diff --git a/docs/sparkr.md b/docs/sparkr.md
index 9fda0ec0e654233da3c0f72c3a1c1a3be9a72c4b..a5235b2bf66a4a900e296e8d42f1579925e5697f 100644
--- a/docs/sparkr.md
+++ b/docs/sparkr.md
@@ -272,11 +272,11 @@ In SparkR, we support several kinds of User-Defined Functions:
##### dapply
Apply a function to each partition of a `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 a `SparkDataFrame`. It must match the R function's output.
+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 a `SparkDataFrame`. It must match to [data types](#data-type-mapping-between-r-and-spark) of returned value.
<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"),
@@ -295,8 +295,8 @@ head(collect(df1))
##### dapplyCollect
Like `dapply`, apply a function to each partition of a `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.
+should be a `data.frame`. But, Schema is not required to be passed. Note that `dapplyCollect` can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
+
<div data-lang="r" markdown="1">
{% highlight r %}
@@ -316,6 +316,136 @@ 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 is the 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><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/DateTimeClasses.html">POSIXct</a></td>
+ <td>timestamp</td>
+</tr>
+<tr>
+ <td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/DateTimeClasses.html">POSIXlt</a></td>
+ <td>timestamp</td>
+</tr>
+<tr>
+ <td><a href="https://stat.ethz.ch/R-manual/R-devel/library/base/html/Dates.html">Date</a></td>
+ <td>date</td>
+</tr>
+<tr>
+ <td>array</td>
+ <td>array</td>
+</tr>
+<tr>
+ <td>list</td>
+ <td>array</td>
+</tr>
+<tr>
+ <td>env</td>
+ <td>map</td>
+</tr>
+</table>
+
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Determine six waiting times with the largest eruption time in minutes.
+schema <- structType(structField("waiting", "double"), structField("max_eruption", "double"))
+result <- gapply(
+ df,
+ "waiting",
+ function(key, x) {
+ y <- data.frame(key, max(x$eruptions))
+ },
+ schema)
+head(collect(arrange(result, "max_eruption", decreasing = TRUE)))
+
+## waiting max_eruption
+##1 64 5.100
+##2 69 5.067
+##3 71 5.033
+##4 87 5.000
+##5 63 4.933
+##6 89 4.900
+{% endhighlight %}
+</div>
+
+##### gapplyCollect
+Like `gapply`, applies a function to each partition of a `SparkDataFrame` and collect the result back to R data.frame. The output of the function should be a `data.frame`. But, the schema is not required to be passed. Note that `gapplyCollect` can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.
+
+<div data-lang="r" markdown="1">
+{% highlight r %}
+
+# Determine six waiting times with the largest eruption time in minutes.
+result <- gapplyCollect(
+ df,
+ "waiting",
+ function(key, x) {
+ y <- data.frame(key, max(x$eruptions))
+ colnames(y) <- c("waiting", "max_eruption")
+ y
+ })
+head(result[order(result$max_eruption, decreasing = TRUE), ])
+
+## waiting max_eruption
+##1 64 5.100
+##2 69 5.067
+##3 71 5.033
+##4 87 5.000
+##5 63 4.933
+##6 89 4.900
+
+{% endhighlight %}
+</div>
+
#### Run local R functions distributed using `spark.lapply`
##### spark.lapply