CRAN release: update description and news version to 0.8.3, [skip vbu…

…mp] (#970) This PR tracks CRAN submission and release --------- Signed-off-by: Joe Zhu <[email protected]> Co-authored-by: github-actions <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: 27856297+dependabot-preview[bot]@users.noreply.github.com <27856297+dependabot-preview[bot]@users.noreply.github.com>
insightsengineering · Jun 20, 2023 · c69a6f7 · c69a6f7
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -7,6 +7,7 @@
 # doc
 ^CODE_OF_CONDUCT\.md$
 ^docs$
+LICENSE
 ^LICENSE\.md$
 ^SECURITY\.md$
 

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: tern
 Title: Create Common TLGs Used in Clinical Trials
-Version: 0.8.2.9014
+Version: 0.8.3
 Date: 2023-06-15
 Authors@R: c(
     person("Joe", "Zhu", , "[email protected]", role = c("aut", "cre")),
@@ -16,7 +16,7 @@ Authors@R: c(
   )
 Description: Table, Listings, and Graphs (TLG) library for common outputs
     used in clinical trials.
-License: Apache License 2.0 | file LICENSE
+License: Apache License 2.0
 URL: https://github.com/insightsengineering/tern
 BugReports: https://github.com/insightsengineering/tern/issues
 Depends:

diff --git a/NAMESPACE b/NAMESPACE
@@ -31,9 +31,11 @@ export(CombinationFunction)
 export(a_compare)
 export(a_count_occurrences)
 export(a_count_occurrences_by_grade)
+export(a_count_patients_sum_exposure)
 export(a_count_patients_with_event)
 export(a_count_patients_with_flags)
 export(a_count_values)
+export(a_coxreg)
 export(a_length_proportion)
 export(a_odds_ratio)
 export(a_proportion)
@@ -45,7 +47,9 @@ export(analyze_num_patients)
 export(analyze_patients_exposure_in_cols)
 export(analyze_vars_in_cols)
 export(append_varlabels)
+export(arrange_grobs)
 export(as.rtable)
+export(combine_counts)
 export(combine_groups)
 export(combine_levels)
 export(combine_vectors)
@@ -110,6 +114,7 @@ export(fit_coxreg_univar)
 export(fit_logistic)
 export(fit_rsp_step)
 export(fit_survival_step)
+export(forest_viewport)
 export(format_count_fraction)
 export(format_count_fraction_fixed_dp)
 export(format_extreme_values)
@@ -125,6 +130,7 @@ export(g_lineplot)
 export(g_step)
 export(g_waterfall)
 export(get_smooths)
+export(groups_list_to_df)
 export(h_adlb_worsen)
 export(h_adsl_adlb_merge_using_worst_flag)
 export(h_ancova)
@@ -202,6 +208,8 @@ export(h_tbl_median_surv)
 export(h_worsen_counter)
 export(h_xticks)
 export(has_count_in_any_col)
+export(has_count_in_cols)
+export(has_counts_difference)
 export(has_fraction_in_any_col)
 export(has_fraction_in_cols)
 export(has_fractions_difference)

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# tern 0.8.2.9014
+# tern 0.8.3
 
 ### Enhancements
 * Added explicit zero counts to `g_km` plot "at risk" annotation tables.
@@ -14,6 +14,8 @@
 * Fixed `tern:::tidy.glm` formals to respect `broom:::tidy.default` formals.
 
 ### Miscellaneous
+* Remove examples for unexported functions.
+* Export functions `has_count_in_cols`, `has_counts_difference`, `combine_counts`, `h_tab_rsp_one_biomarker`, `arrange_grobs`, `a_count_patients_sum_exposure`, `a_coxreg`, `groups_list_to_df`, `forest_viewport`.
 * Updated README to include installation instructions for CRAN.
 * Began deprecation of `indent_mod` argument and replace it with the `.indent_mods` argument in `summarize_num_patients` and `analyze_num_patients`.
 

diff --git a/R/abnormal.R b/R/abnormal.R
@@ -33,7 +33,6 @@ NULL
 #'
 #' @return
 #' * `s_count_abnormal()` returns the statistic `fraction` which is a vector with `num` and `denom` counts of patients.
-#'
 #' @examples
 #' library(dplyr)
 #'
@@ -48,21 +47,6 @@ NULL
 #' # Select only post-baseline records.
 #' df <- df %>%
 #'   filter(ONTRTFL == "Y")
-#'
-#' # Internal function - s_count_abnormal
-#' \dontrun{
-#' # For abnormal level "HIGH" we get the following counts.
-#' s_count_abnormal(df, .var = "ANRIND", abnormal = list(high = "HIGH", low = "LOW"))
-#'
-#' # Optionally exclude patients with abnormality at baseline.
-#' s_count_abnormal(
-#'   df,
-#'   .var = "ANRIND",
-#'   abnormal = list(high = "HIGH", low = "LOW"),
-#'   exclude_base_abn = TRUE
-#' )
-#' }
-#'
 #' @keywords internal
 s_count_abnormal <- function(df,
                              .var,
@@ -112,14 +96,6 @@ s_count_abnormal <- function(df,
 #' @return
 #' * `a_count_abnormal()` returns the corresponding list with formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_abnormal
-#' \dontrun{
-#' # Use the Formatted Analysis function for `analyze()`.
-#' a_fun <- make_afun(a_count_abnormal, .ungroup_stats = "fraction")
-#' a_fun(df, .var = "ANRIND", abnormal = list(low = "LOW", high = "HIGH"))
-#' }
-#'
 #' @keywords internal
 a_count_abnormal <- make_afun(
   s_count_abnormal,

diff --git a/R/abnormal_by_baseline.R b/R/abnormal_by_baseline.R
@@ -64,19 +64,6 @@ d_count_abnormal_by_baseline <- function(abnormal) {
 #' * `s_count_abnormal_by_baseline()` returns statistic `fraction` which is a named list with 3 labeled elements:
 #'   `not_abnormal`, `abnormal`, and `total`. Each element contains a vector with `num` and `denom` patient counts.
 #'
-#' @examples
-#' df <- data.frame(
-#'   USUBJID = as.character(c(1:6)),
-#'   ANRIND = factor(c(rep("LOW", 4), "NORMAL", "HIGH")),
-#'   BNRIND = factor(c("LOW", "NORMAL", "HIGH", NA, "LOW", "NORMAL"))
-#' )
-#' df <- df_explicit_na(df)
-#'
-#' # Internal function - s_count_abnormal_by_baseline
-#' \dontrun{
-#' # Just for one abnormal level.
-#' s_count_abnormal_by_baseline(df, .var = "ANRIND", abnormal = "HIGH")
-#' }
 #'
 #' @keywords internal
 s_count_abnormal_by_baseline <- function(df,
@@ -147,14 +134,6 @@ s_count_abnormal_by_baseline <- function(df,
 #' @return
 #' * `a_count_abnormal_by_baseline()` returns the corresponding list with formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_abnormal_by_baseline
-#' \dontrun{
-#' # Use the Formatted Analysis function for `analyze()`. We need to ungroup `fraction` first
-#' # so that the `rtables` formatting function `format_fraction()` can be applied correctly.
-#' afun <- make_afun(a_count_abnormal_by_baseline, .ungroup_stats = "fraction")
-#' afun(df, .var = "ANRIND", abnormal = "LOW")
-#' }
 #'
 #' @keywords internal
 a_count_abnormal_by_baseline <- make_afun(
@@ -171,6 +150,13 @@ a_count_abnormal_by_baseline <- make_afun(
 #'   the statistics from `s_count_abnormal_by_baseline()` to the table layout.
 #'
 #' @examples
+#' df <- data.frame(
+#'   USUBJID = as.character(c(1:6)),
+#'   ANRIND = factor(c(rep("LOW", 4), "NORMAL", "HIGH")),
+#'   BNRIND = factor(c("LOW", "NORMAL", "HIGH", NA, "LOW", "NORMAL"))
+#' )
+#' df <- df_explicit_na(df)
+#'
 #' # Layout creating function.
 #' basic_table() %>%
 #'   count_abnormal_by_baseline(var = "ANRIND", abnormal = c(High = "HIGH")) %>%

diff --git a/R/abnormal_by_marked.R b/R/abnormal_by_marked.R
@@ -67,15 +67,6 @@ NULL
 #'   full_parent_df = I(full_parent_df),
 #'   cur_col_subset = I(cur_col_subset)
 #' )
-#' # Internal function - s_count_abnormal_by_marked
-#' \dontrun{
-#' s_count_abnormal_by_marked(
-#'   df = df_crp %>% filter(abn_dir == "High"),
-#'   .spl_context = spl_context,
-#'   .var = "AVALCAT1",
-#'   variables = list(id = "USUBJID", param = "PARAMCD", direction = "abn_dir")
-#' )
-#' }
 #'
 #' @keywords internal
 s_count_abnormal_by_marked <- function(df,
@@ -137,18 +128,6 @@ s_count_abnormal_by_marked <- function(df,
 #' @return
 #' * `a_count_abnormal_by_marked()` returns the corresponding list with formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_abnormal_by_marked
-#' \dontrun{
-#' # Use the Formatted Analysis function for `analyze()`. We need to ungroup `count_fraction` first
-#' # so that the `rtables` formatting function `format_count_fraction()` can be applied correctly.
-#' afun <- make_afun(a_count_abnormal_by_marked, .ungroup_stats = "count_fraction")
-#' afun(
-#'   df = df_crp %>% filter(abn_dir == "High"),
-#'   .spl_context = spl_context,
-#'   variables = list(id = "USUBJID", param = "PARAMCD", direction = "abn_dir")
-#' )
-#' }
 #'
 #' @keywords internal
 a_count_abnormal_by_marked <- make_afun(

diff --git a/R/abnormal_by_worst_grade.R b/R/abnormal_by_worst_grade.R
@@ -79,15 +79,6 @@ NULL
 #'   cur_col_subset = I(cur_col_subset)
 #' )
 #'
-#' # Internal function - s_count_abnormal_by_worst_grade
-#' \dontrun{
-#' s_count_abnormal_by_worst_grade(
-#'   df = adlb_f_alt,
-#'   .spl_context = spl_context,
-#'   .var = "GRADE_ANL"
-#' )
-#' }
-#'
 #' @keywords internal
 s_count_abnormal_by_worst_grade <- function(df, # nolint
                                             .var = "GRADE_ANL",
@@ -137,14 +128,6 @@ s_count_abnormal_by_worst_grade <- function(df, # nolint
 #' @return
 #' * `a_count_abnormal_by_worst_grade()` returns the corresponding list with formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_abnormal_by_worst_grade
-#' \dontrun{
-#' # Use the Formatted Analysis function for `analyze()`. We need to ungroup `count_fraction` first
-#' # so that the `rtables` formatting function `format_count_fraction()` can be applied correctly.
-#' afun <- make_afun(a_count_abnormal_by_worst_grade, .ungroup_stats = "count_fraction")
-#' afun(df = adlb_f_alt, .spl_context = spl_context)
-#' }
 #'
 #' @keywords internal
 a_count_abnormal_by_worst_grade <- make_afun( # nolint

diff --git a/R/abnormal_by_worst_grade_worsen.R b/R/abnormal_by_worst_grade_worsen.R
@@ -293,19 +293,6 @@ h_worsen_counter <- function(df, id, .var, baseline_var, direction_var) {
 #'   worst_flag_high = c("WGRHIFL" = "Y"),
 #'   direction_var = "GRADDR"
 #' )
-#' # Internal function - s_count_abnormal_lab_worsen_by_baseline
-#' \dontrun{
-#' # Patients with worsening lab grade for CRP in the direction of low
-#' s_count_abnormal_lab_worsen_by_baseline(
-#'   df = df %>% filter(ARMCD == "ARM A" & PARAMCD == "CRP"),
-#'   .var = "ATOXGR",
-#'   variables = list(
-#'     id = "USUBJID",
-#'     baseline_var = "BTOXGR",
-#'     direction_var = "GRADDR"
-#'   )
-#' )
-#' }
 #'
 #' @keywords internal
 s_count_abnormal_lab_worsen_by_baseline <- function(df, # nolint
@@ -334,16 +321,6 @@ s_count_abnormal_lab_worsen_by_baseline <- function(df, # nolint
 #' * `a_count_abnormal_lab_worsen_by_baseline()` returns the corresponding list with
 #'   formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_abnormal_lab_worsen_by_baseline
-#' \dontrun{
-#' a_count_abnormal_lab_worsen_by_baseline(
-#'   df = df %>% filter(ARMCD == "ARM A" & PARAMCD == "CRP"),
-#'   .var = "ATOXGR",
-#'   variables = list(id = "USUBJID", baseline_var = "BTOXGR", direction_var = "GRADDR")
-#' )
-#' }
-#'
 #' @keywords internal
 a_count_abnormal_lab_worsen_by_baseline <- make_afun( # nolint
   s_count_abnormal_lab_worsen_by_baseline,

diff --git a/R/count_cumulative.R b/R/count_cumulative.R
@@ -97,16 +97,6 @@ d_count_cumulative <- function(threshold, lower_tail, include_eq) {
 #' * `s_count_cumulative()` returns a named list of `count_fraction`s: a list with each `thresholds` value as a
 #'   component, each component containing a vector for the count and fraction.
 #'
-#' @examples
-#' # Internal function - s_count_cumulative
-#' \dontrun{
-#' set.seed(1, kind = "Mersenne-Twister")
-#' x <- c(sample(1:10, 10), NA)
-#' .N_col <- length(x)
-#' s_count_cumulative(x, thresholds = c(0, 5, 11), .N_col = .N_col)
-#' s_count_cumulative(x, thresholds = c(0, 5, 11), include_eq = FALSE, na.rm = FALSE, .N_col = .N_col)
-#' }
-#'
 #' @keywords internal
 s_count_cumulative <- function(x,
                                thresholds,
@@ -132,15 +122,6 @@ s_count_cumulative <- function(x,
 #' @return
 #' * `a_count_cumulative()` returns the corresponding list with formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_cumulative
-#' \dontrun{
-#' # Use the Formatted Analysis function for `analyze()`. We need to ungroup `count_fraction` first
-#' # so that the `rtables` formatting function `format_count_fraction()` can be applied correctly.
-#' afun <- make_afun(a_count_cumulative, .ungroup_stats = "count_fraction")
-#' afun(x, thresholds = c(0, 5, 11), .N_col = .N_col)
-#' }
-#'
 #' @keywords internal
 a_count_cumulative <- make_afun(
   s_count_cumulative,

diff --git a/R/count_missed_doses.R b/R/count_missed_doses.R
@@ -21,11 +21,6 @@ NULL
 #' set.seed(1)
 #' x <- c(sample(1:10, 10), NA)
 #'
-#' # Internal function - s_count_nonmissing
-#' \dontrun{
-#' s_count_nonmissing(x)
-#' }
-#'
 #' @keywords internal
 s_count_nonmissing <- function(x) {
   list(n = n_available(x))
@@ -53,12 +48,6 @@ d_count_missed_doses <- function(thresholds) {
 #' @return
 #' * `s_count_missed_doses()` returns the statistics `n` and `count_fraction` with one element for each threshold.
 #'
-#' @examples
-#' # Internal function - s_count_missed_doses
-#' \dontrun{
-#' s_count_missed_doses(x = c(0, 1, 0, 2, 3, 4, 0, 2), thresholds = c(2, 5), .N_col = 10)
-#' }
-#'
 #' @keywords internal
 s_count_missed_doses <- function(x,
                                  thresholds,
@@ -84,15 +73,6 @@ s_count_missed_doses <- function(x,
 #' @return
 #' * `a_count_missed_doses()` returns the corresponding list with formatted [rtables::CellValue()].
 #'
-#' @examples
-#' # Internal function - a_count_missed_doses
-#' \dontrun{
-#' #  We need to ungroup `count_fraction` first so that the `rtables` formatting
-#' # function `format_count_fraction()` can be applied correctly.
-#' afun <- make_afun(a_count_missed_doses, .ungroup_stats = "count_fraction")
-#' afun(x = c(0, 1, 0, 2, 3, 4, 0, 2), thresholds = c(2, 5), .N_col = 10)
-#' }
-#'
 #' @keywords internal
 a_count_missed_doses <- make_afun(
   s_count_missed_doses,

diff --git a/R/count_patients_events_in_cols.R b/R/count_patients_events_in_cols.R
@@ -40,18 +40,6 @@ NULL
 #'   AEBODSYS = rep(c("SOC1", "SOC2", "SOC3"), c(3, 3, 1))
 #' )
 #'
-#' # Internal function - s_count_patients_and_multiple_events
-#' \dontrun{
-#' s_count_patients_and_multiple_events(
-#'   df = df,
-#'   id = "USUBJID",
-#'   filters_list = list(
-#'     serious = c(AESER = "Y"),
-#'     fatal = c(AESDTH = "Y")
-#'   )
-#' )
-#' }
-#'
 #' @keywords internal
 s_count_patients_and_multiple_events <- function(df, # nolint
                                                  id,