Github user felixcheung commented on a diff in the pull request:
https://github.com/apache/spark/pull/13660#discussion_r67029962
--- Diff: docs/sparkr.md ---
@@ -262,6 +262,67 @@ head(df)
{% endhighlight %}
</div>
+### Applying User-defined Function
+
+#### 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`.
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Convert waiting time from hours to seconds.
+# Note that we can apply UDF to DataFrame.
+
+df1 <- dapply(df, function(x) {x}, schema(df))
+head(collect(df1), 3)
+## eruptions waiting waiting_secs
+##1 3.600 79 4740
+##2 1.800 54 3240
+##3 3.333 74 4440
+
+{% endhighlight %}
+</div>
+
+#### dapplyCollect
+Like `dapply`, apply a function to each partition of `SparkDataFrame` and
collect the result back.
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Convert waiting time from hours to seconds.
+# Note that we can apply UDF to DataFrame.
+ldf <- dapplyCollect(
+ df,
+ function(x) {
+ x <- cbind(x, "waiting_secs"=x$waiting * 60)
+ })
+head(df, 3)
+## eruptions waiting waiting_secs
+##1 3.600 79 4740
+##2 1.800 54 3240
+##3 3.333 74 4440
+
+{% endhighlight %}
+</div>
+
+#### lapply
+Similar to `lapply` in native R, `spark.lapply` runs a function over a
list of elements and distributes the computations with Spark.
+Applies a function in a manner that is similar to `doParallel` or `lapply`
to elements of a list.
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Perform distributed training of multiple models with spark.lapply
+families <- c("gaussian", "poisson")
+train <- function(family) {
+ model <- glm(Sepal.Length ~ Sepal.Width + Species, iris, family = family)
+ summary(model)
+}
+model.summaries <- spark.lapply(sc, families, train)
--- End diff --
perhaps describe more on what will get passed to this udf `train` here?
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