Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/13760#discussion_r68541684
--- Diff: R/pkg/R/group.R ---
@@ -198,62 +198,61 @@ createMethods()
#'
#' 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.
-#' The schema must match to output of `func`. It has to be
defined for each
-#' output column with preferred output column name and
corresponding data type.
-#' @return a SparkDataFrame
+#' @param x A GroupedData
#' @rdname gapply
#' @name gapply
#' @export
-#' @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"))
-#'
-#' Here our output contains three columns, the key which is a combination
of two
-#' columns with data types integer and string and the mean which is a
double.
-#' 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$b), stringsAsFactors = FALSE)
-#' },
-#' schema)
-#' collect(df1)
-#'
-#' Result
-#' ------
-#' a c avg
-#' 3 3 3.0
-#' 1 1 1.5
-#' }
+#' @seealso \link{gapplyCollect}
#' @note gapply(GroupedData) since 2.0.0
setMethod("gapply",
signature(x = "GroupedData"),
function(x, func, schema) {
- try(if (is.null(schema)) stop("schema cannot be NULL"))
- packageNamesArr <- serialize(.sparkREnv[[".packages"]],
- connection = NULL)
- broadcastArr <- lapply(ls(.broadcastNames),
- function(name) { get(name, .broadcastNames)
})
- sdf <- callJStatic(
- "org.apache.spark.sql.api.r.SQLUtils",
- "gapply",
- x@sgd,
- serialize(cleanClosure(func), connection = NULL),
- packageNamesArr,
- broadcastArr,
- schema$jobj)
- dataFrame(sdf)
+ if (is.null(schema)) stop("schema cannot be NULL")
+ gapplyInternal(x, func, schema)
})
+
+#' gapplyCollect
+#'
+#' Applies a R function to each group in the input GroupedData and
collects the result
--- End diff --
I think having this would somewhat duplicates
gapplyCollect(SparkDataFrame), since they go to the same rd file. Could you
check?
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