Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/14090#discussion_r70921996
--- Diff: docs/sparkr.md ---
@@ -316,6 +314,139 @@ 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 represent R function's output schema on the
basis of Spark data types. The column names of the returned `data.frame` are
set by user. Below data type mapping between R
+and Spark.
+
+#### Data type mapping between R and Spark
+<table class="table">
+<tr><th>R</th><th>Spark</th></tr>
+<tr>
+ <td>byte</td>
+ <td>byte</td>
+</tr>
+<tr>
+ <td>integer</td>
+ <td>integer</td>
+</tr>
+<tr>
+ <td>float</td>
+ <td>float</td>
+</tr>
+<tr>
+ <td>double</td>
+ <td>double</td>
+</tr>
+<tr>
+ <td>numeric</td>
+ <td>double</td>
+</tr>
+<tr>
+ <td>character</td>
+ <td>string</td>
+</tr>
+<tr>
+ <td>string</td>
+ <td>string</td>
+</tr>
+<tr>
+ <td>binary</td>
+ <td>binary</td>
+</tr>
+<tr>
+ <td>raw</td>
+ <td>binary</td>
+</tr>
+<tr>
+ <td>logical</td>
+ <td>boolean</td>
+</tr>
+<tr>
+ <td>timestamp</td>
+ <td>timestamp</td>
+</tr>
+<tr>
+ <td>date</td>
+ <td>date</td>
+</tr>
+<tr>
+ <td>array</td>
+ <td>array</td>
+</tr>
+<tr>
+ <td>list</td>
+ <td>array</td>
+</tr>
+<tr>
+ <td>map</td>
+ <td>map</td>
+</tr>
+<tr>
+ <td>env</td>
+ <td>map</td>
+</tr>
+<tr>
+ <td>struct</td>
--- End diff --
Thanks for the explanation, @shivaram !
So, I'll remove map, struct and timestamp and leave the rest as is.
Does it sound fine ?
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