Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/12836#discussion_r67261006
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1266,6 +1266,83 @@ setMethod("dapplyCollect",
ldf
})
+#' gapply
+#'
+#' Group the SparkDataFrame using the specified columns and apply the R
function to each
+#' group.
+#'
+#' @param x A SparkDataFrame
+#' @param cols Grouping columns
+#' @param func A function to be applied to each group partition specified
by grouping
+#' column of the SparkDataFrame. 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.
+#' @family SparkDataFrame functions
+#' @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"))
+#'
+#' 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
+#'
+#' Fits linear models on iris dataset by grouping on the 'Species' column
and
+#' using 'Sepal_Length' as a target variable, 'Sepal_Width', 'Petal_Length'
+#' and 'Petal_Width' as training features.
+#'
+#' df <- createDataFrame (iris)
+#' schema <- structType(structField("(Intercept)", "double"),
--- End diff --
Similar to above, do the column names also have to match ? i.e. is
`(Intercept)` important here or would `Intercept` work as well ?
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