Github user zero323 commented on a diff in the pull request: https://github.com/apache/spark/pull/17807#discussion_r114059534 --- 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} --- End diff -- > which is something we need to clean up actually There are like four functions left for full parity so we can try to figure this out after that. I would like to take a look at the overall structure as well. There is a lot of boilerplate there.
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