[SPARK-6824] Fill the docs for DataFrame API in SparkR

This patch also removes the RDD docs from being built as a part of roxygen just by the method to delete
" ' '" of " \#' ".

Author: hqzizania <qian.huang@intel.com>
Author: qhuang <qian.huang@intel.com>

Closes #5969 from hqzizania/R1 and squashes the following commits:

6d27696 [qhuang] fixes in NAMESPACE
eb4b095 [qhuang] remove more docs
6394579 [qhuang] remove RDD docs in generics.R
6813860 [hqzizania] Fill the docs for DataFrame API in SparkR
857220f [hqzizania] remove the pairRDD docs from being built as a part of roxygen
c045d64 [hqzizania] remove the RDD docs from being built as a part of roxygen

1 files changed, 120 insertions, 120 deletions

diff --git a/R/pkg/R/context.R b/R/pkg/R/context.R
index b4845b6948..43be9c904f 100644
--- a/R/pkg/R/context.R
+++ b/R/pkg/R/context.R
@@ -25,27 +25,27 @@ getMinPartitions <- function(sc, minPartitions) {
   as.integer(minPartitions)
 }
 
-#' Create an RDD from a text file.
-#'
-#' This function reads a text file from HDFS, a local file system (available on all
-#' nodes), or any Hadoop-supported file system URI, and creates an
-#' RDD of strings from it.
-#'
-#' @param sc SparkContext to use
-#' @param path Path of file to read. A vector of multiple paths is allowed.
-#' @param minPartitions Minimum number of partitions to be created. If NULL, the default
-#'  value is chosen based on available parallelism.
-#' @return RDD where each item is of type \code{character}
-#' @export
-#' @examples
-#'\dontrun{
-#'  sc <- sparkR.init()
-#'  lines <- textFile(sc, "myfile.txt")
-#'}
+# Create an RDD from a text file.
+#
+# This function reads a text file from HDFS, a local file system (available on all
+# nodes), or any Hadoop-supported file system URI, and creates an
+# RDD of strings from it.
+#
+# @param sc SparkContext to use
+# @param path Path of file to read. A vector of multiple paths is allowed.
+# @param minPartitions Minimum number of partitions to be created. If NULL, the default
+#  value is chosen based on available parallelism.
+# @return RDD where each item is of type \code{character}
+# @export
+# @examples
+#\dontrun{
+#  sc <- sparkR.init()
+#  lines <- textFile(sc, "myfile.txt")
+#}
 textFile <- function(sc, path, minPartitions = NULL) {
   # Allow the user to have a more flexible definiton of the text file path
   path <- suppressWarnings(normalizePath(path))
-  #' Convert a string vector of paths to a string containing comma separated paths
+  # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
 
   jrdd <- callJMethod(sc, "textFile", path, getMinPartitions(sc, minPartitions))
@@ -53,27 +53,27 @@ textFile <- function(sc, path, minPartitions = NULL) {
   RDD(jrdd, "string")
 }
 
-#' Load an RDD saved as a SequenceFile containing serialized objects.
-#'
-#' The file to be loaded should be one that was previously generated by calling
-#' saveAsObjectFile() of the RDD class.
-#'
-#' @param sc SparkContext to use
-#' @param path Path of file to read. A vector of multiple paths is allowed.
-#' @param minPartitions Minimum number of partitions to be created. If NULL, the default
-#'  value is chosen based on available parallelism.
-#' @return RDD containing serialized R objects.
-#' @seealso saveAsObjectFile
-#' @export
-#' @examples
-#'\dontrun{
-#'  sc <- sparkR.init()
-#'  rdd <- objectFile(sc, "myfile")
-#'}
+# Load an RDD saved as a SequenceFile containing serialized objects.
+#
+# The file to be loaded should be one that was previously generated by calling
+# saveAsObjectFile() of the RDD class.
+#
+# @param sc SparkContext to use
+# @param path Path of file to read. A vector of multiple paths is allowed.
+# @param minPartitions Minimum number of partitions to be created. If NULL, the default
+#  value is chosen based on available parallelism.
+# @return RDD containing serialized R objects.
+# @seealso saveAsObjectFile
+# @export
+# @examples
+#\dontrun{
+#  sc <- sparkR.init()
+#  rdd <- objectFile(sc, "myfile")
+#}
 objectFile <- function(sc, path, minPartitions = NULL) {
   # Allow the user to have a more flexible definiton of the text file path
   path <- suppressWarnings(normalizePath(path))
-  #' Convert a string vector of paths to a string containing comma separated paths
+  # Convert a string vector of paths to a string containing comma separated paths
   path <- paste(path, collapse = ",")
 
   jrdd <- callJMethod(sc, "objectFile", path, getMinPartitions(sc, minPartitions))
@@ -81,24 +81,24 @@ objectFile <- function(sc, path, minPartitions = NULL) {
   RDD(jrdd, "byte")
 }
 
-#' Create an RDD from a homogeneous list or vector.
-#'
-#' This function creates an RDD from a local homogeneous list in R. The elements
-#' in the list are split into \code{numSlices} slices and distributed to nodes
-#' in the cluster.
-#'
-#' @param sc SparkContext to use
-#' @param coll collection to parallelize
-#' @param numSlices number of partitions to create in the RDD
-#' @return an RDD created from this collection
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:10, 2)
-#' # The RDD should contain 10 elements
-#' length(rdd)
-#'}
+# Create an RDD from a homogeneous list or vector.
+#
+# This function creates an RDD from a local homogeneous list in R. The elements
+# in the list are split into \code{numSlices} slices and distributed to nodes
+# in the cluster.
+#
+# @param sc SparkContext to use
+# @param coll collection to parallelize
+# @param numSlices number of partitions to create in the RDD
+# @return an RDD created from this collection
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:10, 2)
+# # The RDD should contain 10 elements
+# length(rdd)
+#}
 parallelize <- function(sc, coll, numSlices = 1) {
   # TODO: bound/safeguard numSlices
   # TODO: unit tests for if the split works for all primitives
@@ -133,33 +133,33 @@ parallelize <- function(sc, coll, numSlices = 1) {
   RDD(jrdd, "byte")
 }
 
-#' Include this specified package on all workers
-#'
-#' This function can be used to include a package on all workers before the
-#' user's code is executed. This is useful in scenarios where other R package
-#' functions are used in a function passed to functions like \code{lapply}.
-#' NOTE: The package is assumed to be installed on every node in the Spark
-#' cluster.
-#'
-#' @param sc SparkContext to use
-#' @param pkg Package name
-#'
-#' @export
-#' @examples
-#'\dontrun{
-#'  library(Matrix)
-#'
-#'  sc <- sparkR.init()
-#'  # Include the matrix library we will be using
-#'  includePackage(sc, Matrix)
-#'
-#'  generateSparse <- function(x) {
-#'    sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3))
-#'  }
-#'
-#'  rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse)
-#'  collect(rdd)
-#'}
+# Include this specified package on all workers
+#
+# This function can be used to include a package on all workers before the
+# user's code is executed. This is useful in scenarios where other R package
+# functions are used in a function passed to functions like \code{lapply}.
+# NOTE: The package is assumed to be installed on every node in the Spark
+# cluster.
+#
+# @param sc SparkContext to use
+# @param pkg Package name
+#
+# @export
+# @examples
+#\dontrun{
+#  library(Matrix)
+#
+#  sc <- sparkR.init()
+#  # Include the matrix library we will be using
+#  includePackage(sc, Matrix)
+#
+#  generateSparse <- function(x) {
+#    sparseMatrix(i=c(1, 2, 3), j=c(1, 2, 3), x=c(1, 2, 3))
+#  }
+#
+#  rdd <- lapplyPartition(parallelize(sc, 1:2, 2L), generateSparse)
+#  collect(rdd)
+#}
 includePackage <- function(sc, pkg) {
   pkg <- as.character(substitute(pkg))
   if (exists(".packages", .sparkREnv)) {
@@ -171,30 +171,30 @@ includePackage <- function(sc, pkg) {
   .sparkREnv$.packages <- packages
 }
 
-#' @title Broadcast a variable to all workers
-#'
-#' @description
-#' Broadcast a read-only variable to the cluster, returning a \code{Broadcast}
-#' object for reading it in distributed functions.
-#'
-#' @param sc Spark Context to use
-#' @param object Object to be broadcast
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' rdd <- parallelize(sc, 1:2, 2L)
-#'
-#' # Large Matrix object that we want to broadcast
-#' randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000))
-#' randomMatBr <- broadcast(sc, randomMat)
-#'
-#' # Use the broadcast variable inside the function
-#' useBroadcast <- function(x) {
-#'   sum(value(randomMatBr) * x)
-#' }
-#' sumRDD <- lapply(rdd, useBroadcast)
-#'}
+# @title Broadcast a variable to all workers
+#
+# @description
+# Broadcast a read-only variable to the cluster, returning a \code{Broadcast}
+# object for reading it in distributed functions.
+#
+# @param sc Spark Context to use
+# @param object Object to be broadcast
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# rdd <- parallelize(sc, 1:2, 2L)
+#
+# # Large Matrix object that we want to broadcast
+# randomMat <- matrix(nrow=100, ncol=10, data=rnorm(1000))
+# randomMatBr <- broadcast(sc, randomMat)
+#
+# # Use the broadcast variable inside the function
+# useBroadcast <- function(x) {
+#   sum(value(randomMatBr) * x)
+# }
+# sumRDD <- lapply(rdd, useBroadcast)
+#}
 broadcast <- function(sc, object) {
   objName <- as.character(substitute(object))
   serializedObj <- serialize(object, connection = NULL)
@@ -205,21 +205,21 @@ broadcast <- function(sc, object) {
   Broadcast(id, object, jBroadcast, objName)
 }
 
-#' @title Set the checkpoint directory
-#'
-#' Set the directory under which RDDs are going to be checkpointed. The
-#' directory must be a HDFS path if running on a cluster.
-#'
-#' @param sc Spark Context to use
-#' @param dirName Directory path
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' setCheckpointDir(sc, "~/checkpoint")
-#' rdd <- parallelize(sc, 1:2, 2L)
-#' checkpoint(rdd)
-#'}
+# @title Set the checkpoint directory
+#
+# Set the directory under which RDDs are going to be checkpointed. The
+# directory must be a HDFS path if running on a cluster.
+#
+# @param sc Spark Context to use
+# @param dirName Directory path
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# setCheckpointDir(sc, "~/checkpoint")
+# rdd <- parallelize(sc, 1:2, 2L)
+# checkpoint(rdd)
+#}
 setCheckpointDir <- function(sc, dirName) {
   invisible(callJMethod(sc, "setCheckpointDir", suppressWarnings(normalizePath(dirName))))
 }