R/ard_stats_wilcox_test.R

In cardx: Extra Analysis Results Data Utilities

#' ARD Wilcoxon Rank-Sum Test
#'
#' @description
#' Analysis results data for paired and non-paired Wilcoxon Rank-Sum tests.
#'
#' @param data (`data.frame`)\cr
#'   a data frame. See below for details.
#' @param by ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
#'   optional column name to compare by.
#' @param variables ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
#'   column names to be compared. Independent tests will be computed for
#'   each variable.
#' @param id ([`tidy-select`][dplyr::dplyr_tidy_select])\cr
#'   column name of the subject or participant ID.
#' @param conf.level (scalar `numeric`)\cr
#'   confidence level for confidence interval. Default is `0.95`.
#' @param ... arguments passed to `wilcox.test(...)`
#'
#' @return ARD data frame
#' @name ard_stats_wilcox_test
#'
#' @details
#' For the `ard_stats_wilcox_test()` function, the data is expected to be one row per subject.
#' The data is passed as `wilcox.test(data[[variable]] ~ data[[by]], paired = FALSE, ...)`.
#'
#' For the `ard_stats_paired_wilcox_test()` function, the data is expected to be one row
#' per subject per by level. Before the test is calculated, the data are
#' reshaped to a wide format to be one row per subject.
#' The data are then passed as
#' `wilcox.test(x = data_wide[[<by level 1>]], y = data_wide[[<by level 2>]], paired = TRUE, ...)`.
#'
#' @examplesIf do.call(asNamespace("cardx")$is_pkg_installed, list(pkg = "broom", reference_pkg = "cardx"))
#' cards::ADSL |>
#'   dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
#'   ard_stats_wilcox_test(by = "ARM", variables = "AGE")
#'
#' # constructing a paired data set,
#' # where patients receive both treatments
#' cards::ADSL[c("ARM", "AGE")] |>
#'   dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
#'   dplyr::mutate(.by = ARM, USUBJID = dplyr::row_number()) |>
#'   dplyr::arrange(USUBJID, ARM) |>
#'   ard_stats_paired_wilcox_test(by = ARM, variables = AGE, id = USUBJID)
NULL

#' @rdname ard_stats_wilcox_test
#' @export
ard_stats_wilcox_test <- function(data, variables, by = NULL, conf.level = 0.95, ...) {
  set_cli_abort_call()

  # check installed packages ---------------------------------------------------
  check_pkg_installed("broom", reference_pkg = "cardx")

  # check/process inputs -------------------------------------------------------
  check_not_missing(data)
  check_not_missing(variables)
  check_data_frame(data)
  data <- dplyr::ungroup(data)
  cards::process_selectors(data, by = {{ by }}, variables = {{ variables }})
  check_scalar(by, allow_empty = TRUE)
  check_range(conf.level, range = c(0, 1))

  # return empty ARD if no variables selected ----------------------------------
  if (is_empty(variables)) {
    return(dplyr::tibble() |> cards::as_card())
  }

  # build ARD ------------------------------------------------------------------
  lapply(
    variables,
    function(variable) {
      .format_wilcoxtest_results(
        by = by,
        variable = variable,
        lst_tidy =
          # styler: off
          cards::eval_capture_conditions(
            if (!is_empty(by)) {
              stats::wilcox.test(data[[variable]] ~ data[[by]], conf.level = conf.level, ...) |>
                broom::tidy()
            }
            else {
              stats::wilcox.test(data[[variable]], ...) |>
                broom::tidy()
            }
          ),
        # styler: on
        paired = FALSE,
        ...
      )
    }
  ) |>
    dplyr::bind_rows()
}

#' @rdname ard_stats_wilcox_test
#' @export
ard_stats_paired_wilcox_test <- function(data, by, variables, id, conf.level = 0.95, ...) {
  set_cli_abort_call()

  # check installed packages ---------------------------------------------------
  check_pkg_installed("broom", reference_pkg = "cardx")

  # check/process inputs -------------------------------------------------------
  check_not_missing(data)
  check_not_missing(variables)
  check_not_missing(by)
  check_not_missing(id)
  check_data_frame(data)
  data <- dplyr::ungroup(data)
  cards::process_selectors(data, by = {{ by }}, variables = {{ variables }}, id = {{ id }})
  check_scalar(by)
  check_scalar(id)

  # return empty ARD if no variables selected ----------------------------------
  if (is_empty(variables)) {
    return(dplyr::tibble() |> cards::as_card())
  }

  # build ARD ------------------------------------------------------------------
  lapply(
    variables,
    function(variable) {
      .format_wilcoxtest_results(
        by = by,
        variable = variable,
        lst_tidy =
          cards::eval_capture_conditions({
            # adding this reshape inside the eval, so if there is an error it's captured in the ARD object
            data_wide <- .paired_data_pivot_wider(data, by = by, variable = variable, id = id)
            # perform paired wilcox test
            stats::wilcox.test(x = data_wide[["by1"]], y = data_wide[["by2"]], paired = TRUE, conf.level = conf.level, ...) |>
              broom::tidy()
          }),
        paired = TRUE,
        ...
      )
    }
  ) |>
    dplyr::bind_rows()
}


#' Convert Wilcoxon test to ARD
#'
#' @inheritParams cards::tidy_as_ard
#' @inheritParams stats::wilcox.test
#' @param by (`string`)\cr by column name
#' @param variable (`string`)\cr variable column name
#' @param ... passed to `stats::wilcox.test(...)`
#'
#' @return ARD data frame
#'
#' @examplesIf do.call(asNamespace("cardx")$is_pkg_installed, list(pkg = "broom", reference_pkg = "cardx"))
#' # Pre-processing ADSL to have grouping factor (ARM here) with 2 levels
#' ADSL <- cards::ADSL |>
#'   dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
#'   ard_stats_wilcox_test(by = "ARM", variables = "AGE")
#'
#' cardx:::.format_wilcoxtest_results(
#'   by = "ARM",
#'   variable = "AGE",
#'   paired = FALSE,
#'   lst_tidy =
#'     cards::eval_capture_conditions(
#'       stats::wilcox.test(ADSL[["AGE"]] ~ ADSL[["ARM"]], paired = FALSE) |>
#'         broom::tidy()
#'     )
#' )
#'
#' @keywords internal
.format_wilcoxtest_results <- function(by = NULL, variable, lst_tidy, paired, ...) {
  # build ARD ------------------------------------------------------------------
  ret <-
    cards::tidy_as_ard(
      lst_tidy = lst_tidy,
      tidy_result_names = c("statistic", "p.value", "method", "alternative"),
      fun_args_to_record = c(
        "mu", "paired", "exact", "correct", "conf.int",
        "conf.level", "tol.root", "digits.rank"
      ),
      formals = formals(asNamespace("stats")[["wilcox.test.default"]]),
      passed_args = c(list(paired = paired), dots_list(...)),
      lst_ard_columns = list(variable = variable, context = "stats_wilcox_test")
    )

  if (!is_empty(by)) {
    ret <- ret |>
      dplyr::mutate(group1 = by)
  }

  # add the stat label ---------------------------------------------------------
  ret |>
    dplyr::left_join(
      .df_wilcoxtest_stat_labels(by),
      by = "stat_name"
    ) |>
    dplyr::mutate(stat_label = dplyr::coalesce(.data$stat_label, .data$stat_name)) |>
    cards::as_card() |>
    cards::tidy_ard_column_order()
}


.df_wilcoxtest_stat_labels <- function(by = NULL) {
  dplyr::tribble(
    ~stat_name, ~stat_label,
    "statistic", ifelse(is.null(by), "V Statistic", "X-squared Statistic"),
    "parameter", "Degrees of Freedom",
    "estimate", "Median of the Difference",
    "p.value", "p-value",
    "conf.low", "CI Lower Bound",
    "conf.high", "CI Upper Bound",
    "paired", "Paired test",
    "conf.level", "CI Confidence Level",
  )
}