Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/14090#discussion_r70172206
--- Diff: docs/sparkr.md ---
@@ -306,6 +306,64 @@ head(ldf, 3)
{% endhighlight %}
</div>
+#### Run a given function on a large dataset grouping by input column(s)
and using `gapply` or `gapplyCollect`
+
+##### gapply
+Apply a function to each group of a `SparkDataFrame`. The function is to
be applied to each group of the `SparkDataFrame` and should have only two
parameters: grouping key and R `data.frame` corresponding to
+that key. The groups are chosen from `SparkDataFrame`s column(s).
+The output of function should be a `data.frame`. Schema specifies the row
format of the resulting
+`SparkDataFrame`. It must match the R function's output.
--- End diff --
I think gapply and dapply are the first important use cases where we
require strict mapping Spark JVM types to R atomic types. It might be
worthwhile to add a section in the programming guide to illustrate and explain
that further.
To be more concrete, what should be the column type of the UDF output R
data.frame if the SparkDataFrame has a column of double? It would be good to
have a table on that.
That could be a separate PR though.
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