Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/13760#discussion_r68116142
--- Diff: R/pkg/R/group.R ---
@@ -199,17 +199,10 @@ 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
#' @rdname gapply
#' @name gapply
#' @export
+#' @seealso \link{gapplyCollect}
#' @examples
#' \dontrun{
--- End diff --
@shivaram, yes, the example for calculating the average is almost the same
- in groups.R I use group_by and in DataFrame not but we could also combine all
in let's say in DataFrame.R and so something like this:
```
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,
c("a", "c"),
function(key, x) {
y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
},
schema)
or we can also group the data and afterwards call gapply on GroupedData:
gdf <- group_by(df, "a", "c")
df1 <- gapply(
gdf,
function(key, x) {
y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
},
schema)
collect(df1)
```
Is this better ?
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