Fix reorder columns

book/tables/ADA/adat01.qmd

@@ -20,12 +20,32 @@ library(tibble)
 adsl <- random.cdisc.data::cadsl
 adab <- random.cdisc.data::cadab
 
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+# Order needed for the columns is c(1, 3, 4, 2, 5)
+reorder_facets <- function(splret, spl, fulldf, ...) {
+  ord <- c(1, 3, 4, 2, 5)
+  make_split_result(
+    splret$values[ord],
+    splret$datasplit[ord],
+    splret$labels[ord]
+  )
+}
+
+# Create a custom split function for adding the new columns (facets) and sorting them
+custom_column_split_fun <- make_split_fun(
+  post = list(
+    add_combo_facet("all_X",
+      label = "All Drug X",
+      levels = c("A: Drug X", "C: Combination")
+    ),
+    add_combo_facet("all_pt",
+      label = "All Patients",
+      levels = c("A: Drug X", "B: Placebo", "C: Combination")
+    ),
+    reorder_facets
+  )
 )
 
+
 # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
 adsl <- df_explicit_na(adsl)
 adab <- adab %>%
@@ -133,12 +153,13 @@ adab_pb <- df_explicit_na(adab) %>%
 ## {{< fa regular file-lines sm fw >}} Preview
 
 <!-- skip strict because of https://github.com/insightsengineering/rtables/issues/830 -->
+
 ```{r variant1, test = list(result_v1 = "result"), opts.label = ifelse(packageVersion("rtables") < "0.6.6.9011", "skip_test_strict", "")}
 # Layout for Baseline Prevalence of NAbs
 lyt_bl <- basic_table(show_colcounts = TRUE) %>%
   split_cols_by(
     "ACTARM",
-    split_fun = add_combo_levels(combodf)
+    split_fun = custom_column_split_fun
   ) %>%
   count_patients_with_flags(
     "USUBJID",
@@ -169,7 +190,7 @@ lyt_bl <- basic_table(show_colcounts = TRUE) %>%
 lyt_pb <- basic_table(show_colcounts = TRUE) %>%
   split_cols_by(
     "ACTARM",
-    split_fun = add_combo_levels(combodf)
+    split_fun = custom_column_split_fun
   ) %>%
   count_patients_with_flags(
     "USUBJID",
@@ -217,12 +238,6 @@ result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
 result_1@col_info <- result_2@col_info
 result <- rbind(result_1, result_2)
 
-# Change the column order.
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
 main_title(result) <- paste(
   "Baseline Prevalence and Incidence of Treatment Emergent ADA"
 )

book/tables/ADA/adat04a.qmd

@@ -20,10 +20,30 @@ library(tibble)
 adsl <- random.cdisc.data::cadsl
 adab <- random.cdisc.data::cadab
 
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+
+# Order needed for the columns is c(1, 3, 4, 2, 5)
+reorder_facets <- function(splret, spl, fulldf, ...) {
+  ord <- c(1, 3, 4, 2, 5)
+  make_split_result(
+    splret$values[ord],
+    splret$datasplit[ord],
+    splret$labels[ord]
+  )
+}
+
+# Create a custom split function for adding the new columns (facets) and sorting them
+custom_column_split_fun <- make_split_fun(
+  post = list(
+    add_combo_facet("all_X",
+      label = "All Drug X",
+      levels = c("A: Drug X", "C: Combination")
+    ),
+    add_combo_facet("all_pt",
+      label = "All Patients",
+      levels = c("A: Drug X", "B: Placebo", "C: Combination")
+    ),
+    reorder_facets
+  )
 )
 
 # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
@@ -160,12 +180,13 @@ adab_pb <- df_explicit_na(adab) %>%
 ## {{< fa regular file-lines sm fw >}} Preview
 
 <!-- skip strict because of https://github.com/insightsengineering/rtables/issues/830 -->
+
 ```{r variant1, test = list(result_v1 = "result"), opts.label = ifelse(packageVersion("rtables") < "0.6.6.9011", "skip_test_strict", "")}
 # Layout for Baseline Prevalence of NAbs
 lyt_bl <- basic_table(show_colcounts = TRUE) %>%
   split_cols_by(
     "ACTARM",
-    split_fun = add_combo_levels(combodf)
+    split_fun = custom_column_split_fun
   ) %>%
   count_patients_with_flags(
     "USUBJID",
@@ -194,7 +215,7 @@ lyt_bl <- basic_table(show_colcounts = TRUE) %>%
 lyt_pb <- basic_table(show_colcounts = TRUE) %>%
   split_cols_by(
     "ACTARM",
-    split_fun = add_combo_levels(combodf)
+    split_fun = custom_column_split_fun
   ) %>%
   count_patients_with_flags(
     "USUBJID",
@@ -241,12 +262,6 @@ result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
 # Combine tables
 result <- rbind(result_1, result_2)
 
-# Change the column order
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
 main_title(result) <- paste(
   "Baseline Prevalence and Incidence of Treatment Emergent NAbs"
 )

book/tables/ADA/adat04b.qmd

@@ -20,10 +20,29 @@ library(tibble)
 adsl <- random.cdisc.data::cadsl
 adab <- random.cdisc.data::cadab
 
-combodf <- tribble(
-  ~valname, ~label, ~levelcombo, ~exargs,
-  "all_X", "All Drug X", c("A: Drug X", "C: Combination"), list(),
-  "all_pt", "All Patients", c("A: Drug X", "B: Placebo", "C: Combination"), list()
+# Order needed for the columns is c(1, 3, 4, 2, 5)
+reorder_facets <- function(splret, spl, fulldf, ...) {
+  ord <- c(1, 3, 4, 2, 5)
+  make_split_result(
+    splret$values[ord],
+    splret$datasplit[ord],
+    splret$labels[ord]
+  )
+}
+
+# Create a custom split function for adding the new columns (facets) and sorting them
+custom_column_split_fun <- make_split_fun(
+  post = list(
+    add_combo_facet("all_X",
+      label = "All Drug X",
+      levels = c("A: Drug X", "C: Combination")
+    ),
+    add_combo_facet("all_pt",
+      label = "All Patients",
+      levels = c("A: Drug X", "B: Placebo", "C: Combination")
+    ),
+    reorder_facets
+  )
 )
 
 # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
@@ -140,12 +159,13 @@ adab_pb <- left_join(adab_pb_ada, adab_pb_adap, by = mergecol) %>%
 ## {{< fa regular file-lines sm fw >}} Preview
 
 <!-- skip strict because of https://github.com/insightsengineering/rtables/issues/830 -->
+
 ```{r variant1, test = list(result_v1 = "result"), opts.label = ifelse(packageVersion("rtables") < "0.6.6.9011", "skip_test_strict", "")}
 # Layout for Baseline Prevalence of NAbs
 lyt_bl <- basic_table(show_colcounts = TRUE) %>%
   split_cols_by(
     "ACTARM",
-    split_fun = add_combo_levels(combodf)
+    split_fun = custom_column_split_fun
   ) %>%
   count_patients_with_flags(
     "USUBJID",
@@ -175,7 +195,7 @@ lyt_bl <- basic_table(show_colcounts = TRUE) %>%
 lyt_pb <- basic_table(show_colcounts = TRUE) %>%
   split_cols_by(
     "ACTARM",
-    split_fun = add_combo_levels(combodf)
+    split_fun = custom_column_split_fun
   ) %>%
   count_patients_with_flags(
     "USUBJID",
@@ -207,12 +227,6 @@ result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)
 # Combine tables.
 result <- rbind(result_1, result_2)
 
-# Change the column order.
-result <- cbind_rtables(result[, 1], result[, 3]) %>%
-  cbind_rtables(result[, 4]) %>%
-  cbind_rtables(result[, 2]) %>%
-  cbind_rtables(result[, 5])
-
 main_title(result) <- paste(
   "Baseline Prevalence and Incidence of Neutralizing Antibodies (NAbs)"
 )

book/tables/efficacy/rbmit01.qmd

@@ -117,10 +117,11 @@ draws_vars$subjid <- "TMP_ID"
 Define which imputation method to use, then create samples for the imputation parameters by running the `draws()` function.
 
 <!-- skip strict because of https://github.com/insightsengineering/rbmi/issues/409 -->
+
 ```{r, warning = FALSE, opts.label = "skip_test_strict"}
 #| code-fold: show
 
-draws_method <- method_bayes()
+draws_method <- method_bayes(seed = 123)
 
 draws_obj <- rbmi::draws(
   data = data_full,
@@ -154,6 +155,7 @@ The next step is to run the analysis model on each imputed dataset.
 This is done by defining an analysis function and then calling `rbmi::analyse()` to apply this function to each imputed dataset.
 
 <!-- skip strict because of https://github.com/insightsengineering/rbmi/issues/409 -->
+
 ```{r, opts.label = "skip_test_strict"}
 #| code-fold: show
 #| opts_label: "skip_test_strict" #
@@ -183,6 +185,7 @@ The `rbmi::pool()` function can be used to summarize the analysis results across
 Using the `broom::tidy()` function the `rbmi` final results are reshaped.
 
 <!-- skip strict because of https://github.com/insightsengineering/rbmi/issues/409 -->
+
 ```{r, opts.label = "skip_test_strict"}
 #| code-fold: show
 
@@ -215,5 +218,5 @@ result
 {{< include ../../_utils/save_results.qmd >}}
 
 {{< include ../../repro.qmd >}}
-
 ::::
+

package/tests/testthat/_create_test_files.R

@@ -1,7 +1,7 @@
 # Script to create test files for all articles in the test book.
 # It would not remove existing test files so in case article location or name is changed, the old test file should be removed manually.
 # run with `source()`, e.g.:
-# source("./tests//testthat/_create_test_files.R")
+# source("./tests/testthat/_create_test_files.R")
 
 article_files <- list.files(
   test_book_path,
@@ -12,6 +12,14 @@ article_files <- list.files(
 ) |>
   grep("tables/|listings/|graphs/", x = _, value = TRUE)
 
+excluded_articles <- c(
+  # Stan is not reproducible on different platforms
+  # https://mc-stan.org/docs/2_27/reference-manual/reproducibility-chapter.html
+  "tables/efficacy/rbmit01.qmd"
+)
+
+article_files <- setdiff(article_files, excluded_articles)
+
 test_files <- article_files |>
   vapply(
     X = _,

package/tests/testthat/_snaps/development/tables-ADA-adat01.md

@@ -5,22 +5,22 @@
     Output
       Baseline Prevalence and Incidence of Treatment Emergent ADA
 
-      —————————————————————————————————————————————————————————————————————————————————————————————————————————————————
-                                                     A: Drug X    C: Combination   <Missing>   B: Placebo   All Drug X 
-                                                      (N=134)        (N=132)         (N=0)      (N=134)       (N=266)  
-      —————————————————————————————————————————————————————————————————————————————————————————————————————————————————
-      Baseline Prevalence of ADAs                                                                                      
-        Baseline evaluable patients                     134            132             0           0            266    
-      Patient with a positive sample at baseline     63 (47.0%)     64 (48.5%)         0           0        127 (47.7%)
-      Patient with no positive samples at baseline       71             68             0           0            139    
-      Incidence of Treatment Emergent ADAs                                                                             
-        Post-baseline evaluable patients                134            132             0           0            266    
-      Patient positive for Treatment Emergent ADA        0              0              0           0             0     
-      Treatment-induced ADA                              0              0              0           0             0     
-      Treatment-enhanced ADA                             0              0              0           0             0     
-      Patient negative for Treatment Emergent ADA        0              0              0           0             0     
-      Treatment unaffected                               0              0              0           0             0     
-      —————————————————————————————————————————————————————————————————————————————————————————————————————————————————
+      ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
+                                                       A: Drug X    C: Combination   <Missing>   B: Placebo   All Drug X 
+                                                        (N=134)        (N=132)         (N=0)      (N=134)       (N=266)  
+      ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
+      Baseline Prevalence of ADAs                                                                                        
+        Baseline evaluable patients                       134            132             0           0            266    
+        Patient with a positive sample at baseline     63 (47.0%)     64 (48.5%)         0           0        127 (47.7%)
+        Patient with no positive samples at baseline       71             68             0           0            139    
+      Incidence of Treatment Emergent ADAs                                                                               
+        Post-baseline evaluable patients                  134            132             0           0            266    
+        Patient positive for Treatment Emergent ADA        0              0              0           0             0     
+          Treatment-induced ADA                            0              0              0           0             0     
+          Treatment-enhanced ADA                           0              0              0           0             0     
+        Patient negative for Treatment Emergent ADA        0              0              0           0             0     
+          Treatment unaffected                             0              0              0           0             0     
+      ———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
 
       ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient = a patient with an ADA assay result from a baseline sample(s)
         Post-baseline evaluable patient = a patient with an ADA assay result from at least one post-baseline sample Number of patients positive for Treatment Emergent