULL used. See formatters::list_valid_aligns() currently supported alignments. format_na_str character(1). String displayed formatted cell's value(s) NA.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/CellValue.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell Value constructor — CellValue","text":"object representing value within single cell within populated table. underlying structure object implementation detail relied upon beyond calling accessors class.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/EmptyColInfo.html","id":null,"dir":"Reference","previous_headings":"","what":"Empty table, column, split objects — EmptyColInfo","title":"Empty table, column, split objects — EmptyColInfo","text":"Empty objects various types compare efficiently.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ManualSplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Manually defined split — ManualSplit","title":"Manually defined split — ManualSplit","text":"Manually defined split","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ManualSplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manually defined split — ManualSplit","text":"","code":"ManualSplit( levels, label, name = \"manual\", extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", page_prefix = NA_character_, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/ManualSplit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manually defined split — ManualSplit","text":"levels character. Levels split (.e. children manual split) label character(1). label (confused name) object/structure. name character(1). Name split/table/row created. Defaults corresponding label, required . extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ManualSplit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manually defined split — ManualSplit","text":"ManualSplit object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ManualSplit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manually defined split — ManualSplit","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/MultiVarSplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split between two or more different variables — MultiVarSplit","title":"Split between two or more different variables — MultiVarSplit","text":"Split two different variables","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/MultiVarSplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split between two or more different variables — MultiVarSplit","text":"","code":"MultiVarSplit( vars, split_label = \"\", varlabels = NULL, varnames = NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = \"multivars\", child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", split_fun = NULL, page_prefix = NA_character_, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/MultiVarSplit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split between two or more different variables — MultiVarSplit","text":"vars character vector. Multiple variable names. split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). varlabels character vector. Labels vars varnames character vector. Names vars appear pathing. vars unique variable names. , variable names suffixes necessary enforce uniqueness. cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. split_name string. Name associated split (pathing, etc) child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. split_fun function/NULL. custom splitting function See custom_split_funs page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/MultiVarSplit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split between two or more different variables — MultiVarSplit","text":"MultiVarSplit object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/MultiVarSplit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split between two or more different variables — MultiVarSplit","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/VarLevelSplit.html","id":null,"dir":"Reference","previous_headings":"","what":"Split on levels within a variable — VarLevelSplit-class","title":"Split on levels within a variable — VarLevelSplit-class","text":"Split levels within variable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/VarLevelSplit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split on levels within a variable — VarLevelSplit-class","text":"","code":"VarLevelSplit( var, split_label, labels_var = NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_fun = NULL, split_format = NULL, split_na_str = NA_character_, valorder = NULL, split_name = var, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), indent_mod = 0L, label_pos = c(\"topleft\", \"hidden\", \"visible\"), cindent_mod = 0L, cvar = \"\", cextra_args = list(), page_prefix = NA_character_, section_div = NA_character_ ) VarLevWBaselineSplit( var, ref_group, labels_var = var, split_label, split_fun = NULL, label_fstr = \"%s - %s\", cfun = NULL, cformat = NULL, cna_str = NA_character_, cvar = \"\", split_format = NULL, split_na_str = NA_character_, valorder = NULL, split_name = var, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/VarLevelSplit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split on levels within a variable — VarLevelSplit-class","text":"var string, variable name split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). labels_var string, name variable containing labels displayed values var cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_fun function/NULL. custom splitting function See custom_split_funs split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. valorder character vector. Order split children appear resulting table. split_name string. Name associated split (pathing, etc) child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. ref_group character. Value var taken ref_group/control compared . label_fstr string. sprintf style format string containing. non-comparison splits, can contain one \"%s\" takes current split value generates row/column label. Comparison-based splits can contain two \"%s\".","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/VarLevelSplit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split on levels within a variable — VarLevelSplit-class","text":"VarLevelSplit object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/VarLevelSplit.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split on levels within a variable — VarLevelSplit-class","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/Viewer.html","id":null,"dir":"Reference","previous_headings":"","what":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"table displayed using bootstrap styling tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/Viewer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"","code":"Viewer(x, y = NULL, ...)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/Viewer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"x object class rtable shiny.tag (defined htmltools package) y optional second argument type x ... arguments passed as_html","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/Viewer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"meaningful. Called side effect opening browser viewer pane.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/Viewer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Display an rtable object in the Viewer pane in RStudio or in a browser — Viewer","text":"","code":"if (interactive()) { sl5 <- factor(iris$Sepal.Length > 5, levels = c(TRUE, FALSE), labels = c(\"S.L > 5\", \"S.L <= 5\") ) df <- cbind(iris, sl5 = sl5) lyt <- basic_table() %>% split_cols_by(\"sl5\") %>% analyze(\"Sepal.Length\") tbl <- build_table(lyt, df) Viewer(tbl) Viewer(tbl, tbl) tbl2 <- htmltools::tags$div( class = \"table-responsive\", as_html(tbl, class_table = \"table\") ) Viewer(tbl, tbl2) }"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Add the column population counts to the header — add_colcounts","title":"Add the column population counts to the header — add_colcounts","text":"Add data derived column counts.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add the column population counts to the header — add_colcounts","text":"","code":"add_colcounts(lyt, format = \"(N=xx)\")"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add the column population counts to the header — add_colcounts","text":"lyt layout object pre-data used tabulation format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add the column population counts to the header — add_colcounts","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add the column population counts to the header — add_colcounts","text":"often case column counts derived input data build_table representative population counts. example, events counted table header display number subjects total number events. case use col_count argument build_table control counts displayed table header.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add the column population counts to the header — add_colcounts","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_colcounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add the column population counts to the header — add_colcounts","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_colcounts() %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze(\"AGE\", afun = function(x) list(min = min(x), max = max(x))) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> RACE (lvls) -> AGE (** analysis **) #> tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> ——————————————————————————————————————————————————————————————————— #> ASIAN #> min 20 21 22 #> max 58 55 53 #> BLACK OR AFRICAN AMERICAN #> min 23 21 24 #> max 60 42 51 #> WHITE #> min 30 25 28 #> max 47 55 47"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_facet.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a combination facet in postprocessing — add_combo_facet","title":"Add a combination facet in postprocessing — add_combo_facet","text":"Add combination facet postprocessing stage custom split fun.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_facet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a combination facet in postprocessing — add_combo_facet","text":"","code":"add_combo_facet(name, label = name, levels, extra = list()) add_overall_facet(name, label, extra = list())"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_facet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a combination facet in postprocessing — add_combo_facet","text":"name character(1). Name resulting facet (use pathing, etc). label character(1). Label resulting facet. levels character. Vector levels combine within resulting facet. extra list. Extra arguments passed analysis functions applied within resulting facet.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_facet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a combination facet in postprocessing — add_combo_facet","text":"function can used within post argument make_split_fun.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_facet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a combination facet in postprocessing — add_combo_facet","text":"add_combo_facet, data associated resulting facet data associated facets level levels, rbound together. particular, means levels overlapping, data appears duplicated.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_facet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a combination facet in postprocessing — add_combo_facet","text":"","code":"mysplfun <- make_split_fun(post = list( add_combo_facet(\"A_B\", label = \"Arms A+B\", levels = c(\"A: Drug X\", \"B: Placebo\") ), add_overall_facet(\"ALL\", label = \"All Arms\") )) lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = mysplfun) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Combination Levels to split — select_all_levels","title":"Add Combination Levels to split — select_all_levels","text":"Add Combination Levels split","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Combination Levels to split — select_all_levels","text":"","code":"select_all_levels add_combo_levels(combosdf, trim = FALSE, first = FALSE, keep_levels = NULL)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Add Combination Levels to split — select_all_levels","text":"object class AllLevelsSentinel length 0.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Combination Levels to split — select_all_levels","text":"combosdf data.frame/tbl_df. Columns valname, label, levelcombo, exargs. levelcombo exargs list columns. Passing select_all_levels object value comblevels column indicates overall/-observations level created. trim logical(1). splits corresponding 0 observations kept tabulating. first logical(1). created split level placed first levels (TRUE) last (FALSE, default). keep_levels character NULL. non-NULL, levels retain across combination individual levels.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Combination Levels to split — select_all_levels","text":"closure suitable use splitting function (splfun) creating table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add Combination Levels to split — select_all_levels","text":"Analysis summary functions order matters never used within tabulation framework.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_combo_levels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Combination Levels to split — select_all_levels","text":"","code":"library(tibble) combodf <- tribble( ~valname, ~label, ~levelcombo, ~exargs, \"A_B\", \"Arms A+B\", c(\"A: Drug X\", \"B: Placebo\"), list(), \"A_C\", \"Arms A+C\", c(\"A: Drug X\", \"C: Combination\"), list() ) lyt <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf)) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination Arms A+B Arms A+C #> (N=121) (N=106) (N=129) (N=227) (N=250) #> ———————————————————————————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 34.03 34.73 lyt1 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf, keep_levels = c( \"A_B\", \"A_C\" ) ) ) %>% analyze(\"AGE\") tbl1 <- build_table(lyt1, DM) tbl1 #> Arms A+B Arms A+C #> (N=227) (N=250) #> —————————————————————————— #> Mean 34.03 34.73 smallerDM <- droplevels(subset(DM, SEX %in% c(\"M\", \"F\") & grepl(\"^(A|B)\", ARM))) lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf[1, ])) %>% split_cols_by(\"SEX\", split_fun = add_overall_level(\"SEX_ALL\", \"All Genders\") ) %>% analyze(\"AGE\") lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = add_combo_levels(combodf)) %>% split_rows_by(\"SEX\", split_fun = add_overall_level(\"SEX_ALL\", \"All Genders\") ) %>% summarize_row_groups() %>% analyze(\"AGE\") tbl3 <- build_table(lyt3, smallerDM) tbl3 #> A: Drug X B: Placebo Arms A+B Arms A+C #> (N=121) (N=106) (N=227) (N=121) #> ——————————————————————————————————————————————————————————————————————— #> All Genders 121 (100.0%) 106 (100.0%) 227 (100.0%) 121 (100.0%) #> Mean 34.91 33.02 34.03 34.91 #> F 70 (57.9%) 56 (52.8%) 126 (55.5%) 70 (57.9%) #> Mean 33.71 33.84 33.77 33.71 #> M 51 (42.1%) 50 (47.2%) 101 (44.5%) 51 (42.1%) #> Mean 36.55 32.10 34.35 36.55"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_existing_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an already calculated table to the layout — add_existing_table","title":"Add an already calculated table to the layout — add_existing_table","text":"Add already calculated table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_existing_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an already calculated table to the layout — add_existing_table","text":"","code":"add_existing_table(lyt, tt, indent_mod = 0)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_existing_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an already calculated table to the layout — add_existing_table","text":"lyt layout object pre-data used tabulation tt TableTree (related class). TableTree object representing populated table. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_existing_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an already calculated table to the layout — add_existing_table","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_existing_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add an already calculated table to the layout — add_existing_table","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_existing_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an already calculated table to the layout — add_existing_table","text":"","code":"lyt1 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = mean, format = \"xx.xx\") tbl1 <- build_table(lyt1, DM) tbl1 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————— #> mean 34.91 33.02 34.57 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = sd, format = \"xx.xx\") %>% add_existing_table(tbl1) tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————— #> sd 7.79 6.34 6.50 #> mean 34.91 33.02 34.57 table_structure(tbl2) #> [TableTree] root #> [ElementaryTable] AGE (1 x 3) #> [ElementaryTable] AGE (1 x 3) row_paths_summary(tbl2) #> rowname node_class path #> ———————————————————————————————————————— #> sd DataRow root, AGE, sd #> mean DataRow root, AGE, mean"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Overall Column — add_overall_col","title":"Add Overall Column — add_overall_col","text":"function add overall column top level splitting, within existing column splits. See add_overall_level recommended way add overall columns generally within existing splits.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Overall Column — add_overall_col","text":"","code":"add_overall_col(lyt, label)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Overall Column — add_overall_col","text":"lyt layout object pre-data used tabulation label character(1). label (confused name) object/structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Overall Column — add_overall_col","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Overall Column — add_overall_col","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% add_overall_col(\"All Patients\") %>% analyze(\"AGE\") lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> (all obs) #> #> Row-Split Structure: #> AGE (** analysis **) #> tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination All Patients #> ————————————————————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 34.22"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_level.html","id":null,"dir":"Reference","previous_headings":"","what":"Add an virtual 'overall' level to split — add_overall_level","title":"Add an virtual 'overall' level to split — add_overall_level","text":"Add virtual 'overall' level split","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_level.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add an virtual 'overall' level to split — add_overall_level","text":"","code":"add_overall_level( valname = \"Overall\", label = valname, extra_args = list(), first = TRUE, trim = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_level.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add an virtual 'overall' level to split — add_overall_level","text":"valname character(1). 'Value' assigned implicit -observations split level. Defaults \"Overall\" label character(1). label (confused name) object/structure. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. first logical(1). implicit level appear first (TRUE) last FALSE. Defaults TRUE. trim logical(1). splits corresponding 0 observations kept tabulating.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_level.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add an virtual 'overall' level to split — add_overall_level","text":"closure suitable use splitting function (splfun) creating table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/add_overall_level.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add an virtual 'overall' level to split — add_overall_level","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\", split_fun = add_overall_level(\"All Patients\", first = FALSE )) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination All Patients #> ————————————————————————————————————————————————————————————— #> Mean 34.91 33.02 34.57 34.22 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", split_fun = add_overall_level(\"All Ethnicities\") ) %>% summarize_row_groups(label_fstr = \"%s (n)\") %>% analyze(\"AGE\") lyt2 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> RACE (lvls) -> AGE (** analysis **) #> tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————————————————————————————— #> All Ethnicities (n) 121 (100.0%) 106 (100.0%) 129 (100.0%) #> Mean 34.91 33.02 34.57 #> ASIAN (n) 79 (65.3%) 68 (64.2%) 84 (65.1%) #> Mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN (n) 28 (23.1%) 24 (22.6%) 27 (20.9%) #> Mean 34.68 31.71 34.00 #> WHITE (n) 14 (11.6%) 14 (13.2%) 18 (14.0%) #> Mean 39.36 36.93 35.11 #> AMERICAN INDIAN OR ALASKA NATIVE (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> MULTIPLE (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> OTHER (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> UNKNOWN (n) 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA"},{"path":"https://insightsengineering.github.io/rtables/main/reference/additional_fun_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","title":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","text":"possible add specific parameters afun cfun, analyze summarize_row_groups respectively. parameters grant access relevant information like row split structure (see spl_context) predefined baseline (.ref_group).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/additional_fun_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","text":"list describe parameters can added custom analysis function: .N_col column-wise N (column count) full column tabulated within .N_total overall N (observation count, defined sum column counts) tabulation .N_row row-wise N (row group count) group observations analyzed (.e. column-based subsetting) .df_row data.frame observations row group analyzed (.e. column-based subsetting) .var variable analyzed .ref_group data.frame vector subset corresponding ref_group column including subsetting defined row-splitting. Optional required/meaningful ref_group column defined .ref_full data.frame vector subset corresponding ref_group column without subsetting defined row-splitting. Optional required/meaningful ref_group column defined .in_ref_col boolean indicates calculation done cells within reference column .spl_context data.frame, row gives information previous/'ancestor' split state. See spl_context .alt_df_row data.frame, .e. alt_count_df row splitting. can used .all_col_exprs .spl_context information retrieve current faceting, alt_count_df. can empty table entries filtered . .alt_df data.frame, .alt_df_row filtered columns expression. data present faceting main data df. also filters NAs related parameters set (e.g. inclNAs analyze). Similarly .alt_df_row, can empty table entries filtered . .all_col_exprs list expressions. represents different column splitting. .all_col_counts vector integers. represents global count column. differs alt_counts_df used (see build_table).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/additional_fun_params.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Additional parameters within analysis and content functions\n(afun/cfun) — additional_fun_params","text":"formals specified incorrectly present tabulation machinery, missing. example.ref_group missing baseline previously defined data splitting (via ref_group parameters , e.g., split_rows_by). Similarly, alt_counts_df provided build_table, .alt_df_row .alt_df present.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Rows Analyzing Variables Across Columns — analyze","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"Adding analyzed variables table layout defines primary tabulation performed. adding calls analyze /analyze_colvars layout pipeline. adding splitting, tabulation occur current/next level nesting default.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"","code":"analyze( lyt, vars, afun = simple_analysis, var_labels = vars, table_names = vars, format = NULL, na_str = NA_character_, nested = TRUE, inclNAs = FALSE, extra_args = list(), show_labels = c(\"default\", \"visible\", \"hidden\"), indent_mod = 0L, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"lyt layout object pre-data used tabulation vars character vector. Multiple variable names. afun function. Analysis function, must take x df first parameter. Can optionally take parameters populated tabulation framework. See Details analyze. var_labels character. Variable labels 1 variables table_names character. Names tables representing atomic analysis. Defaults var. format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. show_labels character(1). variable labels corresponding variable(s) vars visible resulting table. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"non-NULL format used specify formats generated rows, can character vector, function, list functions. repped number rows known tabulation process, overridden formats specified within rcell calls afun. analysis function (afun) take first parameter either x df. function accepts changes behavior tabulation performed. afun's first parameter x, receive corresponding subset vector data relevant column (var ) raw data used build table. afun's first parameter df, receive corresponding subset data.frame (.e. columns) raw data tabulated addition differentiation first argument, analysis function can optionally accept number parameters , present formals passed function tabulation machinery. listed described additional_fun_params.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"None arguments described Details section can overridden via extra_args calling make_afun. .N_col .N_total can overridden via col_counts argument build_table. Alternative values others must calculated within afun based combination extra arguments unmodified values provided tabulation framework.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Rows Analyzing Variables Across Columns — analyze","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> AGE (** analysis **) #> tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————— #> Min. 20.00 21.00 22.00 #> 1st Qu. 29.00 29.00 30.00 #> Median 33.00 32.00 33.00 #> Mean 34.91 33.02 34.57 #> 3rd Qu. 39.00 37.00 38.00 #> Max. 60.00 55.00 53.00 lyt2 <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(head(names(iris), -1), afun = function(x) { list( \"mean / sd\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = rcell(diff(range(x)), format = \"xx.xx\") ) }) lyt2 #> A Pre-data Table Layout #> #> Column-Split Structure: #> Species (lvls) #> #> Row-Split Structure: #> Sepal.Length:Sepal.Width:Petal.Length:Petal.Width (** multivar analysis **) #> tbl2 <- build_table(lyt2, iris) tbl2 #> setosa versicolor virginica #> —————————————————————————————————————————————————————— #> Sepal.Length #> mean / sd 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) #> range 1.50 2.10 3.00 #> Sepal.Width #> mean / sd 3.43 (0.38) 2.77 (0.31) 2.97 (0.32) #> range 2.10 1.40 1.60 #> Petal.Length #> mean / sd 1.46 (0.17) 4.26 (0.47) 5.55 (0.55) #> range 0.90 2.10 2.40 #> Petal.Width #> mean / sd 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) #> range 0.50 0.80 1.10"},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze_colvars.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"Generate Rows Analyzing Different Variables Across Columns","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze_colvars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"","code":"analyze_colvars( lyt, afun, format = NULL, na_str = NA_character_, nested = TRUE, extra_args = list(), indent_mod = 0L, inclNAs = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze_colvars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"lyt layout object pre-data used tabulation afun function list. Function(s) used calculate values column. list repped needed matched position columns tabulation. functions accepts parameters analyze like afun format. information see additional_fun_params. format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze_colvars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze_colvars.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/analyze_colvars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Rows Analyzing Different Variables Across Columns — analyze_colvars","text":"","code":"library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:testthat’: #> #> matches #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union ANL <- DM %>% mutate(value = rnorm(n()), pctdiff = runif(n())) ## toy example where we take the mean of the first variable and the ## count of >.5 for the second. colfuns <- list( function(x) rcell(mean(x), format = \"xx.x\"), function(x) rcell(sum(x > .5), format = \"xx\") ) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"value\", \"pctdiff\")) %>% split_rows_by(\"RACE\", split_label = \"ethnicity\", split_fun = drop_split_levels ) %>% summarize_row_groups() %>% analyze_colvars(afun = colfuns) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> value:pctdiff (vars) #> #> Row-Split Structure: #> RACE (lvls) -> NA (** col-var analysis **) #> tbl <- build_table(lyt, ANL) tbl #> A: Drug X B: Placebo C: Combination #> value pctdiff value pctdiff value pctdiff #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 79 (65.3%) 68 (64.2%) 68 (64.2%) 84 (65.1%) 84 (65.1%) #> 0.0 32 0.2 28 0.1 42 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 28 (23.1%) 24 (22.6%) 24 (22.6%) 27 (20.9%) 27 (20.9%) #> -0.0 15 0.1 9 0.1 15 #> WHITE 14 (11.6%) 14 (11.6%) 14 (13.2%) 14 (13.2%) 18 (14.0%) 18 (14.0%) #> -0.2 10 0.3 7 -0.3 12 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"value\", \"pctdiff\"), varlabels = c(\"Measurement\", \"Pct Diff\") ) %>% split_rows_by(\"RACE\", split_label = \"ethnicity\", split_fun = drop_split_levels ) %>% summarize_row_groups() %>% analyze_colvars(afun = mean, format = \"xx.xx\") tbl2 <- build_table(lyt2, ANL) tbl2 #> A: Drug X B: Placebo C: Combination #> Measurement Pct Diff Measurement Pct Diff Measurement Pct Diff #> —————————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 79 (65.3%) 68 (64.2%) 68 (64.2%) 84 (65.1%) 84 (65.1%) #> mean 0.04 0.45 0.19 0.44 0.14 0.51 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 28 (23.1%) 24 (22.6%) 24 (22.6%) 27 (20.9%) 27 (20.9%) #> mean -0.04 0.53 0.13 0.46 0.06 0.57 #> WHITE 14 (11.6%) 14 (11.6%) 14 (13.2%) 14 (13.2%) 18 (14.0%) 18 (14.0%) #> mean -0.19 0.61 0.33 0.48 -0.27 0.55"},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":null,"dir":"Reference","previous_headings":"","what":"Append a description to the 'top-left' materials for the layout — append_topleft","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"function adds newlines current set \"top-left materials\".","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"","code":"append_topleft(lyt, newlines)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"lyt layout object pre-data used tabulation newlines character. new line(s) added materials","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"Adds newlines set strings representing 'top-left' materials declared layout (content displayed left column labels resulting tables printed). Top-left material strings stored displayed exactly , structure indenting applied either added displayed.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"Currently, construction layout called makes difference, independent actual splitting keywords. may change future. function experimental, name details behavior subject change future versions.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/append_topleft.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Append a description to the 'top-left' materials for the layout — append_topleft","text":"","code":"library(dplyr) DM2 <- DM %>% mutate(RACE = factor(RACE), SEX = factor(SEX)) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% split_rows_by(\"RACE\") %>% append_topleft(\"Ethnicity\") %>% analyze(\"AGE\") %>% append_topleft(\" Age\") tbl <- build_table(lyt, DM2) tbl #> Ethnicity A: Drug X B: Placebo C: Combination #> Age F M F M F M #> ————————————————————————————————————————————————————————————————————————————— #> ASIAN #> Mean 33.55 35.03 34.00 31.10 34.90 34.39 #> BLACK OR AFRICAN AMERICAN #> Mean 33.17 37.40 30.58 32.83 33.85 34.14 #> WHITE #> Mean 35.88 44.00 38.57 35.29 36.50 34.00"},{"path":"https://insightsengineering.github.io/rtables/main/reference/as_html.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an rtable object to a shiny.tag html object — as_html","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"returned html object can immediately used shiny rmarkdown.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/as_html.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"","code":"as_html( x, width = NULL, class_table = \"table table-condensed table-hover\", class_tr = NULL, class_th = NULL, link_label = NULL, bold = c(\"header\"), header_sep_line = TRUE, no_spaces_between_cells = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/as_html.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"x rtable object width string indicate desired width table. Common input formats include percentage viewer window width (e.g. \"100%\") distance value (e.g. \"300px\"). Defaults NULL. class_table class table tag class_tr class tr tag class_th class th tag link_label link anchor label (including tab: prefix) table. bold elements table output bold. Options \"main_title\", \"subtitles\", \"header\", \"row_names\", \"label_rows\", \"content_rows\" (includes non-label rows). Defaults \"header\". header_sep_line whether black line printed table header. Defaults TRUE. no_spaces_between_cells whether spaces table cells collapsed. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/as_html.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"shiny.tag object representing x HTML.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/as_html.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an rtable object to a shiny.tag html object — as_html","text":"","code":"tbl <- rtable( header = LETTERS[1:3], format = \"xx\", rrow(\"r1\", 1, 2, 3), rrow(\"r2\", 4, 3, 2, indent = 1), rrow(\"r3\", indent = 2) ) as_html(tbl) #>
    #>
    #>
    #>

    <\/p> #> <\/div> #>

    <\/div> #> <\/div> #> #> #> #> #> #>
    <\/th> #> A<\/th> #> B<\/th> #> C<\/th> #> <\/tr> #>
    r1<\/td> #> 1<\/td> #> 2<\/td> #> 3<\/td> #> <\/tr> #>
    r2<\/td> #> 4<\/td> #> 3<\/td> #> 2<\/td> #> <\/tr> #>
    r3<\/td> #> <\/td> #> <\/td> #> <\/td> #> <\/tr> #>
    <\/caption> #> <\/table> #>
    <\/div> #> <\/div> as_html(tbl, class_table = \"table\", class_tr = \"row\") #>
    #>
    #>
    #>

    <\/p> #> <\/div> #>

    <\/div> #> <\/div> #> #> #> #> #> #>
    <\/th> #> A<\/th> #> B<\/th> #> C<\/th> #> <\/tr> #>
    r1<\/td> #> 1<\/td> #> 2<\/td> #> 3<\/td> #> <\/tr> #>
    r2<\/td> #> 4<\/td> #> 3<\/td> #> 2<\/td> #> <\/tr> #>
    r3<\/td> #> <\/td> #> <\/td> #> <\/td> #> <\/tr> #>
    <\/caption> #> <\/table> #>
    <\/div> #> <\/div> as_html(tbl, bold = c(\"header\", \"row_names\")) #>
    #>
    #>
    #>

    <\/p> #> <\/div> #>

    <\/div> #> <\/div> #> #> #> #> #> #>
    <\/th> #> A<\/th> #> B<\/th> #> C<\/th> #> <\/tr> #>
    r1<\/td> #> 1<\/td> #> 2<\/td> #> 3<\/td> #> <\/tr> #>
    r2<\/td> #> 4<\/td> #> 3<\/td> #> 2<\/td> #> <\/tr> #>
    r3<\/td> #> <\/td> #> <\/td> #> <\/td> #> <\/tr> #>
    <\/caption> #> <\/table> #>
    <\/div> #> <\/div> if (FALSE) { Viewer(tbl) }"},{"path":"https://insightsengineering.github.io/rtables/main/reference/asvec.html","id":null,"dir":"Reference","previous_headings":"","what":"convert to a vector — asvec","title":"convert to a vector — asvec","text":"Convert rtables framework object vector, possible. unlikely useful realistic scenarios.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/asvec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"convert to a vector — asvec","text":"","code":"# S4 method for VTableTree as.vector(x, mode = \"any\")"},{"path":"https://insightsengineering.github.io/rtables/main/reference/asvec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"convert to a vector — asvec","text":"x . object converted vector mode character(1). Passed .vector","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/asvec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"convert to a vector — asvec","text":"vector chosen mode (error raised one row present).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/asvec.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"convert to a vector — asvec","text":"works table single row row object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/avarspl.html","id":null,"dir":"Reference","previous_headings":"","what":"Define a subset tabulation/analysis — AnalyzeVarSplit","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"Define subset tabulation/analysis Define subset tabulation/analysis","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/avarspl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"","code":"AnalyzeVarSplit( var, split_label = var, afun, defrowlab = \"\", cfun = NULL, cformat = NULL, split_format = NULL, split_na_str = NA_character_, inclNAs = FALSE, split_name = var, extra_args = list(), indent_mod = 0L, label_pos = \"default\", cvar = \"\", section_div = NA_character_ ) AnalyzeColVarSplit( afun, defrowlab = \"\", cfun = NULL, cformat = NULL, split_format = NULL, split_na_str = NA_character_, inclNAs = FALSE, split_name = \"\", extra_args = list(), indent_mod = 0L, label_pos = \"default\", cvar = \"\", section_div = NA_character_ ) AnalyzeMultiVars( var, split_label = \"\", afun, defrowlab = \"\", cfun = NULL, cformat = NULL, split_format = NULL, split_na_str = NA_character_, inclNAs = FALSE, .payload = NULL, split_name = NULL, extra_args = list(), indent_mod = 0L, child_labels = c(\"default\", \"topleft\", \"visible\", \"hidden\"), child_names = var, cvar = \"\", section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/avarspl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"var string, variable name split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). afun function. Analysis function, must take x df first parameter. Can optionally take parameters populated tabulation framework. See Details analyze. defrowlab character. Default row labels specified return value afun cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE split_name string. Name associated split (pathing, etc) extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cvar character(1). variable, , content function accept. Defaults NA. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. .payload Used internally, intended set end users. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. child_names character. Names given sub splits contained compound split (typically AnalyzeMultiVars split object).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/avarspl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"AnalyzeVarSplit object. AnalyzeMultiVars split object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/avarspl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Define a subset tabulation/analysis — AnalyzeVarSplit","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Layout with 1 column and zero rows — basic_table","title":"Layout with 1 column and zero rows — basic_table","text":"Every layout must start basic table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layout with 1 column and zero rows — basic_table","text":"","code":"basic_table( title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), show_colcounts = FALSE, colcount_format = \"(N=xx)\", header_section_div = NA_character_, top_level_section_div = NA_character_, inset = 0L )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layout with 1 column and zero rows — basic_table","text":"title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. show_colcounts logical(1). column counts displayed resulting table layout applied data colcount_format character(1). Format use displaying column counts. Must 1d, 2d one component percent. See details. header_section_div character(1). String used divide header table. See header_section_div() getter setter . Please consider changing last element section_div() concatenating tables need divider . top_level_section_div character(1). assigned single character, first (top level) split division table highlighted line made character. See section_div information. inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layout with 1 column and zero rows — basic_table","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Layout with 1 column and zero rows — basic_table","text":"colcount_format ignored show_colcounts FALSE (default). show_colcounts TRUE, colcount_format 2-dimensional percent component, value component percent always populated 1 (.e. 100%). 1d formats used render counts exactly normally , 2d formats include percent, 3d formats result error. Formats form functions supported colcount format. See formatters::list_valid_format_labels() list valid format labels select .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Layout with 1 column and zero rows — basic_table","text":"percent components colcount_format always populated value 1, can get arguably strange results, individual arm columns combined \"patients\" column list \"100%\" percentage, even though individual arm columns represent strict subsets patients column. Note subtitles (subtitles()) footers (main_footer() prov_footer()) spans one line can supplied character vector maintain indentation multiple lines.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/basic_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Layout with 1 column and zero rows — basic_table","text":"","code":"lyt <- basic_table() %>% analyze(\"AGE\", afun = mean) tbl <- build_table(lyt, DM) tbl #> all obs #> ——————————————————————— #> mean 34.2219101123596 lyt2 <- basic_table( title = \"Title of table\", subtitles = c(\"a number\", \"of subtitles\"), main_footer = \"test footer\", prov_footer = paste( \"test.R program, executed at\", Sys.time() ) ) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", mean) tbl2 <- build_table(lyt2, DM) tbl2 #> Title of table #> a number #> of subtitles #> #> ————————————————————————————————————————————————————————————— #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————— #> mean 34.9090909090909 33.0188679245283 34.5658914728682 #> ————————————————————————————————————————————————————————————— #> #> test footer #> #> test.R program, executed at 2024-02-22 20:28:20.014627 lyt3 <- basic_table( show_colcounts = TRUE, colcount_format = \"xx. (xx.%)\" ) %>% split_cols_by(\"ARM\")"},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve and assign elements of a TableTree — brackets","title":"Retrieve and assign elements of a TableTree — brackets","text":"Retrieve assign elements TableTree","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve and assign elements of a TableTree — brackets","text":"","code":"# S4 method for VTableTree,ANY,ANY,list [(x, i, j, ...) <- value # S4 method for VTableTree,logical,logical [(x, i, j, ..., drop = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve and assign elements of a TableTree — brackets","text":"x TableTree index j index ... Includes keep_topleft logical(1) ([ ) top-left material table retained subsetting. Defaults TRUE rows included (.e. subsetting column), drops otherwise. keep_titles logical(1) title information retained. Defaults FALSE. keep_footers logical(1) non-referential footer information retained. Defaults keep_titles. reindex_refs logical(1). referential footnotes re-indexed resulting subset entire table. Defaults TRUE. value Replacement value (list, TableRow, TableTree) drop logical(1). value cell returned one cell selected combination j. possible return vector values. please consider using cell_values(). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve and assign elements of a TableTree — brackets","text":"TableTree (ElementaryTable) object, unless single cell selected drop=TRUE, case (possibly multi-valued) fully stripped raw value selected cell.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve and assign elements of a TableTree — brackets","text":"default, subsetting drops information title, subtitle, main footer, provenance footer, topleft. column selected rows kept, topleft information remains default. referential footnote kept whenever subset table contains referenced element.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Retrieve and assign elements of a TableTree — brackets","text":"subsetting always preserve original order, even provided indexes preserve . sorting needed, please consider using sort_at_path(). Also note character indices treated paths, vectors names [ [<-.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/brackets.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve and assign elements of a TableTree — brackets","text":"","code":"lyt <- basic_table( title = \"Title\", subtitles = c(\"Sub\", \"titles\"), prov_footer = \"prov footer\", main_footer = \"main footer\" ) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(c(\"AGE\")) tbl <- build_table(lyt, DM) top_left(tbl) <- \"Info\" tbl #> Title #> Sub #> titles #> #> —————————————————————————————————————————————————————————— #> Info A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89 #> M #> Mean 36.55 32.10 34.28 #> U #> Mean NA NA NA #> UNDIFFERENTIATED #> Mean NA NA NA #> —————————————————————————————————————————————————————————— #> #> main footer #> #> prov footer # As default header, footer, and topleft information is lost tbl[1, ] #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————— #> F tbl[1:2, 2] #> B: Placebo #> ——————————————————— #> F #> Mean 33.84 # Also boolean filters can work tbl[, c(FALSE, TRUE, FALSE)] #> Note: method with signature ‘VTableTree#missing#ANY’ chosen for function ‘[’, #> target signature ‘TableTree#missing#logical’. #> \"VTableTree#ANY#logical\" would also be valid #> Info B: Placebo #> ————————————————————————————— #> F #> Mean 33.84 #> M #> Mean 32.10 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA # If drop = TRUE, the content values are directly retrieved tbl[2, 1] #> A: Drug X #> ———————————————— #> Mean 33.71 tbl[2, 1, drop = TRUE] #> [1] 33.71429 # Drop works also if vectors are selected, but not matrices tbl[, 1, drop = TRUE] #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> Info A: Drug X #> ———————————————————————————— #> F #> Mean 33.71 #> M #> Mean 36.55 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA tbl[2, , drop = TRUE] #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————— #> Mean 33.71 33.84 34.89 tbl[1, 1, drop = TRUE] # NULL because it is a label row #> Warning: The value selected with drop = TRUE belongs to a label row. NULL will be returned #> NULL tbl[2, 1:2, drop = TRUE] # vectors can be returned only with cell_values() #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> A: Drug X B: Placebo #> ————————————————————————————— #> Mean 33.71 33.84 tbl[1:2, 1:2, drop = TRUE] # no dropping because it is a matrix #> Warning: Trying to drop more than one subsetted value. We support this only with accessor function `cell_values()`. No drop will be done at this time. #> A: Drug X B: Placebo #> ——————————————————————————————— #> F #> Mean 33.71 33.84 # If all rows are selected, topleft is kept by default tbl[, 2] #> Info B: Placebo #> ————————————————————————————— #> F #> Mean 33.84 #> M #> Mean 32.10 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA tbl[, 1] #> Info A: Drug X #> ———————————————————————————— #> F #> Mean 33.71 #> M #> Mean 36.55 #> U #> Mean NA #> UNDIFFERENTIATED #> Mean NA # It is possible to deselect values tbl[-2, ] #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> M #> Mean 36.55 32.10 34.28 #> U #> Mean NA NA NA #> UNDIFFERENTIATED #> Mean NA NA NA tbl[, -1] #> Info B: Placebo C: Combination #> —————————————————————————————————————————————— #> F #> Mean 33.84 34.89 #> M #> Mean 32.10 34.28 #> U #> Mean NA NA #> UNDIFFERENTIATED #> Mean NA NA # Values can be reassigned tbl[2, 1] <- rcell(999) tbl[2, ] <- list(rrow(\"FFF\", 888, 666, 777)) tbl[6, ] <- list(-111, -222, -333) tbl #> Title #> Sub #> titles #> #> —————————————————————————————————————————————————————————— #> Info A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> FFF 888 666 777 #> M #> Mean 36.55 32.10 34.28 #> U #> Mean -111 -222 -333 #> UNDIFFERENTIATED #> Mean NA NA NA #> —————————————————————————————————————————————————————————— #> #> main footer #> #> prov footer # We can keep some information from the original table if we need tbl[1, 2, keep_titles = TRUE] #> Title #> Sub #> titles #> #> —————————————— #> B: Placebo #> —————————————— #> F #> —————————————— #> #> main footer #> #> prov footer tbl[1, 2, keep_footers = TRUE, keep_titles = FALSE] #> B: Placebo #> —————————————— #> F #> —————————————— #> #> main footer #> #> prov footer tbl[1, 2, keep_footers = FALSE, keep_titles = TRUE] #> Title #> Sub #> titles #> #> —————————————— #> B: Placebo #> —————————————— #> F tbl[1, 2, keep_footers = TRUE] #> B: Placebo #> —————————————— #> F #> —————————————— #> #> main footer #> #> prov footer tbl[1, 2, keep_topleft = TRUE] #> Info B: Placebo #> ————————————————— #> F # Keeps the referential footnotes when subset contains them fnotes_at_path(tbl, rowpath = c(\"SEX\", \"M\", \"AGE\", \"Mean\")) <- \"important\" tbl[4, 1] #> A: Drug X #> ———————————————————— #> Mean {1} 36.55 #> ———————————————————— #> #> {1} - important #> ———————————————————— #> tbl[2, 1] # None present #> A: Drug X #> ——————————————— #> FFF 888 # We can reindex referential footnotes, so that the new table does not depend # on the original one fnotes_at_path(tbl, rowpath = c(\"SEX\", \"U\", \"AGE\", \"Mean\")) <- \"important\" tbl[, 1] # both present #> Info A: Drug X #> ———————————————————————————— #> F #> FFF 888 #> M #> Mean {1} 36.55 #> U #> Mean {1} -111 #> UNDIFFERENTIATED #> Mean NA #> ———————————————————————————— #> #> {1} - important #> ———————————————————————————— #> tbl[5:6, 1] # {1} because it has been indexed again #> A: Drug X #> —————————————————————— #> U #> Mean {1} -111 #> —————————————————————— #> #> {1} - important #> —————————————————————— #> tbl[5:6, 1, reindex_refs = FALSE] # {2} -> not reindexed #> A: Drug X #> —————————————————————— #> U #> Mean {1} -111 #> —————————————————————— #> #> {1} - important #> —————————————————————— #> # Note that order can not be changed with subsetting tbl[c(4, 3, 1), c(3, 1)] # It preserves order and wanted selection #> A: Drug X C: Combination #> ——————————————————————————————————————— #> F #> M #> Mean {1} 34.28 36.55 #> ——————————————————————————————————————— #> #> {1} - important #> ——————————————————————————————————————— #>"},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a table from a layout and data — build_table","title":"Create a table from a layout and data — build_table","text":"Layouts used describe table pre-data. build_table used create table using layout dataset.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a table from a layout and data — build_table","text":"","code":"build_table( lyt, df, alt_counts_df = NULL, col_counts = NULL, col_total = if (is.null(alt_counts_df)) nrow(df) else nrow(alt_counts_df), topleft = NULL, hsep = default_hsep(), ... )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a table from a layout and data — build_table","text":"lyt layout object pre-data used tabulation df dataset (data.frame tibble) alt_counts_df dataset (data.frame tibble). Alternative full data rtables framework use () calculating column counts. col_counts numeric (NULL). Deprecated. non-null, column counts override calculated automatically tabulation. Must specify \"counts\" resulting columns non-NULL. NA elements replaced automatically calculated counts. col_total integer(1). total observations across columns. Defaults nrow(df). topleft character. Override values \"top left\" material displayed printing. hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. ... currently ignored.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a table from a layout and data — build_table","text":"TableTree ElementaryTable object representing table created performing tabulations declared lyt data df.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a table from a layout and data — build_table","text":"alt_counts_df specified, column counts calculated applying exact column subsetting expressions determined applying column splitting main data (df) alt_counts_df counting observations resulting subset. particular, means case splitting based cuts data, dynamic cuts calculated based df simply re-used count calculation.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create a table from a layout and data — build_table","text":"overriding column counts totals care must taken , e.g., length() nrow() called within tabulation functions, give overridden counts. Writing/using tabulation functions accept .N_col .N_total rely column counts (even implicitly) way ensure overridden counts fully respected.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create a table from a layout and data — build_table","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/build_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a table from a layout and data — build_table","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(\"Sepal.Length\", afun = function(x) { list( \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = diff(range(x)) ) }) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> Species (lvls) #> #> Row-Split Structure: #> Sepal.Length (** analysis **) #> tbl <- build_table(lyt, iris) tbl #> setosa versicolor virginica #> ——————————————————————————————————————————————————— #> mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) #> range 1.5 2.1 3 # analyze multiple variables lyt2 <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = function(x) { list( \"mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"range\" = diff(range(x)) ) }) tbl2 <- build_table(lyt2, iris) tbl2 #> setosa versicolor virginica #> —————————————————————————————————————————————————————— #> Sepal.Length #> mean (sd) 5.01 (0.35) 5.94 (0.52) 6.59 (0.64) #> range 1.5 2.1 3 #> Petal.Width #> mean (sd) 0.25 (0.11) 1.33 (0.20) 2.03 (0.27) #> range 0.5 0.8 1.1 # an example more relevant for clinical trials with column counts lyt3 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = function(x) { setNames(as.list(fivenum(x)), c( \"minimum\", \"lower-hinge\", \"median\", \"upper-hinge\", \"maximum\" )) }) tbl3 <- build_table(lyt3, DM) tbl3 #> A: Drug X B: Placebo C: Combination #> (N=121) (N=106) (N=129) #> ————————————————————————————————————————————————————— #> minimum 20 21 22 #> lower-hinge 29 29 30 #> median 33 32 33 #> upper-hinge 39 37 38 #> maximum 60 55 53 tbl4 <- build_table(lyt3, subset(DM, AGE > 40)) tbl4 #> A: Drug X B: Placebo C: Combination #> (N=25) (N=10) (N=21) #> ————————————————————————————————————————————————————— #> minimum 41 41 41 #> lower-hinge 43 42 43 #> median 45 45.5 45 #> upper-hinge 49 48 47 #> maximum 60 55 53 # with column counts calculated based on different data miniDM <- DM[sample(1:NROW(DM), 100), ] tbl5 <- build_table(lyt3, DM, alt_counts_df = miniDM) tbl5 #> A: Drug X B: Placebo C: Combination #> (N=37) (N=30) (N=33) #> ————————————————————————————————————————————————————— #> minimum 20 21 22 #> lower-hinge 29 29 30 #> median 33 32 33 #> upper-hinge 39 37 38 #> maximum 60 55 53 tbl6 <- build_table(lyt3, DM, col_counts = 1:3) tbl6 #> A: Drug X B: Placebo C: Combination #> (N=1) (N=2) (N=3) #> ————————————————————————————————————————————————————— #> minimum 20 21 22 #> lower-hinge 29 29 30 #> median 33 32 33 #> upper-hinge 39 37 38 #> maximum 60 55 53"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cbind_rtables.html","id":null,"dir":"Reference","previous_headings":"","what":"cbind two rtables — cbind_rtables","title":"cbind two rtables — cbind_rtables","text":"cbind two rtables","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cbind_rtables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"cbind two rtables — cbind_rtables","text":"","code":"cbind_rtables(x, ...)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cbind_rtables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"cbind two rtables — cbind_rtables","text":"x table row object ... 1 objects class x","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cbind_rtables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"cbind two rtables — cbind_rtables","text":"formal table object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cbind_rtables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"cbind two rtables — cbind_rtables","text":"","code":"x <- rtable(c(\"A\", \"B\"), rrow(\"row 1\", 1, 2), rrow(\"row 2\", 3, 4)) y <- rtable(\"C\", rrow(\"row 1\", 5), rrow(\"row 2\", 6)) z <- rtable(\"D\", rrow(\"row 1\", 9), rrow(\"row 2\", 10)) t1 <- cbind_rtables(x, y) t1 #> A B C #> ————————————————— #> row 1 1 2 5 #> row 2 3 4 6 t2 <- cbind_rtables(x, y, z) t2 #> A B C D #> —————————————————————— #> row 1 1 2 5 9 #> row 2 3 4 6 10 col_paths_summary(t1) #> label path #> —————————————————— #> A manual, A #> B manual, B #> C manual, C col_paths_summary(t2) #> label path #> —————————————————— #> A manual, A #> B manual, B #> C manual, C #> D manual, D"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cell_values.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve cell values by row and column path — cell_values","title":"Retrieve cell values by row and column path — cell_values","text":"Retrieve cell values row column path","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cell_values.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve cell values by row and column path — cell_values","text":"","code":"cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) value_at(tt, rowpath = NULL, colpath = NULL) # S4 method for VTableTree value_at(tt, rowpath = NULL, colpath = NULL)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cell_values.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve cell values by row and column path — cell_values","text":"tt TableTree (related class). TableTree object representing populated table. rowpath character. Path row-split space desired row(s). Can include \"@content\". colpath character. Path column-split space desired column(s). Can include \"*\". omit_labrows logical(1). label rows underneath rowpath omitted (TRUE, default), return empty lists cell \"values\" (FALSE).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cell_values.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve cell values by row and column path — cell_values","text":"cell_values, list (regardless type value cells hold). rowpath defines path single row, cell_values returns list cell values row, otherwise list lists, one row captured underneath rowpath. occurs subsetting colpath occurred. value_at \"unwrapped\" value single cell, error, combination rowpath colpath define location single cell tt.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cell_values.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Retrieve cell values by row and column path — cell_values","text":"cell_values return single cell's value wrapped list. Use value_at receive \"bare\" cell value.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cell_values.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve cell values by row and column path — cell_values","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% split_rows_by(\"RACE\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% analyze(\"AGE\") library(dplyr) ## for mutate tbl <- build_table(lyt, DM %>% mutate(SEX = droplevels(SEX), RACE = droplevels(RACE))) row_paths_summary(tbl) #> rowname node_class path #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN ContentRow RACE, ASIAN, @content, ASIAN #> A LabelRow RACE, ASIAN, STRATA1, A #> Mean DataRow RACE, ASIAN, STRATA1, A, AGE, Mean #> B LabelRow RACE, ASIAN, STRATA1, B #> Mean DataRow RACE, ASIAN, STRATA1, B, AGE, Mean #> C LabelRow RACE, ASIAN, STRATA1, C #> Mean DataRow RACE, ASIAN, STRATA1, C, AGE, Mean #> BLACK OR AFRICAN AMERICAN ContentRow RACE, BLACK OR AFRICAN AMERICAN, @content, BLACK OR AFRICAN AMERICAN #> A LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A #> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, A, AGE, Mean #> B LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B #> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, B, AGE, Mean #> C LabelRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C #> Mean DataRow RACE, BLACK OR AFRICAN AMERICAN, STRATA1, C, AGE, Mean #> WHITE ContentRow RACE, WHITE, @content, WHITE #> A LabelRow RACE, WHITE, STRATA1, A #> Mean DataRow RACE, WHITE, STRATA1, A, AGE, Mean #> B LabelRow RACE, WHITE, STRATA1, B #> Mean DataRow RACE, WHITE, STRATA1, B, AGE, Mean #> C LabelRow RACE, WHITE, STRATA1, C #> Mean DataRow RACE, WHITE, STRATA1, C, AGE, Mean col_paths_summary(tbl) #> label path #> ————————————————————————————————————————————— #> A: Drug X ARM, A: Drug X #> F ARM, A: Drug X, SEX, F #> M ARM, A: Drug X, SEX, M #> B: Placebo ARM, B: Placebo #> F ARM, B: Placebo, SEX, F #> M ARM, B: Placebo, SEX, M #> C: Combination ARM, C: Combination #> F ARM, C: Combination, SEX, F #> M ARM, C: Combination, SEX, M cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\") ) #> $`A: Drug X.F` #> [1] 33.75 #> # it's also possible to access multiple values by being less specific cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"F\") ) #> $A.AGE.Mean #> $A.AGE.Mean$`A: Drug X.F` #> [1] 30.4 #> #> #> $B.AGE.Mean #> $B.AGE.Mean$`A: Drug X.F` #> [1] 33.75 #> #> #> $C.AGE.Mean #> $C.AGE.Mean$`A: Drug X.F` #> [1] 36.92308 #> #> cell_values(tbl, c(\"RACE\", \"ASIAN\"), c(\"ARM\", \"A: Drug X\", \"SEX\", \"M\")) #> $ASIAN #> $ASIAN$`A: Drug X.M` #> [1] 35.0000000 0.6862745 #> #> #> $STRATA1.A.AGE.Mean #> $STRATA1.A.AGE.Mean$`A: Drug X.M` #> [1] 34.41667 #> #> #> $STRATA1.B.AGE.Mean #> $STRATA1.B.AGE.Mean$`A: Drug X.M` #> [1] 34.875 #> #> #> $STRATA1.C.AGE.Mean #> $STRATA1.C.AGE.Mean$`A: Drug X.M` #> [1] 35.6 #> #> ## any arm, male columns from the ASIAN content (i.e. summary) row cell_values( tbl, c(\"RACE\", \"ASIAN\", \"@content\"), c(\"ARM\", \"B: Placebo\", \"SEX\", \"M\") ) #> $`B: Placebo.M` #> [1] 31.00 0.62 #> cell_values( tbl, c(\"RACE\", \"ASIAN\", \"@content\"), c(\"ARM\", \"*\", \"SEX\", \"M\") ) #> $`A: Drug X.M` #> [1] 35.0000000 0.6862745 #> #> $`B: Placebo.M` #> [1] 31.00 0.62 #> #> $`C: Combination.M` #> [1] 44.0000000 0.6470588 #> ## all columns cell_values(tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\")) #> $`A: Drug X.F` #> [1] 33.75 #> #> $`A: Drug X.M` #> [1] 34.875 #> #> $`B: Placebo.F` #> [1] 32.46154 #> #> $`B: Placebo.M` #> [1] 30.9375 #> #> $`C: Combination.F` #> [1] 33.3 #> #> $`C: Combination.M` #> [1] 35.91667 #> ## all columns for the Combination arm cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\"), c(\"ARM\", \"C: Combination\") ) #> $`C: Combination.F` #> [1] 33.3 #> #> $`C: Combination.M` #> [1] 35.91667 #> cvlist <- cell_values( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\", \"AGE\", \"Mean\"), c(\"ARM\", \"B: Placebo\", \"SEX\", \"M\") ) cvnolist <- value_at( tbl, c(\"RACE\", \"ASIAN\", \"STRATA1\", \"B\", \"AGE\", \"Mean\"), c(\"ARM\", \"B: Placebo\", \"SEX\", \"M\") ) stopifnot(identical(cvlist[[1]], cvnolist))"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cinfo.html","id":null,"dir":"Reference","previous_headings":"","what":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"InstantiatedColumnInfo","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cinfo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"","code":"InstantiatedColumnInfo( treelyt = LayoutColTree(), csubs = list(expression(TRUE)), extras = list(list()), cnts = NA_integer_, total_cnt = NA_integer_, dispcounts = FALSE, countformat = \"(N=xx)\", count_na_str = \"\", topleft = character() )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cinfo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"treelyt LayoutColTree. csubs list. List subsetting expressions extras list. Extra arguments associated columns cnts integer. Counts. total_cnt integer(1). Total observations represented across columns. dispcounts logical(1). counts displayed header info associated table printed. countformat character(1). Format counts displayed count_na_str character. NA string used formatting counts. Defaults \"\". topleft character. Override values \"top left\" material displayed printing.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cinfo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"InstantiatedColumnInfo — InstantiatedColumnInfo-class","text":"InstantiateadColumnInfo object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/clear_imods.html","id":null,"dir":"Reference","previous_headings":"","what":"Clear All Indent Mods from a Table — clear_indent_mods","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"Clear Indent Mods Table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/clear_imods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"","code":"clear_indent_mods(tt) # S4 method for VTableTree clear_indent_mods(tt) # S4 method for TableRow clear_indent_mods(tt)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/clear_imods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"tt TableTree (related class). TableTree object representing populated table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/clear_imods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"class tt, indent mods set zero.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/clear_imods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Clear All Indent Mods from a Table — clear_indent_mods","text":"","code":"lyt1 <- basic_table() %>% summarize_row_groups(\"STUDYID\", label_fstr = \"overall summary\") %>% split_rows_by(\"AEBODSYS\", child_labels = \"visible\") %>% summarize_row_groups(\"STUDYID\", label = \"subgroup summary\") %>% analyze(\"AGE\", indent_mod = -1L) tbl1 <- build_table(lyt1, ex_adae) tbl1 #> all obs #> ———————————————————————————————————— #> overall summary 1934 (100.0%) #> cl A.1 #> subgroup summary 422 (21.8%) #> Mean 34.70 #> cl B.1 #> subgroup summary 178 (9.2%) #> Mean 35.86 #> cl B.2 #> subgroup summary 410 (21.2%) #> Mean 35.42 #> cl C.1 #> subgroup summary 182 (9.4%) #> Mean 33.83 #> cl C.2 #> subgroup summary 166 (8.6%) #> Mean 33.89 #> cl D.1 #> subgroup summary 368 (19.0%) #> Mean 34.39 #> cl D.2 #> subgroup summary 208 (10.8%) #> Mean 34.83 clear_indent_mods(tbl1) #> all obs #> ———————————————————————————————————— #> overall summary 1934 (100.0%) #> cl A.1 #> subgroup summary 422 (21.8%) #> Mean 34.70 #> cl B.1 #> subgroup summary 178 (9.2%) #> Mean 35.86 #> cl B.2 #> subgroup summary 410 (21.2%) #> Mean 35.42 #> cl C.1 #> subgroup summary 182 (9.4%) #> Mean 33.83 #> cl C.2 #> subgroup summary 166 (8.6%) #> Mean 33.89 #> cl D.1 #> subgroup summary 368 (19.0%) #> Mean 34.39 #> cl D.2 #> subgroup summary 208 (10.8%) #> Mean 34.83"},{"path":"https://insightsengineering.github.io/rtables/main/reference/col_accessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Column information/structure accessors — clayout","title":"Column information/structure accessors — clayout","text":"Column information/structure accessors","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/col_accessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Column information/structure accessors — clayout","text":"","code":"clayout(obj) # S4 method for VTableNodeInfo clayout(obj) # S4 method for PreDataTableLayouts clayout(obj) # S4 method for ANY clayout(obj) clayout(object) <- value # S4 method for PreDataTableLayouts clayout(object) <- value col_info(obj) # S4 method for VTableNodeInfo col_info(obj) col_info(obj) <- value # S4 method for TableRow col_info(obj) <- value # S4 method for ElementaryTable col_info(obj) <- value # S4 method for TableTree col_info(obj) <- value coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for InstantiatedColumnInfo coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for PreDataTableLayouts coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for PreDataColLayout coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for LayoutColTree coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for VTableTree coltree(obj, df = NULL, rtpos = TreePos()) # S4 method for TableRow coltree(obj, df = NULL, rtpos = TreePos()) col_exprs(obj, df = NULL) # S4 method for PreDataTableLayouts col_exprs(obj, df = NULL) # S4 method for PreDataColLayout col_exprs(obj, df = NULL) # S4 method for InstantiatedColumnInfo col_exprs(obj, df = NULL) col_counts(obj, path = NULL) # S4 method for InstantiatedColumnInfo col_counts(obj, path = NULL) # S4 method for VTableNodeInfo col_counts(obj, path = NULL) col_counts(obj, path = NULL) <- value # S4 method for InstantiatedColumnInfo col_counts(obj, path = NULL) <- value # S4 method for VTableNodeInfo col_counts(obj, path = NULL) <- value col_total(obj) # S4 method for InstantiatedColumnInfo col_total(obj) # S4 method for VTableNodeInfo col_total(obj) col_total(obj) <- value # S4 method for InstantiatedColumnInfo col_total(obj) <- value # S4 method for VTableNodeInfo col_total(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/col_accessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Column information/structure accessors — clayout","text":"obj . object accessor access modify object object modify -place value new value df data.frame/NULL. Data use column information generated Pre-Data layout object rtpos TreePos. Root position. path character NULL. col_counts getter setter . Path (column structure).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/col_accessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Column information/structure accessors — clayout","text":"LayoutColTree object. Various column information, depending accessor used.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/collect_leaves.html","id":null,"dir":"Reference","previous_headings":"","what":"Collect leaves of a table tree — collect_leaves","title":"Collect leaves of a table tree — collect_leaves","text":"Collect leaves table tree","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/collect_leaves.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Collect leaves of a table tree — collect_leaves","text":"","code":"collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/collect_leaves.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Collect leaves of a table tree — collect_leaves","text":"tt TableTree (related class). TableTree object representing populated table. incl.cont logical. Include rows content tables within tree. Defaults TRUE add.labrows logical. Include label rows. Defaults FALSE","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/collect_leaves.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Collect leaves of a table tree — collect_leaves","text":"list TableRow objects rows table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compare_rtables.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare two rtables — compare_rtables","title":"Compare two rtables — compare_rtables","text":"Prints matrix . means cell matches, X means cell cells match, + cell (row) missing, - cell (row) . structure set TRUE, C indicates columnar structure mismatch, R indicates row-structure mismatch, S indicates mismatch row column structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compare_rtables.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare two rtables — compare_rtables","text":"","code":"compare_rtables( object, expected, tol = 0.1, comp.attr = TRUE, structure = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/compare_rtables.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare two rtables — compare_rtables","text":"object rtable test expected rtable expected tol numerical tolerance comp.attr boolean. Compare format cells. attributes silently ignored. structure boolean. structure (form column row paths cells) compared. Currently defaults FALSE, subject change future versions.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compare_rtables.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare two rtables — compare_rtables","text":"matrix class \"rtables_diff\" representing differences object expected described .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compare_rtables.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Compare two rtables — compare_rtables","text":"current form compare_rtables take structure account, row cell position.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compare_rtables.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare two rtables — compare_rtables","text":"","code":"t1 <- rtable(header = c(\"A\", \"B\"), format = \"xx\", rrow(\"row 1\", 1, 2)) t2 <- rtable(header = c(\"A\", \"B\", \"C\"), format = \"xx\", rrow(\"row 1\", 1, 2, 3)) compare_rtables(object = t1, expected = t2) #> 1 2 3 #> 1 \".\" \".\" \"-\" #> attr(,\"info\") #> [1] \"column names are not the same\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" if (interactive()) { Viewer(t1, t2) } expected <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.xx, xx.xx)\")) ) expected #> ARM A ARM B #> N=100 N=200 #> ——————————————————————————————— #> row 1 10 15 #> #> section title #> row colspan (0.35, 0.44) object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.xx, xx.xx)\")) ) compare_rtables(object, expected, comp.attr = FALSE) #> 1 2 #> 1 \".\" \".\" #> 2 \".\" \".\" #> 3 \"X\" \"X\" #> 4 \"-\" \"-\" #> attr(,\"info\") #> [1] \"cell attributes have not been compared\" #> [2] \"row labels are not the same\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(), rrow(\"section title\") ) compare_rtables(object, expected) #> 1 2 #> 1 \".\" \".\" #> 2 \".\" \".\" #> 3 \".\" \".\" #> 4 \"-\" \"-\" #> attr(,\"info\") #> [1] \"row labels are not the same\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 14, 15.03), rrow(), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.xx, xx.xx)\")) ) compare_rtables(object, expected) #> [,1] [,2] #> [1,] \"X\" \".\" #> [2,] \".\" \".\" #> [3,] \".\" \".\" #> [4,] \"X\" \"X\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\" object <- rtable( header = c(\"ARM A\\nN=100\", \"ARM B\\nN=200\"), format = \"xx\", rrow(\"row 1\", 10, 15), rrow(), rrow(\"section title\"), rrow(\"row colspan\", rcell(c(.345543, .4432423), colspan = 2, format = \"(xx.x, xx.x)\")) ) compare_rtables(object, expected) #> [,1] [,2] #> [1,] \".\" \".\" #> [2,] \".\" \".\" #> [3,] \".\" \".\" #> [4,] \"X\" \"X\" #> attr(,\"class\") #> [1] \"rtables_diff\" \"matrix\" \"array\""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compat_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Compatibility Arg Conventions — compat_args","title":"Compatibility Arg Conventions — compat_args","text":"Compatibility Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compat_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compatibility Arg Conventions — compat_args","text":"","code":"compat_args(.lst, row.name, format, indent, label, inset)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/compat_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compatibility Arg Conventions — compat_args","text":".lst list. already-collected list arguments used instead elements .... Arguments passed via ... ignored specified. row.name NULL empty string used row.name rrow. format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. indent deprecated. label character(1). label (confused name) object/structure. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/compat_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compatibility Arg Conventions — compat_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/constr_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor Arg Conventions — constr_args","title":"Constructor Arg Conventions — constr_args","text":"Constructor Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/constr_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor Arg Conventions — constr_args","text":"","code":"constr_args( kids, cont, lev, iscontent, cinfo, labelrow, vals, cspan, label_pos, cindent_mod, cvar, label, cextra_args, child_names, title, subtitles, main_footer, prov_footer, footnotes, page_title, page_prefix, section_div, trailing_section_div, split_na_str, cna_str, inset, table_inset, header_section_div )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/constr_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor Arg Conventions — constr_args","text":"kids list. List direct children. cont ElementaryTable. Content table. lev integer. Nesting level (roughly, indentation level practical terms). iscontent logical. TableTree/ElementaryTable constructed content table another TableTree. cinfo InstantiatedColumnInfo (NULL). Column structure object created. labelrow LabelRow. LabelRow object assign Table. Constructed label default specified. vals list. cell values row cspan integer. Column span. 1 indicates spanning. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. label character(1). label (confused name) object/structure. cextra_args list. Extra arguments passed content function tabulating row group summaries. child_names character. Names given sub splits contained compound split (typically AnalyzeMultiVars split object). title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. footnotes list NULL. Referential footnotes applied current level. post-processing, can achieved fnotes_at_path<-. page_title character. Page specific title(s). page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. trailing_section_div character(1). String used section divider printing last row contained (sub)-table, unless row also last table row printed overall, NA_character_ none (default). generated via layouting, correspond section_div split table represents single facet. split_na_str character. NA string vector use split_format. cna_str character. NA string use cformat content table. inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). table_inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). header_section_div character(1). String used divide header table. See header_section_div() getter setter . Please consider changing last element section_div() concatenating tables need divider .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/constr_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor Arg Conventions — constr_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/content_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve or set Content Table from a TableTree — content_table","title":"Retrieve or set Content Table from a TableTree — content_table","text":"Returns content table obj TableTree object, NULL otherwise","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/content_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve or set Content Table from a TableTree — content_table","text":"","code":"content_table(obj) content_table(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/content_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve or set Content Table from a TableTree — content_table","text":"obj TableTree. TableTree value ElementaryTable. new content table obj.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/content_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve or set Content Table from a TableTree — content_table","text":"ElementaryTable containing (top level) content rows obj ( NULL obj formal table object).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/counts_wpcts.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"Analysis function count levels factor percentage column total","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/counts_wpcts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"","code":"counts_wpcts(x, .N_col)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/counts_wpcts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"x factor. Vector data, provided rtables pagination machinery .N_col integer(1). Total count column, provided rtables pagination machinery","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/counts_wpcts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"RowsVerticalSection object counts (percents) level factor","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/counts_wpcts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analysis function to count levels of a factor with percentage of the column total — counts_wpcts","text":"","code":"counts_wpcts(DM$SEX, 400) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 F 187 (46.8%) 0 F #> 2 M 169 (42.2%) 0 M #> 3 U 0 (0.0%) 0 U #> 4 UNDIFFERENTIATED 0 (0.0%) 0 UNDIFFERENTIATED"},{"path":"https://insightsengineering.github.io/rtables/main/reference/custom_split_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Custom Split Functions — custom_split_funs","title":"Custom Split Functions — custom_split_funs","text":"Split functions provide work-horse rtables's generalized partitioning. functions accept (sub)set incoming data, split object, return 'splits' data.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/custom_split_funs.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Custom Split Functions — custom_split_funs","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/custom_split_funs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Custom Split Functions — custom_split_funs","text":"","code":"# Example of a picky split function. The number of values in the column variable # var decrees if we are going to print also the column with all observation # or not. picky_splitter <- function(var) { # Main layout function function(df, spl, vals, labels, trim) { orig_vals <- vals # Check for number of levels if all are selected if (is.null(vals)) { vec <- df[[var]] vals <- unique(vec) } # Do a split with or without All obs if (length(vals) == 1) { do_base_split(spl = spl, df = df, vals = vals, labels = labels, trim = trim) } else { fnc_tmp <- add_overall_level(\"Overall\", label = \"All Obs\", first = FALSE) fnc_tmp(df = df, spl = spl, vals = orig_vals, trim = trim) } } } # Data sub-set d1 <- subset(ex_adsl, ARM == \"A: Drug X\" | (ARM == \"B: Placebo\" & SEX == \"F\")) d1 <- subset(d1, SEX %in% c(\"M\", \"F\")) d1$SEX <- factor(d1$SEX) # This table uses the number of values in the SEX column to add the overall col or not lyt <- basic_table() %>% split_cols_by(\"ARM\", split_fun = drop_split_levels) %>% split_cols_by(\"SEX\", split_fun = picky_splitter(\"SEX\")) %>% analyze(\"AGE\", show_labels = \"visible\") tbl <- build_table(lyt, d1) tbl #> A: Drug X B: Placebo #> F M All Obs F #> ————————————————————————————————————————————— #> AGE #> Mean 32.76 35.57 33.86 34.12"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cutsplits.html","id":null,"dir":"Reference","previous_headings":"","what":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"Splits cutting values numeric variable Create static cut static cumulative cut split","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cutsplits.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"","code":"make_static_cut_split( var, split_label, cuts, cutlabels = NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = var, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", cumulative = FALSE, page_prefix = NA_character_, section_div = NA_character_ ) VarDynCutSplit( var, split_label, cutfun, cutlabelfun = function(x) NULL, cfun = NULL, cformat = NULL, cna_str = NA_character_, split_format = NULL, split_na_str = NA_character_, split_name = var, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), cumulative = FALSE, indent_mod = 0L, cindent_mod = 0L, cvar = \"\", cextra_args = list(), label_pos = \"visible\", page_prefix = NA_character_, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/cutsplits.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"var string, variable name split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). cuts numeric. Cuts use cutlabels character (NULL). Labels cuts cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. split_name string. Name associated split (pathing, etc) child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cindent_mod numeric(1). indent modifier content tables generated split. cvar character(1). variable, , content function accept. Defaults NA. cextra_args list. Extra arguments passed content function tabulating row group summaries. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cumulative logical. cuts treated cumulative. Defaults FALSE page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. cutfun function. Function accepts full vector var values returns cut points used (via cut) splitting data tabulation cutlabelfun function. Function returns either labels cuts NULL passed return value cutfun","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/cutsplits.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Splits for cutting by values of a numeric variable — VarStaticCutSplit-class","text":"VarStaticCutSplit, CumulativeCutSplit object make_static_cut_split, VarDynCutSplit object VarDynCutSplit()","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a Result Data Frame — data.frame_export","title":"Generate a Result Data Frame — data.frame_export","text":"Collection utilities exctract data.frame TableTree objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a Result Data Frame — data.frame_export","text":"","code":"as_result_df(tt, spec = \"v0_experimental\", simplify = FALSE, ...) result_df_specs() path_enriched_df(tt, path_fun = collapse_path, value_fun = collapse_values)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a Result Data Frame — data.frame_export","text":"tt TableTree (related class). TableTree object representing populated table. spec character(1). specification use extract result data frame. See details simplify logical(1). TRUE, result data frame visible labels result columns. ... Passed spec-specific result data frame conversion function. Currently can one following parameters (valid v0_experimental spec now): expand_colnames: TRUE, result data frame expanded column names usual output. useful result data frame used processing. simplify: TRUE, result data frame visible labels result columns. as_strings: TRUE, result data frame values strings, appear final table (can also retrieved matrix_form(tt)$strings). also true column counts expand_colnames = TRUE. as_viewer: TRUE, result data frame values appear final table, .e. precision numbers, easy--use numeric form. keep_label_rows: TRUE, result data frame labels appear final table. as_is: TRUE, result data frame values appear final table, without information row structure. Row labels assigned rows work well df_to_tt(). path_fun function. Function transform paths single-string row/column names. value_fun function. Function transform cell values cells data.frame. Defaults collapse_values creates strings multi-valued cells collapsed together, separated |.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a Result Data Frame — data.frame_export","text":"result_df_specs(): returns named list result data frame extraction functions \"specification\". path_enriched_df(): returns data frame tt's cell values (processed value_fun, columns named full column paths (processed path_fun additional row_path column row paths (processed path_fun).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate a Result Data Frame — data.frame_export","text":"as_result_df(): Result data frame specifications may differ exact information include form represent . Specifications whose names end \"_experimental\" subject change without notice, specifications without \"_experimental\" suffix remain available including bugs construction indefinitely.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Generate a Result Data Frame — data.frame_export","text":"result_df_specs(): list functions extract result data frames TableTrees. path_enriched_df(): transform TableTree object Path-Enriched data.frame.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/data.frame_export.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a Result Data Frame — data.frame_export","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"STRATA1\") %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl) as_result_df(tbl) #> spl_var_1 spl_value_1 avar_name row_name label_name row_num is_group_summary #> 1 STRATA1 A AGE Mean Mean 3 FALSE #> 2 STRATA1 A BMRKR2 LOW LOW 5 FALSE #> 3 STRATA1 A BMRKR2 MEDIUM MEDIUM 6 FALSE #> 4 STRATA1 A BMRKR2 HIGH HIGH 7 FALSE #> 5 STRATA1 B AGE Mean Mean 10 FALSE #> 6 STRATA1 B BMRKR2 LOW LOW 12 FALSE #> 7 STRATA1 B BMRKR2 MEDIUM MEDIUM 13 FALSE #> 8 STRATA1 B BMRKR2 HIGH HIGH 14 FALSE #> 9 STRATA1 C AGE Mean Mean 17 FALSE #> 10 STRATA1 C BMRKR2 LOW LOW 19 FALSE #> 11 STRATA1 C BMRKR2 MEDIUM MEDIUM 20 FALSE #> 12 STRATA1 C BMRKR2 HIGH HIGH 21 FALSE #> node_class A: Drug X B: Placebo C: Combination #> 1 DataRow 33.07895 35.11364 34.225 #> 2 DataRow 12 16 14 #> 3 DataRow 10 17 13 #> 4 DataRow 16 11 13 #> 5 DataRow 33.85106 36 36.32558 #> 6 DataRow 19 13 10 #> 7 DataRow 13 22 16 #> 8 DataRow 15 10 17 #> 9 DataRow 34.22449 35.17778 35.63265 #> 10 DataRow 19 16 16 #> 11 DataRow 14 17 13 #> 12 DataRow 16 12 20 result_df_specs() #> $v0_experimental #> function(tt, #> as_viewer = FALSE, #> as_strings = FALSE, #> expand_colnames = FALSE, #> keep_label_rows = FALSE, #> as_is = FALSE) { #> checkmate::assert_flag(as_viewer) #> checkmate::assert_flag(as_strings) #> checkmate::assert_flag(expand_colnames) #> checkmate::assert_flag(keep_label_rows) #> checkmate::assert_flag(as_is) #> #> if (as_is) { #> keep_label_rows <- TRUE #> expand_colnames <- FALSE #> } #> #> raw_cvals <- cell_values(tt) #> ## if the table has one row and multiple columns, sometimes the cell values returns a list of the cell values #> ## rather than a list of length 1 representing the single row. This is bad but may not be changeable #> ## at this point. #> if (nrow(tt) == 1 && length(raw_cvals) > 1) { #> raw_cvals <- list(raw_cvals) #> } #> #> # Flatten the list of lists (rows) of cell values into a data frame #> cellvals <- as.data.frame(do.call(rbind, raw_cvals)) #> row.names(cellvals) <- NULL #> #> if (nrow(tt) == 1 && ncol(tt) == 1) { #> colnames(cellvals) <- names(raw_cvals) #> } #> #> if (as_viewer || as_strings) { #> # we keep previous calculations to check the format of the data #> mf_tt <- matrix_form(tt) #> mf_result_chars <- mf_strings(mf_tt)[-seq_len(mf_nlheader(mf_tt)), -1] #> mf_result_chars <- .remove_empty_elements(mf_result_chars) #> mf_result_numeric <- as.data.frame( #> .make_numeric_char_mf(mf_result_chars) #> ) #> mf_result_chars <- as.data.frame(mf_result_chars) #> if (!setequal(dim(mf_result_numeric), dim(cellvals)) || #> !setequal(dim(mf_result_chars), dim(cellvals))) { #> stop( #> \"The extracted numeric data.frame does not have the same dimension of the\", #> \" cell values extracted with cell_values(). This is a bug. Please report it.\" #> ) # nocov #> } #> if (as_strings) { #> colnames(mf_result_chars) <- colnames(cellvals) #> cellvals <- mf_result_chars #> } else { #> colnames(mf_result_numeric) <- colnames(cellvals) #> cellvals <- mf_result_numeric #> } #> } #> #> rdf <- make_row_df(tt) #> #> df <- rdf[, c(\"name\", \"label\", \"abs_rownumber\", \"path\", \"reprint_inds\", \"node_class\")] #> # Removing initial root elements from path (out of the loop -> right maxlen) #> df$path <- lapply(df$path, .remove_root_elems_from_path, #> which_root_name = c(\"root\", \"rbind_root\"), #> all = TRUE #> ) #> maxlen <- max(lengths(df$path)) #> #> # Loop for metadata (path and details from make_row_df) #> metadf <- do.call( #> rbind.data.frame, #> lapply( #> seq_len(NROW(df)), #> function(ii) { #> handle_rdf_row(df[ii, ], maxlen = maxlen) #> } #> ) #> ) #> #> # Should we keep label rows with NAs instead of values? #> if (keep_label_rows) { #> cellvals_mat_struct <- as.data.frame( #> matrix(NA, nrow = nrow(rdf), ncol = ncol(cellvals)) #> ) #> colnames(cellvals_mat_struct) <- colnames(cellvals) #> cellvals_mat_struct[metadf$node_class != \"LabelRow\", ] <- cellvals #> ret <- cbind(metadf, cellvals_mat_struct) #> } else { #> ret <- cbind( #> metadf[metadf$node_class != \"LabelRow\", ], #> cellvals #> ) #> } #> #> # If we want to expand colnames #> if (expand_colnames) { #> col_name_structure <- .get_formatted_colnames(clayout(tt)) #> number_of_non_data_cols <- which(colnames(ret) == \"node_class\") #> if (NCOL(ret) - number_of_non_data_cols != NCOL(col_name_structure)) { #> stop( #> \"When expanding colnames structure, we were not able to find the same\", #> \" number of columns as in the result data frame. This is a bug. Please report it.\" #> ) # nocov #> } #> #> buffer_rows_for_colnames <- matrix( #> rep(\"\", number_of_non_data_cols * NROW(col_name_structure)), #> nrow = NROW(col_name_structure) #> ) #> #> header_colnames_matrix <- cbind(buffer_rows_for_colnames, data.frame(col_name_structure)) #> colnames(header_colnames_matrix) <- colnames(ret) #> #> count_row <- NULL #> if (disp_ccounts(tt)) { #> ccounts <- col_counts(tt) #> if (as_strings) { #> ccounts <- mf_strings(mf_tt)[mf_nlheader(mf_tt), ] #> ccounts <- .remove_empty_elements(ccounts) #> } #> count_row <- c(rep(\"\", number_of_non_data_cols), ccounts) #> header_colnames_matrix <- rbind(header_colnames_matrix, count_row) #> } #> ret <- rbind(header_colnames_matrix, ret) #> } #> #> # Using only labels for row names and losing information about paths #> if (as_is) { #> tmp_rownames <- ret$label_name #> ret <- ret[, -seq_len(which(colnames(ret) == \"node_class\"))] #> if (length(unique(tmp_rownames)) == length(tmp_rownames)) { #> rownames(ret) <- tmp_rownames #> } else { #> ret <- cbind(\"label_name\" = tmp_rownames, ret) #> rownames(ret) <- NULL #> } #> } else { #> rownames(ret) <- NULL #> } #> #> ret #> } #> #> lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl) path_enriched_df(tbl) #> row_path ARM|A: Drug X ARM|B: Placebo ARM|C: Combination #> 1 ma_AGE_BMRKR2|AGE|Mean 33.76866 35.43284 35.43182 #> 2 ma_AGE_BMRKR2|BMRKR2|LOW 50.00000 45.00000 40.00000 #> 3 ma_AGE_BMRKR2|BMRKR2|MEDIUM 37.00000 56.00000 42.00000 #> 4 ma_AGE_BMRKR2|BMRKR2|HIGH 47.00000 33.00000 50.00000"},{"path":"https://insightsengineering.github.io/rtables/main/reference/df_to_tt.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ElementaryTable from data.frame — df_to_tt","title":"Create ElementaryTable from data.frame — df_to_tt","text":"Create ElementaryTable data.frame","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/df_to_tt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ElementaryTable from data.frame — df_to_tt","text":"","code":"df_to_tt(df)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/df_to_tt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ElementaryTable from data.frame — df_to_tt","text":"df data.frame.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/df_to_tt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ElementaryTable from data.frame — df_to_tt","text":"ElementaryTable object unnested columns corresponding names(df) row labels corresponding row.names(df).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/df_to_tt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ElementaryTable from data.frame — df_to_tt","text":"row names defined df (simple numbers), row names taken column label_name, exists. label_name exists, also removed original data. Remember behavior compatible as_result_df(), as_is = TRUE row names unique.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/df_to_tt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ElementaryTable from data.frame — df_to_tt","text":"","code":"df_to_tt(mtcars) #> mpg cyl disp hp drat wt qsec vs am gear carb #> ————————————————————————————————————————————————————————————————————————————————————————————— #> Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4 #> Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2 #> Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1 #> Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2 #> Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1 #> Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2 #> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4 #> Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6 #> Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8 #> Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2"},{"path":"https://insightsengineering.github.io/rtables/main/reference/dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Table Dimensions — nrow,VTableTree-method","title":"Table Dimensions — nrow,VTableTree-method","text":"Table Dimensions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table Dimensions — nrow,VTableTree-method","text":"","code":"# S4 method for VTableTree nrow(x) # S4 method for VTableNodeInfo ncol(x) # S4 method for VTableNodeInfo dim(x)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table Dimensions — nrow,VTableTree-method","text":"x TableTree ElementaryTable object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table Dimensions — nrow,VTableTree-method","text":"number rows (nrow), columns (ncol) (dim) object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table Dimensions — nrow,VTableTree-method","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"SEX\", \"AGE\")) tbl <- build_table(lyt, ex_adsl) dim(tbl) #> [1] 7 3 nrow(tbl) #> [1] 7 ncol(tbl) #> [1] 3 NROW(tbl) #> [1] 7 NCOL(tbl) #> [1] 3"},{"path":"https://insightsengineering.github.io/rtables/main/reference/do_base_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"function intended use inside custom split functions. applies current split custom splitting function default splits can manipulated.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/do_base_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"","code":"do_base_split(spl, df, vals = NULL, labels = NULL, trim = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/do_base_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"spl Split object defining partitioning analysis/tabulation data. df dataset (data.frame tibble) vals . Already calculated/known values split. Generally left NULL. labels character. Labels associated vals. NULL vals , almost always case. trim logical(1). groups corresponding empty data subsets removed. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/do_base_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"result split applied custom split function, see custom_split_funs","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/do_base_split.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply Basic Split (For Use In Custom Split Functions) — do_base_split","text":"","code":"uneven_splfun <- function(df, spl, vals = NULL, labels = NULL, trim = FALSE) { ret <- do_base_split(spl, df, vals, labels, trim) if (NROW(df) == 0) { ret <- lapply(ret, function(x) x[1]) } ret } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"USUBJID\", \"AESEQ\", \"BMRKR1\"), varlabels = c(\"N\", \"E\", \"BMR1\"), split_fun = uneven_splfun ) %>% analyze_colvars(list( USUBJID = function(x, ...) length(unique(x)), AESEQ = max, BMRKR1 = mean )) tbl <- build_table(lyt, subset(ex_adae, as.numeric(ARM) <= 2)) tbl #> A: Drug X B: Placebo C: Combination #> N E BMR1 N E BMR1 N #> ————————————————————————————————————————————————————————————————————————————— #> 122 10 6.09356345928374 123 10 5.86496605625578 0"},{"path":"https://insightsengineering.github.io/rtables/main/reference/drop_facet_levels.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","title":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","text":"function intended use preprocessing component make_split_fun, called directly end users.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/drop_facet_levels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","text":"","code":"drop_facet_levels(df, spl, ...)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/drop_facet_levels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocessing Functions for use in make_split_fun — drop_facet_levels","text":"df data.frame. incoming data corresponding parent facet spl Split. ... dots. used internally pass parameters.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/export_as_docx.html","id":null,"dir":"Reference","previous_headings":"","what":"Export as word document — export_as_docx","title":"Export as word document — export_as_docx","text":"table, produce self-contained word document attach template word file (template_file). function based tt_to_flextable() transformer officer package.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/export_as_docx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Export as word document — export_as_docx","text":"","code":"export_as_docx( tt, file, doc_metadata = NULL, titles_as_header = FALSE, footers_as_text = TRUE, template_file = NULL, section_properties = NULL ) section_properties_portrait() section_properties_landscape() margins_potrait() margins_landscape()"},{"path":"https://insightsengineering.github.io/rtables/main/reference/export_as_docx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Export as word document — export_as_docx","text":"tt TableTree (related class). TableTree object representing populated table. file character(1). String indicates final file output. needs .docx extension. doc_metadata list character(1)s. value can used metadata ?officer::set_doc_properties. Important text values title, subject, creator, description created date object. titles_as_header logical(1). Defaults TRUE tt_to_flextable(), table self-contained makes additional header rows main_title() string subtitles() character vector (one per element). FALSE suggested export_as_docx(). adds titles subtitles text paragraph table. style applied. footers_as_text logical(1). Defaults FALSE tt_to_flextable(), table self-contained flextable definition footnotes. TRUE used export_as_docx() add footers new paragraph table. style applied, smaller font. template_file character(1). Template file officer use starting point final document. attach table use defaults defined template file. Output doc file nonetheless. section_properties officer::prop_section object. can set margins page size.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/export_as_docx.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Export as word document — export_as_docx","text":"section_properties_portrait(): helper function defines standard portrait properties tables. section_properties_landscape(): helper function defines standard landscape properties tables. margins_potrait(): helper function defines standard portrait margins tables. margins_landscape(): helper function defines standard landscape margins tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/export_as_docx.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Export as word document — export_as_docx","text":"export_as_docx() many options available. suggest, need specific formats details use tt_to_flextable() first export_as_docx. title_as_header footer_as_text need specified changed tt_to_flextable().","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/export_as_docx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Export as word document — export_as_docx","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\", \"COUNTRY\")) tbl <- build_table(lyt, ex_adsl) # See how section_properties_portrait function is built for custom if (FALSE) { tf <- tempfile(fileext = \".docx\") export_as_docx(tbl, file = tf, section_properties = section_properties_portrait()) }"},{"path":"https://insightsengineering.github.io/rtables/main/reference/find_degen_struct.html","id":null,"dir":"Reference","previous_headings":"","what":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"Find degenerate (sub)structures within table (Experimental)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/find_degen_struct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"","code":"find_degen_struct(tt)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/find_degen_struct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"tt TableTree function returns list row-paths structural subtables contain data rows ( even associated content rows).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/find_degen_struct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"list character vectors representing row paths, , degenerate substructures within table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/find_degen_struct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find degenerate (sub)structures within a table (Experimental) — find_degen_struct","text":"","code":"find_degen_struct(rtable(\"hi\")) #> [[1]] #> [1] \"\" #>"},{"path":"https://insightsengineering.github.io/rtables/main/reference/format_rcell.html","id":null,"dir":"Reference","previous_headings":"","what":"Format rcell — format_rcell","title":"Format rcell — format_rcell","text":"wrapper around formatters::format_value use CellValue objects","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/format_rcell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format rcell — format_rcell","text":"","code":"format_rcell( x, format, output = c(\"ascii\", \"html\"), na_str = obj_na_str(x) %||% \"NA\", pr_row_format = NULL, pr_row_na_str = NULL, shell = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/format_rcell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format rcell — format_rcell","text":"x object class CellValue, raw value. format character(1) function. format label (string) formatter function apply x. output character(1). Output type. na_str character(1). String displayed value x missing. Defaults \"NA\". pr_row_format list default format coming general row. pr_row_na_str list default \"NA\" string coming general row. shell logical(1). formats returned instead values formats applied. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/format_rcell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format rcell — format_rcell","text":"formatted text representing cell","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/format_rcell.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Format rcell — format_rcell","text":"","code":"cll <- CellValue(pi, format = \"xx.xxx\") format_rcell(cll) #> [1] \"3.142\" # Cell values precedes the row values cll <- CellValue(pi, format = \"xx.xxx\") format_rcell(cll, pr_row_format = \"xx.x\") #> [1] \"3.142\" # Similarly for NA values cll <- CellValue(NA, format = \"xx.xxx\", format_na_str = \"This is THE NA\") format_rcell(cll, pr_row_na_str = \"This is NA\") #> [1] \"This is THE NA\""},{"path":"https://insightsengineering.github.io/rtables/main/reference/formatters_methods.html","id":null,"dir":"Reference","previous_headings":"","what":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"See formatters documentation descriptions generics.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/formatters_methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"","code":"# S4 method for VNodeInfo obj_name(obj) # S4 method for Split obj_name(obj) # S4 method for VNodeInfo obj_name(obj) <- value # S4 method for Split obj_name(obj) <- value # S4 method for Split obj_label(obj) # S4 method for TableRow obj_label(obj) # S4 method for VTableTree obj_label(obj) # S4 method for ValueWrapper obj_label(obj) # S4 method for Split obj_label(obj) <- value # S4 method for TableRow obj_label(obj) <- value # S4 method for ValueWrapper obj_label(obj) <- value # S4 method for VTableTree obj_label(obj) <- value # S4 method for VTableNodeInfo obj_format(obj) # S4 method for CellValue obj_format(obj) # S4 method for Split obj_format(obj) # S4 method for VTableNodeInfo obj_format(obj) <- value # S4 method for Split obj_format(obj) <- value # S4 method for CellValue obj_format(obj) <- value # S4 method for Split obj_na_str(obj) # S4 method for VTitleFooter main_title(obj) # S4 method for VTitleFooter main_title(obj) <- value # S4 method for TableRow main_title(obj) # S4 method for VTitleFooter subtitles(obj) # S4 method for VTitleFooter subtitles(obj) <- value # S4 method for TableRow subtitles(obj) # S4 method for VTitleFooter main_footer(obj) # S4 method for VTitleFooter main_footer(obj) <- value # S4 method for TableRow main_footer(obj) # S4 method for VTitleFooter prov_footer(obj) # S4 method for VTitleFooter prov_footer(obj) <- value # S4 method for TableRow prov_footer(obj) # S4 method for VTableNodeInfo table_inset(obj) # S4 method for PreDataTableLayouts table_inset(obj) # S4 method for VTableNodeInfo table_inset(obj) <- value # S4 method for PreDataTableLayouts table_inset(obj) <- value # S4 method for InstantiatedColumnInfo table_inset(obj) <- value # S4 method for TableRow nlines(x, colwidths = NULL, max_width = NULL) # S4 method for LabelRow nlines(x, colwidths = NULL, max_width = NULL) # S4 method for RefFootnote nlines(x, colwidths = NULL, max_width = NULL) # S4 method for InstantiatedColumnInfo nlines(x, colwidths = NULL, max_width = NULL) # S4 method for VTableTree make_row_df( tt, colwidths = NULL, visible_only = TRUE, rownum = 0, indent = 0L, path = character(), incontent = FALSE, repr_ext = 0L, repr_inds = integer(), sibpos = NA_integer_, nsibs = NA_integer_, max_width = NULL ) # S4 method for TableRow make_row_df( tt, colwidths = NULL, visible_only = TRUE, rownum = 0, indent = 0L, path = \"root\", incontent = FALSE, repr_ext = 0L, repr_inds = integer(), sibpos = NA_integer_, nsibs = NA_integer_, max_width = NULL ) # S4 method for LabelRow make_row_df( tt, colwidths = NULL, visible_only = TRUE, rownum = 0, indent = 0L, path = \"root\", incontent = FALSE, repr_ext = 0L, repr_inds = integer(), sibpos = NA_integer_, nsibs = NA_integer_, max_width = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/formatters_methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"obj . object accessor access modify value new value x object colwidths numeric vector. Column widths use vertical pagination. max_width numeric(1). Width strings wrapped determining many lines require. tt TableTree (related class). TableTree object representing populated table. visible_only logical(1). visible aspects table structure reflected summary. Defaults TRUE. May supported methods. rownum numeric(1). Internal detail set manually. indent integer(1). Internal detail set manually. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. incontent logical(1). Internal detail set manually. repr_ext integer(1). Internal detail set manually. repr_inds integer. Internal detail set manually. sibpos integer(1). Internal detail set manually. nsibs integer(1). Internal detail set manually.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/formatters_methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"getters, current value component accessed obj, setters, modified copy obj new value.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/formatters_methods.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":" visible_only TRUE ( default), methods return data.frame exactly one row per visible row table-like object. useful reasoning table print, reflect full pathing space structure (though paths given work ). supported, visible_only FALSE, every structural element table (row-space) reflected returned data.frame, meaning full pathing-space represented rows layout summary represent printed rows table displayed. arguments beyond tt visible_only present make_row_df methods can call make_row_df recursively retain information, set top-level call","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/formatters_methods.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Methods for generics in the formatters package — obj_name,VNodeInfo-method","text":"technically present root tree node excluded summary returned make_row_df make_col_df (see rtables::make_col_df), simply row/column structure tt thus useful pathing pagination.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/gen_args.html","id":null,"dir":"Reference","previous_headings":"","what":"General Argument Conventions — gen_args","title":"General Argument Conventions — gen_args","text":"General Argument Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/gen_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"General Argument Conventions — gen_args","text":"","code":"gen_args( df, alt_counts_df, spl, pos, tt, tr, verbose, colwidths, obj, x, value, object, path, label, label_pos, cvar, topleft, page_prefix, hsep, indent_size, section_div, na_str, inset, table_inset, ... )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/gen_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"General Argument Conventions — gen_args","text":"df dataset (data.frame tibble) alt_counts_df dataset (data.frame tibble). Alternative full data rtables framework use () calculating column counts. spl Split object defining partitioning analysis/tabulation data. pos numeric. top-level set nested splits new layout feature added . Defaults current tt TableTree (related class). TableTree object representing populated table. tr TableRow (related class). TableRow object representing single row within populated table. verbose logical(1). extra debugging messages shown. Defaults FALSE. colwidths numeric vector. Column widths use vertical pagination. obj . object accessor access modify x object value new value object object modify -place path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. label character(1). label (confused name) object/structure. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. cvar character(1). variable, , content function accept. Defaults NA. topleft character. Override values \"top left\" material displayed printing. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. indent_size numeric(1). Number spaces use per indent level. Defaults 2 section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. na_str character(1). String displayed value x missing. Defaults \"NA\". inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). table_inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). ... Passed methods tabulation functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/gen_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"General Argument Conventions — gen_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/gfc.html","id":null,"dir":"Reference","previous_headings":"","what":"get formatted cells — get_formatted_cells","title":"get formatted cells — get_formatted_cells","text":"get formatted cells","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/gfc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"get formatted cells — get_formatted_cells","text":"","code":"get_formatted_cells(obj, shell = FALSE) # S4 method for TableTree get_formatted_cells(obj, shell = FALSE) # S4 method for ElementaryTable get_formatted_cells(obj, shell = FALSE) # S4 method for TableRow get_formatted_cells(obj, shell = FALSE) # S4 method for LabelRow get_formatted_cells(obj, shell = FALSE) get_cell_aligns(obj) # S4 method for TableTree get_cell_aligns(obj) # S4 method for ElementaryTable get_cell_aligns(obj) # S4 method for TableRow get_cell_aligns(obj) # S4 method for LabelRow get_cell_aligns(obj)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/gfc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"get formatted cells — get_formatted_cells","text":"obj . object accessor access modify shell logical(1). formats returned instead values formats applied. Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/gfc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"get formatted cells — get_formatted_cells","text":"formatted print-strings (body) cells obj.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/gfc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"get formatted cells — get_formatted_cells","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() tbl <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\") %>% build_table(iris2) get_formatted_cells(tbl) #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] \"\" \"\" \"\" \"\" \"\" \"\" #> [2,] \"4.40\" \"4.30\" \"5.00\" \"4.90\" \"4.90\" \"5.60\" #> [3,] \"4.80\" \"4.80\" \"5.60\" \"5.60\" \"6.20\" \"6.30\" #> [4,] \"5.00\" \"5.00\" \"5.90\" \"5.90\" \"6.50\" \"6.50\" #> [5,] \"5.02\" \"4.99\" \"5.99\" \"5.88\" \"6.50\" \"6.67\" #> [6,] \"5.30\" \"5.10\" \"6.40\" \"6.10\" \"6.70\" \"7.20\" #> [7,] \"5.80\" \"5.70\" \"7.00\" \"6.70\" \"7.70\" \"7.90\" #> [8,] \"\" \"\" \"\" \"\" \"\" \"\" #> [9,] \"0.10\" \"0.10\" \"1.00\" \"1.00\" \"1.40\" \"1.50\" #> [10,] \"0.20\" \"0.20\" \"1.20\" \"1.20\" \"1.90\" \"1.80\" #> [11,] \"0.20\" \"0.20\" \"1.30\" \"1.30\" \"2.10\" \"2.00\" #> [12,] \"0.23\" \"0.26\" \"1.35\" \"1.30\" \"2.08\" \"1.98\" #> [13,] \"0.20\" \"0.30\" \"1.50\" \"1.40\" \"2.30\" \"2.20\" #> [14,] \"0.40\" \"0.60\" \"1.80\" \"1.70\" \"2.50\" \"2.50\""},{"path":"https://insightsengineering.github.io/rtables/main/reference/head_tail.html","id":null,"dir":"Reference","previous_headings":"","what":"Head and tail methods — head","title":"Head and tail methods — head","text":"Head tail methods","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/head_tail.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Head and tail methods — head","text":"","code":"head(x, ...) # S4 method for VTableTree head( x, n = 6, ..., keep_topleft = TRUE, keep_titles = TRUE, keep_footers = keep_titles, reindex_refs = FALSE ) tail(x, ...) # S4 method for VTableTree tail( x, n = 6, ..., keep_topleft = TRUE, keep_titles = TRUE, keep_footers = keep_titles, reindex_refs = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/head_tail.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Head and tail methods — head","text":"x object ... arguments passed methods. n integer vector length dim(x) (1, non-dimensioned objects). logical silently coerced integer. Values specify indices selected corresponding dimension (along length) object. positive value n[] includes first/last n[] indices dimension, negative value excludes last/first abs(n[]), including remaining indices. NA non-specified values (length(n) < length(dim(x))) select indices dimension. Must contain least one non-missing value. keep_topleft logical(1). TRUE (default), top_left material table carried subset. keep_titles logical(1). TRUE (default), title material table carried subset. keep_footers logical(1). TRUE, footer material table carried subset. defaults keep_titles. reindex_refs logical(1). Defaults FALSE. TRUE, referential footnotes reindexed subset.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/horizontal_sep.html","id":null,"dir":"Reference","previous_headings":"","what":"Access or recursively set header-body separator for tables — horizontal_sep","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"Access recursively set header-body separator tables","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/horizontal_sep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"","code":"horizontal_sep(obj) # S4 method for VTableTree horizontal_sep(obj) horizontal_sep(obj) <- value # S4 method for VTableTree horizontal_sep(obj) <- value # S4 method for TableRow horizontal_sep(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/horizontal_sep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"obj . object accessor access modify value character(1). String use new header/body separator.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/horizontal_sep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access or recursively set header-body separator for tables — horizontal_sep","text":"horizontal_sep string acting header separator. horizontal_sep<-, obj, new header separator applied recursively subtables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/in_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Create multiple rows in analysis or summary functions — in_rows","title":"Create multiple rows in analysis or summary functions — in_rows","text":"define cells get placed multiple rows afun","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/in_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create multiple rows in analysis or summary functions — in_rows","text":"","code":"in_rows( ..., .list = NULL, .names = NULL, .labels = NULL, .formats = NULL, .indent_mods = NULL, .cell_footnotes = list(NULL), .row_footnotes = list(NULL), .aligns = NULL, .format_na_strs = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/in_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create multiple rows in analysis or summary functions — in_rows","text":"... single row defining expressions .list list. list cell content, usually rcells, .list concatenated ... .names character NULL. Names returned list/structure. .labels character NULL. labels defined rows .formats character NULL. Formats values .indent_mods integer NULL. Indent modifications defined rows. .cell_footnotes list. Referential footnote messages associated name cells. .row_footnotes list. Referential footnotes messages associated name rows. .aligns character NULL. Alignments cells. Standard NULL \"center\". See formatters::list_valid_aligns() currently supported alignments. .format_na_strs character NULL. NA strings cells","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/in_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create multiple rows in analysis or summary functions — in_rows","text":"RowsVerticalSection object (NULL). details object considered internal implementation detail.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/in_rows.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create multiple rows in analysis or summary functions — in_rows","text":"post-processing, referential footnotes can also added using row column paths fnotes_at_path<-.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/in_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create multiple rows in analysis or summary functions — in_rows","text":"","code":"in_rows(1, 2, 3, .names = c(\"a\", \"b\", \"c\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c in_rows(1, 2, 3, .labels = c(\"a\", \"b\", \"c\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c in_rows(1, 2, 3, .names = c(\"a\", \"b\", \"c\"), .labels = c(\"AAA\", \"BBB\", \"CCC\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 AAA #> 2 b 2 0 BBB #> 3 c 3 0 CCC in_rows(.list = list(a = 1, b = 2, c = 3)) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c in_rows(1, 2, .list = list(3), .names = c(\"a\", \"b\", \"c\")) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 a 1 0 a #> 2 b 2 0 b #> 3 c 3 0 c lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = function(x) { in_rows( \"Mean (sd)\" = rcell(c(mean(x), sd(x)), format = \"xx.xx (xx.xx)\"), \"Range\" = rcell(range(x), format = \"xx.xx - xx.xx\") ) }) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> Range 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00"},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent.html","id":null,"dir":"Reference","previous_headings":"","what":"Change indentation of all rrows in an rtable — indent","title":"Change indentation of all rrows in an rtable — indent","text":"Change indentation rrows rtable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Change indentation of all rrows in an rtable — indent","text":"","code":"indent(x, by = 1)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Change indentation of all rrows in an rtable — indent","text":"x rtable object integer increase indentation rows. Can negative. final indentation smaller 0 indentation set 0.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Change indentation of all rrows in an rtable — indent","text":"x indent modifier incremented .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Change indentation of all rrows in an rtable — indent","text":"","code":"is_setosa <- iris$Species == \"setosa\" m_tbl <- rtable( header = rheader( rrow(row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2)), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"All Species\", mean(iris$Sepal.Length), median(iris$Sepal.Length), mean(iris$Petal.Length), median(iris$Petal.Length), format = \"xx.xx\" ), rrow( row.name = \"Setosa\", mean(iris$Sepal.Length[is_setosa]), median(iris$Sepal.Length[is_setosa]), mean(iris$Petal.Length[is_setosa]), median(iris$Petal.Length[is_setosa]), format = \"xx.xx\" ) ) indent(m_tbl) #> Sepal.Length Petal.Length #> mean median mean median #> ————————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.01 5.00 1.46 1.50 indent(m_tbl, 2) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.01 5.00 1.46 1.50"},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent_string.html","id":null,"dir":"Reference","previous_headings":"","what":"Indent Strings — indent_string","title":"Indent Strings — indent_string","text":"Used rtables indent row names ASCII output.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent_string.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Indent Strings — indent_string","text":"","code":"indent_string(x, indent = 0, incr = 2, including_newline = TRUE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent_string.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Indent Strings — indent_string","text":"x character vector indent vector length length(x) non-negative integers incr non-negative integer: number spaces per indent level including_newline boolean: newlines also indented","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent_string.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Indent Strings — indent_string","text":"x indented left-padding indent*incr white-spaces.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/indent_string.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Indent Strings — indent_string","text":"","code":"indent_string(\"a\", 0) #> [1] \"a\" indent_string(\"a\", 1) #> [1] \" a\" indent_string(letters[1:3], 0:2) #> [1] \"a\" \" b\" \" c\" indent_string(paste0(letters[1:3], \"\\n\", LETTERS[1:3]), 0:2) #> [1] \"a\\nA\" \" b\\n B\" \" c\\n C\""},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_row_at_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Insert Row at Path — insert_row_at_path","title":"Insert Row at Path — insert_row_at_path","text":"Insert row existing table directly directly existing data (.e., non-content non-label) row, specified path.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_row_at_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Insert Row at Path — insert_row_at_path","text":"","code":"insert_row_at_path(tt, path, value, after = FALSE) # S4 method for VTableTree,DataRow insert_row_at_path(tt, path, value, after = FALSE) # S4 method for VTableTree,ANY insert_row_at_path(tt, path, value)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_row_at_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Insert Row at Path — insert_row_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. value new value logical(1). value added row directly (FALSE, default) (TRUE) row specified path.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_row_at_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Insert Row at Path — insert_row_at_path","text":"","code":"lyt <- basic_table() %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(c(\"CHN\", \"USA\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl2 <- insert_row_at_path( tbl, c(\"COUNTRY\", \"CHN\", \"AGE\", \"Mean\"), rrow(\"new row\", 555) ) tbl2 #> all obs #> ——————————————————— #> CHN #> new row 555 #> Mean 34.64 #> USA #> Mean 35.30 tbl3 <- insert_row_at_path(tbl2, c(\"COUNTRY\", \"CHN\", \"AGE\", \"Mean\"), rrow(\"new row redux\", 888), after = TRUE ) tbl3 #> all obs #> ————————————————————————— #> CHN #> new row 555 #> Mean 34.64 #> new row redux 888 #> USA #> Mean 35.30"},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_rrow.html","id":null,"dir":"Reference","previous_headings":"","what":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"function deprecated removed future release rtables. Please use insert_row_at_path label_at_path instead.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_rrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"","code":"insert_rrow(tbl, rrow, at = 1, ascontent = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_rrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"tbl rtable rrow rrow append rtable position put rrow, defaults beginning (.e. 1) ascontent logical. Currently ignored.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_rrow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"TableTree specific class tbl","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_rrow.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"Label rows (.e. row data values, row.name) can inserted positions already contain label row non-trivial nested row structure tbl","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/insert_rrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"[DEPRECATED] insert rrows at (before) a specific location — insert_rrow","text":"","code":"o <- options(warn = 0) lyt <- basic_table() %>% split_cols_by(\"Species\") %>% analyze(\"Sepal.Length\") tbl <- build_table(lyt, iris) insert_rrow(tbl, rrow(\"Hello World\")) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ————————————————————————————————————————————— #> Hello World #> Mean 5.01 5.94 6.59 insert_rrow(tbl, rrow(\"Hello World\"), at = 2) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ————————————————————————————————————————————— #> Mean 5.01 5.94 6.59 #> Hello World lyt2 <- basic_table() %>% split_cols_by(\"Species\") %>% split_rows_by(\"Species\") %>% analyze(\"Sepal.Length\") tbl2 <- build_table(lyt2, iris) insert_rrow(tbl2, rrow(\"Hello World\")) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ————————————————————————————————————————————— #> Hello World #> setosa #> Mean 5.01 NA NA #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"Hello World\"), at = 2) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ——————————————————————————————————————————————— #> setosa #> Hello World #> Mean 5.01 NA NA #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"Hello World\"), at = 4) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ——————————————————————————————————————————————— #> setosa #> Mean 5.01 NA NA #> versicolor #> Hello World #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"new row\", 5, 6, 7)) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ———————————————————————————————————————————— #> new row 5 6 7 #> setosa #> Mean 5.01 NA NA #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 insert_rrow(tbl2, rrow(\"new row\", 5, 6, 7), at = 3) #> Warning: 'insert_rrow' is deprecated. #> Use 'insert_row_at_path or label_at_path(tab)<-' instead. #> See help(\"Deprecated\") #> setosa versicolor virginica #> ———————————————————————————————————————————— #> setosa #> Mean 5.01 NA NA #> new row 5 6 7 #> versicolor #> Mean NA 5.94 NA #> virginica #> Mean NA NA 6.59 options(o)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/int_methods.html","id":null,"dir":"Reference","previous_headings":"","what":"combine SplitVector objects — internal_methods","title":"combine SplitVector objects — internal_methods","text":"internal methods documented satisfy R CMD check. End users pay attention documentation.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/int_methods.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"combine SplitVector objects — internal_methods","text":"","code":"# S4 method for SplitVector c(x, ...) split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for NULL split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for PreDataRowLayout split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for SplitVector split_rows(lyt = NULL, spl, pos, cmpnd_fun = AnalyzeMultiVars) # S4 method for PreDataTableLayouts split_rows(lyt, spl, pos) # S4 method for ANY split_rows(lyt, spl, pos) cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for NULL cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for PreDataRowLayout cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for SplitVector cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for PreDataTableLayouts cmpnd_last_rowsplit(lyt, spl, constructor) # S4 method for ANY cmpnd_last_rowsplit(lyt, spl, constructor) split_cols(lyt = NULL, spl, pos) # S4 method for NULL split_cols(lyt = NULL, spl, pos) # S4 method for PreDataColLayout split_cols(lyt = NULL, spl, pos) # S4 method for SplitVector split_cols(lyt = NULL, spl, pos) # S4 method for PreDataTableLayouts split_cols(lyt = NULL, spl, pos) # S4 method for ANY split_cols(lyt = NULL, spl, pos) .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for PreDataTableLayouts .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for PreDataRowLayout .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for SplitVector .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) # S4 method for Split .add_row_summary( lyt, label, cfun, child_labels = c(\"default\", \"visible\", \"hidden\"), cformat = NULL, cna_str = \"-\", indent_mod = 0L, cvar = \"\", extra_args = list() ) fix_dyncuts(spl, df) # S4 method for Split fix_dyncuts(spl, df) # S4 method for VarDynCutSplit fix_dyncuts(spl, df) # S4 method for VTableTree fix_dyncuts(spl, df) # S4 method for PreDataRowLayout fix_dyncuts(spl, df) # S4 method for PreDataColLayout fix_dyncuts(spl, df) # S4 method for SplitVector fix_dyncuts(spl, df) # S4 method for PreDataTableLayouts fix_dyncuts(spl, df) summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for TableTree summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for ElementaryTable summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for TableRow summarize_rows_inner(obj, depth = 0, indent = 0) # S4 method for LabelRow summarize_rows_inner(obj, depth = 0, indent = 0) table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) str(object, ...) # S4 method for VTableTree str(object, max.level = 3L, ...) # S4 method for TableTree table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for ElementaryTable table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for TableRow table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for LabelRow table_structure_inner(obj, depth = 0, indent = 0, print_indent = 0) # S4 method for TableRow nrow(x) # S4 method for TableRow ncol(x) # S4 method for LabelRow ncol(x) # S4 method for InstantiatedColumnInfo ncol(x) # S4 method for VTree tree_children(x) # S4 method for VTableTree tree_children(x) # S4 method for ANY tree_children(x) # S4 method for VTree tree_children(x) <- value # S4 method for VTableTree tree_children(x) <- value # S4 method for TableTree content_table(obj) # S4 method for ANY content_table(obj) # S4 method for TableTree,ElementaryTable content_table(obj) <- value next_rpos(obj, nested = TRUE, for_analyze = FALSE) # S4 method for PreDataTableLayouts next_rpos(obj, nested = TRUE, for_analyze = FALSE) # S4 method for PreDataRowLayout next_rpos(obj, nested = TRUE, for_analyze = FALSE) # S4 method for ANY next_rpos(obj, nested) next_cpos(obj, nested = TRUE) # S4 method for PreDataTableLayouts next_cpos(obj, nested = TRUE) # S4 method for PreDataColLayout next_cpos(obj, nested = TRUE) # S4 method for ANY next_cpos(obj, nested = TRUE) last_rowsplit(obj) # S4 method for NULL last_rowsplit(obj) # S4 method for SplitVector last_rowsplit(obj) # S4 method for PreDataRowLayout last_rowsplit(obj) # S4 method for PreDataTableLayouts last_rowsplit(obj) rlayout(obj) # S4 method for PreDataTableLayouts rlayout(obj) # S4 method for ANY rlayout(obj) rlayout(object) <- value # S4 method for PreDataTableLayouts rlayout(object) <- value tree_pos(obj) # S4 method for VLayoutNode tree_pos(obj) pos_subset(obj) # S4 method for TreePos pos_subset(obj) # S4 method for VLayoutNode pos_subset(obj) pos_splits(obj) # S4 method for TreePos pos_splits(obj) # S4 method for VLayoutNode pos_splits(obj) pos_splvals(obj) # S4 method for TreePos pos_splvals(obj) # S4 method for VLayoutNode pos_splvals(obj) pos_splval_labels(obj) # S4 method for TreePos pos_splval_labels(obj) spl_payload(obj) # S4 method for Split spl_payload(obj) spl_payload(obj) <- value # S4 method for Split spl_payload(obj) <- value spl_label_var(obj) # S4 method for VarLevelSplit spl_label_var(obj) # S4 method for Split spl_label_var(obj) tt_labelrow(obj) # S4 method for VTableTree tt_labelrow(obj) tt_labelrow(obj) <- value # S4 method for VTableTree,LabelRow tt_labelrow(obj) <- value labelrow_visible(obj) # S4 method for VTableTree labelrow_visible(obj) # S4 method for LabelRow labelrow_visible(obj) # S4 method for VAnalyzeSplit labelrow_visible(obj) labelrow_visible(obj) <- value # S4 method for VTableTree labelrow_visible(obj) <- value # S4 method for LabelRow labelrow_visible(obj) <- value # S4 method for VAnalyzeSplit labelrow_visible(obj) <- value label_kids(spl) # S4 method for Split label_kids(spl) label_kids(spl) <- value # S4 method for Split,character label_kids(spl) <- value # S4 method for Split,logical label_kids(spl) <- value vis_label(spl) # S4 method for Split vis_label(spl) label_position(spl) # S4 method for Split label_position(spl) # S4 method for VAnalyzeSplit label_position(spl) label_position(spl) <- value # S4 method for Split label_position(spl) <- value content_fun(obj) # S4 method for Split content_fun(obj) content_fun(object) <- value # S4 method for Split content_fun(object) <- value analysis_fun(obj) # S4 method for AnalyzeVarSplit analysis_fun(obj) # S4 method for AnalyzeColVarSplit analysis_fun(obj) split_fun(obj) # S4 method for CustomizableSplit split_fun(obj) # S4 method for Split split_fun(obj) split_fun(obj) <- value # S4 method for CustomizableSplit split_fun(obj) <- value # S4 method for Split split_fun(obj) <- value content_extra_args(obj) # S4 method for Split content_extra_args(obj) content_extra_args(object) <- value # S4 method for Split content_extra_args(object) <- value content_var(obj) # S4 method for Split content_var(obj) content_var(object) <- value # S4 method for Split content_var(object) <- value avar_inclNAs(obj) # S4 method for VAnalyzeSplit avar_inclNAs(obj) avar_inclNAs(obj) <- value # S4 method for VAnalyzeSplit avar_inclNAs(obj) <- value spl_labelvar(obj) # S4 method for VarLevelSplit spl_labelvar(obj) spl_child_order(obj) # S4 method for VarLevelSplit spl_child_order(obj) spl_child_order(obj) <- value # S4 method for VarLevelSplit spl_child_order(obj) <- value # S4 method for ManualSplit spl_child_order(obj) # S4 method for MultiVarSplit spl_child_order(obj) # S4 method for AllSplit spl_child_order(obj) # S4 method for VarStaticCutSplit spl_child_order(obj) root_spl(obj) # S4 method for PreDataAxisLayout root_spl(obj) root_spl(obj) <- value # S4 method for PreDataAxisLayout root_spl(obj) <- value spanned_values(obj) # S4 method for TableRow spanned_values(obj) # S4 method for LabelRow spanned_values(obj) spanned_cells(obj) # S4 method for TableRow spanned_cells(obj) # S4 method for LabelRow spanned_cells(obj) spanned_values(obj) <- value # S4 method for TableRow spanned_values(obj) <- value # S4 method for LabelRow spanned_values(obj) <- value # S4 method for CellValue obj_na_str(obj) <- value # S4 method for VTableNodeInfo obj_na_str(obj) <- value # S4 method for Split obj_na_str(obj) <- value # S4 method for VTableNodeInfo obj_na_str(obj) set_format_recursive(obj, format, na_str, override = FALSE) # S4 method for TableRow set_format_recursive(obj, format, na_str, override = FALSE) # S4 method for LabelRow set_format_recursive(obj, format, override = FALSE) content_format(obj) # S4 method for Split content_format(obj) content_format(obj) <- value # S4 method for Split content_format(obj) <- value content_na_str(obj) # S4 method for Split content_na_str(obj) content_na_str(obj) <- value # S4 method for Split content_na_str(obj) <- value # S4 method for TableTree collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for ElementaryTable collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for VTree collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for VLeaf collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for NULL collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) # S4 method for ANY collect_leaves(tt, incl.cont = TRUE, add.labrows = FALSE) row_cspans(obj) # S4 method for TableRow row_cspans(obj) # S4 method for LabelRow row_cspans(obj) row_cspans(obj) <- value # S4 method for TableRow row_cspans(obj) <- value # S4 method for LabelRow row_cspans(obj) <- value cell_cspan(obj) # S4 method for CellValue cell_cspan(obj) cell_cspan(obj) <- value # S4 method for CellValue cell_cspan(obj) <- value cell_align(obj) # S4 method for CellValue cell_align(obj) cell_align(obj) <- value # S4 method for CellValue cell_align(obj) <- value tt_level(obj) # S4 method for VNodeInfo tt_level(obj) tt_level(obj) <- value # S4 method for VNodeInfo tt_level(obj) <- value # S4 method for VTableTree tt_level(obj) <- value indent_mod(obj) # S4 method for Split indent_mod(obj) # S4 method for VTableNodeInfo indent_mod(obj) # S4 method for ANY indent_mod(obj) # S4 method for RowsVerticalSection indent_mod(obj) indent_mod(obj) <- value # S4 method for Split indent_mod(obj) <- value # S4 method for VTableNodeInfo indent_mod(obj) <- value # S4 method for CellValue indent_mod(obj) <- value # S4 method for RowsVerticalSection indent_mod(obj) <- value content_indent_mod(obj) # S4 method for Split content_indent_mod(obj) # S4 method for VTableNodeInfo content_indent_mod(obj) content_indent_mod(obj) <- value # S4 method for Split content_indent_mod(obj) <- value # S4 method for VTableNodeInfo content_indent_mod(obj) <- value rawvalues(obj) # S4 method for ValueWrapper rawvalues(obj) # S4 method for LevelComboSplitValue rawvalues(obj) # S4 method for list rawvalues(obj) # S4 method for ANY rawvalues(obj) # S4 method for CellValue rawvalues(obj) # S4 method for TreePos rawvalues(obj) # S4 method for RowsVerticalSection rawvalues(obj) value_names(obj) # S4 method for ANY value_names(obj) # S4 method for TreePos value_names(obj) # S4 method for list value_names(obj) # S4 method for ValueWrapper value_names(obj) # S4 method for LevelComboSplitValue value_names(obj) # S4 method for RowsVerticalSection value_names(obj) value_labels(obj) # S4 method for ANY value_labels(obj) # S4 method for TreePos value_labels(obj) # S4 method for list value_labels(obj) # S4 method for RowsVerticalSection value_labels(obj) # S4 method for ValueWrapper value_labels(obj) # S4 method for LevelComboSplitValue value_labels(obj) # S4 method for MultiVarSplit value_labels(obj) spl_varlabels(obj) # S4 method for MultiVarSplit spl_varlabels(obj) spl_varlabels(object) <- value # S4 method for MultiVarSplit spl_varlabels(object) <- value splv_extra(obj) # S4 method for SplitValue splv_extra(obj) splv_extra(obj) <- value # S4 method for SplitValue splv_extra(obj) <- value split_exargs(obj) # S4 method for Split split_exargs(obj) split_exargs(obj) <- value # S4 method for Split split_exargs(obj) <- value col_extra_args(obj, df = NULL) # S4 method for InstantiatedColumnInfo col_extra_args(obj, df = NULL) # S4 method for PreDataTableLayouts col_extra_args(obj, df = NULL) # S4 method for PreDataColLayout col_extra_args(obj, df = NULL) # S4 method for LayoutColTree col_extra_args(obj, df = NULL) # S4 method for LayoutColLeaf col_extra_args(obj, df = NULL) disp_ccounts(obj) # S4 method for VTableTree disp_ccounts(obj) # S4 method for InstantiatedColumnInfo disp_ccounts(obj) # S4 method for PreDataTableLayouts disp_ccounts(obj) # S4 method for PreDataColLayout disp_ccounts(obj) disp_ccounts(obj) <- value # S4 method for VTableTree disp_ccounts(obj) <- value # S4 method for InstantiatedColumnInfo disp_ccounts(obj) <- value # S4 method for PreDataColLayout disp_ccounts(obj) <- value # S4 method for LayoutColTree disp_ccounts(obj) <- value # S4 method for PreDataTableLayouts disp_ccounts(obj) <- value colcount_format(obj) # S4 method for InstantiatedColumnInfo colcount_format(obj) # S4 method for VTableNodeInfo colcount_format(obj) # S4 method for PreDataColLayout colcount_format(obj) # S4 method for PreDataTableLayouts colcount_format(obj) colcount_format(obj) <- value # S4 method for InstantiatedColumnInfo colcount_format(obj) <- value # S4 method for VTableNodeInfo colcount_format(obj) <- value # S4 method for PreDataColLayout colcount_format(obj) <- value # S4 method for PreDataTableLayouts colcount_format(obj) <- value # S4 method for TableRow as.vector(x, mode = \"any\") # S4 method for ElementaryTable as.vector(x, mode = \"any\") spl_cuts(obj) # S4 method for VarStaticCutSplit spl_cuts(obj) spl_cutlabels(obj) # S4 method for VarStaticCutSplit spl_cutlabels(obj) spl_cutfun(obj) # S4 method for VarDynCutSplit spl_cutfun(obj) spl_cutlabelfun(obj) # S4 method for VarDynCutSplit spl_cutlabelfun(obj) spl_is_cmlcuts(obj) # S4 method for VarDynCutSplit spl_is_cmlcuts(obj) spl_varnames(obj) # S4 method for MultiVarSplit spl_varnames(obj) spl_varnames(object) <- value # S4 method for MultiVarSplit spl_varnames(object) <- value # S4 method for TableRow row_footnotes(obj) # S4 method for RowsVerticalSection row_footnotes(obj) # S4 method for TableRow row_footnotes(obj) <- value # S4 method for VTableTree row_footnotes(obj) # S4 method for CellValue cell_footnotes(obj) # S4 method for TableRow cell_footnotes(obj) # S4 method for LabelRow cell_footnotes(obj) # S4 method for VTableTree cell_footnotes(obj) # S4 method for CellValue cell_footnotes(obj) <- value # S4 method for DataRow cell_footnotes(obj) <- value # S4 method for ContentRow cell_footnotes(obj) <- value # S4 method for ANY col_fnotes_here(obj) <- value # S4 method for LayoutColTree col_footnotes(obj) # S4 method for LayoutColLeaf col_footnotes(obj) # S4 method for LayoutColTree col_footnotes(obj) <- value # S4 method for LayoutColLeaf col_footnotes(obj) <- value # S4 method for VTableTree col_footnotes(obj) # S4 method for RefFootnote ref_index(obj) # S4 method for RefFootnote ref_index(obj) <- value # S4 method for RefFootnote ref_symbol(obj) # S4 method for RefFootnote ref_symbol(obj) <- value # S4 method for RefFootnote ref_msg(obj) # S4 method for VTableTree,character fnotes_at_path(obj, rowpath = NULL, colpath = NULL, reset_idx = TRUE) <- value # S4 method for VTableTree,NULL fnotes_at_path(obj, rowpath = NULL, colpath = NULL, reset_idx = TRUE) <- value # S4 method for VTableNodeInfo,missing rbind2(x, y) # S4 method for VTableTree tt_at_path(tt, path, ...) # S4 method for VTableTree,ANY,VTableTree tt_at_path(tt, path, ...) <- value # S4 method for VTableTree,ANY,NULL tt_at_path(tt, path, ...) <- value # S4 method for VTableTree,ANY,TableRow tt_at_path(tt, path, ...) <- value # S4 method for VTableTree,ANY,ANY,CellValue [(x, i, j, ...) <- value # S4 method for VTableTree,logical,ANY [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,logical,missing [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,ANY,logical [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,ANY,missing [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,missing,ANY [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,ANY,character [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,character,ANY [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,character,character [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,missing,numeric [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree,numeric,numeric [(x, i, j, ..., drop = FALSE) # S4 method for VTableTree cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) # S4 method for TableRow cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) # S4 method for LabelRow cell_values(tt, rowpath = NULL, colpath = NULL, omit_labrows = TRUE) # S4 method for TableRow value_at(tt, rowpath = NULL, colpath = NULL) # S4 method for LabelRow value_at(tt, rowpath = NULL, colpath = NULL) # S4 method for VTableTree print(x, ...) # S4 method for VTableTree show(object)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/int_methods.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"combine SplitVector objects — internal_methods","text":"x object. ... Splits SplitVector objects lyt layout object pre-data used tabulation spl Split. split. pos numeric(1). Intended internal use. cmpnd_fun function. Intended internal use. constructor function. label character(1). label (confused name) object/structure. cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. cformat format spec. Format content rows cna_str character. NA string use cformat content table. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. cvar character(1). variable, , content function accept. Defaults NA. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. df dataset (data.frame tibble) obj object. depth depth tree indent indent print_indent indent print object table object max.level numeric(1). Passed utils::str. Defaults 3 VTableTree method, unlike underlying default NA. NA appropriate VTableTree objects. value new value nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. for_analyze logical(1). format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". override logical(1). tt TableTree (related class). TableTree object representing populated table. incl.cont logical. Include rows content tables within tree. Defaults TRUE add.labrows logical. Include label rows. Defaults FALSE mode character(1). Passed .vector rowpath character NULL. Path within row structure. NULL indicates footnote go column rather cell. colpath character NULL. Path within column structure. NULL indicates footnote go row rather cell reset_idx logical(1). numbering referential footnotes immediately recalculated. Defaults TRUE. y Second element rbound via rbind2 path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. index j index drop logical(1). value cell returned one cell selected combination j. possible return vector values. please consider using cell_values(). Defaults FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/int_methods.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"combine SplitVector objects — internal_methods","text":"Various, considered implementation details.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/int_methods.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"combine SplitVector objects — internal_methods","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) tbl <- build_table(lyt, iris2) indent_mod(tbl) #> [1] 0 indent_mod(tbl) <- 1L tbl #> setosa versicolor virginica #> a b a b a b #> ——————————————————————————————————————————————————————————— #> Sepal.Length #> Min. 4.40 4.30 5.00 4.90 4.90 5.60 #> 1st Qu. 4.80 4.80 5.60 5.60 6.20 6.30 #> Median 5.00 5.00 5.90 5.90 6.50 6.50 #> Mean 5.02 4.99 5.99 5.88 6.50 6.67 #> 3rd Qu. 5.30 5.10 6.40 6.10 6.70 7.20 #> Max. 5.80 5.70 7.00 6.70 7.70 7.90 #> Petal.Width #> Min. 0.10 0.10 1.00 1.00 1.40 1.50 #> 1st Qu. 0.20 0.20 1.20 1.20 1.90 1.80 #> Median 0.20 0.20 1.30 1.30 2.10 2.00 #> Mean 0.23 0.26 1.35 1.30 2.08 1.98 #> 3rd Qu. 0.20 0.30 1.50 1.40 2.30 2.20 #> Max. 0.40 0.60 1.80 1.70 2.50 2.50"},{"path":"https://insightsengineering.github.io/rtables/main/reference/is_rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Check if an object is a valid rtable — is_rtable","title":"Check if an object is a valid rtable — is_rtable","text":"Check object valid rtable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/is_rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check if an object is a valid rtable — is_rtable","text":"","code":"is_rtable(x)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/is_rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check if an object is a valid rtable — is_rtable","text":"x object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/is_rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check if an object is a valid rtable — is_rtable","text":"TRUE x formal Table object, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/is_rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check if an object is a valid rtable — is_rtable","text":"","code":"is_rtable(build_table(basic_table(), iris)) #> [1] TRUE"},{"path":"https://insightsengineering.github.io/rtables/main/reference/label_at_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Label at Path — label_at_path","title":"Label at Path — label_at_path","text":"Gets sets label path","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/label_at_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Label at Path — label_at_path","text":"","code":"label_at_path(tt, path) label_at_path(tt, path) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/label_at_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Label at Path — label_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/label_at_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Label at Path — label_at_path","text":"path resolves single row, label row retrieved set. , instead, path resolves subtable, text row-label associated path retrieved set. subtable case, label text set non-NA value, labelrow set visible, even . Similarly, label row text subtable set NA, label row bet set non-visible, row appear table printed.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/label_at_path.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Label at Path — label_at_path","text":"changing row labels content rows, important path way row. Paths ending \"@content\" exhibit behavior want, thus error. See row_paths help determining full paths content rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/label_at_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Label at Path — label_at_path","text":"","code":"lyt <- basic_table() %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(c(\"CHN\", \"USA\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) label_at_path(tbl, c(\"COUNTRY\", \"CHN\")) #> [1] \"CHN\" label_at_path(tbl, c(\"COUNTRY\", \"USA\")) <- \"United States\" tbl #> all obs #> ——————————————————————— #> CHN #> Mean 34.64 #> United States #> Mean 35.30"},{"path":"https://insightsengineering.github.io/rtables/main/reference/length-CellValue-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Length of a Cell value — length,CellValue-method","title":"Length of a Cell value — length,CellValue-method","text":"Length Cell value","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/length-CellValue-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Length of a Cell value — length,CellValue-method","text":"","code":"# S4 method for CellValue length(x)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/length-CellValue-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Length of a Cell value — length,CellValue-method","text":"x x.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/length-CellValue-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Length of a Cell value — length,CellValue-method","text":"Always returns 1L","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":null,"dir":"Reference","previous_headings":"","what":"Returns a function that coerces the return values of f to a list — list_wrap_x","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"Returns function coerces return values f list","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"","code":"list_wrap_x(f) list_wrap_df(f)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"f function wrap.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"function calls f converts result list CellValue objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"list_wrap_x generates wrapper takes x first argument, list_wrap_df generates otherwise identical wrapper function whose first argument named df. provide using functions tabulation analyze, functions take df first argument passed full subset dataframe, accept anything else notably including x passed relevant subset variable analyzed.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/list_wrap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Returns a function that coerces the return values of f to a list — list_wrap_x","text":"","code":"summary(iris$Sepal.Length) #> Min. 1st Qu. Median Mean 3rd Qu. Max. #> 4.300 5.100 5.800 5.843 6.400 7.900 f <- list_wrap_x(summary) f(x = iris$Sepal.Length) #> $Min. #> [1] 4.3 #> #> $`1st Qu.` #> [1] 5.1 #> #> $Median #> [1] 5.8 #> #> $Mean #> [1] 5.843333 #> #> $`3rd Qu.` #> [1] 6.4 #> #> $Max. #> [1] 7.9 #> f2 <- list_wrap_df(summary) f2(df = iris$Sepal.Length) #> $Min. #> [1] 4.3 #> #> $`1st Qu.` #> [1] 5.1 #> #> $Median #> [1] 5.8 #> #> $Mean #> [1] 5.843333 #> #> $`3rd Qu.` #> [1] 6.4 #> #> $Max. #> [1] 7.9 #>"},{"path":"https://insightsengineering.github.io/rtables/main/reference/lyt_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Layouting Function Arg Conventions — lyt_args","title":"Layouting Function Arg Conventions — lyt_args","text":"Layouting Function Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/lyt_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Layouting Function Arg Conventions — lyt_args","text":"","code":"lyt_args( lyt, var, vars, label, labels_var, varlabels, varnames, split_format, split_na_str, nested, format, cfun, cformat, cna_str, split_fun, split_name, split_label, afun, inclNAs, valorder, ref_group, compfun, label_fstr, child_labels, extra_args, name, cuts, cutlabels, cutfun, cutlabelfun, cumulative, indent_mod, show_labels, label_pos, var_labels, cvar, table_names, topleft, align, page_by, page_prefix, format_na_str, section_div, na_str )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/lyt_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Layouting Function Arg Conventions — lyt_args","text":"lyt layout object pre-data used tabulation var string, variable name vars character vector. Multiple variable names. label character(1). label (confused name) object/structure. labels_var string, name variable containing labels displayed values var varlabels character vector. Labels vars varnames character vector. Names vars appear pathing. vars unique variable names. , variable names suffixes necessary enforce uniqueness. split_format FormatSpec. Default format associated split created. split_na_str character. NA string vector use split_format. nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. cformat format spec. Format content rows cna_str character. NA string use cformat content table. split_fun function/NULL. custom splitting function See custom_split_funs split_name string. Name associated split (pathing, etc) split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). afun function. Analysis function, must take x df first parameter. Can optionally take parameters populated tabulation framework. See Details analyze. inclNAs boolean. observations NA var variable(s) included performing analysis. Defaults FALSE valorder character vector. Order split children appear resulting table. ref_group character. Value var taken ref_group/control compared . compfun function/string. comparison function accepts analysis function outputs two different partitions returns single value. Defaults subtraction. string, taken name function. label_fstr string. sprintf style format string containing. non-comparison splits, can contain one \"%s\" takes current split value generates row/column label. Comparison-based splits can contain two \"%s\". child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. name character(1). Name split/table/row created. Defaults corresponding label, required . cuts numeric. Cuts use cutlabels character (NULL). Labels cuts cutfun function. Function accepts full vector var values returns cut points used (via cut) splitting data tabulation cutlabelfun function. Function returns either labels cuts NULL passed return value cutfun cumulative logical. cuts treated cumulative. Defaults FALSE indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. show_labels character(1). variable labels corresponding variable(s) vars visible resulting table. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. var_labels character. Variable labels 1 variables cvar character(1). variable, , content function accept. Defaults NA. table_names character. Names tables representing atomic analysis. Defaults var. topleft character. Override values \"top left\" material displayed printing. align character(1) NULL. Alignment value rendered . defaults \"center\" NULL used. See formatters::list_valid_aligns() currently supported alignments. page_by logical(1). pagination forced different children resulting form split. error rise selected split contain least one value NA. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table format_na_str character(1). String displayed formatted cell's value(s) NA. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. na_str character(1). String displayed value x missing. Defaults \"NA\".","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/lyt_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Layouting Function Arg Conventions — lyt_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_afun.html","id":null,"dir":"Reference","previous_headings":"","what":"Create custom analysis function wrapping existing function — make_afun","title":"Create custom analysis function wrapping existing function — make_afun","text":"Create custom analysis function wrapping existing function","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_afun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create custom analysis function wrapping existing function — make_afun","text":"","code":"make_afun( fun, .stats = NULL, .formats = NULL, .labels = NULL, .indent_mods = NULL, .ungroup_stats = NULL, .format_na_strs = NULL, ..., .null_ref_cells = \".in_ref_col\" %in% names(formals(fun)) )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_afun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create custom analysis function wrapping existing function — make_afun","text":"fun function. function wrapped new customized analysis fun. return named list. .stats character. Names elements keep fun's full output. .formats . vector/list formats override defaults applied fun. .labels character. Vector labels override defaults returned fun .indent_mods integer. Named vector indent modifiers generated rows. .ungroup_stats character. Vector names, must match elements .stats .format_na_strs . vector/list na strings override defaults applied fun. ... dots. Additional arguments fun effectively become new defaults. can still overridden extra_args within split. .null_ref_cells logical(1). cells reference column NULL-ed returned analysis function. Defaults TRUE fun accepts .in_ref_col formal argument. Note argument occurs ... must fully specified name set.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_afun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create custom analysis function wrapping existing function — make_afun","text":"function suitable use analyze element selection, reformatting, relabeling performed automatically.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_afun.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create custom analysis function wrapping existing function — make_afun","text":"setting .ungroup_stats non-null changes structure value(s) returned fun, rather just labeling (.labels), formatting (.formats), selecting amongst (.stats) . means subsequent make_afun calls customize output can must operate new structure, original structure returned fun. See final pair examples .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_afun.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create custom analysis function wrapping existing function — make_afun","text":"","code":"s_summary <- function(x) { stopifnot(is.numeric(x)) list( n = sum(!is.na(x)), mean_sd = c(mean = mean(x), sd = sd(x)), min_max = range(x) ) } s_summary(iris$Sepal.Length) #> $n #> [1] 150 #> #> $mean_sd #> mean sd #> 5.8433333 0.8280661 #> #> $min_max #> [1] 4.3 7.9 #> a_summary <- make_afun( fun = s_summary, .formats = c(n = \"xx\", mean_sd = \"xx.xx (xx.xx)\", min_max = \"xx.xx - xx.xx\"), .labels = c(n = \"n\", mean_sd = \"Mean (sd)\", min_max = \"min - max\") ) a_summary(x = iris$Sepal.Length) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 150 0 n #> 2 mean_sd 5.84 (0.83) 0 Mean (sd) #> 3 min_max 4.30 - 7.90 0 min - max a_summary2 <- make_afun(a_summary, .stats = c(\"n\", \"mean_sd\")) a_summary2(x = iris$Sepal.Length) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 n 150 0 n #> 2 mean_sd 5.84 (0.83) 0 Mean (sd) a_summary3 <- make_afun(a_summary, .formats = c(mean_sd = \"(xx.xxx, xx.xxx)\")) s_foo <- function(df, .N_col, a = 1, b = 2) { list( nrow_df = nrow(df), .N_col = .N_col, a = a, b = b ) } s_foo(iris, 40) #> $nrow_df #> [1] 150 #> #> $.N_col #> [1] 40 #> #> $a #> [1] 1 #> #> $b #> [1] 2 #> a_foo <- make_afun(s_foo, b = 4, .formats = c(nrow_df = \"xx.xx\", \".N_col\" = \"xx.\", a = \"xx\", b = \"xx.x\"), .labels = c( nrow_df = \"Nrow df\", \".N_col\" = \"n in cols\", a = \"a value\", b = \"b value\" ), .indent_mods = c(nrow_df = 2L, a = 1L) ) a_foo(iris, .N_col = 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150.00 2 Nrow df #> 2 .N_col 40 0 n in cols #> 3 a 1 1 a value #> 4 b 4.0 0 b value a_foo2 <- make_afun(a_foo, .labels = c(nrow_df = \"Number of Rows\")) a_foo2(iris, .N_col = 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150.00 2 Number of Rows #> 2 .N_col 40 0 n in cols #> 3 a 1 1 a value #> 4 b 4.0 0 b value # grouping and further customization s_grp <- function(df, .N_col, a = 1, b = 2) { list( nrow_df = nrow(df), .N_col = .N_col, letters = list( a = a, b = b ) ) } a_grp <- make_afun(s_grp, b = 3, .labels = c( nrow_df = \"row count\", .N_col = \"count in column\" ), .formats = c(nrow_df = \"xx.\", .N_col = \"xx.\"), .indent_mod = c(letters = 1L), .ungroup_stats = \"letters\" ) a_grp(iris, 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150 0 row count #> 2 .N_col 40 0 count in column #> 3 a 1 1 a #> 4 b 3 1 b a_aftergrp <- make_afun(a_grp, .stats = c(\"nrow_df\", \"b\"), .formats = c(b = \"xx.\") ) a_aftergrp(iris, 40) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 nrow_df 150 0 row count #> 2 b 3 0 b s_ref <- function(x, .in_ref_col, .ref_group) { list( mean_diff = mean(x) - mean(.ref_group) ) } a_ref <- make_afun(s_ref, .labels = c(mean_diff = \"Mean Difference from Ref\") ) a_ref(iris$Sepal.Length, .in_ref_col = TRUE, 1:10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 mean_diff 0 Mean Difference from Ref a_ref(iris$Sepal.Length, .in_ref_col = FALSE, 1:10) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 mean_diff 0.343333333333334 0 Mean Difference from Ref"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Column Layout Summary — make_col_df","title":"Column Layout Summary — make_col_df","text":"Generate structural summary columns rtables table return data.frame.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Column Layout Summary — make_col_df","text":"","code":"make_col_df(tt, colwidths = NULL, visible_only = TRUE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Column Layout Summary — make_col_df","text":"tt . Object representing table-like object summarized. colwidths numeric. Internal detail set manually. visible_only logical(1). visible aspects table structure reflected summary. Defaults TRUE. May supported methods.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Column Layout Summary — make_col_df","text":"Used Pagination","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_row_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Return List with Table Row/Col Paths — row_paths","title":"Return List with Table Row/Col Paths — row_paths","text":"Return List Table Row/Col Paths","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_row_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Return List with Table Row/Col Paths — row_paths","text":"","code":"row_paths(x) col_paths(x)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_row_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Return List with Table Row/Col Paths — row_paths","text":"x rtable object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_row_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Return List with Table Row/Col Paths — row_paths","text":"list paths row/column within x","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_col_row_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Return List with Table Row/Col Paths — row_paths","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"SEX\", \"AGE\")) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————— #> SEX #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 #> AGE #> Mean 33.77 35.43 35.43 row_paths(tbl) #> [[1]] #> [1] \"ma_SEX_AGE\" \"SEX\" #> #> [[2]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"F\" #> #> [[3]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"M\" #> #> [[4]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"U\" #> #> [[5]] #> [1] \"ma_SEX_AGE\" \"SEX\" \"UNDIFFERENTIATED\" #> #> [[6]] #> [1] \"ma_SEX_AGE\" \"AGE\" #> #> [[7]] #> [1] \"ma_SEX_AGE\" \"AGE\" \"Mean\" #> col_paths(tbl) #> [[1]] #> [1] \"ARM\" \"A: Drug X\" #> #> [[2]] #> [1] \"ARM\" \"B: Placebo\" #> #> [[3]] #> [1] \"ARM\" \"C: Combination\" #> cell_values(tbl, c(\"AGE\", \"Mean\"), c(\"ARM\", \"B: Placebo\")) #> $`B: Placebo` #> [1] 35.43284 #>"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_fun.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Custom Splitting Function — make_split_fun","title":"Create a Custom Splitting Function — make_split_fun","text":"Create Custom Splitting Function","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_fun.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Custom Splitting Function — make_split_fun","text":"","code":"make_split_fun(pre = list(), core_split = NULL, post = list())"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_fun.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Custom Splitting Function — make_split_fun","text":"pre list. Zero functions operate incoming data return new data frame split via core_split. called data order appear list. core_split function NULL. NULL, function accepts arguments do_base_split , returns type named list. Custom functions override behavior used column splits. post list. Zero functions called list output splitting.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_fun.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Custom Splitting Function — make_split_fun","text":"function use custom split function.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_fun.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a Custom Splitting Function — make_split_fun","text":"Custom split functions can thought () 3 different types manipulations splitting process Preprocessing incoming data split (Row-splitting ) Customization core mapping incoming data facets, Postprocessing operations set facets (groups) generated split. function provides interface create custom split functions implementing specifying sets operations classes customization independently. Preprocessing functions (1), must accept: df, spl, vals, labels, can optionally accept .spl_context. manipulate df ( incoming data split) return modified data.frame. modified data.frame must contain columns present incoming data.frame, can add columns necessary (though note new columns used layout split analysis variables, present validity checking done). preprocessing component useful things manipulating factor levels, e.g., trim unobserved ones reorder levels based observed counts, etc. Customization core splitting (2) currently supported row splits. Core splitting functions override fundamental splitting procedure, necessary rare cases. must accept spl, df, vals, labels, can optionally accept .spl_context. must return named list elements, length, follows: - datasplit (containing list data.frames), - values containing values associated facets, must character SplitValue objects. values appear paths resulting table. - labels containing character labels associated values Postprocessing functions (3) must accept result core split first argument ( writing can anything), addition spl, fulldf, can optionally accept .spl_context. must return modified version structure specified core splitting. pre- post-processing cases, multiple functions can specified. happens, applied sequentially, order appear list passed relevant argument (pre post, respectively).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_fun.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Custom Splitting Function — make_split_fun","text":"","code":"mysplitfun <- make_split_fun( pre = list(drop_facet_levels), post = list(add_overall_facet(\"ALL\", \"All Arms\")) ) basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = mysplitfun) %>% analyze(\"AGE\") %>% build_table(subset(DM, ARM %in% c(\"B: Placebo\", \"C: Combination\"))) #> B: Placebo C: Combination All Arms #> (N=106) (N=129) (N=235) #> ————————————————————————————————————————————— #> Mean 33.02 34.57 33.87 ## post (and pre) arguments can take multiple functions, here ## we add an overall facet and the reorder the facets reorder_facets <- function(splret, spl, fulldf, ...) { ord <- order(names(splret$values)) make_split_result( splret$values[ord], splret$datasplit[ord], splret$labels[ord] ) } mysplitfun2 <- make_split_fun( pre = list(drop_facet_levels), post = list( add_overall_facet(\"ALL\", \"All Arms\"), reorder_facets ) ) basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\", split_fun = mysplitfun2) %>% analyze(\"AGE\") %>% build_table(subset(DM, ARM %in% c(\"B: Placebo\", \"C: Combination\"))) #> All Arms B: Placebo C: Combination #> (N=235) (N=106) (N=129) #> ————————————————————————————————————————————— #> Mean 33.87 33.02 34.57 very_stupid_core <- function(spl, df, vals, labels, .spl_context) { make_split_result(c(\"stupid\", \"silly\"), datasplit = list(df[1:10, ], df[11:30, ]), labels = c(\"first 10\", \"second 20\") ) } dumb_30_facet <- add_combo_facet(\"dumb\", label = \"thirty patients\", levels = c(\"stupid\", \"silly\") ) nonsense_splfun <- make_split_fun( core_split = very_stupid_core, post = list(dumb_30_facet) ) ## recall core split overriding is not supported in column space ## currently, but we can see it in action in row space lyt_silly <- basic_table() %>% split_rows_by(\"ARM\", split_fun = nonsense_splfun) %>% summarize_row_groups() %>% analyze(\"AGE\") silly_table <- build_table(lyt_silly, DM) silly_table #> all obs #> ——————————————————————————— #> first 10 10 (2.8%) #> Mean 31.10 #> second 20 20 (5.6%) #> Mean 34.25 #> thirty patients 30 (8.4%) #> Mean 33.20"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_result.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct split result object — make_split_result","title":"Construct split result object — make_split_result","text":"functions can used create add split result functions implement core splitting post-processing within custom split function.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_result.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct split result object — make_split_result","text":"","code":"make_split_result(values, datasplit, labels, extras = NULL) add_to_split_result(splres, values, datasplit, labels, extras = NULL)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_result.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct split result object — make_split_result","text":"values character list(SplitValue). values associated facet datasplit list(data.frame). facet data facet generated split labels character. labels associated facet extras NULL list. Extra values associated facets passed analysis functions applied within facet. splres list. list representing result splitting.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_result.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Construct split result object — make_split_result","text":"named list representing facets generated split elements values, datasplit, labels, length correspond elementwise.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_result.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct split result object — make_split_result","text":"functions various housekeeping ensure split result list rtables internals expect , relevant end users.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/make_split_result.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Construct split result object — make_split_result","text":"","code":"splres <- make_split_result( values = c(\"hi\", \"lo\"), datasplit = list(hi = mtcars, lo = mtcars[1:10, ]), labels = c(\"more data\", \"less data\") ) splres2 <- add_to_split_result(splres, values = \"med\", datasplit = list(med = mtcars[1:20, ]), labels = \"kinda some data\" )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/manual_cols.html","id":null,"dir":"Reference","previous_headings":"","what":"Manual column declaration — manual_cols","title":"Manual column declaration — manual_cols","text":"Manual column declaration","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/manual_cols.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manual column declaration — manual_cols","text":"","code":"manual_cols(..., .lst = list(...))"},{"path":"https://insightsengineering.github.io/rtables/main/reference/manual_cols.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manual column declaration — manual_cols","text":"... One vectors levels appear column space. one set levels given, values second nested within value first, . .lst list sets levels, default populated via list(...).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/manual_cols.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manual column declaration — manual_cols","text":"InstantiatedColumnInfo object, suitable use declaring column structure manually constructed table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/manual_cols.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Manual column declaration — manual_cols","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/manual_cols.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manual column declaration — manual_cols","text":"","code":"# simple one level column space rows <- lapply(1:5, function(i) { DataRow(rep(i, times = 3)) }) tbl <- TableTree(kids = rows, cinfo = manual_cols(split = c(\"a\", \"b\", \"c\"))) tbl #> a b c #> ———————————— #> 1 1 1 #> 2 2 2 #> 3 3 3 #> 4 4 4 #> 5 5 5 # manually declared nesting tbl2 <- TableTree( kids = list(DataRow(as.list(1:4))), cinfo = manual_cols( Arm = c(\"Arm A\", \"Arm B\"), Gender = c(\"M\", \"F\") ) ) tbl2 #> Arm A Arm B #> M F M F #> —————————————————————— #> 1 2 3 4"},{"path":"https://insightsengineering.github.io/rtables/main/reference/matrix_form-VTableTree-method.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"Although rtables represented tree data structure outputting table ASCII HTML useful map rtable state formatted cells matrix form.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/matrix_form-VTableTree-method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"","code":"# S4 method for VTableTree matrix_form( obj, indent_rownames = FALSE, expand_newlines = TRUE, indent_size = 2 )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/matrix_form-VTableTree-method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"obj . object accessor access modify indent_rownames logical(1), TRUE column row names strings matrix indented row names (strings pre-fixed) expand_newlines logical(1). matrix form generated expand rows whose values contain newlines multiple 'physical' rows ( appear rendered ASCII). Defaults TRUE indent_size numeric(1). Number spaces use per indent level. Defaults 2","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/matrix_form-VTableTree-method.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"list following elements: strings content, printed, top-left material, column headers, row labels , cell values tt spans column-span information print-string strings matrix aligns text alignment print-string strings matrix display Whether print-string strings matrix printed row_info data.frame generated make_row_df additional nrow_header attribute indicating number pseudo \"rows\" column structure defines.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/matrix_form-VTableTree-method.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"strings return object defined follows: row labels determined make_row_df cell values determined using get_formatted_cells. (Column labels calculated using non-exported internal function.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/matrix_form-VTableTree-method.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform rtable to a list of matrices which can be used for outputting — matrix_form,VTableTree-method","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> Species (lvls) -> group (lvls) #> #> Row-Split Structure: #> Sepal.Length:Petal.Width (** multivar analysis **) #> tbl <- build_table(lyt, iris2) matrix_form(tbl) #> $strings #> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] \"\" \"setosa\" \"setosa\" \"versicolor\" \"versicolor\" \"virginica\" #> [2,] \"\" \"a\" \"b\" \"a\" \"b\" \"a\" #> [3,] \"Sepal.Length\" \"\" \"\" \"\" \"\" \"\" #> [4,] \"Min.\" \"4.40\" \"4.30\" \"5.00\" \"4.90\" \"4.90\" #> [5,] \"1st Qu.\" \"4.80\" \"4.80\" \"5.60\" \"5.60\" \"6.20\" #> [6,] \"Median\" \"5.00\" \"5.00\" \"5.90\" \"5.90\" \"6.50\" #> [7,] \"Mean\" \"5.02\" \"4.99\" \"5.99\" \"5.88\" \"6.50\" #> [8,] \"3rd Qu.\" \"5.30\" \"5.10\" \"6.40\" \"6.10\" \"6.70\" #> [9,] \"Max.\" \"5.80\" \"5.70\" \"7.00\" \"6.70\" \"7.70\" #> [10,] \"Petal.Width\" \"\" \"\" \"\" \"\" \"\" #> [11,] \"Min.\" \"0.10\" \"0.10\" \"1.00\" \"1.00\" \"1.40\" #> [12,] \"1st Qu.\" \"0.20\" \"0.20\" \"1.20\" \"1.20\" \"1.90\" #> [13,] \"Median\" \"0.20\" \"0.20\" \"1.30\" \"1.30\" \"2.10\" #> [14,] \"Mean\" \"0.23\" \"0.26\" \"1.35\" \"1.30\" \"2.08\" #> [15,] \"3rd Qu.\" \"0.20\" \"0.30\" \"1.50\" \"1.40\" \"2.30\" #> [16,] \"Max.\" \"0.40\" \"0.60\" \"1.80\" \"1.70\" \"2.50\" #> [,7] #> [1,] \"virginica\" #> [2,] \"b\" #> [3,] \"\" #> [4,] \"5.60\" #> [5,] \"6.30\" #> [6,] \"6.50\" #> [7,] \"6.67\" #> [8,] \"7.20\" #> [9,] \"7.90\" #> [10,] \"\" #> [11,] \"1.50\" #> [12,] \"1.80\" #> [13,] \"2.00\" #> [14,] \"1.98\" #> [15,] \"2.20\" #> [16,] \"2.50\" #> #> $spans #> setosa.a setosa.b versicolor.a versicolor.b virginica.a virginica.b #> [1,] 1 2 2 2 2 2 2 #> [2,] 1 1 1 1 1 1 1 #> [3,] 1 1 1 1 1 1 1 #> [4,] 1 1 1 1 1 1 1 #> [5,] 1 1 1 1 1 1 1 #> [6,] 1 1 1 1 1 1 1 #> [7,] 1 1 1 1 1 1 1 #> [8,] 1 1 1 1 1 1 1 #> [9,] 1 1 1 1 1 1 1 #> [10,] 1 1 1 1 1 1 1 #> [11,] 1 1 1 1 1 1 1 #> [12,] 1 1 1 1 1 1 1 #> [13,] 1 1 1 1 1 1 1 #> [14,] 1 1 1 1 1 1 1 #> [15,] 1 1 1 1 1 1 1 #> [16,] 1 1 1 1 1 1 1 #> #> $aligns #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [2,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [3,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [4,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [5,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [6,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [7,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [8,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [9,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [10,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [11,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [12,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [13,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [14,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [15,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> [16,] \"left\" \"center\" \"center\" \"center\" \"center\" \"center\" \"center\" #> #> $display #> virginica.b1 virginica.b2 virginica.b3 virginica.b4 virginica.b5 #> [1,] TRUE TRUE FALSE TRUE FALSE TRUE #> [2,] TRUE TRUE TRUE TRUE TRUE TRUE #> [3,] TRUE TRUE TRUE TRUE TRUE TRUE #> [4,] TRUE TRUE TRUE TRUE TRUE TRUE #> [5,] TRUE TRUE TRUE TRUE TRUE TRUE #> [6,] TRUE TRUE TRUE TRUE TRUE TRUE #> [7,] TRUE TRUE TRUE TRUE TRUE TRUE #> [8,] TRUE TRUE TRUE TRUE TRUE TRUE #> [9,] TRUE TRUE TRUE TRUE TRUE TRUE #> [10,] TRUE TRUE TRUE TRUE TRUE TRUE #> [11,] TRUE TRUE TRUE TRUE TRUE TRUE #> [12,] TRUE TRUE TRUE TRUE TRUE TRUE #> [13,] TRUE TRUE TRUE TRUE TRUE TRUE #> [14,] TRUE TRUE TRUE TRUE TRUE TRUE #> [15,] TRUE TRUE TRUE TRUE TRUE TRUE #> [16,] TRUE TRUE TRUE TRUE TRUE TRUE #> virginica.b6 #> [1,] FALSE #> [2,] TRUE #> [3,] TRUE #> [4,] TRUE #> [5,] TRUE #> [6,] TRUE #> [7,] TRUE #> [8,] TRUE #> [9,] TRUE #> [10,] TRUE #> [11,] TRUE #> [12,] TRUE #> [13,] TRUE #> [14,] TRUE #> [15,] TRUE #> [16,] TRUE #> #> $formats #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] \"\" \"\" \"\" \"\" \"\" \"\" \"\" #> [2,] \"\" \"\" \"\" \"\" \"\" \"\" \"\" #> [3,] \"\" \"-\" \"-\" \"-\" \"-\" \"-\" \"-\" #> [4,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [5,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [6,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [7,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [8,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [9,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [10,] \"\" \"-\" \"-\" \"-\" \"-\" \"-\" \"-\" #> [11,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [12,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [13,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [14,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [15,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> [16,] \"\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" \"xx.xx\" #> #> $row_info #> label name abs_rownumber path pos_in_siblings #> 1 Sepal.Length Sepal.Length 1 ma_Sepal.... NA #> 2 Min. Min. 2 ma_Sepal.... 1 #> 3 1st Qu. 1st Qu. 3 ma_Sepal.... 2 #> 4 Median Median 4 ma_Sepal.... 3 #> 5 Mean Mean 5 ma_Sepal.... 4 #> 6 3rd Qu. 3rd Qu. 6 ma_Sepal.... 5 #> 7 Max. Max. 7 ma_Sepal.... 6 #> 8 Petal.Width Petal.Width 8 ma_Sepal.... NA #> 9 Min. Min. 9 ma_Sepal.... 1 #> 10 1st Qu. 1st Qu. 10 ma_Sepal.... 2 #> 11 Median Median 11 ma_Sepal.... 3 #> 12 Mean Mean 12 ma_Sepal.... 4 #> 13 3rd Qu. 3rd Qu. 13 ma_Sepal.... 5 #> 14 Max. Max. 14 ma_Sepal.... 6 #> n_siblings self_extent par_extent reprint_inds node_class indent nrowrefs #> 1 NA 1 0 LabelRow 0 0 #> 2 6 1 1 1 DataRow 1 0 #> 3 6 1 1 1 DataRow 1 0 #> 4 6 1 1 1 DataRow 1 0 #> 5 6 1 1 1 DataRow 1 0 #> 6 6 1 1 1 DataRow 1 0 #> 7 6 1 1 1 DataRow 1 0 #> 8 NA 1 0 LabelRow 0 0 #> 9 6 1 1 8 DataRow 1 0 #> 10 6 1 1 8 DataRow 1 0 #> 11 6 1 1 8 DataRow 1 0 #> 12 6 1 1 8 DataRow 1 0 #> 13 6 1 1 8 DataRow 1 0 #> 14 6 1 1 8 DataRow 1 0 #> ncellrefs nreflines force_page page_title trailing_sep #> 1 0 0 FALSE #> 2 0 0 FALSE #> 3 0 0 FALSE #> 4 0 0 FALSE #> 5 0 0 FALSE #> 6 0 0 FALSE #> 7 0 0 FALSE #> 8 0 0 FALSE #> 9 0 0 FALSE #> 10 0 0 FALSE #> 11 0 0 FALSE #> 12 0 0 FALSE #> 13 0 0 FALSE #> 14 0 0 FALSE #> #> $line_grouping #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 #> #> $ref_footnotes #> character(0) #> #> $main_title #> [1] \"\" #> #> $subtitles #> character(0) #> #> $page_titles #> character(0) #> #> $main_footer #> character(0) #> #> $prov_footer #> character(0) #> #> $header_section_div #> [1] NA #> #> $horizontal_sep #> [1] \"—\" #> #> $col_gap #> [1] 3 #> #> $table_inset #> [1] 0 #> #> $has_topleft #> [1] TRUE #> #> $indent_size #> [1] 2 #> #> $col_widths #> [1] 12 4 4 5 5 5 4 #> #> $ref_fnote_df #> [1] row_path col_path row col symbol ref_index msg #> [8] nlines #> <0 rows> (or 0-length row.names) #> #> attr(,\"nrow_header\") #> [1] 2 #> attr(,\"ncols\") #> [1] 6 #> attr(,\"class\") #> [1] \"MatrixPrintForm\" \"list\""},{"path":"https://insightsengineering.github.io/rtables/main/reference/names.html","id":null,"dir":"Reference","previous_headings":"","what":"Names of a TableTree — names,VTableNodeInfo-method","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"Names TableTree","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"","code":"# S4 method for VTableNodeInfo names(x) # S4 method for InstantiatedColumnInfo names(x) # S4 method for LayoutColTree names(x) # S4 method for VTableTree row.names(x)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"x object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"column names x, defined details .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/names.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Names of a TableTree — names,VTableNodeInfo-method","text":"TableTrees one level splitting columns, names defined top-level split values repped across columns span.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/no_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Exported for use in tern — no_colinfo","title":"Exported for use in tern — no_colinfo","text":"table/row/InstantiatedColumnInfo object contain column structure information?","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/no_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exported for use in tern — no_colinfo","text":"","code":"no_colinfo(obj) # S4 method for VTableNodeInfo no_colinfo(obj) # S4 method for InstantiatedColumnInfo no_colinfo(obj)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/no_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Exported for use in tern — no_colinfo","text":"obj . object accessor access modify","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/no_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Exported for use in tern — no_colinfo","text":"TRUE object /empty instantiated column information, FALSE otherwise.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":null,"dir":"Reference","previous_headings":"","what":"Pagination of a TableTree — pag_tt_indices","title":"Pagination of a TableTree — pag_tt_indices","text":"Paginate rtables table vertical / horizontal direction, required specified page size.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pagination of a TableTree — pag_tt_indices","text":"","code":"pag_tt_indices( tt, lpp = 15, min_siblings = 2, nosplitin = character(), colwidths = NULL, max_width = NULL, verbose = FALSE ) paginate_table( tt, page_type = \"letter\", font_family = \"Courier\", font_size = 8, lineheight = 1, landscape = FALSE, pg_width = NULL, pg_height = NULL, margins = c(top = 0.5, bottom = 0.5, left = 0.75, right = 0.75), lpp = NA_integer_, cpp = NA_integer_, min_siblings = 2, nosplitin = character(), colwidths = NULL, tf_wrap = FALSE, max_width = NULL, verbose = FALSE )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pagination of a TableTree — pag_tt_indices","text":"tt TableTree (related class). TableTree object representing populated table. lpp numeric. Maximum lines per page including (re)printed header context rows min_siblings numeric. Minimum sibling rows must appear either side pagination row mid-subtable split valid. Defaults 2. nosplitin character. List names sub-tables page-breaks allowed, regardless considerations. Defaults none. colwidths numeric vector. Column widths use vertical pagination. max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE. verbose logical(1). extra debugging messages shown. Defaults FALSE. page_type character(1). Name page type. See page_types. Ignored pg_width pg_height set directly. font_family character(1). Name font family. error thrown family named monospaced. Defaults Courier. font_size numeric(1). Font size, defaults 12. lineheight numeric(1). Line height, defaults 1. landscape logical(1). dimensions page_type inverted landscape? Defaults FALSE, ignored pg_width pg_height set directly. pg_width numeric(1). Page width inches. pg_height numeric(1). Page height inches. margins numeric(4). Named numeric vector containing 'bottom', 'left', 'top', 'right' margins inches. Defaults .5 inches vertical margins .75 horizontal margins. cpp numeric(1) NULL. Width (characters) pages horizontal pagination. NA (default) indicates cpp inferred page size; NULL indicates horizontal pagination done regardless page size. tf_wrap logical(1). texts title, subtitle, footnotes wrapped?","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pagination of a TableTree — pag_tt_indices","text":"pag_tt_indices list paginated-groups row-indices tt. paginate_table, subtables defined subsetting indices defined pag_tt_indices.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pagination of a TableTree — pag_tt_indices","text":"rtables pagination context aware, meaning label rows row-group summaries (content rows) repeated (vertical) pagination, appropriate. allows reader immediately understand table turning new page, also mean rendered, paginated table take lines text rendering table without pagination . Pagination also takes account word-wrapping title, footer, column-label, formatted cell value content. Vertical pagination information (pagination data.frame) created using (make_row_df) Horizontal pagination performed creating pagination dataframe columns, applying algorithm used vertical pagination . physical page size font information specified, used derive lines-per-page (lpp) characters-per-page (cpp) values. full multi-direction pagination algorithm follows: Adjust lpp cpp account rendered elements rows (columns) titles/footers/column labels, horizontal dividers vertical pagination case row-labels, table_inset, top-left materials horizontal case Perform 'forced pagination' representing page-row splits, generating 1 tables Perform vertical pagination separately table generated (1) Perform horizontal pagination entire table apply results table page generated (1)-(2) Return list subtables representing full bi-directional pagination Pagination directions done using Core Pagination Algorithm implemented formatters package:","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":"pagination-algorithm","dir":"Reference","previous_headings":"","what":"Pagination Algorithm","title":"Pagination of a TableTree — pag_tt_indices","text":"Pagination performed independently vertical horizontal directions based solely pagination data.frame, includes following information row/column: number lines/characters rendering row take word-wrapping (self_extent) indices (reprint_inds) number lines (par_extent) rows act context row row's number siblings position within siblings Given lpp (cpp) already adjusted rendered elements rows/columns dataframe pagination information, pagination performed via following algorithm, start = 1: Core Pagination Algorithm: Initial guess pagination point start + lpp (start + cpp) guess valid pagination position, guess > start, decrement guess repeat error thrown possible pagination positions start start + lpp (start + cpp) ever < start decrementing Retain pagination index pagination point less NROW(tt) (ncol(tt)), set start pos + 1, repeat steps (1) - (4). Validating pagination position: Given (already adjusted) lpp cpp value, pagination invalid : rows/columns page take (adjusted) lpp lines/cpp characters render including word-wrapping (vertical ) context repetition (vertical ) footnote messages section divider lines take many lines rendering rows (vertical ) row label content (row-group summary) row (vertical ) row pagination point siblings, less min_siblings preceding following siblings pagination occur within sub-table listed nosplitin","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/paginate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Pagination of a TableTree — pag_tt_indices","text":"","code":"s_summary <- function(x) { if (is.numeric(x)) { in_rows( \"n\" = rcell(sum(!is.na(x)), format = \"xx\"), \"Mean (sd)\" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = \"xx.xx (xx.xx)\" ), \"IQR\" = rcell(IQR(x, na.rm = TRUE), format = \"xx.xx\"), \"min - max\" = rcell(range(x, na.rm = TRUE), format = \"xx.xx - xx.xx\") ) } else if (is.factor(x)) { vs <- as.list(table(x)) do.call(in_rows, lapply(vs, rcell, format = \"xx\")) } else { ( stop(\"type not supported\") ) } } lyt <- basic_table() %>% split_cols_by(var = \"ARM\") %>% analyze(c(\"AGE\", \"SEX\", \"BEP01FL\", \"BMRKR1\", \"BMRKR2\", \"COUNTRY\"), afun = s_summary) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> AGE #> n 134 134 132 #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> IQR 11.00 10.00 10.00 #> min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 #> SEX #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 #> BEP01FL #> Y 68 63 66 #> N 66 71 66 #> BMRKR1 #> n 134 134 132 #> Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) #> IQR 4.16 4.06 3.88 #> min - max 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 #> COUNTRY #> CHN 74 81 64 #> USA 10 13 17 #> BRA 13 7 10 #> PAK 12 9 10 #> NGA 8 7 11 #> RUS 5 8 6 #> JPN 5 4 9 #> GBR 4 3 2 #> CAN 3 2 3 #> CHE 0 0 0 nrow(tbl) #> [1] 33 row_paths_summary(tbl) #> rowname node_class path #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> AGE LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE #> n DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, n #> Mean (sd) DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, Mean (sd) #> IQR DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, IQR #> min - max DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, AGE, min - max #> SEX LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX #> F DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, F #> M DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, M #> U DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, U #> UNDIFFERENTIATED DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, SEX, UNDIFFERENTIATED #> BEP01FL LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BEP01FL #> Y DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BEP01FL, Y #> N DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BEP01FL, N #> BMRKR1 LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1 #> n DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, n #> Mean (sd) DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, Mean (sd) #> IQR DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, IQR #> min - max DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR1, min - max #> BMRKR2 LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2 #> LOW DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2, LOW #> MEDIUM DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2, MEDIUM #> HIGH DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, BMRKR2, HIGH #> COUNTRY LabelRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY #> CHN DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, CHN #> USA DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, USA #> BRA DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, BRA #> PAK DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, PAK #> NGA DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, NGA #> RUS DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, RUS #> JPN DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, JPN #> GBR DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, GBR #> CAN DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, CAN #> CHE DataRow ma_AGE_SEX_BEP01FL_BMRKR1_BMRKR2_COUNTRY, COUNTRY, CHE tbls <- paginate_table(tbl, lpp = 15) mf <- matrix_form(tbl, indent_rownames = TRUE) w_tbls <- propose_column_widths(mf) # so that we have the same column widths tmp <- lapply(tbls, function(tbli) { cat(toString(tbli, widths = w_tbls)) cat(\"\\n\\n\") cat(\"~~~~ PAGE BREAK ~~~~\") cat(\"\\n\\n\") }) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> AGE #> n 134 134 132 #> Mean (sd) 33.77 (6.55) 35.43 (7.90) 35.43 (7.72) #> IQR 11.00 10.00 10.00 #> min - max 21.00 - 50.00 21.00 - 62.00 20.00 - 69.00 #> SEX #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 #> BEP01FL #> Y 68 63 66 #> N 66 71 66 #> #> #> ~~~~ PAGE BREAK ~~~~ #> #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> BMRKR1 #> n 134 134 132 #> Mean (sd) 5.97 (3.55) 5.70 (3.31) 5.62 (3.49) #> IQR 4.16 4.06 3.88 #> min - max 0.41 - 17.67 0.65 - 14.24 0.17 - 21.39 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 #> COUNTRY #> CHN 74 81 64 #> USA 10 13 17 #> BRA 13 7 10 #> #> #> ~~~~ PAGE BREAK ~~~~ #> #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> COUNTRY #> PAK 12 9 10 #> NGA 8 7 11 #> RUS 5 8 6 #> JPN 5 4 9 #> GBR 4 3 2 #> CAN 3 2 3 #> CHE 0 0 0 #> #> #> ~~~~ PAGE BREAK ~~~~ #>"},{"path":"https://insightsengineering.github.io/rtables/main/reference/prune_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Recursively prune a TableTree — prune_table","title":"Recursively prune a TableTree — prune_table","text":"Recursively prune TableTree","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/prune_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recursively prune a TableTree — prune_table","text":"","code":"prune_table( tt, prune_func = prune_empty_level, stop_depth = NA_real_, depth = 0 )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/prune_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recursively prune a TableTree — prune_table","text":"tt TableTree (related class). TableTree object representing populated table. prune_func function. Function called subtree returns TRUE entire subtree removed. stop_depth numeric(1). depth subtrees checked pruning. Defaults NA indicates pruning happen levels depth numeric(1). Used internally, intended set end user.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/prune_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recursively prune a TableTree — prune_table","text":"TableTree pruned via recursive application prune_func.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/prune_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recursively prune a TableTree — prune_table","text":"","code":"adsl <- ex_adsl levels(adsl$SEX) <- c(levels(ex_adsl$SEX), \"OTHER\") tbl_to_prune <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") %>% build_table(adsl) tbl_to_prune %>% prune_table() #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 44.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 28.00 NA 46.00"},{"path":"https://insightsengineering.github.io/rtables/main/reference/qtable_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Generalized Frequency Table — qtable_layout","title":"Generalized Frequency Table — qtable_layout","text":"function provides convenience interface generating generalizations 2-way frequency table. Row column space can facetted variables, analysis function can specified. function builds layout specified layout applies data provided.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/qtable_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generalized Frequency Table — qtable_layout","text":"","code":"qtable_layout( data, row_vars = character(), col_vars = character(), avar = NULL, row_labels = NULL, afun = NULL, summarize_groups = FALSE, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), show_colcounts = TRUE, drop_levels = TRUE, ..., .default_rlabel = NULL ) qtable( data, row_vars = character(), col_vars = character(), avar = NULL, row_labels = NULL, afun = NULL, summarize_groups = FALSE, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), show_colcounts = TRUE, drop_levels = TRUE, ... )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/qtable_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generalized Frequency Table — qtable_layout","text":"data data.frame. data tabulate. row_vars character. names variables used row facetting. col_vars character. names variables used column facetting. avar character(1). variable analyzed. Defaults first variable data. row_labels character NULL. Row label(s) applied analysis rows. length must match number rows generated afun. See details. afun function. function generate analysis row cell values. can proper analysis function, function returns vector list. Vectors taken multi-valued single cells, whereas lists interpreted multiple cells. summarize_groups logical(1). level nesting include marginal summary rows. Defaults FALSE title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. show_colcounts logical(1). column counts displayed resulting table layout applied data drop_levels logical(1). unobserved factor levels dropped facetting. Defaults TRUE. ... passed afun, specified. Otherwise ignored. .default_rlabel character(1). implementation detail set end users.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/qtable_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generalized Frequency Table — qtable_layout","text":"qtable built TableTree object representing desired table, qtable_layout, PreDataTableLayouts object declaring structure desired table, suitable passing build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/qtable_layout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generalized Frequency Table — qtable_layout","text":"function creates table single top-level structure row column dimensions involving faceting 0 variables . display table depends certain details tabulation. case afun returns single cell's contents (either scalar vector 2 3 elements), label rows deepest-nested row facets hidden labels used used analysis row labels. case afun returns list (corresponding multiple cells), names list used analysis row labels deepest-nested facet row labels visible. table annotated top-left area informative label displaying analysis variable (avar), set, function used (captured via substitute) possible, 'count' . One exception user may directly modify top-left area (via row_labels) case table row facets afun returns single row.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/qtable_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generalized Frequency Table — qtable_layout","text":"","code":"qtable(ex_adsl) #> all obs #> (N=400) #> ——————————————— #> count 400 qtable(ex_adsl, row_vars = \"ARM\") #> all obs #> count (N=400) #> ———————————————————————— #> A: Drug X 134 #> B: Placebo 134 #> C: Combination 132 qtable(ex_adsl, col_vars = \"ARM\") #> A: Drug X B: Placebo C: Combination #> (N=134) (N=134) (N=132) #> ——————————————————————————————————————————————— #> count 134 134 132 qtable(ex_adsl, row_vars = \"SEX\", col_vars = \"ARM\") #> A: Drug X B: Placebo C: Combination #> count (N=134) (N=134) (N=132) #> —————————————————————————————————————————————————————————— #> F 79 77 66 #> M 51 55 60 #> U 3 2 4 #> UNDIFFERENTIATED 1 0 2 qtable(ex_adsl, row_vars = c(\"COUNTRY\", \"SEX\"), col_vars = c(\"ARM\", \"STRATA1\")) #> A: Drug X B: Placebo C: Combination #> A B C A B C A B C #> count (N=38) (N=47) (N=49) (N=44) (N=45) (N=45) (N=40) (N=43) (N=49) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> CHN #> F 11 15 18 12 15 18 8 9 12 #> M 9 7 11 8 12 15 10 10 10 #> U 1 0 1 1 0 0 1 0 2 #> UNDIFFERENTIATED 0 0 1 0 0 0 1 0 1 #> USA #> F 1 3 3 1 4 3 2 4 4 #> M 1 2 0 3 1 1 1 3 3 #> BRA #> F 3 1 1 2 1 0 0 3 2 #> M 1 7 0 4 0 0 2 3 0 #> PAK #> F 3 1 4 4 0 0 2 1 1 #> M 2 1 1 2 2 1 1 3 2 #> NGA #> F 0 1 2 2 1 2 1 0 4 #> M 2 1 1 0 1 1 4 1 1 #> U 0 1 0 0 0 0 0 0 0 #> RUS #> F 0 2 2 2 1 2 1 1 2 #> M 1 0 0 1 1 1 0 0 1 #> U 0 0 0 0 0 0 0 1 0 #> JPN #> F 1 1 2 0 2 1 3 2 2 #> M 0 1 0 0 0 0 1 0 1 #> U 0 0 0 0 1 0 0 0 0 #> GBR #> F 1 0 1 0 2 0 0 1 0 #> M 0 1 1 1 0 0 0 0 1 #> CAN #> F 1 1 0 1 1 0 1 0 0 #> M 0 1 0 0 0 0 1 1 0 qtable(ex_adsl, row_vars = c(\"COUNTRY\", \"SEX\"), col_vars = c(\"ARM\", \"STRATA1\"), avar = \"AGE\", afun = mean ) #> A: Drug X B: Placebo C: Combination #> A B C A B C A B C #> AGE - mean (N=38) (N=47) (N=49) (N=44) (N=45) (N=45) (N=40) (N=43) (N=49) #> ——————————————————————————————————————————————————————————————————————————————————————————————————— #> CHN #> F 31.09 30.67 32.56 33.08 35.73 35.28 35.25 33.89 35.75 #> M 34.44 36.43 35.73 38.38 35.25 37.60 30.80 35.20 38.60 #> U 33.00 NA 34.00 27.00 NA NA 38.00 NA 33.00 #> UNDIFFERENTIATED NA NA 28.00 NA NA NA 44.00 NA 46.00 #> USA #> F 24.00 38.00 35.00 46.00 40.75 32.33 36.50 30.50 36.00 #> M 40.00 34.50 NA 38.67 53.00 30.00 47.00 38.00 29.00 #> BRA #> F 35.33 38.00 44.00 27.50 25.00 NA NA 46.33 33.00 #> M 43.00 35.14 NA 36.00 NA NA 28.50 40.33 NA #> PAK #> F 28.00 38.00 29.50 30.75 NA NA 33.00 23.00 49.00 #> M 39.00 37.00 31.00 41.50 28.50 33.00 40.00 35.33 35.50 #> NGA #> F NA 25.00 35.00 26.50 37.00 32.00 31.00 NA 32.75 #> M 29.50 29.00 50.00 NA 40.00 24.00 34.50 32.00 28.00 #> U NA 28.00 NA NA NA NA NA NA NA #> RUS #> F NA 36.50 39.50 31.00 36.00 39.50 30.00 44.00 26.00 #> M 40.00 NA NA 36.00 58.00 29.00 NA NA 28.00 #> U NA NA NA NA NA NA NA 37.00 NA #> JPN #> F 29.00 34.00 37.50 NA 29.00 28.00 32.33 47.50 34.00 #> M NA 48.00 NA NA NA NA 45.00 NA 26.00 #> U NA NA NA NA 35.00 NA NA NA NA #> GBR #> F 28.00 NA 36.00 NA 28.00 NA NA 40.00 NA #> M NA 27.00 28.00 62.00 NA NA NA NA 69.00 #> CAN #> F 41.00 39.00 NA 34.00 43.00 NA 37.00 NA NA #> M NA 31.00 NA NA NA NA 36.00 32.00 NA summary_list <- function(x, ...) as.list(summary(x)) qtable(ex_adsl, row_vars = \"SEX\", col_vars = \"ARM\", avar = \"AGE\", afun = summary_list) #> A: Drug X B: Placebo C: Combination #> AGE - summary_list (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————————— #> F #> Min. 21.00 23.00 21.00 #> 1st Qu. 28.00 29.00 31.25 #> Median 32.00 32.00 35.00 #> Mean 32.76 34.12 35.20 #> 3rd Qu. 37.00 37.00 38.00 #> Max. 47.00 58.00 64.00 #> M #> Min. 23.00 21.00 20.00 #> 1st Qu. 29.50 32.50 29.00 #> Median 37.00 37.00 33.50 #> Mean 35.57 37.44 35.38 #> 3rd Qu. 40.50 41.50 40.00 #> Max. 50.00 62.00 69.00 #> U #> Min. 28.00 27.00 31.00 #> 1st Qu. 30.50 29.00 34.00 #> Median 33.00 31.00 36.00 #> Mean 31.67 31.00 35.25 #> 3rd Qu. 33.50 33.00 37.25 #> Max. 34.00 35.00 38.00 #> UNDIFFERENTIATED #> Min. 28.00 NA 44.00 #> 1st Qu. 28.00 NA 44.50 #> Median 28.00 NA 45.00 #> Mean 28.00 NA 45.00 #> 3rd Qu. 28.00 NA 45.50 #> Max. 28.00 NA 46.00 suppressWarnings(qtable(ex_adsl, row_vars = \"SEX\", col_vars = \"ARM\", avar = \"AGE\", afun = range )) #> A: Drug X B: Placebo C: Combination #> AGE - range (N=134) (N=134) (N=132) #> ————————————————————————————————————————————————————————————— #> F 21.0 / 47.0 23.0 / 58.0 21.0 / 64.0 #> M 23.0 / 50.0 21.0 / 62.0 20.0 / 69.0 #> U 28.0 / 34.0 27.0 / 35.0 31.0 / 38.0 #> UNDIFFERENTIATED 28.0 / 28.0 Inf / -Inf 44.0 / 46.0"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rbind.html","id":null,"dir":"Reference","previous_headings":"","what":"rbind TableTree and related objects — rbindl_rtables","title":"rbind TableTree and related objects — rbindl_rtables","text":"rbind TableTree related objects","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rbind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"rbind TableTree and related objects — rbindl_rtables","text":"","code":"rbindl_rtables(x, gap = 0, check_headers = TRUE) # S4 method for VTableNodeInfo rbind(..., deparse.level = 1) # S4 method for VTableNodeInfo,ANY rbind2(x, y)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rbind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"rbind TableTree and related objects — rbindl_rtables","text":"x VTableNodeInfo. TableTree, ElementaryTable TableRow object. gap deprecated. Ignored. check_headers deprecated. Ignored. ... . Elements stacked. deparse.level numeric(1). Currently Ignored. y VTableNodeInfo. TableTree, ElementaryTable TableRow object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rbind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"rbind TableTree and related objects — rbindl_rtables","text":"formal table object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rbind.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"rbind TableTree and related objects — rbindl_rtables","text":"objects rbinded, titles footer information retained first object (exists) objects titles/footers identical titles/footers. Otherwise, titles/footers removed must set bound table via main_title(), subtitles(), main_footer(), prov_footer() functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rbind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"rbind TableTree and related objects — rbindl_rtables","text":"","code":"mtbl <- rtable( header = rheader( rrow(row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2)), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"All Species\", mean(iris$Sepal.Length), median(iris$Sepal.Length), mean(iris$Petal.Length), median(iris$Petal.Length), format = \"xx.xx\" ) ) mtbl2 <- with(subset(iris, Species == \"setosa\"), rtable( header = rheader( rrow(row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2)), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"Setosa\", mean(Sepal.Length), median(Sepal.Length), mean(Petal.Length), median(Petal.Length), format = \"xx.xx\" ) )) rbind(mtbl, mtbl2) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.01 5.00 1.46 1.50 rbind(mtbl, rrow(), mtbl2) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> #> Setosa 5.01 5.00 1.46 1.50 rbind(mtbl, rrow(\"aaa\"), indent(mtbl2)) #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> aaa #> Setosa 5.01 5.00 1.46 1.50"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rcell.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell value constructors — rcell","title":"Cell value constructors — rcell","text":"Construct cell value associate formatting, labeling, indenting, column spanning information .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rcell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell value constructors — rcell","text":"","code":"rcell( x, format = NULL, colspan = 1L, label = NULL, indent_mod = NULL, footnotes = NULL, align = NULL, format_na_str = NULL ) non_ref_rcell( x, is_ref, format = NULL, colspan = 1L, label = NULL, indent_mod = NULL, refval = NULL, align = \"center\", format_na_str = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rcell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell value constructors — rcell","text":"x . Cell value. format character(1) function. format label (string) formatters function apply x. See formatters::list_valid_format_labels() currently supported format labels. colspan integer(1). Column span value. label character(1). Label NULL. non-null, looked determining row labels. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. footnotes list NULL. Referential footnote messages cell. align character(1) NULL. Alignment value rendered . defaults \"center\" NULL used. See formatters::list_valid_aligns() currently supported alignments. format_na_str character(1). String displayed formatted cell's value(s) NA. is_ref logical(1). reference column (.e. .in_ref_col passed argument) refval . Value use reference column. Defaults NULL","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rcell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell value constructors — rcell","text":"object representing value within single cell within populated table. underlying structure object implementation detail relied upon beyond calling accessors class.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rcell.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cell value constructors — rcell","text":"non_ref_rcell provides common blank cells reference column, value otherwise, passed value .in_ref_col used.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rcell.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Cell value constructors — rcell","text":"Currently column spanning supported defining header structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. formatters export_as_pdf, export_as_txt","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/reexports.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Objects exported from other packages — reexports","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\", \"COUNTRY\")) tbl <- build_table(lyt, ex_adsl) cat(export_as_txt(tbl, file = NULL, paginate = TRUE, lpp = 8)) #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> AGE #> Mean 33.77 35.43 35.43 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 #> \\s\\n A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> COUNTRY #> CHN 74 81 64 #> USA 10 13 17 #> BRA 13 7 10 #> PAK 12 9 10 #> NGA 8 7 11 #> \\s\\n A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> COUNTRY #> RUS 5 8 6 #> JPN 5 4 9 #> GBR 4 3 2 #> CAN 3 2 3 #> CHE 0 0 0 if (FALSE) { tf <- tempfile(fileext = \".txt\") export_as_txt(tbl, file = tf) system2(\"cat\", tf) } lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\", \"COUNTRY\")) tbl <- build_table(lyt, ex_adsl) if (FALSE) { tf <- tempfile(fileext = \".pdf\") export_as_pdf(tbl, file = tf, pg_height = 4) tf <- tempfile(fileext = \".pdf\") export_as_pdf(tbl, file = tf, lpp = 8) }"},{"path":"https://insightsengineering.github.io/rtables/main/reference/ref_fnotes.html","id":null,"dir":"Reference","previous_headings":"","what":"Referential Footnote Accessors — row_footnotes","title":"Referential Footnote Accessors — row_footnotes","text":"Get set referential footnotes aspects built table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ref_fnotes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Referential Footnote Accessors — row_footnotes","text":"","code":"row_footnotes(obj) row_footnotes(obj) <- value cell_footnotes(obj) cell_footnotes(obj) <- value col_fnotes_here(obj) # S4 method for ANY col_fnotes_here(obj) col_fnotes_here(obj) <- value col_footnotes(obj) col_footnotes(obj) <- value ref_index(obj) ref_index(obj) <- value ref_symbol(obj) ref_symbol(obj) <- value ref_msg(obj) fnotes_at_path(obj, rowpath = NULL, colpath = NULL, reset_idx = TRUE) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/ref_fnotes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Referential Footnote Accessors — row_footnotes","text":"obj . object accessor access modify value new value rowpath character NULL. Path within row structure. NULL indicates footnote go column rather cell. colpath character NULL. Path within column structure. NULL indicates footnote go row rather cell reset_idx logical(1). numbering referential footnotes immediately recalculated. Defaults TRUE.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/ref_fnotes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Referential Footnote Accessors — row_footnotes","text":"","code":"# How to add referencial footnotes after having created a table lyt <- basic_table() %>% split_rows_by(\"SEX\", page_by = TRUE) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl <- trim_rows(tbl) # Check the row and col structure to add precise references # row_paths(tbl) # col_paths(t) # row_paths_summary(tbl) # col_paths_summary(tbl) # Add the citation numbers on the table and relative references in the footnotes fnotes_at_path(tbl, rowpath = c(\"SEX\", \"F\", \"AGE\", \"Mean\")) <- \"Famous paper 1\" fnotes_at_path(tbl, rowpath = c(\"SEX\", \"UNDIFFERENTIATED\")) <- \"Unfamous paper 2\" # tbl"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rheader.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a header — rheader","title":"Create a header — rheader","text":"Create header","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rheader.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a header — rheader","text":"","code":"rheader(..., format = \"xx\", .lst = NULL)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rheader.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a header — rheader","text":"... row specifications (either character vectors output rrow DataRow, LabelRow, etc. format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. .lst list. already-collected list arguments used instead elements .... Arguments passed via ... ignored specified.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rheader.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a header — rheader","text":"InstantiatedColumnInfo object.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/rheader.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a header — rheader","text":"","code":"h1 <- rheader(c(\"A\", \"B\", \"C\")) h2 <- rheader( rrow(NULL, rcell(\"group 1\", colspan = 2), rcell(\"group 2\", colspan = 2)), rrow(NULL, \"A\", \"B\", \"A\", \"B\") ) h1 #> An InstantiatedColumnInfo object #> Columns: #> A (mnl) #> B (mnl) #> C (mnl) #> h2 #> An InstantiatedColumnInfo object #> Columns: #> group 1 (mnl) -> A (mnl) #> group 1 (mnl) -> B (mnl) #> group 2 (mnl) -> A (mnl) #> group 2 (mnl) -> B (mnl) #>"},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_accessors.html","id":null,"dir":"Reference","previous_headings":"","what":"Row attribute accessors — obj_avar","title":"Row attribute accessors — obj_avar","text":"Row attribute accessors","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_accessors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row attribute accessors — obj_avar","text":"","code":"obj_avar(obj) # S4 method for TableRow obj_avar(obj) # S4 method for ElementaryTable obj_avar(obj) row_cells(obj) # S4 method for TableRow row_cells(obj) row_cells(obj) <- value # S4 method for TableRow row_cells(obj) <- value row_values(obj) # S4 method for TableRow row_values(obj) row_values(obj) <- value # S4 method for TableRow row_values(obj) <- value # S4 method for LabelRow row_values(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_accessors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row attribute accessors — obj_avar","text":"obj . object accessor access modify value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_accessors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row attribute accessors — obj_avar","text":"various, depending accessor called.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_paths_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Print Row/Col Paths Summary — row_paths_summary","title":"Print Row/Col Paths Summary — row_paths_summary","text":"Print Row/Col Paths Summary","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_paths_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print Row/Col Paths Summary — row_paths_summary","text":"","code":"row_paths_summary(x) col_paths_summary(x)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_paths_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print Row/Col Paths Summary — row_paths_summary","text":"x rtable object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_paths_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print Row/Col Paths Summary — row_paths_summary","text":"data.frame summarizing row- column-structure x.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/row_paths_summary.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print Row/Col Paths Summary — row_paths_summary","text":"","code":"library(dplyr) ex_adsl_MF <- ex_adsl %>% filter(SEX %in% c(\"M\", \"F\")) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl_MF) tbl #> A: Drug X B: Placebo C: Combination #> F M F M F M #> ———————————————————————————————————————————————————————————— #> AGE #> Mean 32.76 35.57 34.12 37.44 35.20 35.38 #> BMRKR2 #> LOW 26 21 21 23 26 11 #> MEDIUM 21 15 38 18 17 23 #> HIGH 32 15 18 14 23 26 df <- row_paths_summary(tbl) #> rowname node_class path #> ——————————————————————————————————————————————————————— #> AGE LabelRow ma_AGE_BMRKR2, AGE #> Mean DataRow ma_AGE_BMRKR2, AGE, Mean #> BMRKR2 LabelRow ma_AGE_BMRKR2, BMRKR2 #> LOW DataRow ma_AGE_BMRKR2, BMRKR2, LOW #> MEDIUM DataRow ma_AGE_BMRKR2, BMRKR2, MEDIUM #> HIGH DataRow ma_AGE_BMRKR2, BMRKR2, HIGH df #> label indent node_class path #> 1 AGE 0 LabelRow ma_AGE_B.... #> 2 Mean 1 DataRow ma_AGE_B.... #> 3 BMRKR2 0 LabelRow ma_AGE_B.... #> 4 LOW 1 DataRow ma_AGE_B.... #> 5 MEDIUM 1 DataRow ma_AGE_B.... #> 6 HIGH 1 DataRow ma_AGE_B.... col_paths_summary(tbl) #> label path #> ————————————————————————————————————————————— #> A: Drug X ARM, A: Drug X #> F ARM, A: Drug X, SEX, F #> M ARM, A: Drug X, SEX, M #> B: Placebo ARM, B: Placebo #> F ARM, B: Placebo, SEX, F #> M ARM, B: Placebo, SEX, M #> C: Combination ARM, C: Combination #> F ARM, C: Combination, SEX, F #> M ARM, C: Combination, SEX, M # manually constructed table tbl2 <- rtable( rheader( rrow( \"row 1\", rcell(\"a\", colspan = 2), rcell(\"b\", colspan = 2) ), rrow(\"h2\", \"a\", \"b\", \"c\", \"d\") ), rrow(\"r1\", 1, 2, 1, 2), rrow(\"r2\", 3, 4, 2, 1) ) col_paths_summary(tbl2) #> label path #> ————————————————————————————— #> a manual, a #> a manual, a, manual, a #> b manual, a, manual, b #> b manual, b #> c manual, b, manual, c #> d manual, b, manual, d"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rowclasses.html","id":null,"dir":"Reference","previous_headings":"","what":"Row classes and constructors — LabelRow","title":"Row classes and constructors — LabelRow","text":"Row classes constructors Row constructors Classes","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rowclasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Row classes and constructors — LabelRow","text":"","code":"LabelRow( lev = 1L, label = \"\", name = label, vis = !is.na(label) && nzchar(label), cinfo = EmptyColInfo, indent_mod = 0L, table_inset = 0L, trailing_section_div = NA_character_ ) .tablerow( vals = list(), name = \"\", lev = 1L, label = name, cspan = rep(1L, length(vals)), cinfo = EmptyColInfo, var = NA_character_, format = NULL, na_str = NA_character_, klass, indent_mod = 0L, footnotes = list(), table_inset = 0L, trailing_section_div = NA_character_ ) DataRow(...) ContentRow(...)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rowclasses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Row classes and constructors — LabelRow","text":"lev integer. Nesting level (roughly, indentation level practical terms). label character(1). label (confused name) object/structure. name character(1). Name split/table/row created. Defaults corresponding label, required . vis logical. row visible (LabelRow ). cinfo InstantiatedColumnInfo (NULL). Column structure object created. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. table_inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). trailing_section_div character(1). String used section divider printing last row contained (sub)-table, unless row also last table row printed overall, NA_character_ none (default). generated via layouting, correspond section_div split table represents single facet. vals list. cell values row cspan integer. Column span. 1 indicates spanning. var string, variable name format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". klass Internal detail. footnotes list NULL. Referential footnotes applied current level. post-processing, can achieved fnotes_at_path<-. ... passed shared constructor (.tablerow).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rowclasses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Row classes and constructors — LabelRow","text":"formal object representing table row constructed type.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rowclasses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Row classes and constructors — LabelRow","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrow.html","id":null,"dir":"Reference","previous_headings":"","what":"row — rrow","title":"row — rrow","text":"row","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrow.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"row — rrow","text":"","code":"rrow(row.name = \"\", ..., format = NULL, indent = 0, inset = 0L)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrow.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"row — rrow","text":"row.name NULL empty string used row.name rrow. ... cell values format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. indent deprecated. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrow.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"row — rrow","text":"row object context-appropriate type (label data)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrow.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"row — rrow","text":"","code":"rrow(\"ABC\", c(1, 2), c(3, 2), format = \"xx (xx.%)\") #> [DataRow indent_mod 0]: ABC 1 (200%) 3 (200%) rrow(\"\") #> [LabelRow indent_mod 0]:"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrowl.html","id":null,"dir":"Reference","previous_headings":"","what":"rrowl — rrowl","title":"rrowl — rrowl","text":"rrowl","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrowl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"rrowl — rrowl","text":"","code":"rrowl(row.name, ..., format = NULL, indent = 0, inset = 0L)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrowl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"rrowl — rrowl","text":"row.name NULL empty string used row.name rrow. ... values vector/list form format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. indent deprecated. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrowl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"rrowl — rrowl","text":"row object context-appropriate type (label data)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/rrowl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"rrowl — rrowl","text":"","code":"rrowl(\"a\", c(1, 2, 3), format = \"xx\") #> [DataRow indent_mod 0]: a 1 2 3 rrowl(\"a\", c(1, 2, 3), c(4, 5, 6), format = \"xx\") #> [DataRow indent_mod 0]: a 1 2 3 4 5 6 rrowl(\"N\", table(iris$Species)) #> [DataRow indent_mod 0]: N 50 50 50 rrowl(\"N\", table(iris$Species), format = \"xx\") #> [DataRow indent_mod 0]: N 50 50 50 x <- tapply(iris$Sepal.Length, iris$Species, mean, simplify = FALSE) rrow(row.name = \"row 1\", x) #> [DataRow indent_mod 0]: row 1 5.006, 5.936, 6.588 rrow(\"ABC\", 2, 3) #> [DataRow indent_mod 0]: ABC 2 3 rrowl(row.name = \"row 1\", c(1, 2), c(3, 4)) #> [DataRow indent_mod 0]: row 1 1 2 3 4 rrow(row.name = \"row 2\", c(1, 2), c(3, 4)) #> [DataRow indent_mod 0]: row 2 1, 2 3, 4"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtable.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a Table — rtable","title":"Create a Table — rtable","text":"Create Table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a Table — rtable","text":"","code":"rtable(header, ..., format = NULL, hsep = default_hsep(), inset = 0L) rtablel(header, ..., format = NULL, hsep = default_hsep(), inset = 0L)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a Table — rtable","text":"header Information defining header (column structure) table. can row objects (legacy), character vectors InstantiatedColumnInfo object. ... Rows place table. format character(1) function. format label (string) formatter function apply cell values passed via .... See list_valid_format_labels currently supported format labels. hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. inset integer(1). table inset row table constructed. See table_inset.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a Table — rtable","text":"formal table object appropriate type (ElementaryTable TableTree)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a Table — rtable","text":"","code":"rtable( header = LETTERS[1:3], rrow(\"one to three\", 1, 2, 3), rrow(\"more stuff\", rcell(pi, format = \"xx.xx\"), \"test\", \"and more\") ) #> A B C #> ————————————————————————————————————— #> one to three 1 2 3 #> more stuff 3.14 test and more # Table with multirow header sel <- iris$Species == \"setosa\" mtbl <- rtable( header = rheader( rrow( row.name = NULL, rcell(\"Sepal.Length\", colspan = 2), rcell(\"Petal.Length\", colspan = 2) ), rrow(NULL, \"mean\", \"median\", \"mean\", \"median\") ), rrow( row.name = \"All Species\", mean(iris$Sepal.Length), median(iris$Sepal.Length), mean(iris$Petal.Length), median(iris$Petal.Length), format = \"xx.xx\" ), rrow( row.name = \"Setosa\", mean(iris$Sepal.Length[sel]), median(iris$Sepal.Length[sel]), mean(iris$Petal.Length[sel]), median(iris$Petal.Length[sel]) ) ) mtbl #> Sepal.Length Petal.Length #> mean median mean median #> ——————————————————————————————————————————————— #> All Species 5.84 5.80 3.76 4.35 #> Setosa 5.006 5 1.462 1.5 names(mtbl) # always first row of header #> [1] \"Sepal.Length\" \"Sepal.Length\" \"Petal.Length\" \"Petal.Length\" # Single row header tbl <- rtable( header = c(\"Treatement\\nN=100\", \"Comparison\\nN=300\"), format = \"xx (xx.xx%)\", rrow(\"A\", c(104, .2), c(100, .4)), rrow(\"B\", c(23, .4), c(43, .5)), rrow(\"\"), rrow(\"this is a very long section header\"), rrow(\"estimate\", rcell(55.23, \"xx.xx\", colspan = 2)), rrow(\"95% CI\", indent = 1, rcell(c(44.8, 67.4), format = \"(xx.x, xx.x)\", colspan = 2)) ) tbl #> Treatement Comparison #> N=100 N=300 #> ———————————————————————————————————————————————————————————————— #> A 104 (20.00%) 100 (40.00%) #> B 23 (40.00%) 43 (50.00%) #> #> this is a very long section header #> estimate 55.23 #> 95% CI (44.8, 67.4) row.names(tbl) #> [1] \"A\" \"B\" #> [3] \"\" \"this is a very long section header\" #> [5] \"estimate\" \"95% CI\" names(tbl) #> [1] \"Treatement\" \"Comparison\" # Subsetting tbl[1, ] #> Treatement Comparison #> N=100 N=300 #> ——————————————————————————————— #> A 104 (20.00%) 100 (40.00%) tbl[, 1] #> Treatement #> N=100 #> ————————————————————————————————————————————————— #> A 104 (20.00%) #> B 23 (40.00%) #> #> this is a very long section header #> estimate 55.23 #> 95% CI (44.8, 67.4) tbl[1, 2] #> Comparison #> N=300 #> ———————————————— #> A 100 (40.00%) tbl[2, 1] #> Treatement #> N=100 #> ——————————————— #> B 23 (40.00%) tbl[3, 2] #> Comparison #> N=300 #> ————————————— #> tbl[5, 1] #> Treatement #> N=100 #> ————————————————————— #> estimate 55.23 tbl[5, 2] #> Comparison #> N=300 #> ————————————————————— #> estimate 55.23 # # Data Structure methods dim(tbl) #> [1] 6 2 nrow(tbl) #> [1] 6 ncol(tbl) #> [1] 2 names(tbl) #> [1] \"Treatement\" \"Comparison\" # Colspans tbl2 <- rtable( c(\"A\", \"B\", \"C\", \"D\", \"E\"), format = \"xx\", rrow(\"r1\", 1, 2, 3, 4, 5), rrow(\"r2\", rcell(\"sp2\", colspan = 2), \"sp1\", rcell(\"sp2-2\", colspan = 2)) ) tbl2 #> A B C D E #> ———————————————————————————— #> r1 1 2 3 4 5 #> r2 sp2 sp1 sp2-2"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":null,"dir":"Reference","previous_headings":"","what":"Default tabulation — simple_analysis","title":"Default tabulation — simple_analysis","text":"function used analyze invoked","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default tabulation — simple_analysis","text":"","code":"simple_analysis(x, ...) # S4 method for numeric simple_analysis(x, ...) # S4 method for logical simple_analysis(x, ...) # S4 method for factor simple_analysis(x, ...) # S4 method for ANY simple_analysis(x, ...)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Default tabulation — simple_analysis","text":"x already split data tabulated particular cell/set cells ... passed directly","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Default tabulation — simple_analysis","text":"RowsVerticalSection object (NULL). details object considered internal implementation detail.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Default tabulation — simple_analysis","text":"function following behavior given particular types inputs: numeric calls mean x logical calls sum x factor calls length x in_rows called resulting value(s). classes input currently lead error.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Default tabulation — simple_analysis","text":"Gabriel Becker Adrian Waddell","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/rtinner.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Default tabulation — simple_analysis","text":"","code":"simple_analysis(1:3) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Mean 2.00 0 Mean simple_analysis(iris$Species) #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 setosa 50 0 setosa #> 2 versicolor 50 0 versicolor #> 3 virginica 50 0 virginica simple_analysis(iris$Species == \"setosa\") #> RowsVerticalSection (in_rows) object print method: #> ---------------------------- #> row_name formatted_cell indent_mod row_label #> 1 Count 50 0 Count"},{"path":"https://insightsengineering.github.io/rtables/main/reference/sanitize_table_struct.html","id":null,"dir":"Reference","previous_headings":"","what":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"Experimental function correct structure degenerate tables adding messaging rows empty sub-structures.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sanitize_table_struct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"","code":"sanitize_table_struct(tt, empty_msg = \"-- This Section Contains No Data --\")"},{"path":"https://insightsengineering.github.io/rtables/main/reference/sanitize_table_struct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"tt TableTree empty_msg character(1). string spanned across inserted empty rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sanitize_table_struct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"tt already valid, returned unmodified. tt degenerate, modified, non-degenerate version table returned.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sanitize_table_struct.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"function locates degenerate portions table (including table overall case table data rows) inserts row spans columns message empty_msg one, generating table guaranteed non-degenerate.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sanitize_table_struct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sanitize degenerate table structures (Experimental) — sanitize_table_struct","text":"","code":"sanitize_table_struct(rtable(\"cool beans\")) #> cool beans #> —————————————————————————————————————— #> -- This Section Contains No Data -- lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() ## Degenerate because it doesn't have any analyze calls -> no data rows badtab <- build_table(lyt, DM) sanitize_table_struct(badtab) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————— #> F 70 (57.9%) 56 (52.8%) 61 (47.3%) #> -- This Section Contains No Data -- #> M 51 (42.1%) 50 (47.2%) 68 (52.7%) #> -- This Section Contains No Data -- #> U 0 (0.0%) 0 (0.0%) 0 (0.0%) #> -- This Section Contains No Data -- #> UNDIFFERENTIATED 0 (0.0%) 0 (0.0%) 0 (0.0%) #> -- This Section Contains No Data --"},{"path":"https://insightsengineering.github.io/rtables/main/reference/score_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Score functions for sorting TableTrees — cont_n_allcols","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"Score functions sorting TableTrees","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/score_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"","code":"cont_n_allcols(tt) cont_n_onecol(j)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/score_funs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"tt TableTree (related class). TableTree object representing populated table. j numeric(1). Number column used scoring.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/score_funs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Score functions for sorting TableTrees — cont_n_allcols","text":"single numeric value indicating score according relevant metric tt, used sorting.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/section_div.html","id":null,"dir":"Reference","previous_headings":"","what":"Section dividers getter and setter — section_div","title":"Section dividers getter and setter — section_div","text":"section_div can used set get section divider table object produced build_table(). assigned post-processing (section_div<-) table can section divider every row, assigned independently. assigning layout creation, split_rows_by() (related row-wise splits) analyze() section_div parameter produce separators split sections data subgroups, respectively.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/section_div.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Section dividers getter and setter — section_div","text":"","code":"section_div(obj) # S4 method for VTableTree section_div(obj) # S4 method for list section_div(obj) # S4 method for TableRow section_div(obj) section_div(obj, only_sep_sections = FALSE) <- value # S4 method for VTableTree section_div(obj, only_sep_sections = FALSE) <- value # S4 method for list section_div(obj, only_sep_sections = FALSE) <- value # S4 method for TableRow section_div(obj, only_sep_sections = FALSE) <- value # S4 method for LabelRow section_div(obj, only_sep_sections = FALSE) <- value header_section_div(obj) # S4 method for PreDataTableLayouts header_section_div(obj) # S4 method for VTableTree header_section_div(obj) header_section_div(obj) <- value # S4 method for PreDataTableLayouts header_section_div(obj) <- value # S4 method for VTableTree header_section_div(obj) <- value top_level_section_div(obj) # S4 method for PreDataTableLayouts top_level_section_div(obj) top_level_section_div(obj) <- value # S4 method for PreDataTableLayouts top_level_section_div(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/section_div.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Section dividers getter and setter — section_div","text":"obj Table object. can class inherits VTableTree TableRow/LabelRow. only_sep_sections logical(1). Defaults FALSE section_div<-. Allows set section divider sections splits analyses number values less number rows table. TRUE, section divider set rows table. value character. Vector single characters use section dividers. character repeated section dividers span width table. character NA_character_ produce trailing separator row table. value length reflect number rows, 1 number splits/levels. See Details section information.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/section_div.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Section dividers getter and setter — section_div","text":"section divider string. line trailing separator NA_character_ section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/section_div.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Section dividers getter and setter — section_div","text":"Assigned value section divider must character vector. value NA_character_ section divider absent row section. want affect sections splits, please use only_sep_sections provide shorter vector number rows. Ideally, length vector less number splits , eventually, leaf-level, .e. DataRow analyze results . Note one value inserted, first split affected. only_sep_sections = TRUE, default section_div() produced table construction, section divider set splits eventually analyses, header row table. can set header_section_div basic_table() , eventually, hsep build_table(). FALSE, section divider set rows table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/section_div.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Section dividers getter and setter — section_div","text":"","code":"# Data df <- data.frame( cat = c( \"really long thing its so \", \"long\" ), value = c(6, 3, 10, 1) ) fast_afun <- function(x) list(\"m\" = rcell(mean(x), format = \"xx.\"), \"m/2\" = max(x) / 2) tbl <- basic_table() %>% split_rows_by(\"cat\", section_div = \"~\") %>% analyze(\"value\", afun = fast_afun, section_div = \" \") %>% build_table(df) # Getter section_div(tbl) #> [1] NA \" \" \"~\" NA \" \" \"~\" # Setter section_div(tbl) <- letters[seq_len(nrow(tbl))] tbl #> all obs #> ——————————————————————————————————— #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5 # last letter can appear if there is another table rbind(tbl, tbl) #> all obs #> ——————————————————————————————————— #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5 #> fffffffffffffffffffffffffffffffffff #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5 # header_section_div header_section_div(tbl) <- \"+\" tbl #> all obs #> ——————————————————————————————————— #> +++++++++++++++++++++++++++++++++++ #> really long thing its so #> aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa #> m 8 #> bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb #> m/2 5 #> ccccccccccccccccccccccccccccccccccc #> long #> ddddddddddddddddddddddddddddddddddd #> m 2 #> eeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee #> m/2 1.5"},{"path":"https://insightsengineering.github.io/rtables/main/reference/sf_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Function Arg Conventions — sf_args","title":"Split Function Arg Conventions — sf_args","text":"Split Function Arg Conventions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sf_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Function Arg Conventions — sf_args","text":"","code":"sf_args(trim, label, first)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/sf_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Function Arg Conventions — sf_args","text":"trim logical(1). splits corresponding 0 observations kept tabulating. label character(1). label (confused name) object/structure. first logical(1). created split level placed first levels (TRUE) last (FALSE, default).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sf_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Function Arg Conventions — sf_args","text":"NULL (argument template dummy function)","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/sort_at_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Sorting a Table at a Specific Path — sort_at_path","title":"Sorting a Table at a Specific Path — sort_at_path","text":"Main sorting function order substructure TableTree particular Path table tree.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sort_at_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sorting a Table at a Specific Path — sort_at_path","text":"","code":"sort_at_path( tt, path, scorefun, decreasing = NA, na.pos = c(\"omit\", \"last\", \"first\"), .prev_path = character() )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/sort_at_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sorting a Table at a Specific Path — sort_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. scorefun function. Scoring function, accept type children directly position path (either VTableTree, VTableRow, VTableNodeInfo, covers ) return numeric value sorted. decreasing logical(1). scores generated scorefun sorted decreasing order. unset (default NA), set TRUE generated scores numeric FALSE characters. na.pos character(1). done children (sub-trees/rows) NA scores. Defaults \"omit\", removes , allowed values \"last\" \"first\" indicate placed order. .prev_path character. Internal detail, set manually.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sort_at_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Sorting a Table at a Specific Path — sort_at_path","text":"TableTree structure tt exception requested sorting done path.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/sort_at_path.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sorting a Table at a Specific Path — sort_at_path","text":"path can include \"wildcard\" \"*\" step, translates roughly node/branching element means child step separately sorted based scorefun remaining path entries. can occur multiple times path. Note sorting needs deeper understanding table structure rtables. Please consider reading related vignette (Sorting Pruning) explore table structure useful functions like table_structure() row_paths_summary(). also important understand difference \"content\" rows \"data\" rows. first one analyzes describes split variable generally generated summarize_row_groups(), second one commonly produced calling one various analyze() instances. Built-score functions cont_n_allcols() cont_n_onecol(). working content rows (coming summarize_row_groups()) custom score function needs used DataRows. , useful descriptor accessor functions (coming related vignette): cell_values() - Retrieves named list TableRow TableTree object's values. obj_name() - Retrieves name object. Note can differ label displayed () printing. obj_label() - Retrieves display label object. Note can differ name appears path. content_table() - Retrieves TableTree object's content table (contains summary rows). tree_children() - Retrieves TableTree object's direct children (either subtables, rows possibly mix thereof, though happen practice).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/sort_at_path.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sorting a Table at a Specific Path — sort_at_path","text":"","code":"# Creating a table to sort # Function that gives two statistics per table-tree \"leaf\" more_analysis_fnc <- function(x) { in_rows( \"median\" = median(x), \"mean\" = mean(x), .formats = \"xx.x\" ) } # Main layout of the table raw_lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by( \"RACE\", split_fun = drop_and_remove_levels(\"WHITE\") # dropping WHITE levels ) %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\", afun = more_analysis_fnc) # Creating the table and pruning empty and NAs tbl <- build_table(raw_lyt, DM) %>% prune_table() # Peek at the table structure to understand how it is built table_structure(tbl) #> [TableTree] RACE #> [TableTree] ASIAN [cont: 1 x 3] #> [TableTree] STRATA1 #> [TableTree] A [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] B [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] C [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] BLACK OR AFRICAN AMERICAN [cont: 1 x 3] #> [TableTree] STRATA1 #> [TableTree] A [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] B [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) #> [TableTree] C [cont: 1 x 3] #> [ElementaryTable] AGE (2 x 3) # Sorting only ASIAN sub-table, or, in other words, sorting STRATA elements for # the ASIAN group/row-split. This uses content_table() accessor function as it # is a \"ContentRow\". In this case, we also base our sorting only on the second column. sort_at_path(tbl, c(\"ASIAN\", \"STRATA1\"), cont_n_onecol(2)) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> median 32.5 32.0 34.0 #> mean 34.1 31.6 34.7 #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> median 30.0 33.0 36.0 #> mean 32.2 33.9 36.8 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> median 36.5 34.0 33.0 #> mean 36.2 33.0 32.4 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> median 32.0 29.0 32.5 #> mean 31.5 28.6 33.6 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> median 33.0 30.0 33.5 #> mean 35.6 30.8 33.7 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> median 33.0 36.0 32.0 #> mean 35.5 34.2 35.0 # Custom scoring function that is working on \"DataRow\"s scorefun <- function(tt) { # Here we could use browser() sum(unlist(row_values(tt))) # Different accessor function } # Sorting mean and median for all the AGE leaves! sort_at_path(tbl, c(\"RACE\", \"*\", \"STRATA1\", \"*\", \"AGE\"), scorefun) #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 68 (64.2%) 84 (65.1%) #> A 27 (22.3%) 20 (18.9%) 31 (24.0%) #> mean 32.2 33.9 36.8 #> median 30.0 33.0 36.0 #> B 24 (19.8%) 29 (27.4%) 22 (17.1%) #> mean 34.1 31.6 34.7 #> median 32.5 32.0 34.0 #> C 28 (23.1%) 19 (17.9%) 31 (24.0%) #> median 36.5 34.0 33.0 #> mean 36.2 33.0 32.4 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 24 (22.6%) 27 (20.9%) #> A 6 (5.0%) 7 (6.6%) 8 (6.2%) #> mean 31.5 28.6 33.6 #> median 32.0 29.0 32.5 #> B 10 (8.3%) 6 (5.7%) 12 (9.3%) #> mean 35.6 30.8 33.7 #> median 33.0 30.0 33.5 #> C 12 (9.9%) 11 (10.4%) 7 (5.4%) #> mean 35.5 34.2 35.0 #> median 33.0 36.0 32.0"},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_context.html","id":null,"dir":"Reference","previous_headings":"","what":".spl_context within analysis and split functions — spl_context","title":".spl_context within analysis and split functions — spl_context","text":".spl_context optional parameter rtables' special functions, afun (analysis function analyze), cfun (content label function summarize_row_groups), split_fun (e.g. split_rows_by).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_context.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":".spl_context within analysis and split functions — spl_context","text":".spl_context data.frame gives information subsets data corresponding splits within-current analyze action nested. Taken together, correspond path resulting (set ) rows analysis function creating, although information slightly different form. split (correspond groups rows resulting table), well initial 'root' \"split\", represented via following columns: split name split (often variable split simple case) value string representation value split full_parent_df dataframe containing full data (.e. across columns) corresponding path defined combination split value row rows row all_cols_n number observations corresponding row grouping (union columns) (row-split analyze contexts ) <1 column column table structure list columns (named names(col_exprs(tab))) contain logical vectors corresponding subset row's full_parent_df corresponding column cur_col_id Identifier current column. may internal name, constructed pasting column path together cur_col_subset List column containing logical vectors indicating subset row's full_parent_df column currently created analysis function cur_col_expr List current column expression. may used filter .alt_df_row external data column. Filtering .alt_df_row columns produces .alt_df. cur_col_n integer column containing observation counts split cur_col_split Current column split names. recovered current column path cur_col_split_val Current column split values. recovered current column path note Within analysis functions accept .spl_context, all_cols_n cur_col_n columns dataframe contain 'true' observation counts corresponding row-group row-group x column subsets data. numbers , currently , reflect alternate column observation counts provided alt_counts_df, col_counts col_total arguments build_table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_context_to_disp_path.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"Translate spl_context Path display error messages","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_context_to_disp_path.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"","code":"spl_context_to_disp_path(ctx)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_context_to_disp_path.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"ctx data.frame. spl_context data.frame error occurred","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_context_to_disp_path.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Translate spl_context to Path for display in error messages — spl_context_to_disp_path","text":"character string containing description row path corresponding ctx","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_variable.html","id":null,"dir":"Reference","previous_headings":"","what":"Variable Associated With a Split — spl_variable","title":"Variable Associated With a Split — spl_variable","text":"function intended use writing custom splitting logic. cases split associated single variable, name variable returned. time writing includes splits generated via split_rows_by, split_cols_by, split_rows_by_cuts, split_cols_by_cuts, split_rows_by_cutfun, split_cols_by_cutfun layout directives.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_variable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Variable Associated With a Split — spl_variable","text":"","code":"spl_variable(spl) # S4 method for VarLevelSplit spl_variable(spl) # S4 method for VarDynCutSplit spl_variable(spl) # S4 method for VarStaticCutSplit spl_variable(spl) # S4 method for Split spl_variable(spl)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_variable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Variable Associated With a Split — spl_variable","text":"spl Split. split object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/spl_variable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Variable Associated With a Split — spl_variable","text":"splits single variable associated , split, others, error raised.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Declaring a column-split based on levels of a variable — split_cols_by","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"generate children subset categorical variable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"","code":"split_cols_by( lyt, var, labels_var = var, split_label = var, split_fun = NULL, format = NULL, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), ref_group = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"lyt layout object pre-data used tabulation var string, variable name labels_var string, name variable containing labels displayed values var split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). split_fun function/NULL. custom splitting function See custom_split_funs format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. ref_group character(1) NULL. Level var considered ref_group/reference","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Declaring a column-split based on levels of a variable — split_cols_by","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% analyze(c(\"AGE\", \"BMRKR2\")) tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————— #> AGE #> Mean 33.77 35.43 35.43 #> BMRKR2 #> LOW 50 45 40 #> MEDIUM 37 56 42 #> HIGH 47 33 50 # Let's look at the splits in more detail lyt1 <- basic_table() %>% split_cols_by(\"ARM\") lyt1 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> () #> # add an analysis (summary) lyt2 <- lyt1 %>% analyze(c(\"AGE\", \"COUNTRY\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) lyt2 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> AGE:COUNTRY (** multivar analysis **) #> tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> AGE #> Min. 20.00 21.00 22.00 #> 1st Qu. 29.00 29.00 30.00 #> Median 33.00 32.00 33.00 #> Mean 34.91 33.02 34.57 #> 3rd Qu. 39.00 37.00 38.00 #> Max. 60.00 55.00 53.00 #> COUNTRY #> CHN 62.00 48.00 69.00 #> USA 13.00 14.00 17.00 #> BRA 9.00 13.00 7.00 #> PAK 8.00 8.00 12.00 #> NGA 10.00 5.00 9.00 #> RUS 9.00 5.00 6.00 #> JPN 5.00 8.00 5.00 #> GBR 2.00 3.00 2.00 #> CAN 3.00 2.00 2.00 #> CHE 0.00 0.00 0.00 # By default sequentially adding layouts results in nesting library(dplyr) DM_MF <- DM %>% filter(SEX %in% c(\"M\", \"F\")) %>% mutate(SEX = droplevels(SEX)) lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% analyze(c(\"AGE\", \"COUNTRY\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) lyt3 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> SEX (lvls) #> #> Row-Split Structure: #> AGE:COUNTRY (** multivar analysis **) #> tbl3 <- build_table(lyt3, DM_MF) tbl3 #> A: Drug X B: Placebo C: Combination #> F M F M F M #> ————————————————————————————————————————————————————————————— #> AGE #> Min. 20.00 24.00 21.00 21.00 22.00 25.00 #> 1st Qu. 29.00 31.00 29.00 28.00 30.00 29.00 #> Median 32.00 35.00 33.00 31.00 35.00 32.00 #> Mean 33.71 36.55 33.84 32.10 34.89 34.28 #> 3rd Qu. 38.00 41.50 38.00 35.75 39.00 38.00 #> Max. 58.00 60.00 55.00 47.00 53.00 53.00 #> COUNTRY #> CHN 34.00 28.00 29.00 19.00 31.00 38.00 #> USA 8.00 5.00 6.00 8.00 10.00 7.00 #> BRA 6.00 3.00 6.00 7.00 3.00 4.00 #> PAK 2.00 6.00 5.00 3.00 5.00 7.00 #> NGA 6.00 4.00 2.00 3.00 5.00 4.00 #> RUS 7.00 2.00 1.00 4.00 2.00 4.00 #> JPN 2.00 3.00 3.00 5.00 4.00 1.00 #> GBR 2.00 0.00 3.00 0.00 1.00 1.00 #> CAN 3.00 0.00 1.00 1.00 0.00 2.00 #> CHE 0.00 0.00 0.00 0.00 0.00 0.00 # nested=TRUE vs not lyt4 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze(\"AGE\") lyt4 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> SEX (lvls) -> RACE (lvls) -> AGE (** analysis **) #> tbl4 <- build_table(lyt4, DM) tbl4 #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————————————— #> F #> ASIAN #> Mean 33.55 34.00 34.90 #> BLACK OR AFRICAN AMERICAN #> Mean 33.17 30.58 33.85 #> WHITE #> Mean 35.88 38.57 36.50 #> M #> ASIAN #> Mean 35.03 31.10 34.39 #> BLACK OR AFRICAN AMERICAN #> Mean 37.40 32.83 34.14 #> WHITE #> Mean 44.00 35.29 34.00 lyt5 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"AGE\") %>% split_rows_by(\"RACE\", nested = FALSE, split_fun = drop_split_levels) %>% analyze(\"AGE\") lyt5 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> SEX (lvls) -> AGE (** analysis **) #> RACE (lvls) -> AGE (** analysis **) #> tbl5 <- build_table(lyt5, DM) tbl5 #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89 #> M #> Mean 36.55 32.10 34.28 #> ASIAN #> Mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN #> Mean 34.68 31.71 34.00 #> WHITE #> Mean 39.36 36.93 35.11"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by_multivar.html","id":null,"dir":"Reference","previous_headings":"","what":"Associate Multiple Variables with Columns — split_cols_by_multivar","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"cases, variable ultimately analyzed naturally defined column, row basis. need columns reflect different variables entirely, rather different levels single variable, use split_cols_by_multivar","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by_multivar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"","code":"split_cols_by_multivar( lyt, vars, split_fun = NULL, varlabels = vars, varnames = NULL, nested = TRUE, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by_multivar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"lyt layout object pre-data used tabulation vars character vector. Multiple variable names. split_fun function/NULL. custom splitting function See custom_split_funs varlabels character vector. Labels vars varnames character vector. Names vars appear pathing. vars unique variable names. , variable names suffixes necessary enforce uniqueness. nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by_multivar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by_multivar.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_cols_by_multivar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Associate Multiple Variables with Columns — split_cols_by_multivar","text":"","code":"library(dplyr) ANL <- DM %>% mutate(value = rnorm(n()), pctdiff = runif(n())) ## toy example where we take the mean of the first variable and the ## count of >.5 for the second. colfuns <- list( function(x) in_rows(mean = mean(x), .formats = \"xx.x\"), function(x) in_rows(\"# x > 5\" = sum(x > .5), .formats = \"xx\") ) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_multivar(c(\"value\", \"pctdiff\")) %>% split_rows_by(\"RACE\", split_label = \"ethnicity\", split_fun = drop_split_levels ) %>% summarize_row_groups() %>% analyze_colvars(afun = colfuns) lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> value:pctdiff (vars) #> #> Row-Split Structure: #> RACE (lvls) -> NA (** col-var analysis **) #> tbl <- build_table(lyt, ANL) tbl #> A: Drug X B: Placebo C: Combination #> value pctdiff value pctdiff value pctdiff #> ——————————————————————————————————————————————————————————————————————————————————————————————————————— #> ASIAN 79 (65.3%) 79 (65.3%) 68 (64.2%) 68 (64.2%) 84 (65.1%) 84 (65.1%) #> mean 0.0 39 -0.1 31 -0.0 33 #> BLACK OR AFRICAN AMERICAN 28 (23.1%) 28 (23.1%) 24 (22.6%) 24 (22.6%) 27 (20.9%) 27 (20.9%) #> mean 0.2 11 0.1 10 -0.3 10 #> WHITE 14 (11.6%) 14 (11.6%) 14 (13.2%) 14 (13.2%) 18 (14.0%) 18 (14.0%) #> mean 0.3 8 0.4 7 -0.2 9"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_funcs.html","id":null,"dir":"Reference","previous_headings":"","what":"Split functions — split_funcs","title":"Split functions — split_funcs","text":"Split functions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_funcs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split functions — split_funcs","text":"","code":"remove_split_levels(excl) keep_split_levels(only, reorder = TRUE) drop_split_levels(df, spl, vals = NULL, labels = NULL, trim = FALSE) drop_and_remove_levels(excl) reorder_split_levels(neworder, newlabels = neworder, drlevels = TRUE) trim_levels_in_group(innervar, drop_outlevs = TRUE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_funcs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split functions — split_funcs","text":"excl character. Levels excluded (reflected resulting table structure regardless presence data). character. Levels retain (others dropped). reorder logical(1). order used order children split. defaults TRUE df dataset (data.frame tibble) spl Split object defining partitioning analysis/tabulation data. vals . internal use . labels character. Labels use remaining levels instead existing ones. trim logical(1). splits corresponding 0 observations kept tabulating. neworder character. New order factor levels. newlabels character. Labels (new order ) factor levels drlevels logical(1). levels data appear neworder dropped. Defaults TRUE innervar character(1). Variable whose factor levels trimmed (e.g., empty levels dropped) separately within grouping defined point structure drop_outlevs logical(1). empty levels variable split (.e. 'outer' variable, innervar) dropped? Defaults TRUE","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_funcs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split functions — split_funcs","text":"closure suitable use splitting function (splfun) creating table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_funcs.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Split functions — split_funcs","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_funcs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split functions — split_funcs","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = remove_split_levels(c( \"USA\", \"CAN\", \"CHE\", \"BRA\" )) ) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> CHN #> Mean 36.08 34.12 33.71 #> PAK #> Mean 35.38 33.12 36.75 #> NGA #> Mean 31.20 31.40 35.78 #> RUS #> Mean 33.33 34.20 33.00 #> JPN #> Mean 31.20 32.50 36.20 #> GBR #> Mean 32.00 29.00 30.00 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = keep_split_levels(c(\"USA\", \"CAN\", \"BRA\")) ) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> USA #> Mean 36.77 32.57 36.41 #> CAN #> Mean 36.00 34.00 29.50 #> BRA #> Mean 31.78 30.62 36.14 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_split_levels) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89 #> M #> Mean 36.55 32.10 34.28 lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\", split_fun = drop_and_remove_levels(c(\"M\", \"U\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————— #> F #> Mean 33.71 33.84 34.89"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Rows according to levels of a variable — split_rows_by","title":"Add Rows according to levels of a variable — split_rows_by","text":"Add Rows according levels variable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Rows according to levels of a variable — split_rows_by","text":"","code":"split_rows_by( lyt, var, labels_var = var, split_label = var, split_fun = NULL, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), label_pos = \"hidden\", indent_mod = 0L, page_by = FALSE, page_prefix = split_label, section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Rows according to levels of a variable — split_rows_by","text":"lyt layout object pre-data used tabulation var string, variable name labels_var string, name variable containing labels displayed values var split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). split_fun function/NULL. custom splitting function See custom_split_funs format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. page_by logical(1). pagination forced different children resulting form split. error rise selected split contain least one value NA. page_prefix character(1). Prefix, appended split value, forcing pagination children split/table section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Rows according to levels of a variable — split_rows_by","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Add Rows according to levels of a variable — split_rows_by","text":"var factor empty unobserved levels labels_var specified, must also factor number levels var. Currently error occurs case informative, change future.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"custom-splitting-function-details","dir":"Reference","previous_headings":"","what":"Custom Splitting Function Details","title":"Add Rows according to levels of a variable — split_rows_by","text":"User-defined custom split functions can perform type computation incoming data provided meet contract generating 'splits' incoming data 'based ' split object. Split functions functions accept: df data.frame incoming data split spl Split object. largely internal detail custom functions need worry , obj_name(spl), example, give name split appear paths resulting table vals pre-calculated values. given non-null values, values returned match . NULL cases can likely ignored labels pre-calculated value labels. values trim TRUE, resulting splits empty removed (Optional) .spl_context data.frame describing previously performed splits collectively arrived df function must output named list following elements: values vector values corresponding splits df datasplit list data.frames representing groupings actual observations df. labels character vector giving string label value listed values element (Optional) extras present, extra arguments passed summary analysis functions whenever executed corresponding element datasplit subset thereof One way generate custom splitting functions wrap existing split functions modify either incoming data called outputs.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add Rows according to levels of a variable — split_rows_by","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add Rows according to levels of a variable — split_rows_by","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\", split_fun = drop_split_levels) %>% analyze(\"AGE\", mean, var_labels = \"Age\", format = \"xx.xx\") tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————— #> ASIAN #> mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN #> mean 34.68 31.71 34.00 #> WHITE #> mean 39.36 36.93 35.11 lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"RACE\") %>% analyze(\"AGE\", mean, var_labels = \"Age\", format = \"xx.xx\") tbl2 <- build_table(lyt2, DM) tbl2 #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————————————————————————————— #> ASIAN #> mean 34.20 32.68 34.63 #> BLACK OR AFRICAN AMERICAN #> mean 34.68 31.71 34.00 #> WHITE #> mean 39.36 36.93 35.11 #> AMERICAN INDIAN OR ALASKA NATIVE #> mean NA NA NA #> MULTIPLE #> mean NA NA NA #> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER #> mean NA NA NA #> OTHER #> mean NA NA NA #> UNKNOWN #> mean NA NA NA lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% summarize_row_groups(label_fstr = \"Overall (N)\") %>% split_rows_by(\"RACE\", split_label = \"Ethnicity\", labels_var = \"ethn_lab\", split_fun = drop_split_levels ) %>% summarize_row_groups(\"RACE\", label_fstr = \"%s (n)\") %>% analyze(\"AGE\", var_labels = \"Age\", afun = mean, format = \"xx.xx\") lyt3 #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) -> SEX (lvls) #> #> Row-Split Structure: #> RACE (lvls) -> AGE (** analysis **) #> library(dplyr) DM2 <- DM %>% filter(SEX %in% c(\"M\", \"F\")) %>% mutate( SEX = droplevels(SEX), gender_lab = c( \"F\" = \"Female\", \"M\" = \"Male\", \"U\" = \"Unknown\", \"UNDIFFERENTIATED\" = \"Undifferentiated\" )[SEX], ethn_lab = c( \"ASIAN\" = \"Asian\", \"BLACK OR AFRICAN AMERICAN\" = \"Black or African American\", \"WHITE\" = \"White\", \"AMERICAN INDIAN OR ALASKA NATIVE\" = \"American Indian or Alaska Native\", \"MULTIPLE\" = \"Multiple\", \"NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER\" = \"Native Hawaiian or Other Pacific Islander\", \"OTHER\" = \"Other\", \"UNKNOWN\" = \"Unknown\" )[RACE] ) tbl3 <- build_table(lyt3, DM2) tbl3 #> A: Drug X B: Placebo C: Combination #> F M F M F M #> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> Overall (N) 70 (100.0%) 51 (100.0%) 56 (100.0%) 50 (100.0%) 61 (100.0%) 68 (100.0%) #> Asian (n) 44 (62.9%) 35 (68.6%) 37 (66.1%) 31 (62.0%) 40 (65.6%) 44 (64.7%) #> mean 33.55 35.03 34.00 31.10 34.90 34.39 #> Black or African American (n) 18 (25.7%) 10 (19.6%) 12 (21.4%) 12 (24.0%) 13 (21.3%) 14 (20.6%) #> mean 33.17 37.40 30.58 32.83 33.85 34.14 #> White (n) 8 (11.4%) 6 (11.8%) 7 (12.5%) 7 (14.0%) 8 (13.1%) 10 (14.7%) #> mean 35.88 44.00 38.57 35.29 36.50 34.00"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by_multivar.html","id":null,"dir":"Reference","previous_headings":"","what":"Associate Multiple Variables with Rows — split_rows_by_multivar","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"need rows reflect different variables rather different levels single variable, use split_rows_by_multivar.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by_multivar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"","code":"split_rows_by_multivar( lyt, vars, split_fun = NULL, split_label = \"\", varlabels = vars, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), indent_mod = 0L, section_div = NA_character_, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by_multivar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"lyt layout object pre-data used tabulation vars character vector. Multiple variable names. split_fun function/NULL. custom splitting function See custom_split_funs split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). varlabels character vector. Labels vars format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by_multivar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/split_rows_by_multivar.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Associate Multiple Variables with Rows — split_rows_by_multivar","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by_multivar(c(\"SEX\", \"STRATA1\")) %>% summarize_row_groups() %>% analyze(c(\"AGE\", \"SEX\")) tbl <- build_table(lyt, DM) tbl #> A: Drug X B: Placebo C: Combination #> ————————————————————————————————————————————————————————————————————— #> #> SEX 121 (100.0%) 106 (100.0%) 129 (100.0%) #> AGE #> Mean 34.91 33.02 34.57 #> SEX #> F 70 56 61 #> M 51 50 68 #> U 0 0 0 #> UNDIFFERENTIATED 0 0 0 #> STRATA1 121 (100.0%) 106 (100.0%) 129 (100.0%) #> AGE #> Mean 34.91 33.02 34.57 #> SEX #> F 70 56 61 #> M 51 50 68 #> U 0 0 0 #> UNDIFFERENTIATED 0 0 0"},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a content row of summary counts — summarize_row_groups","title":"Add a content row of summary counts — summarize_row_groups","text":"Add content row summary counts","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a content row of summary counts — summarize_row_groups","text":"","code":"summarize_row_groups( lyt, var = \"\", label_fstr = \"%s\", format = \"xx (xx.x%)\", na_str = \"-\", cfun = NULL, indent_mod = 0L, extra_args = list() )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a content row of summary counts — summarize_row_groups","text":"lyt layout object pre-data used tabulation var string, variable name label_fstr string. sprintf style format string containing. non-comparison splits, can contain one \"%s\" takes current split value generates row/column label. Comparison-based splits can contain two \"%s\". format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". cfun list/function/NULL. tabulation function(s) creating content rows. Must accept x df first parameter. Must accept labelstr second argument. Can optionally accept optional arguments accepted analysis functions. See analyze. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add a content row of summary counts — summarize_row_groups","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Add a content row of summary counts — summarize_row_groups","text":"format expects 1 value (.e. specified format string xx appears two values (.e. xx appears twice format string) specified function, raw percent column total counts calculated. format format string xx appears one time, raw counts used. cfun must accept x df first argument. df argument cfun receive subset data.frame corresponding row- column-splitting cell calculated. Must accept labelstr second parameter, accepts label level parent split currently summarized. Can additionally take optional argument supported analysis functions. (see analyze). addition, complex custom functions needed, suggest checking available additional_fun_params apply afun.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Add a content row of summary counts — summarize_row_groups","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_row_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add a content row of summary counts — summarize_row_groups","text":"","code":"DM2 <- subset(DM, COUNTRY %in% c(\"USA\", \"CAN\", \"CHN\")) lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = drop_split_levels) %>% summarize_row_groups(label_fstr = \"%s (n)\") %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") lyt #> A Pre-data Table Layout #> #> Column-Split Structure: #> ARM (lvls) #> #> Row-Split Structure: #> COUNTRY (lvls) -> AGE (** analysis **) #> tbl <- build_table(lyt, DM2) tbl #> A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————— #> CHN (n) 62 (79.5%) 48 (75.0%) 69 (78.4%) #> Min. 22.00 25.00 24.00 #> 1st Qu. 29.25 30.00 30.00 #> Median 34.00 33.50 33.00 #> Mean 36.08 34.12 33.71 #> 3rd Qu. 41.00 38.00 37.00 #> Max. 60.00 55.00 51.00 #> USA (n) 13 (16.7%) 14 (21.9%) 17 (19.3%) #> Min. 23.00 24.00 22.00 #> 1st Qu. 31.00 28.00 31.00 #> Median 36.00 30.00 37.00 #> Mean 36.77 32.57 36.41 #> 3rd Qu. 41.00 37.50 41.00 #> Max. 58.00 47.00 51.00 #> CAN (n) 3 (3.8%) 2 (3.1%) 2 (2.3%) #> Min. 29.00 30.00 28.00 #> 1st Qu. 32.50 32.00 28.75 #> Median 36.00 34.00 29.50 #> Mean 36.00 34.00 29.50 #> 3rd Qu. 39.50 36.00 30.25 #> Max. 43.00 38.00 31.00 row_paths_summary(tbl) # summary count is a content table #> rowname node_class path #> —————————————————————————————————————————————————————————— #> CHN (n) ContentRow COUNTRY, CHN, @content, CHN (n) #> Min. DataRow COUNTRY, CHN, AGE, Min. #> 1st Qu. DataRow COUNTRY, CHN, AGE, 1st Qu. #> Median DataRow COUNTRY, CHN, AGE, Median #> Mean DataRow COUNTRY, CHN, AGE, Mean #> 3rd Qu. DataRow COUNTRY, CHN, AGE, 3rd Qu. #> Max. DataRow COUNTRY, CHN, AGE, Max. #> USA (n) ContentRow COUNTRY, USA, @content, USA (n) #> Min. DataRow COUNTRY, USA, AGE, Min. #> 1st Qu. DataRow COUNTRY, USA, AGE, 1st Qu. #> Median DataRow COUNTRY, USA, AGE, Median #> Mean DataRow COUNTRY, USA, AGE, Mean #> 3rd Qu. DataRow COUNTRY, USA, AGE, 3rd Qu. #> Max. DataRow COUNTRY, USA, AGE, Max. #> CAN (n) ContentRow COUNTRY, CAN, @content, CAN (n) #> Min. DataRow COUNTRY, CAN, AGE, Min. #> 1st Qu. DataRow COUNTRY, CAN, AGE, 1st Qu. #> Median DataRow COUNTRY, CAN, AGE, Median #> Mean DataRow COUNTRY, CAN, AGE, Mean #> 3rd Qu. DataRow COUNTRY, CAN, AGE, 3rd Qu. #> Max. DataRow COUNTRY, CAN, AGE, Max. ## use a cfun and extra_args to customize summarization ## behavior sfun <- function(x, labelstr, trim) { in_rows( c(mean(x, trim = trim), trim), .formats = \"xx.x (xx.x%)\", .labels = sprintf( \"%s (Trimmed mean and trim %%)\", labelstr ) ) } lyt2 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by(\"COUNTRY\", split_fun = drop_split_levels) %>% summarize_row_groups(\"AGE\", cfun = sfun, extra_args = list(trim = .2) ) %>% analyze(\"AGE\", afun = list_wrap_x(summary), format = \"xx.xx\") %>% append_topleft(c(\"Country\", \" Age\")) tbl2 <- build_table(lyt2, DM2) tbl2 #> Country A: Drug X B: Placebo C: Combination #> Age (N=78) (N=64) (N=88) #> ———————————————————————————————————————————————————————————————————————————— #> CHN (Trimmed mean and trim %) 35.1 (20.0%) 33.4 (20.0%) 33.4 (20.0%) #> Min. 22.00 25.00 24.00 #> 1st Qu. 29.25 30.00 30.00 #> Median 34.00 33.50 33.00 #> Mean 36.08 34.12 33.71 #> 3rd Qu. 41.00 38.00 37.00 #> Max. 60.00 55.00 51.00 #> USA (Trimmed mean and trim %) 36.1 (20.0%) 31.9 (20.0%) 36.1 (20.0%) #> Min. 23.00 24.00 22.00 #> 1st Qu. 31.00 28.00 31.00 #> Median 36.00 30.00 37.00 #> Mean 36.77 32.57 36.41 #> 3rd Qu. 41.00 37.50 41.00 #> Max. 58.00 47.00 51.00 #> CAN (Trimmed mean and trim %) 36.0 (20.0%) 34.0 (20.0%) 29.5 (20.0%) #> Min. 29.00 30.00 28.00 #> 1st Qu. 32.50 32.00 28.75 #> Median 36.00 34.00 29.50 #> Mean 36.00 34.00 29.50 #> 3rd Qu. 39.50 36.00 30.25 #> Max. 43.00 38.00 31.00"},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"summarize_rows — summarize_rows","title":"summarize_rows — summarize_rows","text":"summarize_rows deprecated favor make_row_df.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"summarize_rows — summarize_rows","text":"","code":"summarize_rows(obj)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"summarize_rows — summarize_rows","text":"obj VTableTree.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/summarize_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"summarize_rows — summarize_rows","text":"data.frame summarizing rows obj.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tabclasses.html","id":null,"dir":"Reference","previous_headings":"","what":"TableTree classes — ElementaryTable-class","title":"TableTree classes — ElementaryTable-class","text":"TableTree classes Table Constructors Classes","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tabclasses.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TableTree classes — ElementaryTable-class","text":"","code":"ElementaryTable( kids = list(), name = \"\", lev = 1L, label = \"\", labelrow = LabelRow(lev = lev, label = label, vis = !isTRUE(iscontent) && !is.na(label) && nzchar(label)), rspans = data.frame(), cinfo = NULL, iscontent = NA, var = NA_character_, format = NULL, na_str = NA_character_, indent_mod = 0L, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), header_section_div = NA_character_, hsep = default_hsep(), trailing_section_div = NA_character_, inset = 0L ) TableTree( kids = list(), name = if (!is.na(var)) var else \"\", cont = EmptyElTable, lev = 1L, label = name, labelrow = LabelRow(lev = lev, label = label, vis = nrow(cont) == 0 && !is.na(label) && nzchar(label)), rspans = data.frame(), iscontent = NA, var = NA_character_, cinfo = NULL, format = NULL, na_str = NA_character_, indent_mod = 0L, title = \"\", subtitles = character(), main_footer = character(), prov_footer = character(), page_title = NA_character_, hsep = default_hsep(), header_section_div = NA_character_, trailing_section_div = NA_character_, inset = 0L )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/tabclasses.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TableTree classes — ElementaryTable-class","text":"kids list. List direct children. name character(1). Name split/table/row created. Defaults corresponding label, required . lev integer. Nesting level (roughly, indentation level practical terms). label character(1). label (confused name) object/structure. labelrow LabelRow. LabelRow object assign Table. Constructed label default specified. rspans data.frame. Currently stored otherwise ignored. cinfo InstantiatedColumnInfo (NULL). Column structure object created. iscontent logical. TableTree/ElementaryTable constructed content table another TableTree. var string, variable name format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior. title character(1). Main title (main_title()) single string. Ignored subtables. subtitles character. Subtitles (subtitles()) can vector strings, every element printed separate line. Ignored subtables. main_footer character. Main global (non-referential) footer materials (main_footer()). vector strings, printed separate lines. prov_footer character. Provenance-related global footer materials (prov_footer()). can also vector strings, printed different lines. Generally modified hand. header_section_div character(1). String used divide header table. See header_section_div() getter setter . Please consider changing last element section_div() concatenating tables need divider . hsep character(1). Set character(s) repeated separator header body table rendered text. Defaults connected horizontal line (unicode 2014) locals use UTF charset, - elsewhere (per session warning). See formatters::set_default_hsep() information. trailing_section_div character(1). String used section divider printing last row contained (sub)-table, unless row also last table row printed overall, NA_character_ none (default). generated via layouting, correspond section_div split table represents single facet. inset numeric(1). Number spaces inset table header, table body, referential footnotes, main_footer, compared alignment title, subtitle, provenance footer. Defaults 0 (inset). cont ElementaryTable. Content table. page_title character. Page specific title(s).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tabclasses.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TableTree classes — ElementaryTable-class","text":"formal object representing populated table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tabclasses.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"TableTree classes — ElementaryTable-class","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_shell.html","id":null,"dir":"Reference","previous_headings":"","what":"Table shells — table_shell","title":"Table shells — table_shell","text":"table shell rendering table maintains structure, display values, rather displaying formatting instructions cell.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_shell.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table shells — table_shell","text":"","code":"table_shell( tt, widths = NULL, col_gap = 3, hsep = default_hsep(), tf_wrap = FALSE, max_width = NULL ) table_shell_str( tt, widths = NULL, col_gap = 3, hsep = default_hsep(), tf_wrap = FALSE, max_width = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_shell.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table shells — table_shell","text":"tt TableTree (related class). TableTree object representing populated table. widths numeric ( NULL). (proposed) widths columns x. expected length numeric vector can retrieved ncol() + 1 column row names must also considered. col_gap numeric(1). Space (characters) columns hsep character(1). Characters repeat create header/body separator line. NULL, object value used. \" \", empty separator printed. Check default_hsep() information. tf_wrap logical(1). texts title, subtitle, footnotes wrapped? max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_shell.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table shells — table_shell","text":"table_shell_str string representing table shell, table_shell, NULL, function called side effect printing shell console.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_shell.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table shells — table_shell","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\") tbl <- build_table(lyt, iris2) table_shell(tbl) #> setosa versicolor virginica #> a b a b a b #> ———————————————————————————————————————————————————————————— #> Sepal.Length - - - - - - #> Min. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 1st Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Median xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Mean xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 3rd Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Max. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Petal.Width - - - - - - #> Min. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 1st Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Median xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Mean xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> 3rd Qu. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx #> Max. xx.xx xx.xx xx.xx xx.xx xx.xx xx.xx"},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_structure.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize Table — table_structure","title":"Summarize Table — table_structure","text":"Summarize Table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_structure.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize Table — table_structure","text":"","code":"table_structure(x, detail = c(\"subtable\", \"row\"))"},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_structure.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize Table — table_structure","text":"x table object detail either row subtable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_structure.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize Table — table_structure","text":"currently return value. Called side-effect printing row- subtable-structure summary x.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/table_structure.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summarize Table — table_structure","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\" ) tbl <- build_table(lyt, iris2) tbl #> setosa versicolor virginica #> a b a b a b #> ————————————————————————————————————————————————————————— #> Sepal.Length #> Min. 4.40 4.30 5.00 4.90 4.90 5.60 #> 1st Qu. 4.80 4.80 5.60 5.60 6.20 6.30 #> Median 5.00 5.00 5.90 5.90 6.50 6.50 #> Mean 5.02 4.99 5.99 5.88 6.50 6.67 #> 3rd Qu. 5.30 5.10 6.40 6.10 6.70 7.20 #> Max. 5.80 5.70 7.00 6.70 7.70 7.90 #> Petal.Width #> Min. 0.10 0.10 1.00 1.00 1.40 1.50 #> 1st Qu. 0.20 0.20 1.20 1.20 1.90 1.80 #> Median 0.20 0.20 1.30 1.30 2.10 2.00 #> Mean 0.23 0.26 1.35 1.30 2.08 1.98 #> 3rd Qu. 0.20 0.30 1.50 1.40 2.30 2.20 #> Max. 0.40 0.60 1.80 1.70 2.50 2.50 row_paths(tbl) #> [[1]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> #> [[2]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Min.\" #> #> [[3]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"1st Qu.\" #> #> [[4]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Median\" #> #> [[5]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Mean\" #> #> [[6]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"3rd Qu.\" #> #> [[7]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Sepal.Length\" #> [3] \"Max.\" #> #> [[8]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> #> [[9]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Min.\" #> #> [[10]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"1st Qu.\" #> #> [[11]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Median\" #> #> [[12]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Mean\" #> #> [[13]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"3rd Qu.\" #> #> [[14]] #> [1] \"ma_Sepal.Length_Petal.Width\" \"Petal.Width\" #> [3] \"Max.\" #> table_structure(tbl) #> [TableTree] ma_Sepal.Length_Petal.Width #> [ElementaryTable] Sepal.Length (6 x 6) #> [ElementaryTable] Petal.Width (6 x 6) table_structure(tbl, detail = \"row\") #> TableTree: [ma_Sepal.Length_Petal.Width] () #> labelrow: [] () - #> children: #> ElementaryTable: [Sepal.Length] (Sepal.Length) #> labelrow: [Sepal.Length] (Sepal.Length) #> children: #> DataRow: [Min.] (Min.) #> DataRow: [1st Qu.] (1st Qu.) #> DataRow: [Median] (Median) #> DataRow: [Mean] (Mean) #> DataRow: [3rd Qu.] (3rd Qu.) #> DataRow: [Max.] (Max.) #> ElementaryTable: [Petal.Width] (Petal.Width) #> labelrow: [Petal.Width] (Petal.Width) #> children: #> DataRow: [Min.] (Min.) #> DataRow: [1st Qu.] (1st Qu.) #> DataRow: [Median] (Median) #> DataRow: [Mean] (Mean) #> DataRow: [3rd Qu.] (3rd Qu.) #> DataRow: [Max.] (Max.)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/top_left.html","id":null,"dir":"Reference","previous_headings":"","what":"Top Left Material (Experimental) — top_left","title":"Top Left Material (Experimental) — top_left","text":"TableTree object can top left material sequence strings printed area table column header display label first row. functions access modify material.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/top_left.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Top Left Material (Experimental) — top_left","text":"","code":"top_left(obj) # S4 method for VTableTree top_left(obj) # S4 method for InstantiatedColumnInfo top_left(obj) # S4 method for PreDataTableLayouts top_left(obj) top_left(obj) <- value # S4 method for VTableTree top_left(obj) <- value # S4 method for InstantiatedColumnInfo top_left(obj) <- value # S4 method for PreDataTableLayouts top_left(obj) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/top_left.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Top Left Material (Experimental) — top_left","text":"obj . object accessor access modify value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/top_left.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Top Left Material (Experimental) — top_left","text":"character vector representing top-left material obj (obj modification, case setter).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tostring.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an rtable object to a string — tostring","title":"Convert an rtable object to a string — tostring","text":"Transform complex object string representation ready printed written plain-text file objects printed console pass toString. function allows fundamental formatting specifications final output, like column widths relative wrapping (width), title footer wrapping (tf_wrap = TRUE max_width), horizontal separator character (e.g. hsep = \"+\").","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tostring.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an rtable object to a string — tostring","text":"","code":"# S4 method for VTableTree toString( x, widths = NULL, col_gap = 3, hsep = horizontal_sep(x), indent_size = 2, tf_wrap = FALSE, max_width = NULL )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/tostring.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an rtable object to a string — tostring","text":"x . Object prepared rendering. widths numeric ( NULL). (proposed) widths columns x. expected length numeric vector can retrieved ncol() + 1 column row names must also considered. col_gap numeric(1). Space (characters) columns hsep character(1). Characters repeat create header/body separator line. NULL, object value used. \" \", empty separator printed. Check default_hsep() information. indent_size numeric(1). Number spaces use per indent level. Defaults 2 tf_wrap logical(1). texts title, subtitle, footnotes wrapped? max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tostring.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an rtable object to a string — tostring","text":"string representation x appears printed.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tostring.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert an rtable object to a string — tostring","text":"Manual insertion newlines supported tf_wrap result warning undefined wrapping behavior. Passing vectors already split strings remains supported, however case string word-wrapped separately behavior described .","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/tostring.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an rtable object to a string — tostring","text":"","code":"library(dplyr) iris2 <- iris %>% group_by(Species) %>% mutate(group = as.factor(rep_len(c(\"a\", \"b\"), length.out = n()))) %>% ungroup() lyt <- basic_table() %>% split_cols_by(\"Species\") %>% split_cols_by(\"group\") %>% analyze(c(\"Sepal.Length\", \"Petal.Width\"), afun = list_wrap_x(summary), format = \"xx.xx\") tbl <- build_table(lyt, iris2) cat(toString(tbl, col_gap = 3)) #> setosa versicolor virginica #> a b a b a b #> ————————————————————————————————————————————————————————— #> Sepal.Length #> Min. 4.40 4.30 5.00 4.90 4.90 5.60 #> 1st Qu. 4.80 4.80 5.60 5.60 6.20 6.30 #> Median 5.00 5.00 5.90 5.90 6.50 6.50 #> Mean 5.02 4.99 5.99 5.88 6.50 6.67 #> 3rd Qu. 5.30 5.10 6.40 6.10 6.70 7.20 #> Max. 5.80 5.70 7.00 6.70 7.70 7.90 #> Petal.Width #> Min. 0.10 0.10 1.00 1.00 1.40 1.50 #> 1st Qu. 0.20 0.20 1.20 1.20 1.90 1.80 #> Median 0.20 0.20 1.30 1.30 2.10 2.00 #> Mean 0.23 0.26 1.35 1.30 2.08 1.98 #> 3rd Qu. 0.20 0.30 1.50 1.40 2.30 2.20 #> Max. 0.40 0.60 1.80 1.70 2.50 2.50"},{"path":"https://insightsengineering.github.io/rtables/main/reference/tree_children.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve or set the direct children of a Tree-style object — tree_children","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"Retrieve set direct children Tree-style object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tree_children.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"","code":"tree_children(x) tree_children(x) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/tree_children.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"x object Tree structure value New list children.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tree_children.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve or set the direct children of a Tree-style object — tree_children","text":"List direct children x","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_in_facets.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"Trim Levels Another Variable Facet (Postprocessing split step)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_in_facets.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"","code":"trim_levels_in_facets(innervar)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_in_facets.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"innervar character. variable(s) trim (remove unobserved levels) independently within facet.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_in_facets.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim Levels of Another Variable From Each Facet (Postprocessing split step) — trim_levels_in_facets","text":"function suitable use pre (list) argument make_split_fun","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_to_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim Levels to map — trim_levels_to_map","title":"Trim Levels to map — trim_levels_to_map","text":"split function constructor creates split function trims levels variable reflect restrictions possible combinations two variables split (along axis) within layout.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_to_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim Levels to map — trim_levels_to_map","text":"","code":"trim_levels_to_map(map = NULL)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_to_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim Levels to map — trim_levels_to_map","text":"map data.frame. data.frame defining allowed combinations variables. combination level split present map removed data, variable split present data associated split parents .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_to_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim Levels to map — trim_levels_to_map","text":"fun","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_to_map.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trim Levels to map — trim_levels_to_map","text":"splitting occurs, map subset values previously performed splits. levels variable split pruned still present within subset map representing current hierarchical splitting context. Splitting performed via keep_split_levels split function. resulting element partition trimmed pruning values remaining variables specified map values allowed combination previous current split.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_levels_to_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trim Levels to map — trim_levels_to_map","text":"","code":"map <- data.frame( LBCAT = c(\"CHEMISTRY\", \"CHEMISTRY\", \"CHEMISTRY\", \"IMMUNOLOGY\"), PARAMCD = c(\"ALT\", \"CRP\", \"CRP\", \"IGA\"), ANRIND = c(\"LOW\", \"LOW\", \"HIGH\", \"HIGH\"), stringsAsFactors = FALSE ) lyt <- basic_table() %>% split_rows_by(\"LBCAT\") %>% split_rows_by(\"PARAMCD\", split_fun = trim_levels_to_map(map = map)) %>% analyze(\"ANRIND\") tbl <- build_table(lyt, ex_adlb)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_prune_funs.html","id":null,"dir":"Reference","previous_headings":"","what":"Trimming and Pruning Criteria — all_zero_or_na","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"Criteria functions (constructors thereof) trimming pruning tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_prune_funs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"","code":"all_zero_or_na(tr) all_zero(tr) content_all_zeros_nas(tt, criteria = all_zero_or_na) prune_empty_level(tt) prune_zeros_only(tt) low_obs_pruner(min, type = c(\"sum\", \"mean\"))"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_prune_funs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"tr TableRow (related class). TableRow object representing single row within populated table. tt TableTree (related class). TableTree object representing populated table. criteria function. Function takes TableRow object returns TRUE row removed. Defaults all_zero_or_na min numeric(1). (low_obs_pruner ). Minimum aggregate count value. Subtables whose combined/average count threshold pruned type character(1). count values aggregated. Must \"sum\" (default) \"mean\"","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_prune_funs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"logical value indicating whether tr included (TRUE) pruned (FALSE) pruning.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_prune_funs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"all_zero_or_na returns TRUE (thus indicates trimming/pruning) non-LabelRow TableRow contain mix NA (including NaN), 0, Inf -Inf values. all_zero returns TRUE non-Label row contains (non-missing) zero values. content_all_zeros_nas Prunes subtable ) content table exactly one row , b) all_zero_or_na returns TRUE single content row. practice, default summary/content function used, represents pruning subtable corresponds empty set input data (e.g., factor variable used split_rows_by levels present data). prune_empty_level combines all_zero_or_na behavior TableRow objects, content_all_zeros_nas content_table(tt) TableTree objects, additional check returns TRUE tt children. prune_zeros_only behaves prune_empty_level , except like all_zero prunes case non-missing zero values. low_obs_pruner constructor function , called, returns pruning criteria function prune content rows comparing sum mean (dictated type) count portions cell values (defined first value per cell regardless many values per cell ) min.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_prune_funs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trimming and Pruning Criteria — all_zero_or_na","text":"","code":"adsl <- ex_adsl levels(adsl$SEX) <- c(levels(ex_adsl$SEX), \"OTHER\") adsl$AGE[adsl$SEX == \"UNDIFFERENTIATED\"] <- 0 adsl$BMRKR1 <- 0 tbl_to_prune <- basic_table() %>% analyze(\"BMRKR1\") %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") %>% build_table(adsl) tbl_to_prune %>% prune_table(all_zero_or_na) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 tbl_to_prune %>% prune_table(all_zero) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 0.00 #> B 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 0.00 NA 0.00 #> OTHER 0 (0.0%) 0 (0.0%) 0 (0.0%) #> A 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> B 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA #> C 0 (0.0%) 0 (0.0%) 0 (0.0%) #> Mean NA NA NA tbl_to_prune %>% prune_table(content_all_zeros_nas) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> Mean 0.00 0.00 0.00 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 0.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 0.00 NA 0.00 tbl_to_prune %>% prune_table(prune_empty_level) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 tbl_to_prune %>% prune_table(prune_zeros_only) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 0.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 0.00 NA 0.00 min_prune <- low_obs_pruner(70, \"sum\") tbl_to_prune %>% prune_table(min_prune) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————— #> Mean 0.00 0.00 0.00 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim rows from a populated table without regard for table structure — trim_rows","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"Trim rows populated table without regard table structure","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"","code":"trim_rows(tt, criteria = all_zero_or_na)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"tt TableTree (related class). TableTree object representing populated table. criteria function. Function takes TableRow object returns TRUE row removed. Defaults all_zero_or_na","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"table rows NA 0 cell values removed","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"function deprecated future favor elegant versatile prune_table() function can perform function trim_rows() powerful takes table structure account.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"Visible LabelRows including trimming, can lead either label rows trimmed label rows remaining data rows trimmed, depending criteria returns called LabelRow object. avoid , use structurally-aware prune_table machinery instead.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_rows.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Trim rows from a populated table without regard for table structure — trim_rows","text":"","code":"adsl <- ex_adsl levels(adsl$SEX) <- c(levels(ex_adsl$SEX), \"OTHER\") tbl_to_trim <- basic_table() %>% analyze(\"BMRKR1\") %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% summarize_row_groups() %>% split_rows_by(\"STRATA1\") %>% summarize_row_groups() %>% analyze(\"AGE\") %>% build_table(adsl) tbl_to_trim %>% trim_rows() #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> Mean 5.97 5.70 5.62 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 44.00 #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 28.00 NA 46.00 tbl_to_trim %>% trim_rows(all_zero) #> A: Drug X B: Placebo C: Combination #> ——————————————————————————————————————————————————————————— #> Mean 5.97 5.70 5.62 #> F 79 (59.0%) 77 (57.5%) 66 (50.0%) #> A 21 (15.7%) 24 (17.9%) 18 (13.6%) #> Mean 31.14 32.08 34.22 #> B 25 (18.7%) 27 (20.1%) 21 (15.9%) #> Mean 32.84 35.33 36.57 #> C 33 (24.6%) 26 (19.4%) 27 (20.5%) #> Mean 33.73 34.73 34.78 #> M 51 (38.1%) 55 (41.0%) 60 (45.5%) #> A 16 (11.9%) 19 (14.2%) 20 (15.2%) #> Mean 35.62 39.37 33.55 #> B 21 (15.7%) 17 (12.7%) 21 (15.9%) #> Mean 35.33 37.12 36.05 #> C 14 (10.4%) 19 (14.2%) 19 (14.4%) #> Mean 35.86 35.79 36.58 #> U 3 (2.2%) 2 (1.5%) 4 (3.0%) #> A 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 33.00 27.00 38.00 #> B 1 (0.7%) 1 (0.7%) 1 (0.8%) #> Mean 28.00 35.00 37.00 #> C 1 (0.7%) 0 (0.0%) 2 (1.5%) #> Mean 34.00 NA 33.00 #> UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%) #> A 0 (0.0%) 0 (0.0%) 1 (0.8%) #> Mean NA NA 44.00 #> Mean NA NA NA #> C 1 (0.7%) 0 (0.0%) 1 (0.8%) #> Mean 28.00 NA 46.00 #> Mean NA NA NA #> Mean NA NA NA #> Mean NA NA NA"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_zero_rows.html","id":null,"dir":"Reference","previous_headings":"","what":"Trim Zero Rows — trim_zero_rows","title":"Trim Zero Rows — trim_zero_rows","text":"Trim Zero Rows","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_zero_rows.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Trim Zero Rows — trim_zero_rows","text":"","code":"trim_zero_rows(tbl)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_zero_rows.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Trim Zero Rows — trim_zero_rows","text":"tbl table object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/trim_zero_rows.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Trim Zero Rows — trim_zero_rows","text":"rtable object","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tsv_io.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Enriched flat value table with paths — export_as_tsv","title":"Create Enriched flat value table with paths — export_as_tsv","text":"function creates flat tabular file cell values corresponding paths via path_enriched_df(). writes data.frame tsv file. default (.e. value_func specified, List columns least one value length > 1 collapsed character vectors collapsing list element \"|\".","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tsv_io.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Enriched flat value table with paths — export_as_tsv","text":"","code":"export_as_tsv( tt, file = NULL, path_fun = collapse_path, value_fun = collapse_values ) import_from_tsv(file)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/tsv_io.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Enriched flat value table with paths — export_as_tsv","text":"tt TableTree (related class). TableTree object representing populated table. file character(1). path file written read . path_fun function. Function transform paths single-string row/column names. value_fun function. Function transform cell values cells data.frame. Defaults collapse_values creates strings multi-valued cells collapsed together, separated |.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tsv_io.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Enriched flat value table with paths — export_as_tsv","text":"NULL silently export_as_tsv, data.frame re-constituted list values export_as_tsv.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tsv_io.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Create Enriched flat value table with paths — export_as_tsv","text":"currently round-trip capability type export. can read values exported way back via import_from_tsv receive data.frame version back, TableTree.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/tt_to_flextable.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a FlexTable from an rtables table — tt_to_flextable","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"Principally used export (export_as_docx()), function produces flextable rtables table. theme = NULL, rtables-like style used. Otherwise, theme_docx_default() produce .docx-friendly table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tt_to_flextable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"","code":"tt_to_flextable( tt, theme = theme_docx_default(tt), border = flextable::fp_border_default(width = 0.5), indent_size = NULL, titles_as_header = TRUE, footers_as_text = FALSE, counts_in_newline = FALSE, paginate = FALSE, lpp = NULL, cpp = NULL, ..., colwidths = propose_column_widths(matrix_form(tt, indent_rownames = TRUE)), tf_wrap = !is.null(cpp), max_width = cpp, total_width = 10 ) theme_docx_default( tt = NULL, font = \"Arial\", font_size = 9, bold = c(\"header\", \"content_rows\", \"label_rows\"), bold_manual = NULL, border = flextable::fp_border_default(width = 0.5) )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/tt_to_flextable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"tt TableTree (related class). TableTree object representing populated table. theme function(1). Defaults theme_docx_default(tt). expects theme function designed internally function flextable object changes layout style. set NULL, produce table similar rtables default. border officer border object. Defaults officer::fp_border(width = 0.5). indent_size integer(1). NULL, default indent size table (see matrix_form() indent_size) used. work docx, size multiplied 2 mm (5.67 pt) default. titles_as_header logical(1). Defaults TRUE tt_to_flextable(), table self-contained makes additional header rows main_title() string subtitles() character vector (one per element). FALSE suggested export_as_docx(). adds titles subtitles text paragraph table. style applied. footers_as_text logical(1). Defaults FALSE tt_to_flextable(), table self-contained flextable definition footnotes. TRUE used export_as_docx() add footers new paragraph table. style applied, smaller font. counts_in_newline logical(1). Defaults FALSE. rtables text printing (formatters::toString()), column counts, .e. (N=xx), always new line. noticed docx exports necessary line. paginate logical(1). need .docx export use export_as_docx, suggest relying word pagination system. Cooperation two mechanisms guaranteed. option splits tt different \"pages\" multiple flextables. Defaults FALSE. lpp numeric. Maximum lines per page including (re)printed header context rows cpp numeric(1) NULL. Width (characters) pages horizontal pagination. NA (default) indicates cpp inferred page size; NULL indicates horizontal pagination done regardless page size. ... Passed methods tabulation functions. colwidths numeric vector. Column widths use vertical pagination. tf_wrap logical(1). texts title, subtitle, footnotes wrapped? max_width integer(1), character(1) NULL. Width title footer (including footnotes) materials word-wrapped . NULL, set current print width session (getOption(\"width\")). set \"auto\", width table (plus table inset) used. Ignored completely tf_wrap FALSE. total_width numeric(1). Total width inches resulting flextable(s). Defaults 10. font character(1). Defaults \"Arial\". font vailable, flextable default used. font_size integer(1). Positive integerish value defaults 9. bold character vector. can combination c(\"header\", \"content_rows\", \"label_rows\"). first one renders column names bold (topleft content). Second third option use make_row_df() render content /label rows bold. bold_manual named list. List indexes lists. See example needed structure. Accepted groupings/names c(\"header\", \"body\").","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tt_to_flextable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"flextable object.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/tt_to_flextable.html","id":"functions","dir":"Reference","previous_headings":"","what":"Functions","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"theme_docx_default(): main theme function export_as_docx()","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/tt_to_flextable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a FlexTable from an rtables table — tt_to_flextable","text":"Title says Whaaaat Oh, ok. : Drug X B: Placebo C: Combination row1 {1} 5 5 5 row2 1, 2 {2} 1, 2 {2} 1, 2 {2} {1} - row 1 - row footnote {2} - row 2 - cell footnote ha HA! Footer! Title says Whaaaat Oh, ok. : Drug X B: Placebo C: Combination row1 {1} 5 5 5 row2 1, 2 {2} 1, 2 {2} 1, 2 {2} {1} - row 1 - row footnote {2} - row 2 - cell footnote ha HA! Footer! Title says Whaaaat Oh, ok. : Drug X B: Placebo C: Combination row1 {1} 5 5 5 row2 1, 2 {2} 1, 2 {2} 1, 2 {2} {1} - row 1 - row footnote {2} - row 2 - cell footnote ha HA! Footer!","code":"analysisfun <- function(x, ...) { in_rows( row1 = 5, row2 = c(1, 2), .row_footnotes = list(row1 = \"row 1 - row footnote\"), .cell_footnotes = list(row2 = \"row 2 - cell footnote\") ) } lyt <- basic_table( title = \"Title says Whaaaat\", subtitles = \"Oh, ok.\", main_footer = \"ha HA! Footer!\" ) %>% split_cols_by(\"ARM\") %>% analyze(\"AGE\", afun = analysisfun) tbl <- build_table(lyt, ex_adsl) # rtables style tt_to_flextable(tbl, theme = NULL) .cl-17070c7a{table-layout:auto;}.cl-16ff1970{font-family:'DejaVu Sans';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1702b102{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1702b10c{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1702b116{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:0.3pt;padding-right:0.3pt;line-height: 1;background-color:transparent;}.cl-1702c9b2{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9bc{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9c6{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9c7{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9d0{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9d1{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9d2{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9da{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9db{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9dc{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9e4{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9e5{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9ee{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9ef{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9f8{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9f9{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702c9fa{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca02{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca03{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca0c{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca0d{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca0e{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca16{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1702ca17{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}Title says WhaaaatOh, ok.A: Drug XB: PlaceboC: Combinationrow1 {1}555row21, 2 {2}1, 2 {2}1, 2 {2}{1} - row 1 - row footnote{2} - row 2 - cell footnoteha HA! Footer! tt_to_flextable(tbl, theme = theme_docx_default(tbl, font_size = 7)) .cl-17476806{table-layout:auto;}.cl-1740ed8c{font-family:'Arial';font-size:7pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1740ed96{font-family:'Arial';font-size:7pt;font-weight:bold;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1740eda0{font-family:'Arial';font-size:6pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1743ccaa{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1743ccb4{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:3pt;padding-top:0;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1743ccbe{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:1pt;padding-top:1pt;padding-left:0.3pt;padding-right:0.3pt;line-height: 1;background-color:transparent;}.cl-1743ccbf{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-1743defc{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df06{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df07{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df10{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df1a{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df1b{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df24{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df25{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df2e{background-color:transparent;vertical-align: middle;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0.5pt solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df2f{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df38{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df39{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0.5pt solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df42{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.5pt solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df43{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df4c{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df4d{background-color:transparent;vertical-align: top;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0.5pt solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df4e{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0.5pt solid rgba(102, 102, 102, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df56{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df60{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df61{background-color:transparent;vertical-align: top;border-bottom: 0.5pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0.5pt solid rgba(102, 102, 102, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df6a{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df6b{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df74{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-1743df75{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}Title says WhaaaatOh, ok.A: Drug XB: PlaceboC: Combinationrow1 {1}555row21, 2 {2}1, 2 {2}1, 2 {2}{1} - row 1 - row footnote{2} - row 2 - cell footnoteha HA! Footer! # Custom theme special_bold <- list( \"header\" = list(\"i\" = 1, \"j\" = c(1, 3)), \"body\" = list(\"i\" = c(1, 2), \"j\" = 1) ) custom_theme <- theme_docx_default(tbl, font_size = 10, font = \"Brush Script MT\", border = flextable::fp_border_default(color = \"pink\", width = 2), bold = NULL, bold_manual = special_bold ) tt_to_flextable(tbl, border = flextable::fp_border_default(color = \"pink\", width = 2), theme = custom_theme ) .cl-177f04dc{table-layout:auto;}.cl-1776dad2{font-family:'Brush Script MT';font-size:10pt;font-weight:bold;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1776dae6{font-family:'Brush Script MT';font-size:10pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-1776daf0{font-family:'Brush Script MT';font-size:9pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-177a8cea{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-177a8cfe{margin:0;text-align:center;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:3pt;padding-top:0;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-177a8cff{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:1pt;padding-top:1pt;padding-left:0.3pt;padding-right:0.3pt;line-height: 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1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa541{background-color:transparent;vertical-align: top;border-bottom: 2pt solid rgba(255, 192, 203, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa542{background-color:transparent;vertical-align: top;border-bottom: 2pt solid rgba(255, 192, 203, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa54a{background-color:transparent;vertical-align: top;border-bottom: 2pt solid rgba(255, 192, 203, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 2pt solid rgba(255, 192, 203, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa54b{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa54c{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa554{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-177aa555{background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(255, 255, 255, 0.00);border-top: 0 solid rgba(255, 255, 255, 0.00);border-left: 0 solid rgba(255, 255, 255, 0.00);border-right: 0 solid rgba(255, 255, 255, 0.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}Title says WhaaaatOh, ok.A: Drug XB: PlaceboC: Combinationrow1 {1}555row21, 2 {2}1, 2 {2}1, 2 {2}{1} - row 1 - row footnote{2} - row 2 - cell footnoteha HA! Footer!"},{"path":"https://insightsengineering.github.io/rtables/main/reference/ttap.html","id":null,"dir":"Reference","previous_headings":"","what":"Get or set table elements at specified path — tt_at_path","title":"Get or set table elements at specified path — tt_at_path","text":"Get set table elements specified path","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ttap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get or set table elements at specified path — tt_at_path","text":"","code":"tt_at_path(tt, path, ...) tt_at_path(tt, path, ...) <- value"},{"path":"https://insightsengineering.github.io/rtables/main/reference/ttap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get or set table elements at specified path — tt_at_path","text":"tt TableTree (related class). TableTree object representing populated table. path character. vector path position within structure tabletree. element represents subsequent choice amongst children previous choice. ... unused. value new value","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ttap.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Get or set table elements at specified path — tt_at_path","text":"Setting NULL defined path removes corresponding sub table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/ttap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get or set table elements at specified path — tt_at_path","text":"","code":"# Accessing sub table. lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% split_rows_by(\"BMRKR2\") %>% analyze(\"AGE\") tbl <- build_table(lyt, ex_adsl) %>% prune_table() sub_tbl <- tt_at_path(tbl, path = c(\"SEX\", \"F\", \"BMRKR2\")) # Removing sub table. tbl2 <- tbl tt_at_path(tbl2, path = c(\"SEX\", \"F\")) <- NULL tbl2 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> M #> LOW #> Mean 34.43 37.13 32.73 #> MEDIUM #> Mean 37.67 38.78 34.35 #> HIGH #> Mean 35.07 36.21 37.42 #> U #> LOW #> Mean 31.00 27.00 37.00 #> MEDIUM #> Mean 33.00 NA 33.00 #> HIGH #> Mean NA 35.00 38.00 #> UNDIFFERENTIATED #> LOW #> Mean 28.00 NA 45.00 # Setting sub table. lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by(\"SEX\") %>% analyze(\"BMRKR2\") tbl3 <- build_table(lyt3, ex_adsl) %>% prune_table() tt_at_path(tbl3, path = c(\"SEX\", \"F\", \"BMRKR2\")) <- sub_tbl tbl3 #> A: Drug X B: Placebo C: Combination #> —————————————————————————————————————————————————————————— #> F #> LOW #> Mean 32.19 34.05 33.73 #> MEDIUM #> Mean 32.00 33.21 33.82 #> HIGH #> Mean 33.72 36.11 37.87 #> M #> LOW 21 23 11 #> MEDIUM 15 18 23 #> HIGH 15 14 26 #> U #> LOW 2 1 1 #> MEDIUM 1 0 2 #> HIGH 0 1 1 #> UNDIFFERENTIATED #> LOW 1 0 2"},{"path":"https://insightsengineering.github.io/rtables/main/reference/update_ref_indexing.html","id":null,"dir":"Reference","previous_headings":"","what":"Update footnote indexes on a built table — update_ref_indexing","title":"Update footnote indexes on a built table — update_ref_indexing","text":"Re-indexes footnotes within built table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/update_ref_indexing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update footnote indexes on a built table — update_ref_indexing","text":"","code":"update_ref_indexing(tt)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/update_ref_indexing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update footnote indexes on a built table — update_ref_indexing","text":"tt TableTree (related class). TableTree object representing populated table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/update_ref_indexing.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Update footnote indexes on a built table — update_ref_indexing","text":"adding removing referential footnotes manually, subsetting table, reference indexes (.e. number associated specific footnotes) may incorrect. function recalculates based full table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/update_ref_indexing.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Update footnote indexes on a built table — update_ref_indexing","text":"future generally need called manually.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/validate_table_struct.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate and Assert valid table structure (Experimental). — validate_table_struct","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"Validate Assert valid table structure (Experimental).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/validate_table_struct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"","code":"validate_table_struct(tt) assert_valid_table(tt, warn_only = FALSE)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/validate_table_struct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"tt TableTree TableTree (rtables-built table) considered degenerate contains subtables data rows (content rows count) contains subtable degenerate criterion validate_table_struct assesses whether tt valid (non-degenerate) structure. assert_valid_table asserts table must valid structure, throws informative error (default) warning (warn_only TRUE) table degenerate (invalid structure contains one invalid substructures warn_only logical(1). warning thrown instead error? Defaults FALSE","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/validate_table_struct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"validate_table_struct logical value indicating valid structure; assert_valid_table called side-effect throwing error warning degenerate tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/validate_table_struct.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"function experimental exact text warning/error subject change future releases.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/validate_table_struct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Validate and Assert valid table structure (Experimental). — validate_table_struct","text":"","code":"validate_table_struct(rtable(\"hahaha\")) #> [1] FALSE if (FALSE) { assert_valid_table(rtable(\"oops\")) }"},{"path":"https://insightsengineering.github.io/rtables/main/reference/value_formats.html","id":null,"dir":"Reference","previous_headings":"","what":"Value Formats — value_formats","title":"Value Formats — value_formats","text":"Returns matrix formats cells table","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/value_formats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Value Formats — value_formats","text":"","code":"value_formats(obj, default = obj_format(obj)) # S4 method for ANY value_formats(obj, default = obj_format(obj)) # S4 method for TableRow value_formats(obj, default = obj_format(obj)) # S4 method for LabelRow value_formats(obj, default = obj_format(obj)) # S4 method for VTableTree value_formats(obj, default = obj_format(obj))"},{"path":"https://insightsengineering.github.io/rtables/main/reference/value_formats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Value Formats — value_formats","text":"obj table row object. default FormatSpec.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/value_formats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Value Formats — value_formats","text":"Matrix (storage mode list) containing effective format cell position table (including 'virtual' cells implied label rows, whose formats always NULL).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/reference/value_formats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Value Formats — value_formats","text":"","code":"lyt <- basic_table() %>% split_rows_by(\"RACE\", split_fun = keep_split_levels(c(\"ASIAN\", \"WHITE\"))) %>% analyze(\"AGE\") tbl <- build_table(lyt, DM) value_formats(tbl) #> all obs #> ASIAN NULL #> Mean \"xx.xx\" #> WHITE NULL #> Mean \"xx.xx\""},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":null,"dir":"Reference","previous_headings":"","what":"Split on static or dynamic cuts of the data — split_cols_by_cuts","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"Create columns (row splits) based values (quartiles) var.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"","code":"split_cols_by_cuts( lyt, var, cuts, cutlabels = NULL, split_label = var, nested = TRUE, cumulative = FALSE ) split_rows_by_cuts( lyt, var, cuts, cutlabels = NULL, split_label = var, format = NULL, na_str = NA_character_, nested = TRUE, cumulative = FALSE, label_pos = \"hidden\", section_div = NA_character_ ) split_cols_by_cutfun( lyt, var, cutfun = qtile_cuts, cutlabelfun = function(x) NULL, split_label = var, nested = TRUE, extra_args = list(), cumulative = FALSE ) split_cols_by_quartiles( lyt, var, split_label = var, nested = TRUE, extra_args = list(), cumulative = FALSE ) split_rows_by_quartiles( lyt, var, split_label = var, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), cumulative = FALSE, indent_mod = 0L, label_pos = \"hidden\", section_div = NA_character_ ) split_rows_by_cutfun( lyt, var, cutfun = qtile_cuts, cutlabelfun = function(x) NULL, split_label = var, format = NULL, na_str = NA_character_, nested = TRUE, child_labels = c(\"default\", \"visible\", \"hidden\"), extra_args = list(), cumulative = FALSE, indent_mod = 0L, label_pos = \"hidden\", section_div = NA_character_ )"},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"lyt layout object pre-data used tabulation var string, variable name cuts numeric. Cuts use cutlabels character (NULL). Labels cuts split_label string. Label string associated table generated split. confused labels assigned child (based data type split tabulation). nested boolean. layout instruction applied within existing layout structure possible (TRUE, default) new top-level element (`FALSE). Ignored nest split underneath analyses, allowed. cumulative logical. cuts treated cumulative. Defaults FALSE format FormatSpec. Format associated split. Formats can declared via strings (\"xx.x\") function. cases analyze calls, can character vectors lists functions. na_str character(1). String displayed value x missing. Defaults \"NA\". label_pos character(1). Location variable label displayed, Accepts \"hidden\" (default non-analyze row splits), \"visible\", \"topleft\", - analyze splits - \"default\". analyze calls, \"default\" indicates variable visible multiple variables analyzed level nesting. section_div character(1). String repeated section divider group defined split instruction, NA_character_ (default) section divider. cutfun function. Function accepts full vector var values returns cut points passed cut. cutlabelfun function. Function returns either labels cuts NULL passed return value cutfun extra_args list. Extra arguments passed tabulation function. Element position list corresponds children split. Named elements child-specific lists ignored match formal argument tabulation function. child_labels string. One \"default\", \"visible\", \"hidden\". display behavior labels (.e. label rows) children split. Defaults \"default\" flags label row visible child 0 content rows. indent_mod numeric. Modifier default indent position structure created function(subtable, content table, row) structure's children. Defaults 0, corresponds unmodified default behavior.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"PreDataTableLayouts object suitable passing layouting functions, build_table.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"dynamic cuts, cut transformed static cut build_table based full dataset, proceeding. Thus even nested within another split column/row space, resulting split reflect overall values (e.g., quartiles) dataset, values subset nested .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"Gabriel Becker","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/varcuts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split on static or dynamic cuts of the data — split_cols_by_cuts","text":"","code":"library(dplyr) # split_cols_by_cuts lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_cuts(\"AGE\", split_label = \"Age\", cuts = c(0, 25, 35, 1000), cutlabels = c(\"young\", \"medium\", \"old\") ) %>% analyze(c(\"BMRKR2\", \"STRATA2\")) %>% append_topleft(\"counts\") tbl <- build_table(lyt, ex_adsl) tbl #> A: Drug X B: Placebo C: Combination #> counts young medium old young medium old young medium old #> ————————————————————————————————————————————————————————————————————————————— #> BMRKR2 #> LOW 4 30 16 4 17 24 5 19 16 #> MEDIUM 6 12 19 2 28 26 4 25 13 #> HIGH 4 24 19 2 17 14 1 21 28 #> STRATA2 #> S1 8 33 32 2 27 38 5 25 26 #> S2 6 33 22 6 35 26 5 40 31 # split_rows_by_cuts lyt2 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_rows_by_cuts(\"AGE\", split_label = \"Age\", cuts = c(0, 25, 35, 1000), cutlabels = c(\"young\", \"medium\", \"old\") ) %>% analyze(c(\"BMRKR2\", \"STRATA2\")) %>% append_topleft(\"counts\") tbl2 <- build_table(lyt2, ex_adsl) tbl2 #> counts A: Drug X B: Placebo C: Combination #> ———————————————————————————————————————————————————— #> young #> BMRKR2 #> LOW 4 4 5 #> MEDIUM 6 2 4 #> HIGH 4 2 1 #> STRATA2 #> S1 8 2 5 #> S2 6 6 5 #> medium #> BMRKR2 #> LOW 30 17 19 #> MEDIUM 12 28 25 #> HIGH 24 17 21 #> STRATA2 #> S1 33 27 25 #> S2 33 35 40 #> old #> BMRKR2 #> LOW 16 24 16 #> MEDIUM 19 26 13 #> HIGH 19 14 28 #> STRATA2 #> S1 32 38 26 #> S2 22 26 31 # split_cols_by_quartiles lyt3 <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by_quartiles(\"AGE\", split_label = \"Age\") %>% analyze(c(\"BMRKR2\", \"STRATA2\")) %>% append_topleft(\"counts\") tbl3 <- build_table(lyt3, ex_adsl) tbl3 #> A: Drug X B: Placebo C: Combination #> counts [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] [min, Q1] (Q1, Q2] (Q2, Q3] (Q3, max] #> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————— #> BMRKR2 #> LOW 18 16 7 9 12 8 10 15 8 11 13 8 #> MEDIUM 11 7 9 10 14 15 14 13 12 13 7 10 #> HIGH 14 11 14 8 6 10 9 8 7 12 13 18 #> STRATA2 #> S1 22 18 18 15 15 11 22 19 11 14 12 19 #> S2 21 16 12 12 17 22 11 17 16 22 21 17 # split_rows_by_quartiles lyt4 <- basic_table(show_colcounts = TRUE) %>% split_cols_by(\"ARM\") %>% split_rows_by_quartiles(\"AGE\", split_label = \"Age\") %>% analyze(\"BMRKR2\") %>% append_topleft(c(\"Age Quartiles\", \" Counts BMRKR2\")) tbl4 <- build_table(lyt4, ex_adsl) tbl4 #> Age Quartiles A: Drug X B: Placebo C: Combination #> Counts BMRKR2 (N=134) (N=134) (N=132) #> ———————————————————————————————————————————————————————— #> Age #> [min, Q1] #> LOW 18 12 8 #> MEDIUM 11 14 12 #> HIGH 14 6 7 #> (Q1, Q2] #> LOW 16 8 11 #> MEDIUM 7 15 13 #> HIGH 11 10 12 #> (Q2, Q3] #> LOW 7 10 13 #> MEDIUM 9 14 7 #> HIGH 14 9 13 #> (Q3, max] #> LOW 9 15 8 #> MEDIUM 10 13 10 #> HIGH 8 8 18 # split_cols_by_cutfun cutfun <- function(x) { cutpoints <- c( min(x), mean(x), max(x) ) names(cutpoints) <- c(\"\", \"Younger\", \"Older\") cutpoints } lyt5 <- basic_table() %>% split_cols_by_cutfun(\"AGE\", cutfun = cutfun) %>% analyze(\"SEX\") tbl5 <- build_table(lyt5, ex_adsl) tbl5 #> Younger Older #> —————————————————————————————————— #> F 124 98 #> M 75 91 #> U 5 4 #> UNDIFFERENTIATED 1 2 # split_rows_by_cutfun lyt6 <- basic_table() %>% split_cols_by(\"SEX\") %>% split_rows_by_cutfun(\"AGE\", cutfun = cutfun) %>% analyze(\"BMRKR2\") tbl6 <- build_table(lyt6, ex_adsl) tbl6 #> F M U UNDIFFERENTIATED #> ——————————————————————————————————————————— #> AGE #> Younger #> LOW 43 26 3 1 #> MEDIUM 47 23 2 0 #> HIGH 34 26 0 0 #> Older #> LOW 30 29 1 2 #> MEDIUM 29 33 1 0 #> HIGH 39 29 2 0"},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":null,"dir":"Reference","previous_headings":"","what":"List Variables required by a pre-data table layout — vars_in_layout","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"List Variables required pre-data table layout","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"","code":"vars_in_layout(lyt) # S4 method for PreDataTableLayouts vars_in_layout(lyt) # S4 method for PreDataAxisLayout vars_in_layout(lyt) # S4 method for SplitVector vars_in_layout(lyt) # S4 method for Split vars_in_layout(lyt) # S4 method for CompoundSplit vars_in_layout(lyt) # S4 method for ManualSplit vars_in_layout(lyt)"},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"lyt Layout (component thereof)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"character vector containing unique variables explicitly used layout (see Notes).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"walk layout declaration return vector names unique variables used following ways: Variable split (directly via cuts) Element Multi-variable column split Content variable Value-label variable","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"function detect dependencies implicit analysis summary functions accept x df rely existence particular variables split / analyzed. order variable names appear within return vector undefined relied upon.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/reference/vil.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List Variables required by a pre-data table layout — vars_in_layout","text":"","code":"lyt <- basic_table() %>% split_cols_by(\"ARM\") %>% split_cols_by(\"SEX\") %>% summarize_row_groups(label_fstr = \"Overall (N)\") %>% split_rows_by(\"RACE\", split_label = \"Ethnicity\", labels_var = \"ethn_lab\", split_fun = drop_split_levels ) %>% summarize_row_groups(\"RACE\", label_fstr = \"%s (n)\") %>% analyze(\"AGE\", var_labels = \"Age\", afun = mean, format = \"xx.xx\") vars_in_layout(lyt) #> [1] \"ARM\" \"SEX\" \"RACE\" \"ethn_lab\" \"AGE\""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"new-features-0-6-6-9008","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.6.9008","text":"Added top_level_section_div basic_table set section dividers top level rows. Added keep_label_rows as_result_df lines visible.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"bug-fixes-0-6-6-9008","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.6.9008","text":"Fixed rlistings decoration (e.g. titles footers) expansion new lines. Moved relevant handling rtables’ matrix_form function formatters’ dedicated mform_handle_newlines function. Fixed issue rtables_root removed using as_result_df. Fixed edge case bug as_result_df rows table \"root\" path index.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-066","dir":"Changelog","previous_headings":"","what":"rtables 0.6.6","title":"rtables 0.6.6","text":"CRAN release: 2023-12-08","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"new-features-0-6-6","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.6","text":"Removed ref_group reordering column splits change order. Added bold argument as_html bold specified elements, header_sep_line argument print horizontal line table header rendered HTML output. Duplicate referential footnotes consolidated tables rendered. Section divisors can set analysis rows. Added setter getter section dividers (section_div section_div<-). also accept split section structure assignment. Added header_section_div setters getters layout table objects along related basic_table parameter. Added na_str argument analyze_colvars set custom string print place missing values. Added flat data.frame outputs as_result_df() via flag parameters as_viewer, as_strings, expand_colnames. Migrated export_as_pdf function formatters.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"bug-fixes-0-6-6","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.6","text":"Fixed bug failing wrapping section dividers used time. Fixed bug as_result_df causing misalignment column names. Fixed bug allowing path indexing row_paths() giving different path due made named values. Fixed bug as_result_df called tables less 3 rows.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"miscellaneous-0-6-6","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.6","text":"Applied styler resolved package lint. Changed default indentation 4 spaces 2. Added Developer Guide pkgdown site Debugging, Split Machinery, Tabulation sections. Whitespace trimmed rendering tables as_html. Started deprecation cycle col_fnotes_here replaced col_footnotes. Exported section_div methods now dedicated documentation page visible users. tables exported txt, preserve horizontal separator table. Added imports stringi checkmate fundamental packages string handling argument checking. Updated introduction vignette split two. Section introspecting tables now located separate vignette.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"new-features-0-6-5","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.5","text":"Added support white spaces labels text redesigning wrapping functions formatters. Added support new line characters across rtables (titles, column names, row names, footers, na_str). Modified top left information vertical alignment stay bottom header.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"bug-fixes-0-6-5","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.5","text":"Fixed bug causing Viewer as_html fail new line characters added.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"miscellaneous-0-6-5","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.5","text":"Added slide decks advanced training internal files.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"new-features-0-6-4","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.4","text":"Added support .docx exports export_as_docx(). Expanded support flextable customization theme function specific word documents (theme_docx_default()).","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"bug-fixes-0-6-4","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.4","text":"Fixed bug causing -NA rows included every .df_row split.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"miscellaneous-0-6-4","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.4","text":"Specified minimal version package dependencies.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-063","dir":"Changelog","previous_headings":"","what":"rtables 0.6.3","title":"rtables 0.6.3","text":"CRAN release: 2023-08-30","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"new-features-0-6-3","dir":"Changelog","previous_headings":"","what":"New Features","title":"rtables 0.6.3","text":"Analysis functions (cfun/afun) can use new parameters extend analysis calculations: .alt_df_row .alt_df give access alt_counts_df across columns, .all_col_exprs .all_col_counts contains global information columns. Binding objects via rbind retain titles/footer information identical objects present first object bound.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"enhancements-0-6-3","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"rtables 0.6.3","text":"Analysis functions (cfun/afun) information current column split; .spl_context access cur_col_id, cur_col_expr, cur_col_split, cur_col_split_val. Added vignette exploratory analysis qtable. Extracted qtable_layout qtable.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"bug-fixes-0-6-3","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"rtables 0.6.3","text":"Page-splits generate zero facets (thus tables zero pages rendered) now throw informative error table build time. Removed superfluous warning arose custom split functions reference group set (https://github.com/insightsengineering/rtables/issues/707#issuecomment-1678810598). Fixed qtable labeling via row_labels (#698). Error catching test coverage cases alt_counts_df presents different splits df.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"miscellaneous-0-6-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"rtables 0.6.3","text":"Cleaned spelling documentation (#685) Custom appearance vignette updated decimal alignment support. Alignment checks moved formatters: formatters::check_aligns superseded internal function chk_rtables_align formatters::list_valid_aligns superseded rtables_aligns.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-062","dir":"Changelog","previous_headings":"","what":"rtables 0.6.2","title":"rtables 0.6.2","text":"Fixed major regressions page_by machinery caused migration formatters 0.5.1 pagination framework. Fixed page_by labels become missing one level exist split_rows_by. Fixed bug dropping var levels lblvar levels. Added checks catch bad labels ({}) throw informative error. Added qtable function create table single top-level structure row column dimensions involving faceting 0 variables . Added as_result_df function flatten table dataframe. Added sanitize_table_struct, validate_table_struct, find_degen_struct support degenerative table rendering.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-061","dir":"Changelog","previous_headings":"","what":"rtables 0.6.1","title":"rtables 0.6.1","text":"CRAN release: 2023-05-25 Improved resilience pagination machinery (paginate_table) generalizing parameters’ defaults (cpp, lpp, font_size). Moved export_as_txt formatters. Added reexports. Migrated export_as_rtf formatters. re-exported. add r2rtf Suggests pagination logic migrated completely (excepting page_by splits) formatters now invoked . paginate_table remains convenience function. Removed warning str method called upon table objects. Provide str method VTableTree objects default max.level 3, infinite default base useful informative. default font_size value now 8 across pagination export machinery margins argument pagination export machinery now (correctly) interpreted inches. change inherited formatters lpp cpp now default NA_integer_, interpreted inferring value physical page size specified. Horizontal pagination now occurs default due (default page type - \"letter\". Pagination can still turned either direction setting l/cpp NULL explicitly. Referential footnotes now symbol index. Messages associated symbols appear per page footer materials regardless number elements referenced page symbol. Matches inherits changes formatters Started deprecation cycle trim_zero_rows. Fixed bug occurring extracting cell_values sorting. Removed deprecated function vpaginate_table. Added examples details sort_at_path. Added split_label function split_rows_by_multivar extra_args function split_cols_by_multivar. Added split_rows_by_multivar documentation.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-060","dir":"Changelog","previous_headings":"","what":"rtables 0.6.0","title":"rtables 0.6.0","text":"CRAN release: 2023-03-02 added make_split_fun function creation custom split functions basic_table now accepts colcount_format 2d formats now allowed column counts provided one element percent, automatically set 100% spl_context now includes root row row-split contexts. Added vignette format precedence Added vignette split functions Added custom appearance vignette Significant overhaul sorting vignette extended clarified documentation export_as_pdf now correctly takes margins account calculating lpp cpp page size. exporters now pass non-default colwidths values correctly nlines TableRow method (used rows column label extent pagination) now correctly handles column spanning pagination verbose = TRUE now includes original adjusted lines-per page information cont_n_allcols cont_n_onecol score functions now throw errors applied subtables content table, instead previously returned NA sort_at_path now emits informative error message score functions fail. paginate_table now accepts colwidths paginates assuming column label cell values wrapped widths. make_row_df now accepts colwidths calculates row extents assuming cell values wrapped widths nlines TableRow method now uses provided colwidths assume cell-value wrapping export_to_txt now automatically paginates form page dimension provided (previously default unconditionally paginating). Versioned dependency formatters increased >=0.4.0","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-053","dir":"Changelog","previous_headings":"","what":"rtables 0.5.3","title":"rtables 0.5.3","text":"[<- now treats character j values paths, [ always . [<- CellValue method now preserves CellValue attributes (e.g., format) detailed subsetting modification vignette nlines methods now accept colwidths max_width max_width now used pagination determine lines taken referential footnotes make_col_df now accepts colwidths argument, can called directly InstantiatedColumnInfo objects versioned dependency formatters increase >0.3.3.12 word wrapping title/footer materials longer fails presence \"\" values. versioned dependency formatters increase >0.3.3.11 paginate_table now accepts tf_wrap max_width respects title/footer word wrapping present export functions now accepts tf_wrap max_width use pagination (turned ) toString used (pdf, txt exporters). versioned dependency formatters increased >0.3.3.10 export_as_pdf now accepts standard page/font size parameters original parameters (width, height, fontsize soft deprecated (warning) fully deprecated removed future. toString method VTableTree now accepts tf_wrap max_width export_as_txt export_as_pdf now accept cpp, well tf_wrap max_width default tf_wrap max_width = cpp cpp non-NULL. basic_table now accepts inset argument declaring table inset Table Layout object classes now table_inset slot, accessor functions. matrix_form method VTableTree sets table_inset value Increase versioned dependency formatters >0.3.3.5 table_inset support Use exact=TRUE calls attr within access functions Increase versioned dependency formatters >0.3.3.4 layouting instructions now accept na_str argument, specifies na string inheritance rules formats (pre-data) Split (post tabulation) Table/row S4 classes now carry around na_str information Increase versioned dependency formatters >= 0.3.3.3 support na_strs NA_character_ values paginate_table now takes page dimension font information uses formatters::page_lcpp calculate lpp cpp automatically provided. Increase versioned dependency formatters >= 0.3.3.2 page_lcpp","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-052","dir":"Changelog","previous_headings":"","what":"rtables 0.5.2","title":"rtables 0.5.2","text":"paginate_table now accepts cpp perform vertical pagination non-null vpaginate_table now deprecated Increased versioned dependency formatters >=0.3.2.4","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0515","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.5","title":"rtables 0.5.1.5","text":"Support section dividers (section_div argument split_rows_by* function) Updated versioned dependency formatters >=0.3.2.3 Equivalent split functions different enclosing environments (e.g., 2 identical calls add_combo_levels #340) longer block rbinding Fixed various documentation bugs description section added header.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0514","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.4","title":"rtables 0.5.1.4","text":"empty level check splitting variables reinstated.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0513","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.3","title":"rtables 0.5.1.3","text":"Throw informative error messages custom analysis, content split functions fail (#329)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0512","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.2","title":"rtables 0.5.1.2","text":"empty level check splitting variables temporarily removed. likely reinstated future release.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0511","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1.1","title":"rtables 0.5.1.1","text":"col_counts getter setter now accept path argument. empty levels splitting variable now result informative error message (character factor cases). fixed bug handling column extra arguments preventing cbinding tables working correctly ([#324]](https://github.com/insightsengineering/rtables/issues/324))","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-051","dir":"Changelog","previous_headings":"","what":"rtables 0.5.1","title":"rtables 0.5.1","text":"CRAN release: 2022-05-21 empty factor levels now dropped column splits ref_group set (#323) linesep argument toString related functions renamed hsep Increase versioned dependency formatters >=0.3.0 Default “line separator” header body now falls back “-” non-UTF charset locales. New hsep argument build_table sets horizontal separator constructed table (subtables thereof) New horizontal_sep horizontal_sep<- accessors constructed tables, latter mandatorily recursive. split_rows_by(var, child_labels=\"hidden\") longer removes structural subtable corresponding levels var (#314)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-050","dir":"Changelog","previous_headings":"","what":"rtables 0.5.0","title":"rtables 0.5.0","text":"CRAN release: 2022-04-01 formatable dependency renamed formatters suitability release CRAN Update versioned dependency formatters (previously formatable) >=0.2.0","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0410004","dir":"Changelog","previous_headings":"","what":"rtables 0.4.1.0004","title":"rtables 0.4.1.0004","text":"Fix bug function format combined NULL cfun caused error (#307) Fix bug path_enriched_df (powers tsv export), related (#308)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0410002","dir":"Changelog","previous_headings":"","what":"rtables 0.4.1.0002","title":"rtables 0.4.1.0002","text":"added table_shell display shell table formats","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0410001","dir":"Changelog","previous_headings":"","what":"rtables 0.4.1.0001","title":"rtables 0.4.1.0001","text":"added linesep argument toString specify character create line separator. Previously used en dash line separator character, now changed default em dash reducing gap dash line elements.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-040","dir":"Changelog","previous_headings":"","what":"rtables 0.4.0","title":"rtables 0.4.0","text":"CRAN release: 2021-10-06 Initializing layouts NULL now deprecated insert_rrow deprecated favor new insert_row_at_path label_at_path<- functions split analysis/content functions can now depend values splits nested inside accepting using new .spl_context optional argument new trim_levels_to_map split function dictating exact combinations values appear across splits value_formats function now exported returns/displays effective formats cells table compare_rtables now much faster tables many cells compare_rtables now accepts structure argument adds comparison structure (way row- column-path positions) new tt_to_flextable coercion function new export_as_pdf exporter function value_at cell_values functions now methods TableRow objects making usable sorting/pruning functions","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0389001","dir":"Changelog","previous_headings":"","what":"rtables 0.3.8.9001","title":"rtables 0.3.8.9001","text":"new trim_levels_to_map split function based [@wwojciech](https://github.com/wwojciech)’s work #203 support column referential footnotes support adding footnotes existing table via fnotes_at_path<- function trim_levels_in_group now trims empty levels outer (split) variable default value_at cell_values now work tablerow objects Fixed as_html bug multivar split columns case Fixed pagination --one error","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-038","dir":"Changelog","previous_headings":"","what":"rtables 0.3.8","title":"rtables 0.3.8","text":"CRAN release: 2021-07-13 Add experimental support newlines column names, row labels, cell values (supported top-left annotations) as_html refactored support newlines respecting table structure self_extent column df returned make_row_df now reflects extent lines, thus return larger values row-label cell values contain newlines. Fix bug tables output using as_html (viewed Viewer) missing table class attribute (#194) inserting DataRow incorrect number columns now error (#199) Referential footer machinery now works colspan case. Fix extraneous footnote attribute bug (#198) Fix max -Inf warning content rows appear positions whose children 0 visible rows (#200) Resync NEWS.md file Introduce titles footnotes Support automatic population top-left Introduce referential footnote support cells row labels Added vars_in_layout list (explicitly named ) variables used layout Fix column label ordering bug value label variable factor (#173)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-037","dir":"Changelog","previous_headings":"","what":"rtables 0.3.7","title":"rtables 0.3.7","text":"Synchronize release GitHub commit sha.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-036","dir":"Changelog","previous_headings":"","what":"rtables 0.3.6","title":"rtables 0.3.6","text":"CRAN release: 2021-01-22 Documentation revisions requested CRAN. change package code.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-035","dir":"Changelog","previous_headings":"","what":"rtables 0.3.5","title":"rtables 0.3.5","text":"Documentation-text changes introduction vignette pass CRAN’s URL checks. package, example, test, vignette code fully identical tagged GitHub release 0.3.4","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-034","dir":"Changelog","previous_headings":"","what":"rtables 0.3.4","title":"rtables 0.3.4","text":"Minor changes 0.3.3 version order submit rtables CRAN.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-033","dir":"Changelog","previous_headings":"","what":"rtables 0.3.3","title":"rtables 0.3.3","text":"version completely refactors rtables package. provide backwards compatibility layer rtable, rcell, rrow, rheader, rtabulate family functions. However table data structure main tabulation framework changed. provide extensive documentation manuals help(package = \"rtables\") vignettes vignette(package = \"rtables\") package. changes rtables undertaken better meet requirements creating analyzing & reporting tables context clinical trials.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179046","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9046","title":"rtables 0.3.2.17.9046","text":"make_afun now force()s customization arguments immediately, prevents problems called within loop/lapply constructs.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179045","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9045","title":"rtables 0.3.2.17.9045","text":"Tabulation machinery longer removes NAs mandatorily cases, including multivar column splits analyze_colvars’s inclNAs argument now respected.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179044","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9044","title":"rtables 0.3.2.17.9044","text":"Fix indent modifier propagation tabulation Fix indent calculation make_pagdf Add significant testing ensure make_pagdf indent calculation remains correct","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179043","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9043","title":"rtables 0.3.2.17.9043","text":"Rework reference columns handled analyses use .in_ref_col .ref_group work correctly custom splitting used (including provided combination-levels mechanism)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179042","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9042","title":"rtables 0.3.2.17.9042","text":"Fix naming/pathing columns multivar case (split now default name \"multivars\") Fix labeling bug variable appears multiple times MultiVarSplit different associated levels","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179041","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9041","title":"rtables 0.3.2.17.9041","text":"Allow single variable used within split_cols_by_multivar Various removal defunct","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179040","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9040","title":"rtables 0.3.2.17.9040","text":"Fix regression caused 0.3.2.17.9039 column split values displayed name rather label.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179039","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9039","title":"rtables 0.3.2.17.9039","text":"Fix bug display column information column structure symmetric, recursive cbinds.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179036","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9036","title":"rtables 0.3.2.17.9036","text":"Fixed bug row subsetting table content rows. Basic compare_rtables function now works previous versions, awareness row column structure.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179036-1","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9036","title":"rtables 0.3.2.17.9036","text":"summarize_row_groups can now accept list functions cfun argument analyze_colvars .","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179035","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9035","title":"rtables 0.3.2.17.9035","text":"Fix bug unearthed change 0.3.2.17.9034 cell formats retained column subsetting","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179034","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9034","title":"rtables 0.3.2.17.9034","text":"Fix internal value_formats accessor operates CellValues rather raw contained values (thus always returning NULL) rrow constructor longer interprets cell formats row format across cells. Fixes bug “correct way” code discussed #112","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179033","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9033","title":"rtables 0.3.2.17.9033","text":"Interpret .formats in_rows cell formats rather row formats.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179031","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9031","title":"rtables 0.3.2.17.9031","text":"cbind_rtables can now take 2 tables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179029","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9029","title":"rtables 0.3.2.17.9029","text":"Fix issue underlying spurious length-mismatch warning cases using analyze_colvars","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179028","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9028","title":"rtables 0.3.2.17.9028","text":"analyze_colvars now takes adheres inclNAs argument","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-032179027","dir":"Changelog","previous_headings":"","what":"rtables 0.3.2.17.9027","title":"rtables 0.3.2.17.9027","text":"issues news:","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-017","dir":"Changelog","previous_headings":"","what":"rtables 0.1.7","title":"rtables 0.1.7","text":"added format xx.xx (xx.xx - xx.xx) x.xxxx | (<0.0001)","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-016","dir":"Changelog","previous_headings":"","what":"rtables 0.1.6","title":"rtables 0.1.6","text":"Minor changes.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-015","dir":"Changelog","previous_headings":"","what":"rtables 0.1.5","title":"rtables 0.1.5","text":"Changed testing approach fit internal pipelines.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-014","dir":"Changelog","previous_headings":"","what":"rtables 0.1.4","title":"rtables 0.1.4","text":"Replaced dots underscore class checking functions.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-013","dir":"Changelog","previous_headings":"","what":"rtables 0.1.3","title":"rtables 0.1.3","text":"col_by_to_matrix, col_by_to_factor, by_factor_to_matrix. by_add_total, by_all, by_combine, by_quartile, by_compare_subset, by_hierarchical, by_drop_empty_cols. label, var_labels<-, var_labels, var_labels_remove, var_relabel, with_label. cbing_rtables. empty_rtables, is_empty_rtable, is_non_empty_rtable, is_rtable. header_indent, header_indent<-, header_row.names, header_row.names<-. insert_rrow.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-012","dir":"Changelog","previous_headings":"","what":"rtables 0.1.2","title":"rtables 0.1.2","text":"rbind.rtable now supports binding rtables rows, e.g. rbind(tbl1, rrow(), tbl2) rbind(tbl1, rrow(\"row name\"), tbl2). rbindl_rtables supports NULL objects list (except first element). Add indent function. header_add_N deals gracefully NULL objects.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-011","dir":"Changelog","previous_headings":"","what":"rtables 0.1.1","title":"rtables 0.1.1","text":"rtablulate family functions support row_*_data_args arguments anymore. Instead, col_wise_args argument introduced. Functions order_rrows, sort_rrows, order_rtables, sort_rtables introduced. Prevent rtables unlisted unlist.rtables.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0106","dir":"Changelog","previous_headings":"","what":"rtables 0.1.0.6","title":"rtables 0.1.0.6","text":"Viewer now also accepts objects class shiny.tag (defined package htmltools). .html accepts class.table, class.tr, class.th, class.td argument.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-0105","dir":"Changelog","previous_headings":"","what":"rtables 0.1.0.5","title":"rtables 0.1.0.5","text":"Added sprintf_format formatting rcells (thanks Doug Kelkhoff suggestion). Added \"(N=xx)\" \">999.9\" format labels. rtabulate now argument col_N function col_N().","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-010","dir":"Changelog","previous_headings":"","what":"rtables 0.1.0","title":"rtables 0.1.0","text":"Redesign: rtable now header argument instead col.names. header can created rheader collection rrows. header set c(\"\", \"B\") rtable create rheader single rrow setting row.name NULL. header header<- function added. Renamed get_rcell_formats list_rcell_format_labels. rcell format NULL cell content converted string paste(.character(x), collapse = ', '). Accessor [,] works now subset table. rbind method rtables. row.names<-.rtable method. rtabulate added creating tables. indented_row.names function added.","code":""},{"path":"https://insightsengineering.github.io/rtables/main/news/index.html","id":"rtables-001","dir":"Changelog","previous_headings":"","what":"rtables 0.0.1","title":"rtables 0.0.1","text":"Initial public release","code":""}]