[SPARK-15672][R][DOC] R programming guide update

## What changes were proposed in this pull request?
Guide for
- UDFs with dapply, dapplyCollect
- spark.lapply for running parallel R functions

## How was this patch tested?
build locally
<img width="654" alt="screen shot 2016-06-14 at 03 12 56" src="https://cloud.githubusercontent.com/assets/3419881/16039344/12a3b6a0-31de-11e6-8d77-fe23308075c0.png">

Author: Kai Jiang <jiangkai@gmail.com>

Closes #13660 from vectorijk/spark-15672-R-guide-update.

R/pkg/R/context.R

@@ -246,7 +246,7 @@ setCheckpointDir <- function(sc, dirName) {
 #'   \preformatted{
 #'     train <- function(hyperparam) {
 #'       library(MASS)
-#'       lm.ridge(“y ~ x+z”, data, lambda=hyperparam)
+#'       lm.ridge("y ~ x+z", data, lambda=hyperparam)
 #'       model
 #'     }
 #'   }

docs/sparkr.md

@@ -255,6 +255,83 @@ head(df)
 {% endhighlight %}
 </div>
 
+### Applying User-Defined Function
+In SparkR, we support several kinds of User-Defined Functions:
+
+#### Run a given function on a large dataset using `dapply` or `dapplyCollect`
+
+##### 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.
+<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$waiting * 60)}, schema)
+head(collect(df1))
+##  eruptions waiting waiting_secs
+##1     3.600      79         4740
+##2     1.800      54         3240
+##3     3.333      74         4440
+##4     2.283      62         3720
+##5     4.533      85         5100
+##6     2.883      55         3300
+{% endhighlight %}
+</div>
+
+##### dapplyCollect
+Like `dapply`, apply a function to each partition of a `SparkDataFrame` and collect the result back. The output of function
+should be a `data.frame`. But, Schema is not required to be passed. Note that `dapplyCollect` only can be used if the
+output of UDF run on all the partitions can fit in driver memory.
+<div data-lang="r"  markdown="1">
+{% highlight r %}
+
+# Convert waiting time from hours to seconds.
+# Note that we can apply UDF to DataFrame and return a R's data.frame
+ldf <- dapplyCollect(
+         df,
+         function(x) {
+           x <- cbind(x, "waiting_secs" = x$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 local R functions distributed using `spark.lapply`
+
+##### spark.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. The results of all the computations
+should fit in a single machine. If that is not the case they can do something like `df <- createDataFrame(list)` and then use
+`dapply`
+<div data-lang="r"  markdown="1">
+{% highlight r %}
+
+# Perform distributed training of multiple models with spark.lapply. Here, we pass
+# a read-only list of arguments which specifies family the generalized linear model should be.
+families <- c("gaussian", "poisson")
+train <- function(family) {
+  model <- glm(Sepal.Length ~ Sepal.Width + Species, iris, family = family)
+  summary(model)
+}
+# Return a list of model's summaries
+model.summaries <- spark.lapply(families, train)
+
+# Print the summary of each model
+print(model.summaries)
+
+{% endhighlight %}
+</div>
+
 ## Running SQL Queries from SparkR
 A SparkDataFrame can also be registered as a temporary view in Spark SQL and that allows you to run SQL queries over its data.
 The `sql` function enables applications to run SQL queries programmatically and returns the result as a `SparkDataFrame`.