Github user sun-rui commented on a diff in the pull request:
https://github.com/apache/spark/pull/12836#discussion_r66721485
--- Diff: R/pkg/R/group.R ---
@@ -142,3 +142,58 @@ createMethods <- function() {
}
createMethods()
+
+#' gapply
+#'
+#' Applies a R function to each group in the input GroupedData
+#'
+#' @param x a GroupedData
+#' @param func A function to be applied to each group partition specified
by GroupedData.
+#' The function `func` takes as argument a key - grouping
columns and
+#' a data frame - a local R data.frame.
+#' The output of `func` is a local R data.frame.
+#' @param schema The schema of the resulting SparkDataFrame after the
function is applied.
+#' It must match the output of func.
+#' @return a SparkDataFrame
+#' @rdname gapply
+#' @name gapply
+#' @examples
+#' \dontrun{
+#' Computes the arithmetic mean of the second column by grouping
+#' on the first and third columns. Output the grouping values and the
average.
+#'
+#' df <- createDataFrame (
+#' list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3",
0.3)),
+#' c("a", "b", "c", "d"))
+#'
+#' schema <- structType(structField("a", "integer"), structField("c",
"string"),
+#' structField("avg", "double"))
+#' df1 <- gapply(
+#' df,
+#' list("a", "c"),
+#' function(key, x) {
+#' y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
+#' },
+#' schema)
+#' collect(df1)
+#'
+#' Result
+#' ------
+#' a c avg
+#' 3 3 3.0
+#' 1 1 1.5
+#' }
+setMethod("gapply",
+ signature(x = "GroupedData"),
+ function(x, func, schema) {
+ packageNamesArr <- serialize(.sparkREnv[[".packages"]],
+ connection = NULL)
+ broadcastArr <- lapply(ls(.broadcastNames),
+ function(name) { get(name, .broadcastNames)
})
+ sdf <- callJMethod(x@sgd, "flatMapGroupsInR",
+ serialize(cleanClosure(func), connection = NULL),
+ packageNamesArr,
+ broadcastArr,
+ if (is.null(schema)) { schema } else { schema$jobj })
--- End diff --
same as dapply, we don't support NULL schema for now. fails when schema is
NULL
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