Possibility to have a data.frame in input

src/quantile.R

@@ -1,17 +1,26 @@
-# quantile
-# 
-# Description:
-# Compute empirical quantiles of a sample data array corresponding to given 
-# probabilities. 
-#
-# About:
-# This code is intended as a supporting material for the following publication:
+#' quantile
+#' 
+#' Compute empirical quantiles of a sample data array corresponding to given 
+#  probabilities. 
+#'
+#' @param x: a numeric vector or a character value (the variable name), if `data` is not null, 
+#' giving the values taken by the variable of interest. 
+#' @param type: an integer between 1 and 11 selecting one of the nine quantile algorithms detailed
+# in Hyndman and Fan (1996), alternatively the one inspired from Cunnane (1978) or the one
+# in Filiben (1975), as in the Python scipy library. See the references for more details. 
+#' @param probs: numeric vector giving the probabilities with values between 0 and 1. 
+#'
+#' @return a vector containing the quantile values.
+#' @export
+#' 
+#' @references This code is intended as a supporting material for the following publication:
 #    Grazzini J. and Lamarche P. (2017): Production of social statistics... goes social!, 
 #    in Proc. New Techniques and Technologies for Statistics.
-# Copyright (c) 2017, J.Grazzini & P.Lamarche, European Commission
-# Licensed under [European Union Public License](https://joinup.ec.europa.eu/community/eupl/og_page/european-union-public-licence-eupl-v11)
-
-quantile <- function(x, type = 1:11, probs = 0.5) {
+#   Copyright (c) 2017, J.Grazzini & P.Lamarche, European Commission
+#   Licensed under [European Union Public License](https://joinup.ec.europa.eu/community/eupl/og_page/european-union-public-licence-eupl-v11)
+#'
+#' @examples
+quantile <- function(x, type = 1:11, probs = 0.5, data = NULL) {
   # Compute the quantile for weighted data (e.g. survey data). Includes the 9 methods
   # described by Hyndman and Fan (1996) + the one by Cunnane (1978) + the one by Filiben (1975).
   #
@@ -25,15 +34,17 @@ quantile <- function(x, type = 1:11, probs = 0.5) {
   # Returns:
   #  The vector of quantile values.
   if (!type %in% 1:11) stop("Parameter type must be an integer between 1 and 11.")
+  if (!is.null(data))
+    X <- data[,x]
 
   if (type %in% 1:9) {
-    stats::quantile(x = x, type = type, probs = probs)
+    stats::quantile(x = X, type = type, probs = probs)
   } else {
     if (type == 10) 
       m <- 0.4 + 02*probs else m <- 0.3175 + 0.365*probs
-    n <- length(x)
-    orderInit <- order(x)
-    xx <- x[order(x)]
+    n <- length(X)
+    orderInit <- order(X)
+    xx <- X[order(X)]
     pp <- (1:n)/n
 
     J <- sapply(probs + m/n, indexQj, x = xx, wcum = pp)