Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/14090#discussion_r70194370
--- 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 see. I think we can describe the following type mapping in the
programming guide.
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala#L91
Those are the types used in the StructType's fields.
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