R/gg-miss-fct.R

In naniar: Data Structures, Summaries, and Visualisations for Missing Data

#' Plot the number of missings for each variable, broken down by a factor
#'
#' This function draws a ggplot plot of the number of missings in each column,
#'   broken down by a categorical variable from the dataset. A default minimal
#'   theme is used, which can be customised as normal for ggplot.
#'
#' @param x data.frame
#' @param fct column containing the factor variable to visualise
#'
#' @return ggplot object depicting the % missing of each factor level for
#'   each variable.
#'
#' @seealso [geom_miss_point()] [gg_miss_case()] [gg_miss_case_cumsum] [gg_miss_span()] [gg_miss_var()] [gg_miss_var_cumsum()] [gg_miss_which()]
#'
#' @export
#'
#' @examples
#'
#' gg_miss_fct(x = riskfactors, fct = marital)
#' \dontrun{
#' library(ggplot2)
#' gg_miss_fct(x = riskfactors, fct = marital) + labs(title = "NA in Risk Factors and Marital status")
#'}
#'
gg_miss_fct <- function(x, fct){

  fct <- rlang::enquo(fct)

  data <- x %>%
    # protect against error where grouping by missing value leads to
    # warning message from dplyr about explicit
    dplyr::mutate_at(vars(!!fct), .funs = coerce_fct_na_explicit) %>%
    dplyr::group_by(!!fct) %>%
    miss_var_summary() %>%
    # coerce to numeric due to num error
    # reported in https://github.com/tidyverse/ggplot2/issues/5284
    dplyr::mutate(
      pct_miss = as.numeric(pct_miss)
    )

  ggobject <-
    ggplot(data,
           aes(
             x = .data[[fct]],
             y = variable,
             fill = pct_miss
           )) +
    geom_tile() +
    viridis::scale_fill_viridis(name = "% Miss") +
    theme_minimal() +
    theme(axis.text.x = element_text(angle = 45,
                                     hjust = 1))

  return(ggobject)
}