Merge branch 'main' into remove_lookups

Signed-off-by: Lluís Revilla <185338939+llrs-roche@users.noreply.github.com>

Author: llrs-roche

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: cardx
 Title: Extra Analysis Results Data Utilities
-Version: 0.2.2.9020
+Version: 0.2.2.9024
 Authors@R: c(
     person("Daniel D.", "Sjoberg", , "danield.sjoberg@gmail.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-0862-2018")),
@@ -20,7 +20,7 @@ BugReports: https://github.com/insightsengineering/cardx/issues
 Depends:
     R (>= 4.1)
 Imports:
-    cards (>= 0.4.0.9028),
+    cards (>= 0.5.0),
     cli (>= 3.6.1),
     dplyr (>= 1.1.2),
     glue (>= 1.6.2),

NEWS.md

@@ -1,16 +1,24 @@
-# cardx 0.2.2.9020
+# cardx 0.2.2.9024
 
-* Added `as_cards_fun()` to `ard_categorical_ci()` so when there is an error, the user gets an ARD with the expected ARD structure. (#262)
+## New Features and Updates
+
+* Added function `ard_categorical_max()` to calculate categorical occurrence rates by maximum level per unique ID. (#240)
+
+## Other Updates
 
 * Little `n` is now returned with the results of the `proportion_ci_*()` functions, which then flows into the results of `ard_proportion_ci()`. (#256)
 
+* Added `as_cards_fun()` to `ard_categorical_ci()` so when there is an error, the user gets an ARD with the expected ARD structure. (#262)
+
 * Update in `ard_categorical.survey.design()` for factor variables that are all missing. These variables can now be tabulated, where previously this resulted in an error. 
 
 * Update in `ard_missing.survey.design()` where we can now tabulate the missing rate of design variables, such as the weights.
 
+## Bug Fixes
+
 * Fixed a bug in `ard_survival_survfit()` causing an error when "=" character is present in stratification variable level labels. (#252)
 
-* Added function `ard_categorical_max()` to calculate categorical occurrence rates by maximum level per unique ID. (#240)
+* Bug fix in `ard_categorical_ci(denominator='cell')` when missing values were present in the `by` variable.
 
 # cardx 0.2.2
 

R/ard_categorical_ci.R

@@ -5,15 +5,15 @@
 #' @inheritParams cards::ard_categorical
 #' @param variables ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
 #'   columns to include in summaries. Columns must be class `<logical>`
-#'   or `<numeric>` values coded as `c(0, 1)`.
+#'   or `<numeric>` values coded as `c(0,1)`.
 #' @param by ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
 #'   columns to stratify calculations by.
 #' @param denominator (`string`)\cr
 #'   Must be one of `'column'` (default), `'row'`, and `'cell'`, which specifies
 #'   the direction of the calculation/denominator. Argument is similar to
 #'   `cards::ard_categorical(denominator)`.
-#' @param conf.level (`numeric`)\cr
-#'   a scalar in `(0, 1)` indicating the confidence level.
+#' @param conf.level (scalar `numeric`)\cr
+#'   a scalar in `(0,1)` indicating the confidence level.
 #'   Default is `0.95`
 #' @param method (`string`)\cr
 #'   string indicating the type of confidence interval to calculate.
@@ -241,7 +241,7 @@ ard_categorical_ci.data.frame <- function(data,
 }
 
 .levels_for_row <- function(data, by) {
-  cards::nest_for_ard(data = data, by = by, include_data = FALSE) |>
+  suppressMessages(cards::nest_for_ard(data = data, by = by, include_data = FALSE)) |>
     dplyr::select(cards::all_ard_groups(types = "levels")) |>
     map(unlist) |>
     reduce(.f = \(.x, .y) paste0(.x, .y)) |>
@@ -318,7 +318,7 @@ ard_categorical_ci.data.frame <- function(data,
   }
 
 
-  df_grouping_cols <- cards::nest_for_ard(data, by = by, include_data = FALSE)
+  df_grouping_cols <- suppressMessages(cards::nest_for_ard(data, by = by, include_data = FALSE))
   levels <- .levels_for_row(data = data, by = by)
 
   suppressMessages(
@@ -413,19 +413,27 @@ ard_categorical_ci.data.frame <- function(data,
     dplyr::mutate(
       ...a_name_anyone_would_ever_pick... =
         dplyr::coalesce(.data$...a_name_anyone_would_ever_pick..., paste(levels, collapse = ""))
-    ) |>
+    )
+
+  # make dummy vector missing if any of the other variables are missing
+  dummy_data[["...a_name_anyone_would_ever_pick..."]][apply(is.na(dummy_data[c(variable, by, strata)]), MARGIN = 1, FUN = any)] <- NA
+
+  # finish processing dummy data
+  dummy_data <- dummy_data |>
     dplyr::select(-any_of(c(variable, by))) %>%
-    {
-      dplyr::bind_cols(
-        .,
-        map(
-          levels,
-          \(level) (.[["...a_name_anyone_would_ever_pick..."]] == level)
-        ) |>
-          stats::setNames(paste0("level_", levels)) |>
-          dplyr::as_tibble()
-      )
-    } |>
+    # styler: off
+    {dplyr::bind_cols(
+      .,
+      map(
+        levels,
+        \(level) {
+          (.[["...a_name_anyone_would_ever_pick..."]] == level)
+        }
+      ) |>
+        stats::setNames(paste0("level_", levels)) |>
+        dplyr::as_tibble()
+    )} |>
+    # styler: on
     dplyr::select(-"...a_name_anyone_would_ever_pick...")
 
   prop_ci_fun <-

R/proportion_ci.R

@@ -3,7 +3,8 @@
 #' Functions to calculate different proportion confidence intervals for use in `ard_proportion()`.
 #'
 #' @inheritParams ard_categorical_ci
-#' @param x vector of a binary values, i.e. a logical vector, or numeric with values `c(0, 1)`
+#' @param x (binary `numeric`/`logical`)\cr
+#'   vector of a binary values, i.e. a logical vector, or numeric with values `c(0, 1)`
 #' @return Confidence interval of a proportion.
 #'
 #' @name proportion_ci
@@ -238,12 +239,12 @@ proportion_ci_jeffreys <- function(x, conf.level = 0.95) {
 #'
 #'
 #' @param strata (`factor`)\cr variable with one level per stratum and same length as `x`.
-#' @param weights (`numeric` or `NULL`)\cr weights for each level of the strata. If `NULL`, they are
+#' @param weights (`numeric`)\cr weights for each level of the strata. If `NULL`, they are
 #'   estimated using the iterative algorithm that
 #'   minimizes the weighted squared length of the confidence interval.
-#' @param max.iterations (`count`)\cr maximum number of iterations for the iterative procedure used
+#' @param max.iterations (positive `integer`)\cr maximum number of iterations for the iterative procedure used
 #'   to find estimates of optimal weights.
-#' @param correct (`flag`)\cr include the continuity correction. For further information, see for example
+#' @param correct (scalar `logical`)\cr include the continuity correction. For further information, see for example
 #'   [stats::prop.test()].
 #'
 #' @examples

cran-comments.md

@@ -6,13 +6,20 @@
 
 ## R CMD check results
 
-0 errors ✔ | 0 warnings ✔ | 0 notes ✔
+(I added my middle initial to my name to match my other packages on CRAN.)
+
+  New maintainer:
+    Daniel D. Sjoberg <danield.sjoberg@gmail.com>
+  Old maintainer(s):
+    Daniel Sjoberg <danield.sjoberg@gmail.com>
+
+0 errors ✔ | 0 warnings ✔ | 1 note ✖
 
 ## revdepcheck results
 
 We checked 4 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
- * We saw 1 new problem in gtsummary, and it is slated to be released next week (I am the maintainer).
+ * We saw 0 new problems
  * We failed to check 0 packages
 
 ## Additional Comments