Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/13660#discussion_r67771773
--- Diff: docs/sparkr.md ---
@@ -262,6 +262,79 @@ head(df)
{% endhighlight %}
</div>
+### Applying User-defined Function
+In SparkR, we support several kinds for User-defined Functions:
+
+#### Run a given function on a large dataset using `dapply` or
`dapplyCollect`
+
+##### dapply
+Apply a function to each partition of `SparkDataFrame`. The function to be
applied to each partition of the `SparkDataFrame`
+and should have only one parameter, to which a `data.frame` corresponds to
each partition will be passed. 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.
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Convert waiting time from hours to seconds.
+# Note that we can apply UDF to DataFrame.
+schema <- structType(structField("eruptions", "double"),
structField("waiting", "double"),
+ structField("waiting_secs", "double"))
+df1 <- dapply(df, function(x) {x <- cbind(x, x$waiting * 60)}, schema)
+head(collect(df1))
+## eruptions waiting waiting_secs
+##1 3.600 79 4740
+##2 1.800 54 3240
+##3 3.333 74 4440
+##4 2.283 62 3720
+##5 4.533 85 5100
+##6 2.883 55 3300
+{% endhighlight %}
+</div>
+
+##### dapplyCollect
+Like `dapply`, apply a function to each partition of `SparkDataFrame` and
collect the result back.
--- End diff --
I think its good to say a couple of things here. First that we don't
require any schema to be passed in to `dapplyCollect` (unlike `dapply`). The
other thing is that its good to remind users that this should be used only if
the output of the UDF run on all the partitions can fit in driver memory.
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