Github user mhnatiuk commented on a diff in the pull request:
https://github.com/apache/spark/pull/13295#discussion_r65063712
--- Diff: R/pkg/R/group.R ---
@@ -126,6 +126,47 @@ 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
+#'
+#' Pivot GroupedData column to expand unique values of this column into
set of column named accordingly and perform
+#' aggregation. Same purpose as dcast in reshape2 or pivot table in Excel
+#'
+#' @param x: a GroupedData object
+#' @param by: name of column to pivot data by
+#' @param values: a unique list of values that will be translated to
columns in the output SparkDataFrame.
+#' If not given it will be computed on the fly which will influence
performance
+#' @return GroupedData object
+#' @rdname pivot
+#' @export
+#' @examples
+#' \dontrun{
+#' library(magrittr)
+#' df <- 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)
+#' )
+#' SparkRdf <- createDataFrame(sqlContext, df)
+#' values <- list("R", "Python")
+#' sums <- groupBy(SparkRdf, "year") %>%
+#' pivot("course", values) %>%
+#' SparkR::summarize(sumOfEarnings = sum(SparkRdf$earnings) ) %>%
+#' collect()
+#' }
+
+setMethod("pivot",
+ signature(x = "GroupedData"),
+ function(x, colname, values=NULL){
--- End diff --
OK, so I'll add a test to verify that list ain't empty, right? Or does the
underlaying scala method accept empty lists?
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