adding proportion CIs

DESCRIPTION

@@ -15,12 +15,14 @@ Imports:
     cards (>= 0.0.0.9024),
     cli (>= 3.6.1),
     dplyr (>= 1.1.2),
+    glue (>= 1.6.2),
     rlang (>= 1.1.1),
     tidyr (>= 1.3.0)
 Suggests:
     broom (>= 1.0.5),
     spelling,
-    testthat (>= 3.2.0)
+    testthat (>= 3.2.0),
+    withr
 Remotes: 
     insightsengineering/cards
 Config/Needs/website: insightsengineering/nesttemplate

NAMESPACE

@@ -7,6 +7,7 @@ export(ard_chisqtest)
 export(ard_fishertest)
 export(ard_paired_ttest)
 export(ard_paired_wilcoxtest)
+export(ard_proportion_ci)
 export(ard_ttest)
 export(ard_wilcoxtest)
 export(contains)
@@ -16,6 +17,12 @@ export(last_col)
 export(matches)
 export(num_range)
 export(one_of)
+export(proportion_ci_agresti_coull)
+export(proportion_ci_clopper_pearson)
+export(proportion_ci_jeffreys)
+export(proportion_ci_strat_wilson)
+export(proportion_ci_wald)
+export(proportion_ci_wilson)
 export(starts_with)
 export(where)
 import(rlang)

R/ard_proportion_ci.R

@@ -0,0 +1,82 @@
+#' ARD Proportion Confidence Intervals
+#'
+#' `r lifecycle::badge('experimental')`\cr
+#' Calculate confidence intervals for proportions.
+#'
+#' @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)`.
+#' @param by ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
+#'   columns to stratify calculations by
+#' @param conf.level (`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.
+#'   Must be one of `r formals(ard_proportion_ci)[["method"]] |> eval() |> shQuote()`.
+#'   See `?proportion_ci` for details.
+#' @param strata,weights,max.iterations arguments passed to `proportion_ci_strat_wilson()`,
+#'   when `method='strat_wilson'`
+#'
+#' @return an ARD data frame
+#' @export
+#'
+#' @examples
+#' ard_proportion_ci(mtcars, variables = c(vs, am), method = "wilson")
+ard_proportion_ci <- function(data, variables, by = dplyr::group_vars(data),
+                              conf.level = 0.95,
+                              strata,
+                              weights = NULL,
+                              max.iterations = 10,
+                              method = c(
+                                "waldcc", "wald", "clopper-pearson",
+                                "wilson", "wilsoncc",
+                                "strat_wilson", "strat_wilsoncc",
+                                "agresti-coull", "jeffreys"
+                              )) {
+  # process inputs -------------------------------------------------------------
+  cards::process_selectors(data, variables = {{ variables }}, by = {{ by }})
+  method <- arg_match(method)
+  if (method %in% c("strat_wilson", "strat_wilsoncc")) {
+    cards::process_selectors(data, strata = strata)
+    check_scalar(strata)
+  }
+
+  # calculate confidence intervals ---------------------------------------------
+  cards::ard_complex(
+    data = data,
+    variables = {{ variables }},
+    by = {{ by }},
+    statistics =
+      ~ list(
+        prop_ci =
+          switch(method,
+            "waldcc" = \(x, ...) proportion_ci_wald(x, conf.level = conf.level, correct = TRUE),
+            "wald" = \(x, ...) proportion_ci_wald(x, conf.level = conf.level, correct = FALSE),
+            "wilsoncc" = \(x, ...) proportion_ci_wilson(x, conf.level = conf.level, correct = TRUE),
+            "wilson" = \(x, ...) proportion_ci_wilson(x, conf.level = conf.level, correct = FALSE),
+            "clopper-pearson" = \(x, ...) proportion_ci_clopper_pearson(x, conf.level = conf.level),
+            "agresti-coull" = \(x, ...) proportion_ci_agresti_coull(x, conf.level = conf.level),
+            "jeffreys" = \(x, ...) proportion_ci_jeffreys(x, conf.level = conf.level),
+            "strat_wilsoncc" = \(x, data, ...) {
+              proportion_ci_strat_wilson(x,
+                strata = data[[strata]], weights = weights,
+                max.iterations = max.iterations,
+                conf.level = conf.level, correct = TRUE
+              )
+            },
+            "strat_wilson" = \(x, data, ...) {
+              proportion_ci_strat_wilson(x,
+                strata = data[[strata]], weights = weights,
+                max.iterations = max.iterations,
+                conf.level = conf.level, correct = FALSE
+              )
+            }
+          )
+      )
+  ) |>
+    dplyr::mutate(
+      context = "proportion_ci"
+    )
+}