miss-scan-count.R

#' Search and present different kinds of missing values
#'
#' Searching for different kinds of missing values is really annoying. If
#'     you have values like -99 in your data, when they shouldn't be there,
#'     or they should be encoded as missing, it can be difficult to ascertain
#'     if they are there, and if so, where they are. `miss_scan_count` makes it
#'     easier for users to search for particular occurrences of these values
#'     across their variables. Note that the searches are done with regular
#'     expressions, which are special ways of searching for text. See the
#'     example below to see how to look for characters like `?`.
#'
#' @param data data
#' @param search values to search for
#'
#' @return a dataframe of the occurrences of the values you searched for
#'
#' @seealso  [pct_miss_case()] [prop_miss_case()] [pct_miss_var()] [prop_miss_var()] [pct_complete_case()] [prop_complete_case()] [pct_complete_var()] [prop_complete_var()] [miss_prop_summary()] [miss_case_summary()] [miss_case_table()] [miss_summary()] [miss_var_prop()] [miss_var_run()] [miss_var_span()] [miss_var_summary()] [miss_var_table()]
#'
#' @export
#'
#' @examples
#'
#' dat_ms <- tibble::tribble(~x,  ~y,    ~z,  ~specials,
#'                          1,   "A",   -100, "?",
#'                          3,   "N/A", -99,  "!",
#'                          NA,  NA,    -98,  ".",
#'                          -99, "E",   -101, "*",
#'                          -98, "F",   -1,  "-")
#'
#' miss_scan_count(dat_ms,-99)
#' miss_scan_count(dat_ms,c(-99,-98))
#' miss_scan_count(dat_ms,c("-99","-98","N/A"))
#' miss_scan_count(dat_ms, "\\?")
#' miss_scan_count(dat_ms, "\\!")
#' miss_scan_count(dat_ms, "\\.")
#' miss_scan_count(dat_ms, "\\*")
#' miss_scan_count(dat_ms, "-")
#' miss_scan_count(dat_ms,common_na_strings)
#'
#'
miss_scan_count <- function(data,search){
  # if there is only one value to search
  if (length(search) == 1) {
    res <- purrr::map_dfc(data,
                         ~length(grep(search,
                                      x = .))) %>%
      # return the dataframe with the columns "
      tidyr::pivot_longer(cols = dplyr::everything(),
                          names_to = "Variable",
                          values_to = "n")
    # but if there are more than one, we need to combine the search terms
  }

  if (length(search) > 1) {
    res <- purrr::map_dfc(data,
                         ~length(grep(paste0(search,
                                             collapse ="|"),
                                      x = .))) %>%
      tidyr::pivot_longer(cols = dplyr::everything(),
                          names_to = "Variable",
                          values_to = "n")
  }

  return(res)
}