Fill the docs for DataFrame API in SparkR

Author: hqzizania

R/pkg/R/DataFrame.R

@@ -45,6 +45,9 @@ setMethod("initialize", "DataFrame", function(.Object, sdf, isCached) {
 
 #' @rdname DataFrame
 #' @export
+#'
+#' @param sdf A Java object reference to the backing Scala DataFrame
+#' @param isCached TRUE if the dataFrame is cached
 dataFrame <- function(sdf, isCached = FALSE) {
   new("DataFrame", sdf, isCached)
 }
@@ -244,7 +247,7 @@ setMethod("columns",
           })
 
 #' @rdname columns
-#' @export
+#' @aliases names,DataFrame,function-method
 setMethod("names",
           signature(x = "DataFrame"),
           function(x) {
@@ -399,23 +402,23 @@ setMethod("repartition",
             dataFrame(sdf)     
           })
 
-#' toJSON
-#'
-#' Convert the rows of a DataFrame into JSON objects and return an RDD where
-#' each element contains a JSON string.
-#'
-#' @param x A SparkSQL DataFrame
-#' @return A StringRRDD of JSON objects
-#' @rdname tojson
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' path <- "path/to/file.json"
-#' df <- jsonFile(sqlCtx, path)
-#' newRDD <- toJSON(df)
-#'}
+# toJSON
+#
+# Convert the rows of a DataFrame into JSON objects and return an RDD where
+# each element contains a JSON string.
+#
+#@param x A SparkSQL DataFrame
+# @return A StringRRDD of JSON objects
+# @rdname tojson
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# path <- "path/to/file.json"
+# df <- jsonFile(sqlCtx, path)
+# newRDD <- toJSON(df)
+#}
 setMethod("toJSON",
           signature(x = "DataFrame"),
           function(x) {
@@ -578,8 +581,8 @@ setMethod("limit",
             dataFrame(res)
           })
 
-# Take the first NUM rows of a DataFrame and return a the results as a data.frame
-
+#' Take the first NUM rows of a DataFrame and return a the results as a data.frame
+#' 
 #' @rdname take
 #' @export
 #' @examples
@@ -644,22 +647,22 @@ setMethod("first",
             take(x, 1)
           })
 
-#' toRDD()
-#' 
-#' Converts a Spark DataFrame to an RDD while preserving column names.
-#' 
-#' @param x A Spark DataFrame
-#' 
-#' @rdname DataFrame
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' path <- "path/to/file.json"
-#' df <- jsonFile(sqlCtx, path)
-#' rdd <- toRDD(df)
-#' }
+# toRDD()
+# 
+# Converts a Spark DataFrame to an RDD while preserving column names.
+# 
+# @param x A Spark DataFrame
+# 
+# @rdname DataFrame
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# path <- "path/to/file.json"
+# df <- jsonFile(sqlCtx, path)
+# rdd <- toRDD(df)
+# }
 setMethod("toRDD",
           signature(x = "DataFrame"),
           function(x) {
@@ -706,6 +709,7 @@ setMethod("groupBy",
 #'
 #' Compute aggregates by specifying a list of columns
 #'
+#' @param x a DataFrame
 #' @rdname DataFrame
 #' @export
 setMethod("agg",
@@ -721,53 +725,53 @@ setMethod("agg",
 # the requested map function.                                                     #
 ###################################################################################
 
-#' @rdname lapply
+# @rdname lapply
 setMethod("lapply",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             rdd <- toRDD(X)
             lapply(rdd, FUN)
           })
 
-#' @rdname lapply
+# @rdname lapply
 setMethod("map",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             lapply(X, FUN)
           })
 
-#' @rdname flatMap
+# @rdname flatMap
 setMethod("flatMap",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             rdd <- toRDD(X)
             flatMap(rdd, FUN)
           })
 
-#' @rdname lapplyPartition
+# @rdname lapplyPartition
 setMethod("lapplyPartition",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             rdd <- toRDD(X)
             lapplyPartition(rdd, FUN)
           })
 
-#' @rdname lapplyPartition
+# @rdname lapplyPartition
 setMethod("mapPartitions",
           signature(X = "DataFrame", FUN = "function"),
           function(X, FUN) {
             lapplyPartition(X, FUN)
           })
 
-#' @rdname foreach
+# @rdname foreach
 setMethod("foreach",
           signature(x = "DataFrame", func = "function"),
           function(x, func) {
             rdd <- toRDD(x)
             foreach(rdd, func)
           })
 
-#' @rdname foreach
+# @rdname foreach
 setMethod("foreachPartition",
           signature(x = "DataFrame", func = "function"),
           function(x, func) {
@@ -1009,7 +1013,7 @@ setMethod("sortDF",
           })
 
 #' @rdname sortDF
-#' @export
+#' @aliases orderBy,DataFrame,function-method
 setMethod("orderBy",
           signature(x = "DataFrame", col = "characterOrColumn"),
           function(x, col) {
@@ -1046,7 +1050,7 @@ setMethod("filter",
           })
 
 #' @rdname filter
-#' @export
+#' @aliases where,DataFrame,function-method
 setMethod("where",
           signature(x = "DataFrame", condition = "characterOrColumn"),
           function(x, condition) {

R/pkg/R/SQLContext.R

@@ -150,21 +150,21 @@ createDataFrame <- function(sqlCtx, data, schema = NULL, samplingRatio = 1.0) {
   dataFrame(sdf)
 }
 
-#' toDF
-#'
-#' Converts an RDD to a DataFrame by infer the types.
-#'
-#' @param x An RDD
-#'
-#' @rdname DataFrame
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x)))
-#' df <- toDF(rdd)
-#' }
+# toDF
+#
+# Converts an RDD to a DataFrame by infer the types.
+#
+# @param x An RDD
+#
+# @rdname DataFrame
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# rdd <- lapply(parallelize(sc, 1:10), function(x) list(a=x, b=as.character(x)))
+# df <- toDF(rdd)
+# }
 
 setGeneric("toDF", function(x, ...) { standardGeneric("toDF") })
 
@@ -207,23 +207,23 @@ jsonFile <- function(sqlCtx, path) {
 }
 
 
-#' JSON RDD
-#'
-#' Loads an RDD storing one JSON object per string as a DataFrame.
-#'
-#' @param sqlCtx SQLContext to use
-#' @param rdd An RDD of JSON string
-#' @param schema A StructType object to use as schema
-#' @param samplingRatio The ratio of simpling used to infer the schema
-#' @return A DataFrame
-#' @export
-#' @examples
-#'\dontrun{
-#' sc <- sparkR.init()
-#' sqlCtx <- sparkRSQL.init(sc)
-#' rdd <- texFile(sc, "path/to/json")
-#' df <- jsonRDD(sqlCtx, rdd)
-#' }
+# JSON RDD
+#
+# Loads an RDD storing one JSON object per string as a DataFrame.
+#
+# @param sqlCtx SQLContext to use
+# @param rdd An RDD of JSON string
+# @param schema A StructType object to use as schema
+# @param samplingRatio The ratio of simpling used to infer the schema
+# @return A DataFrame
+# @export
+# @examples
+#\dontrun{
+# sc <- sparkR.init()
+# sqlCtx <- sparkRSQL.init(sc)
+# rdd <- texFile(sc, "path/to/json")
+# df <- jsonRDD(sqlCtx, rdd)
+# }
 
 # TODO: support schema
 jsonRDD <- function(sqlCtx, rdd, schema = NULL, samplingRatio = 1.0) {