R/counts_labels.R

In RandiLGarcia/dyadr: dyadr: Dyadic Data Analysis

#' Counts the number of observations in each category of a variable that has
#' value labels, returns counts and percent of total.
#' @export
#' @title counts_labels
#' @param data a data.frame
#' @param x a column in 'data'. Must be a string.
#' @import dplyr
counts_labels <- function(data, x) {
  if(!is.data.frame(data)) {
    stop("data.frame expected.")
  }
  if(is.factor(data[[x]])) {
    data[[x]] <- as.numeric(data[[x]])
    cat("Factor converted to numeric.")
  }
  if(is.character(data[[x]])) {
    data[[x]] <- as.numeric(data[[x]])
    cat("Factor converted to numeric.")
  }
  if(is.null(sjlabelled::get_labels(data[[x]]))) {
    stop(paste0("'", x, "' does not have value labels.")) 
  }
  dims <- dim(data)
  counts <- data %>%
    select_(x) %>%
    group_by_(x) %>%
    count_()
  labels <- tibble::tibble(value_labels = 
                     get_labels(data[[x]]))
  labels <- rownames_to_column(labels, var = "rn")
  labels$rn <- as.numeric(labels[["rn"]])
  names(counts)[1] <- "x"
  x_bins <- counts %>%
    right_join(labels, by = c("x" = "rn"))
  names(x_bins)[1] <- c(x, "n", "value_labels")
  x_bins[["n"]][is.na(x_bins[["n"]])] <- 0
  x_bins <- x_bins %>%
    mutate(percent = round((n / dims[[1]]) * 100))
  return(x_bins)
}

globalVariables(c("get_labels","rownames_to_column"))