Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/13786#discussion_r67797294
--- Diff: R/pkg/R/group.R ---
@@ -129,6 +129,48 @@ methods <- c("avg", "max", "mean", "min", "sum")
# These are not exposed on GroupedData: "kurtosis", "skewness", "stddev",
"stddev_samp", "stddev_pop",
# "variance", "var_samp", "var_pop"
+#' Pivot a column of the GroupedData and perform the specified aggregation.
+#'
+#' Pivot a column of the GroupedData and perform the specified aggregation.
+#' There are two versions of pivot function: one that requires the caller
to specify the list
+#' of distinct values to pivot on, and one that does not. The latter is
more concise but less
+#' efficient, because Spark needs to first compute the list of distinct
values internally.
+#'
+#' @param x a GroupedData object
+#' @param colname A column name
+#' @param values A value or a list/vector of distinct values for the
output columns.
+#' @return GroupedData object
+#' @rdname pivot
+#' @name pivot
+#' @export
+#' @examples
+#' \dontrun{
+#' df <- createDataFrame(data.frame(
+#' earnings = c(10000, 10000, 11000, 15000, 12000, 20000, 21000,
22000),
+#' course = c("R", "Python", "R", "Python", "R", "Python", "R",
"Python"),
+#' year = c(2013, 2013, 2014, 2014, 2015, 2015, 2016, 2016)
+#' ))
+#' collect(sum(pivot(groupBy(df, "year"), "course"), "earnings"))
+#' collect(sum(pivot(groupBy(df, "year"), "course", "R"), "earnings"))
+#' collect(sum(pivot(groupBy(df, "year"), "course", c("Python", "R")),
"earnings"))
+#' collect(sum(pivot(groupBy(df, "year"), "course", list("Python", "R")),
"earnings"))
--- End diff --
... and change one from `sum(` to `mean(` for example.
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