The rtables R package was designed to create and display complex
tables with R. The cells in an rtable may contain any high-dimensional
data structure which can then be displayed with cell-specific formatting
instructions. Currently, rtables can be outputted in ascii and
html.
Note: we have completely refactored the rtables package which is
officially released on CRAN in December 2020. With this significant
change please familiarize yourself with the new framework by reading the
package vignettes.
rtables is developed and copy written by F. Hoffmann-La Roche and it
is released open source under Apache License Version 2.
rtables development is driven by the need to create regulatory ready
tables for health authority review. Some of the key requirements for
this undertaking are listed below:
- cell values and their visualization separate (i.e. no string based
tables)
- values need to be programmatically accessible in their non-rounded state for cross-checking
- multiple values displayed within a cell
- flexible tabulation framework
- flexible formatting (cell spans, rounding, alignment, etc.)
- multiple output formats (html, ascii, latex, pdf, xml)
- flexible pagination
- distinguish between name and label in the data structure to work with CDISC standards
- title, footnotes, cell cell/row/column references
Note that the current state of rtables does not fulfill all of those
requirements, however, rtables is still under active development and
we are working on adding the missing features.
rtables is now available on CRAN and you can install the latest
released version with:
install.packages("rtables")or you can install the latest stable version directly from GitHub with:
devtools::install_github("Roche/rtables")To install a frozen pre-release version of rtables based on the new
Layouting and Tabulation API as presented at user!2020 and JSM2020 run
the following command in R:
devtools::install_github("roche/rtables", ref="v0.3.3")To install the latest development version of the new test version of
rtables run
devtools::install_github("roche/rtables", ref = "gabe_tabletree_work")We first begin with a demographic table alike example and then show the creation of a more complex table.
library(rtables)
#> Loading required package: magrittr
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 supproted")
}
})
build_table(lyt, ex_adsl)
#> A: Drug X B: Placebo C: Combination
#> ----------------------------------------------------------
#> AGE
#> Mean (sd) 33.77 (6.55) 35.43 (7.9) 35.43 (7.72)
#> Median 33 35 35
#> Min - Max 21 - 50 21 - 62 20 - 69
#> BMRKR1
#> Mean (sd) 5.97 (3.55) 5.7 (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 50library(rtables)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
## 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() %>%
split_cols_by("ARM") %>%
split_cols_by("BMRKR2") %>%
add_colcounts() %>%
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%) 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%) 10 (21.7%) 12 (28.6%) 10 (20.8%) 8 (25.8%) 5 (12.5%) 11 (28.2%) 16 (34%)
#> 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%) 1 (3.2%) 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%) 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%) 1 (2.5%) 1 (2.6%) 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%) 2 (5%) 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%) 4 (8.7%) 5 (11.9%) 6 (12.5%) 6 (19.4%) 6 (15%) 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%) 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%) 1 (2.4%) 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
#> AMERICAN INDIAN OR ALASKA NATIVE
#> MULTIPLE
#> NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER
#> OTHER
#> UNKNOWNWe would like to thank everyone who has made rtables a better project
by providing feedback and improving examples & vignettes. The following
list of contributors is alphabetical:
Maximo Carreras, Francois Collins, Saibah Chohan, Tadeusz Lewandowski, Nick Paszty, Nina Qi, Jana Stoilova, Heng Wang, Godwin Yung
-
baselR November 2017, this presentation was written for version
v0.0.1