R/quantile_cut.R

In stRoke: Clinical Stroke Research

#' MOVED Easy function for splitting numeric variable in quantiles
#'
#' Using base/stats functions cut() and quantile().
#'
#' @param x Variable to cut.
#' @param groups Number of groups.
#' @param y alternative vector to draw quantile cuts from. Limits has 
#' to be within x. Default is NULL.
#' @param na.rm Remove NA's. Default is TRUE.
#' @param group.names Names of groups to split to. Default is NULL, 
#' giving intervals as names.
#' @param ordered.f Set resulting vector as ordered. Default is FALSE.
#' @param detail.list flag to include details or not
#' @param inc.outs Flag to include min(x) and max(x) 
#' as borders in case of y!=NULL.
#' 
#' @return vector or list with vector and details (length 2)
#'
#' @keywords quantile
#' @export
#' @examples
#' aa <- as.numeric(sample(1:1000,2000,replace = TRUE))
#' x <- 1:450
#' y <- 6:750
#' summary(quantile_cut(aa,groups=4,detail.list=FALSE)) ## Cuts quartiles
quantile_cut <- function (x,
                          groups,
                          y = NULL,
                          na.rm = TRUE,
                          group.names = NULL,
                          ordered.f = FALSE,
                          inc.outs = FALSE,
                          detail.list = FALSE) {
  if (!is.null(y)) {
    q <- quantile(
      y,
      probs = seq(0, 1, 1 / groups),
      na.rm = na.rm,
      names = TRUE,
      type = 7
    )
    if (inc.outs) {
      # Setting cut borders to include outliers in x compared to y.
      q[1] <- min(x, na.rm = TRUE)
      q[length(q)] <- max(x, na.rm = TRUE)
    }
  }
  if (is.null(y)) {
    q <- quantile(
      x,
      probs = seq(0, 1, 1 / groups),
      na.rm = na.rm,
      names = TRUE,
      type = 7
    )
    
  }
  d <- cut(
    x,
    q,
    include.lowest = TRUE,
    labels = group.names,
    ordered_result = ordered.f
  )
  if (detail.list)
    list(d, q)
  else
    d
  
}