Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/13660#discussion_r67915708
--- Diff: docs/sparkr.md ---
@@ -262,6 +262,83 @@ 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$waiting * 60)}, schema)
+head(collect(df1))
+## eruptions waiting waiting_secs
+##1 3.600 79 4740
+##2 1.800 54 3240
+##3 3.333 74 4440
+##4 2.283 62 3720
+##5 4.533 85 5100
+##6 2.883 55 3300
+{% endhighlight %}
+</div>
+
+##### dapplyCollect
+Like `dapply`, apply a function to each partition of `SparkDataFrame` and
collect the result back. The output of function
+should be a `data.frame`. But, Schema is not required to be passed. Note
that `dapplyCollect` only can be used if the
+output of UDF run on all the partitions can fit in driver memory.
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Convert waiting time from hours to seconds.
+# Note that we can apply UDF to DataFrame and return a R's data.frame
+ldf <- dapplyCollect(
+ df,
+ function(x) {
+ x <- cbind(x, "waiting_secs"=x$waiting * 60)
+ })
+head(ldf, 3)
+## eruptions waiting waiting_secs
+##1 3.600 79 4740
+##2 1.800 54 3240
+##3 3.333 74 4440
+
+{% endhighlight %}
+</div>
+
+#### Run many functions in parallel using `spark.lapply`
+
+##### lapply
--- End diff --
I think spark.lapply is more about running native R distributed vs Dataset
would be typed data distributed (without native R), that seems fairly
orthogonal to me.
speaking of, how should we address local R here? should this say "Run local
R functions in parallel using spark.lapply" or "Run local R functions
distributed using spark.lapply"?
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