Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/17807#discussion_r114058157
--- Diff: R/pkg/R/functions.R ---
@@ -3803,3 +3803,89 @@ setMethod("repeat_string",
jc <- callJStatic("org.apache.spark.sql.functions", "repeat",
x@jc, numToInt(n))
column(jc)
})
+
+#' is_grouping
+#'
+#' Indicates whether a specified column in a GROUP BY list is aggregated
or not,
+#' returns 1 for aggregated or 0 for not aggregated in the result set.
+#'
+#' Same as \code{GROUPING} in SQL and \code{grouping} function in Scala.
+#'
+#' @param x Column to compute on
+#'
+#' @rdname is_grouping
+#' @name is_grouping
+#' @family agg_funcs
+#' @aliases is_grouping,Column-method
+#' @export
+#' @examples \dontrun{
+#' df <- createDataFrame(mtcars)
+#'
+#' # With cube
+#' agg(
+#' cube(df, "cyl", "gear", "am"),
+#' mean(df$mpg),
+#' is_grouping(df$cyl), is_grouping(df$gear), is_grouping(df$am)
+#' )
+#'
+#' # With rollup
+#' agg(
+#' rollup(df, "cyl", "gear", "am"),
+#' mean(df$mpg),
+#' is_grouping(df$cyl), is_grouping(df$gear), is_grouping(df$am)
+#' )
+#' }
+#' @note is_grouping since 2.3.0
+#' @seealso \link{cube}, \link{grouping_id}, \link{rollup}
+setMethod("is_grouping",
+ signature(x = "Column"),
+ function(x) {
+ jc <- callJStatic("org.apache.spark.sql.functions",
"grouping", x@jc)
+ column(jc)
+ })
+
+#' grouping_id
+#'
+#' Returns the level of grouping.
+#'
+#' Equals to \code{
+#' (is_grouping(c1) <<; (n-1)) + (is_grouping(c2) <<; (n-2)) + ... +
is_grouping(cn)
--- End diff --
also perhaps it's more appropriate to use the R-equivalent operator instead?
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