R/add-n-prop-miss.R

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

#' Add column containing number of missing data values
#'
#' It can be useful when doing data analysis to add the number of missing data
#'   points into your dataframe. `add_n_miss` adds a column named "n_miss",
#'   which contains the number of missing values in that row.
#'
#' @param data a dataframe
#' @param ... Variable names to use instead of the whole dataset. By default this
#'   looks at the whole dataset. Otherwise, this is one or more unquoted
#'   expressions separated by commas. These also respect the dplyr verbs
#'   `starts_with`, `contains`, `ends_with`, etc. By default will add "_all" to
#'   the label if left blank, otherwise will add "_vars" to distinguish that it
#'   has not been used on all of the variables.
#' @param label character default is "n_miss".
#'
#' @return a dataframe
#'
#' @export
#'
#' @seealso [bind_shadow()] [add_any_miss()] [add_label_missings()] [add_label_shadow()] [add_miss_cluster()] [add_prop_miss()] [add_shadow_shift()] [cast_shadow()]
#'
#' @examples
#'
#' airquality %>% add_n_miss()
#' airquality %>% add_n_miss(Ozone, Solar.R)
#' airquality %>% add_n_miss(dplyr::contains("o"))
#'
#'
add_n_miss <- function(data, ..., label = "n_miss"){

  if (missing(...)) {
    data[[paste0(label, "_all")]] <- n_miss_row(data)

    return(data)
  }

  selected_data <- dplyr::select(data, ...)

  data[[paste0(label, "_vars")]] <- n_miss_row(selected_data)

  return(data)
}

#' Add column containing proportion of missing data values
#'
#' It can be useful when doing data analysis to add the proportion of missing
#'   data values into your dataframe. `add_prop_miss` adds a column named
#'   "prop_miss", which contains the proportion of missing values in that row.
#'   You can specify the variables that you would like to show the missingness
#'   for.
#'
#' @param data a dataframe
#' @param ... Variable names to use instead of the whole dataset. By default this
#'   looks at the whole dataset. Otherwise, this is one or more unquoted
#'   expressions separated by commas. These also respect the dplyr verbs
#'   `starts_with`, `contains`, `ends_with`, etc. By default will add "_all" to
#'   the label if left blank, otherwise will add "_vars" to distinguish that it
#'   has not been used on all of the variables.
#' @param label character string of what you need to name variable
#'
#' @return a dataframe
#'
#' @export
#'
#' @seealso [bind_shadow()] [add_any_miss()] [add_label_missings()] [add_label_shadow()] [add_miss_cluster()] [add_prop_miss()] [add_shadow_shift()] [cast_shadow()]
#'
#' @examples
#'
#' airquality %>% add_prop_miss()
#' airquality %>% add_prop_miss(Solar.R, Ozone)
#' airquality %>% add_prop_miss(Solar.R, Ozone, label = "testing")
#'
#' # this can be applied to model the proportion of missing data
#' # as in Tierney et al \doi{10.1136/bmjopen-2014-007450}
#' # see "Modelling missingness" in vignette "Getting Started with naniar"
#' # for details

add_prop_miss <- function(data, ..., label = "prop_miss"){

  if (missing(...)) {
    data[[paste0(label, "_all")]] <- prop_miss_row(data)

    return(data)
  }

  selected_data <- dplyr::select(data, ...)

  data[[paste0(label, "_vars")]] <- prop_miss_row(selected_data)

  return(data)

}