[SPARK-11263][SPARKR] lintr Throws Warnings on Commented Code in Documentation
Clean out hundreds of `style: Commented code should be removed.` from lintr Like these: ``` /opt/spark-1.6.0-bin-hadoop2.6/R/pkg/R/DataFrame.R:513:3: style: Commented code should be removed. # sc <- sparkR.init() ^~~~~~~~~~~~~~~~~~~ /opt/spark-1.6.0-bin-hadoop2.6/R/pkg/R/DataFrame.R:514:3: style: Commented code should be removed. # sqlContext <- sparkRSQL.init(sc) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /opt/spark-1.6.0-bin-hadoop2.6/R/pkg/R/DataFrame.R:515:3: style: Commented code should be removed. # path <- "path/to/file.json" ^~~~~~~~~~~~~~~~~~~~~~~~~~~ ``` tried without export or rdname, neither work instead, added this `#' noRd` to suppress .Rd file generation also updated `family` for DataFrame functions for longer descriptive text instead of `dataframe_funcs`  this covers *most* of 'Commented code' but I left out a few that looks legitimate. Author: felixcheung <felixcheun...@hotmail.com> Closes #9463 from felixcheung/rlintr. (cherry picked from commit ed04846e144db5bdab247c0e1fe2a47b99155c82) Signed-off-by: Shivaram Venkataraman <shiva...@cs.berkeley.edu> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/ea9f7c58 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/ea9f7c58 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/ea9f7c58 Branch: refs/heads/branch-1.6 Commit: ea9f7c580385d9fd94b36248b2bae9b8e5a60b06 Parents: 874cd29 Author: felixcheung <felixcheun...@hotmail.com> Authored: Thu Nov 12 20:02:49 2015 -0800 Committer: Shivaram Venkataraman <shiva...@cs.berkeley.edu> Committed: Thu Nov 12 20:02:57 2015 -0800 ---------------------------------------------------------------------- R/pkg/R/DataFrame.R | 232 +++--- R/pkg/R/RDD.R | 1585 ++++++++++++++++++++------------------- R/pkg/R/SQLContext.R | 66 +- R/pkg/R/context.R | 235 +++--- R/pkg/R/generics.R | 18 - R/pkg/R/pairRDD.R | 910 +++++++++++----------- R/pkg/R/sparkR.R | 3 +- R/pkg/inst/profile/shell.R | 2 +- 8 files changed, 1539 insertions(+), 1512 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/ea9f7c58/R/pkg/R/DataFrame.R ---------------------------------------------------------------------- diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index cc86806..fd105ba 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -25,7 +25,7 @@ setOldClass("jobj") #' @title S4 class that represents a DataFrame #' @description DataFrames can be created using functions like \link{createDataFrame}, #' \link{jsonFile}, \link{table} etc. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname DataFrame #' @docType class #' @@ -68,7 +68,7 @@ dataFrame <- function(sdf, isCached = FALSE) { #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname printSchema #' @name printSchema #' @export @@ -93,7 +93,7 @@ setMethod("printSchema", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname schema #' @name schema #' @export @@ -117,7 +117,7 @@ setMethod("schema", #' #' @param x A SparkSQL DataFrame #' @param extended Logical. If extended is False, explain() only prints the physical plan. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname explain #' @name explain #' @export @@ -148,7 +148,7 @@ setMethod("explain", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname isLocal #' @name isLocal #' @export @@ -173,7 +173,7 @@ setMethod("isLocal", #' @param x A SparkSQL DataFrame #' @param numRows The number of rows to print. Defaults to 20. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname showDF #' @name showDF #' @export @@ -198,7 +198,7 @@ setMethod("showDF", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname show #' @name show #' @export @@ -225,7 +225,7 @@ setMethod("show", "DataFrame", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname dtypes #' @name dtypes #' @export @@ -251,7 +251,7 @@ setMethod("dtypes", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname columns #' @name columns #' @aliases names @@ -272,7 +272,7 @@ setMethod("columns", }) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname columns #' @name names setMethod("names", @@ -281,7 +281,7 @@ setMethod("names", columns(x) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname columns #' @name names<- setMethod("names<-", @@ -300,7 +300,7 @@ setMethod("names<-", #' @param x A SparkSQL DataFrame #' @param tableName A character vector containing the name of the table #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname registerTempTable #' @name registerTempTable #' @export @@ -328,7 +328,7 @@ setMethod("registerTempTable", #' @param overwrite A logical argument indicating whether or not to overwrite #' the existing rows in the table. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname insertInto #' @name insertInto #' @export @@ -353,7 +353,7 @@ setMethod("insertInto", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname cache #' @name cache #' @export @@ -381,7 +381,7 @@ setMethod("cache", #' #' @param x The DataFrame to persist #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname persist #' @name persist #' @export @@ -409,7 +409,7 @@ setMethod("persist", #' @param x The DataFrame to unpersist #' @param blocking Whether to block until all blocks are deleted #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname unpersist-methods #' @name unpersist #' @export @@ -437,7 +437,7 @@ setMethod("unpersist", #' @param x A SparkSQL DataFrame #' @param numPartitions The number of partitions to use. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname repartition #' @name repartition #' @export @@ -456,25 +456,24 @@ 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 -# -# @family dataframe_funcs -# @rdname tojson -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# sqlContext <- sparkRSQL.init(sc) -# path <- "path/to/file.json" -# df <- jsonFile(sqlContext, 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 +#' @family DataFrame functions +#' @rdname tojson +#' @noRd +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlContext <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlContext, path) +#' newRDD <- toJSON(df) +#'} setMethod("toJSON", signature(x = "DataFrame"), function(x) { @@ -491,7 +490,7 @@ setMethod("toJSON", #' @param x A SparkSQL DataFrame #' @param path The directory where the file is saved #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname saveAsParquetFile #' @name saveAsParquetFile #' @export @@ -515,7 +514,7 @@ setMethod("saveAsParquetFile", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname distinct #' @name distinct #' @export @@ -538,7 +537,7 @@ setMethod("distinct", # #' @description Returns a new DataFrame containing distinct rows in this DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname unique #' @name unique #' @aliases distinct @@ -556,7 +555,7 @@ setMethod("unique", #' @param withReplacement Sampling with replacement or not #' @param fraction The (rough) sample target fraction #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname sample #' @aliases sample_frac #' @export @@ -580,7 +579,7 @@ setMethod("sample", dataFrame(sdf) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname sample #' @name sample_frac setMethod("sample_frac", @@ -596,7 +595,7 @@ setMethod("sample_frac", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname count #' @name count #' @aliases nrow @@ -620,7 +619,7 @@ setMethod("count", #' #' @name nrow #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname nrow #' @aliases count setMethod("nrow", @@ -633,7 +632,7 @@ setMethod("nrow", #' #' @param x a SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname ncol #' @name ncol #' @export @@ -654,7 +653,7 @@ setMethod("ncol", #' Returns the dimentions (number of rows and columns) of a DataFrame #' @param x a SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname dim #' @name dim #' @export @@ -678,7 +677,7 @@ setMethod("dim", #' @param stringsAsFactors (Optional) A logical indicating whether or not string columns #' should be converted to factors. FALSE by default. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname collect #' @name collect #' @export @@ -746,7 +745,7 @@ setMethod("collect", #' @param num The number of rows to return #' @return A new DataFrame containing the number of rows specified. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname limit #' @name limit #' @export @@ -767,7 +766,7 @@ setMethod("limit", #' Take the first NUM rows of a DataFrame and return a the results as a data.frame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname take #' @name take #' @export @@ -796,7 +795,7 @@ setMethod("take", #' @param num The number of rows to return. Default is 6. #' @return A data.frame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname head #' @name head #' @export @@ -819,7 +818,7 @@ setMethod("head", #' #' @param x A SparkSQL DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname first #' @name first #' @export @@ -837,23 +836,21 @@ setMethod("first", take(x, 1) }) -# toRDD -# -# Converts a Spark DataFrame to an RDD while preserving column names. -# -# @param x A Spark DataFrame -# -# @family dataframe_funcs -# @rdname DataFrame -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# sqlContext <- sparkRSQL.init(sc) -# path <- "path/to/file.json" -# df <- jsonFile(sqlContext, path) -# rdd <- toRDD(df) -# } +#' toRDD +#' +#' Converts a Spark DataFrame to an RDD while preserving column names. +#' +#' @param x A Spark DataFrame +#' +#' @noRd +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlContext <- sparkRSQL.init(sc) +#' path <- "path/to/file.json" +#' df <- jsonFile(sqlContext, path) +#' rdd <- toRDD(df) +#'} setMethod("toRDD", signature(x = "DataFrame"), function(x) { @@ -874,7 +871,7 @@ setMethod("toRDD", #' @return a GroupedData #' @seealso GroupedData #' @aliases group_by -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname groupBy #' @name groupBy #' @export @@ -899,7 +896,7 @@ setMethod("groupBy", groupedData(sgd) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname groupBy #' @name group_by setMethod("group_by", @@ -913,7 +910,7 @@ setMethod("group_by", #' Compute aggregates by specifying a list of columns #' #' @param x a DataFrame -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname agg #' @name agg #' @aliases summarize @@ -924,7 +921,7 @@ setMethod("agg", agg(groupBy(x), ...) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname agg #' @name summarize setMethod("summarize", @@ -940,8 +937,8 @@ setMethod("summarize", # the requested map function. # ################################################################################### -# @family dataframe_funcs -# @rdname lapply +#' @rdname lapply +#' @noRd setMethod("lapply", signature(X = "DataFrame", FUN = "function"), function(X, FUN) { @@ -949,24 +946,25 @@ setMethod("lapply", lapply(rdd, FUN) }) -# @family dataframe_funcs -# @rdname lapply +#' @rdname lapply +#' @noRd setMethod("map", signature(X = "DataFrame", FUN = "function"), function(X, FUN) { lapply(X, FUN) }) -# @family dataframe_funcs -# @rdname flatMap +#' @rdname flatMap +#' @noRd setMethod("flatMap", signature(X = "DataFrame", FUN = "function"), function(X, FUN) { rdd <- toRDD(X) flatMap(rdd, FUN) }) -# @family dataframe_funcs -# @rdname lapplyPartition + +#' @rdname lapplyPartition +#' @noRd setMethod("lapplyPartition", signature(X = "DataFrame", FUN = "function"), function(X, FUN) { @@ -974,16 +972,16 @@ setMethod("lapplyPartition", lapplyPartition(rdd, FUN) }) -# @family dataframe_funcs -# @rdname lapplyPartition +#' @rdname lapplyPartition +#' @noRd setMethod("mapPartitions", signature(X = "DataFrame", FUN = "function"), function(X, FUN) { lapplyPartition(X, FUN) }) -# @family dataframe_funcs -# @rdname foreach +#' @rdname foreach +#' @noRd setMethod("foreach", signature(x = "DataFrame", func = "function"), function(x, func) { @@ -991,8 +989,8 @@ setMethod("foreach", foreach(rdd, func) }) -# @family dataframe_funcs -# @rdname foreach +#' @rdname foreach +#' @noRd setMethod("foreachPartition", signature(x = "DataFrame", func = "function"), function(x, func) { @@ -1091,7 +1089,7 @@ setMethod("[", signature(x = "DataFrame", i = "Column"), #' @param select expression for the single Column or a list of columns to select from the DataFrame #' @return A new DataFrame containing only the rows that meet the condition with selected columns #' @export -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname subset #' @name subset #' @aliases [ @@ -1122,7 +1120,7 @@ setMethod("subset", signature(x = "DataFrame"), #' @param col A list of columns or single Column or name #' @return A new DataFrame with selected columns #' @export -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname select #' @name select #' @family subsetting functions @@ -1150,7 +1148,7 @@ setMethod("select", signature(x = "DataFrame", col = "character"), } }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname select #' @export setMethod("select", signature(x = "DataFrame", col = "Column"), @@ -1162,7 +1160,7 @@ setMethod("select", signature(x = "DataFrame", col = "Column"), dataFrame(sdf) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname select #' @export setMethod("select", @@ -1187,7 +1185,7 @@ setMethod("select", #' @param expr A string containing a SQL expression #' @param ... Additional expressions #' @return A DataFrame -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname selectExpr #' @name selectExpr #' @export @@ -1215,7 +1213,7 @@ setMethod("selectExpr", #' @param colName A string containing the name of the new column. #' @param col A Column expression. #' @return A DataFrame with the new column added. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname withColumn #' @name withColumn #' @aliases mutate transform @@ -1241,7 +1239,7 @@ setMethod("withColumn", #' @param .data A DataFrame #' @param col a named argument of the form name = col #' @return A new DataFrame with the new columns added. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname withColumn #' @name mutate #' @aliases withColumn transform @@ -1275,7 +1273,7 @@ setMethod("mutate", }) #' @export -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname withColumn #' @name transform #' @aliases withColumn mutate @@ -1293,7 +1291,7 @@ setMethod("transform", #' @param existingCol The name of the column you want to change. #' @param newCol The new column name. #' @return A DataFrame with the column name changed. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname withColumnRenamed #' @name withColumnRenamed #' @export @@ -1325,7 +1323,7 @@ setMethod("withColumnRenamed", #' @param x A DataFrame #' @param newCol A named pair of the form new_column_name = existing_column #' @return A DataFrame with the column name changed. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname withColumnRenamed #' @name rename #' @aliases withColumnRenamed @@ -1370,7 +1368,7 @@ setClassUnion("characterOrColumn", c("character", "Column")) #' @param decreasing A logical argument indicating sorting order for columns when #' a character vector is specified for col #' @return A DataFrame where all elements are sorted. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname arrange #' @name arrange #' @aliases orderby @@ -1397,7 +1395,7 @@ setMethod("arrange", dataFrame(sdf) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname arrange #' @export setMethod("arrange", @@ -1429,7 +1427,7 @@ setMethod("arrange", do.call("arrange", c(x, jcols)) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname arrange #' @name orderby setMethod("orderBy", @@ -1446,7 +1444,7 @@ setMethod("orderBy", #' @param condition The condition to filter on. This may either be a Column expression #' or a string containing a SQL statement #' @return A DataFrame containing only the rows that meet the condition. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname filter #' @name filter #' @family subsetting functions @@ -1470,7 +1468,7 @@ setMethod("filter", dataFrame(sdf) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname filter #' @name where setMethod("where", @@ -1491,7 +1489,7 @@ setMethod("where", #' 'inner', 'outer', 'full', 'fullouter', leftouter', 'left_outer', 'left', #' 'right_outer', 'rightouter', 'right', and 'leftsemi'. The default joinType is "inner". #' @return A DataFrame containing the result of the join operation. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname join #' @name join #' @export @@ -1550,7 +1548,7 @@ setMethod("join", #' be returned. If all.x is set to FALSE and all.y is set to TRUE, a right #' outer join will be returned. If all.x and all.y are set to TRUE, a full #' outer join will be returned. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname merge #' @export #' @examples @@ -1682,7 +1680,7 @@ generateAliasesForIntersectedCols <- function (x, intersectedColNames, suffix) { #' @param x A Spark DataFrame #' @param y A Spark DataFrame #' @return A DataFrame containing the result of the union. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname unionAll #' @name unionAll #' @export @@ -1705,7 +1703,7 @@ setMethod("unionAll", #' #' @description Returns a new DataFrame containing rows of all parameters. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname rbind #' @name rbind #' @aliases unionAll @@ -1727,7 +1725,7 @@ setMethod("rbind", #' @param x A Spark DataFrame #' @param y A Spark DataFrame #' @return A DataFrame containing the result of the intersect. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname intersect #' @name intersect #' @export @@ -1754,7 +1752,7 @@ setMethod("intersect", #' @param x A Spark DataFrame #' @param y A Spark DataFrame #' @return A DataFrame containing the result of the except operation. -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname except #' @name except #' @export @@ -1794,7 +1792,7 @@ setMethod("except", #' @param source A name for external data source #' @param mode One of 'append', 'overwrite', 'error', 'ignore' save mode #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname write.df #' @name write.df #' @aliases saveDF @@ -1830,7 +1828,7 @@ setMethod("write.df", callJMethod(df@sdf, "save", source, jmode, options) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname write.df #' @name saveDF #' @export @@ -1861,7 +1859,7 @@ setMethod("saveDF", #' @param source A name for external data source #' @param mode One of 'append', 'overwrite', 'error', 'ignore' save mode #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname saveAsTable #' @name saveAsTable #' @export @@ -1902,7 +1900,7 @@ setMethod("saveAsTable", #' @param col A string of name #' @param ... Additional expressions #' @return A DataFrame -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname describe #' @name describe #' @aliases summary @@ -1925,7 +1923,7 @@ setMethod("describe", dataFrame(sdf) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname describe #' @name describe setMethod("describe", @@ -1940,7 +1938,7 @@ setMethod("describe", #' #' @description Computes statistics for numeric columns of the DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname summary #' @name summary setMethod("summary", @@ -1965,7 +1963,7 @@ setMethod("summary", #' @param cols Optional list of column names to consider. #' @return A DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname nafunctions #' @name dropna #' @aliases na.omit @@ -1995,7 +1993,7 @@ setMethod("dropna", dataFrame(sdf) }) -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname nafunctions #' @name na.omit #' @export @@ -2023,7 +2021,7 @@ setMethod("na.omit", #' column is simply ignored. #' @return A DataFrame #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname nafunctions #' @name fillna #' @export @@ -2087,7 +2085,7 @@ setMethod("fillna", #' @title Download data from a DataFrame into a data.frame #' @param x a DataFrame #' @return a data.frame -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname as.data.frame #' @examples \dontrun{ #' @@ -2108,7 +2106,7 @@ setMethod("as.data.frame", #' the DataFrame is searched by R when evaluating a variable, so columns in #' the DataFrame can be accessed by simply giving their names. #' -#' @family dataframe_funcs +#' @family DataFrame functions #' @rdname attach #' @title Attach DataFrame to R search path #' @param what (DataFrame) The DataFrame to attach http://git-wip-us.apache.org/repos/asf/spark/blob/ea9f7c58/R/pkg/R/RDD.R ---------------------------------------------------------------------- diff --git a/R/pkg/R/RDD.R b/R/pkg/R/RDD.R index 051e441..47945c2 100644 --- a/R/pkg/R/RDD.R +++ b/R/pkg/R/RDD.R @@ -19,16 +19,15 @@ setOldClass("jobj") -# @title S4 class that represents an RDD -# @description RDD can be created using functions like -# \code{parallelize}, \code{textFile} etc. -# @rdname RDD -# @seealso parallelize, textFile -# -# @slot env An R environment that stores bookkeeping states of the RDD -# @slot jrdd Java object reference to the backing JavaRDD -# to an RDD -# @export +#' @title S4 class that represents an RDD +#' @description RDD can be created using functions like +#' \code{parallelize}, \code{textFile} etc. +#' @rdname RDD +#' @seealso parallelize, textFile +#' @slot env An R environment that stores bookkeeping states of the RDD +#' @slot jrdd Java object reference to the backing JavaRDD +#' to an RDD +#' @noRd setClass("RDD", slots = list(env = "environment", jrdd = "jobj")) @@ -111,14 +110,13 @@ setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) .Object }) -# @rdname RDD -# @export -# -# @param jrdd Java object reference to the backing JavaRDD -# @param serializedMode Use "byte" if the RDD stores data serialized in R, "string" if the RDD -# stores strings, and "row" if the RDD stores the rows of a DataFrame -# @param isCached TRUE if the RDD is cached -# @param isCheckpointed TRUE if the RDD has been checkpointed +#' @rdname RDD +#' @noRd +#' @param jrdd Java object reference to the backing JavaRDD +#' @param serializedMode Use "byte" if the RDD stores data serialized in R, "string" if the RDD +#' stores strings, and "row" if the RDD stores the rows of a DataFrame +#' @param isCached TRUE if the RDD is cached +#' @param isCheckpointed TRUE if the RDD has been checkpointed RDD <- function(jrdd, serializedMode = "byte", isCached = FALSE, isCheckpointed = FALSE) { new("RDD", jrdd, serializedMode, isCached, isCheckpointed) @@ -201,19 +199,20 @@ setValidity("RDD", ############ Actions and Transformations ############ -# Persist an RDD -# -# Persist this RDD with the default storage level (MEMORY_ONLY). -# -# @param x The RDD to cache -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10, 2L) -# cache(rdd) -#} -# @rdname cache-methods -# @aliases cache,RDD-method +#' Persist an RDD +#' +#' Persist this RDD with the default storage level (MEMORY_ONLY). +#' +#' @param x The RDD to cache +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' cache(rdd) +#'} +#' @rdname cache-methods +#' @aliases cache,RDD-method +#' @noRd setMethod("cache", signature(x = "RDD"), function(x) { @@ -222,22 +221,23 @@ setMethod("cache", x }) -# Persist an RDD -# -# Persist this RDD with the specified storage level. For details of the -# supported storage levels, refer to -# http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence. -# -# @param x The RDD to persist -# @param newLevel The new storage level to be assigned -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10, 2L) -# persist(rdd, "MEMORY_AND_DISK") -#} -# @rdname persist -# @aliases persist,RDD-method +#' Persist an RDD +#' +#' Persist this RDD with the specified storage level. For details of the +#' supported storage levels, refer to +#' http://spark.apache.org/docs/latest/programming-guide.html#rdd-persistence. +#' +#' @param x The RDD to persist +#' @param newLevel The new storage level to be assigned +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' persist(rdd, "MEMORY_AND_DISK") +#'} +#' @rdname persist +#' @aliases persist,RDD-method +#' @noRd setMethod("persist", signature(x = "RDD", newLevel = "character"), function(x, newLevel = "MEMORY_ONLY") { @@ -246,21 +246,22 @@ setMethod("persist", x }) -# Unpersist an RDD -# -# Mark the RDD as non-persistent, and remove all blocks for it from memory and -# disk. -# -# @param x The RDD to unpersist -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10, 2L) -# cache(rdd) # rdd@@env$isCached == TRUE -# unpersist(rdd) # rdd@@env$isCached == FALSE -#} -# @rdname unpersist-methods -# @aliases unpersist,RDD-method +#' Unpersist an RDD +#' +#' Mark the RDD as non-persistent, and remove all blocks for it from memory and +#' disk. +#' +#' @param x The RDD to unpersist +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' cache(rdd) # rdd@@env$isCached == TRUE +#' unpersist(rdd) # rdd@@env$isCached == FALSE +#'} +#' @rdname unpersist-methods +#' @aliases unpersist,RDD-method +#' @noRd setMethod("unpersist", signature(x = "RDD"), function(x) { @@ -269,24 +270,25 @@ setMethod("unpersist", x }) -# Checkpoint an RDD -# -# Mark this RDD for checkpointing. It will be saved to a file inside the -# checkpoint directory set with setCheckpointDir() and all references to its -# parent RDDs will be removed. This function must be called before any job has -# been executed on this RDD. It is strongly recommended that this RDD is -# persisted in memory, otherwise saving it on a file will require recomputation. -# -# @param x The RDD to checkpoint -# @examples -#\dontrun{ -# sc <- sparkR.init() -# setCheckpointDir(sc, "checkpoint") -# rdd <- parallelize(sc, 1:10, 2L) -# checkpoint(rdd) -#} -# @rdname checkpoint-methods -# @aliases checkpoint,RDD-method +#' Checkpoint an RDD +#' +#' Mark this RDD for checkpointing. It will be saved to a file inside the +#' checkpoint directory set with setCheckpointDir() and all references to its +#' parent RDDs will be removed. This function must be called before any job has +#' been executed on this RDD. It is strongly recommended that this RDD is +#' persisted in memory, otherwise saving it on a file will require recomputation. +#' +#' @param x The RDD to checkpoint +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' setCheckpointDir(sc, "checkpoint") +#' rdd <- parallelize(sc, 1:10, 2L) +#' checkpoint(rdd) +#'} +#' @rdname checkpoint-methods +#' @aliases checkpoint,RDD-method +#' @noRd setMethod("checkpoint", signature(x = "RDD"), function(x) { @@ -296,18 +298,19 @@ setMethod("checkpoint", x }) -# Gets the number of partitions of an RDD -# -# @param x A RDD. -# @return the number of partitions of rdd as an integer. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10, 2L) -# numPartitions(rdd) # 2L -#} -# @rdname numPartitions -# @aliases numPartitions,RDD-method +#' Gets the number of partitions of an RDD +#' +#' @param x A RDD. +#' @return the number of partitions of rdd as an integer. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' numPartitions(rdd) # 2L +#'} +#' @rdname numPartitions +#' @aliases numPartitions,RDD-method +#' @noRd setMethod("numPartitions", signature(x = "RDD"), function(x) { @@ -316,24 +319,25 @@ setMethod("numPartitions", callJMethod(partitions, "size") }) -# Collect elements of an RDD -# -# @description -# \code{collect} returns a list that contains all of the elements in this RDD. -# -# @param x The RDD to collect -# @param ... Other optional arguments to collect -# @param flatten FALSE if the list should not flattened -# @return a list containing elements in the RDD -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10, 2L) -# collect(rdd) # list from 1 to 10 -# collectPartition(rdd, 0L) # list from 1 to 5 -#} -# @rdname collect-methods -# @aliases collect,RDD-method +#' Collect elements of an RDD +#' +#' @description +#' \code{collect} returns a list that contains all of the elements in this RDD. +#' +#' @param x The RDD to collect +#' @param ... Other optional arguments to collect +#' @param flatten FALSE if the list should not flattened +#' @return a list containing elements in the RDD +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 2L) +#' collect(rdd) # list from 1 to 10 +#' collectPartition(rdd, 0L) # list from 1 to 5 +#'} +#' @rdname collect-methods +#' @aliases collect,RDD-method +#' @noRd setMethod("collect", signature(x = "RDD"), function(x, flatten = TRUE) { @@ -344,12 +348,13 @@ setMethod("collect", }) -# @description -# \code{collectPartition} returns a list that contains all of the elements -# in the specified partition of the RDD. -# @param partitionId the partition to collect (starts from 0) -# @rdname collect-methods -# @aliases collectPartition,integer,RDD-method +#' @description +#' \code{collectPartition} returns a list that contains all of the elements +#' in the specified partition of the RDD. +#' @param partitionId the partition to collect (starts from 0) +#' @rdname collect-methods +#' @aliases collectPartition,integer,RDD-method +#' @noRd setMethod("collectPartition", signature(x = "RDD", partitionId = "integer"), function(x, partitionId) { @@ -362,17 +367,18 @@ setMethod("collectPartition", serializedMode = getSerializedMode(x)) }) -# @description -# \code{collectAsMap} returns a named list as a map that contains all of the elements -# in a key-value pair RDD. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L) -# collectAsMap(rdd) # list(`1` = 2, `3` = 4) -#} -# @rdname collect-methods -# @aliases collectAsMap,RDD-method +#' @description +#' \code{collectAsMap} returns a named list as a map that contains all of the elements +#' in a key-value pair RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(list(1, 2), list(3, 4)), 2L) +#' collectAsMap(rdd) # list(`1` = 2, `3` = 4) +#'} +#' @rdname collect-methods +#' @aliases collectAsMap,RDD-method +#' @noRd setMethod("collectAsMap", signature(x = "RDD"), function(x) { @@ -382,19 +388,20 @@ setMethod("collectAsMap", as.list(map) }) -# Return the number of elements in the RDD. -# -# @param x The RDD to count -# @return number of elements in the RDD. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# count(rdd) # 10 -# length(rdd) # Same as count -#} -# @rdname count -# @aliases count,RDD-method +#' Return the number of elements in the RDD. +#' +#' @param x The RDD to count +#' @return number of elements in the RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' count(rdd) # 10 +#' length(rdd) # Same as count +#'} +#' @rdname count +#' @aliases count,RDD-method +#' @noRd setMethod("count", signature(x = "RDD"), function(x) { @@ -406,31 +413,32 @@ setMethod("count", sum(as.integer(vals)) }) -# Return the number of elements in the RDD -# @export -# @rdname count +#' Return the number of elements in the RDD +#' @rdname count +#' @noRd setMethod("length", signature(x = "RDD"), function(x) { count(x) }) -# Return the count of each unique value in this RDD as a list of -# (value, count) pairs. -# -# Same as countByValue in Spark. -# -# @param x The RDD to count -# @return list of (value, count) pairs, where count is number of each unique -# value in rdd. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, c(1,2,3,2,1)) -# countByValue(rdd) # (1,2L), (2,2L), (3,1L) -#} -# @rdname countByValue -# @aliases countByValue,RDD-method +#' Return the count of each unique value in this RDD as a list of +#' (value, count) pairs. +#' +#' Same as countByValue in Spark. +#' +#' @param x The RDD to count +#' @return list of (value, count) pairs, where count is number of each unique +#' value in rdd. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, c(1,2,3,2,1)) +#' countByValue(rdd) # (1,2L), (2,2L), (3,1L) +#'} +#' @rdname countByValue +#' @aliases countByValue,RDD-method +#' @noRd setMethod("countByValue", signature(x = "RDD"), function(x) { @@ -438,23 +446,24 @@ setMethod("countByValue", collect(reduceByKey(ones, `+`, numPartitions(x))) }) -# Apply a function to all elements -# -# This function creates a new RDD by applying the given transformation to all -# elements of the given RDD -# -# @param X The RDD to apply the transformation. -# @param FUN the transformation to apply on each element -# @return a new RDD created by the transformation. -# @rdname lapply -# @aliases lapply -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# multiplyByTwo <- lapply(rdd, function(x) { x * 2 }) -# collect(multiplyByTwo) # 2,4,6... -#} +#' Apply a function to all elements +#' +#' This function creates a new RDD by applying the given transformation to all +#' elements of the given RDD +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each element +#' @return a new RDD created by the transformation. +#' @rdname lapply +#' @noRd +#' @aliases lapply +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' multiplyByTwo <- lapply(rdd, function(x) { x * 2 }) +#' collect(multiplyByTwo) # 2,4,6... +#'} setMethod("lapply", signature(X = "RDD", FUN = "function"), function(X, FUN) { @@ -464,31 +473,33 @@ setMethod("lapply", lapplyPartitionsWithIndex(X, func) }) -# @rdname lapply -# @aliases map,RDD,function-method +#' @rdname lapply +#' @aliases map,RDD,function-method +#' @noRd setMethod("map", signature(X = "RDD", FUN = "function"), function(X, FUN) { lapply(X, FUN) }) -# Flatten results after apply a function to all elements -# -# This function return a new RDD by first applying a function to all -# elements of this RDD, and then flattening the results. -# -# @param X The RDD to apply the transformation. -# @param FUN the transformation to apply on each element -# @return a new RDD created by the transformation. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# multiplyByTwo <- flatMap(rdd, function(x) { list(x*2, x*10) }) -# collect(multiplyByTwo) # 2,20,4,40,6,60... -#} -# @rdname flatMap -# @aliases flatMap,RDD,function-method +#' Flatten results after apply a function to all elements +#' +#' This function return a new RDD by first applying a function to all +#' elements of this RDD, and then flattening the results. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each element +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' multiplyByTwo <- flatMap(rdd, function(x) { list(x*2, x*10) }) +#' collect(multiplyByTwo) # 2,20,4,40,6,60... +#'} +#' @rdname flatMap +#' @aliases flatMap,RDD,function-method +#' @noRd setMethod("flatMap", signature(X = "RDD", FUN = "function"), function(X, FUN) { @@ -501,83 +512,88 @@ setMethod("flatMap", lapplyPartition(X, partitionFunc) }) -# Apply a function to each partition of an RDD -# -# Return a new RDD by applying a function to each partition of this RDD. -# -# @param X The RDD to apply the transformation. -# @param FUN the transformation to apply on each partition. -# @return a new RDD created by the transformation. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# partitionSum <- lapplyPartition(rdd, function(part) { Reduce("+", part) }) -# collect(partitionSum) # 15, 40 -#} -# @rdname lapplyPartition -# @aliases lapplyPartition,RDD,function-method +#' Apply a function to each partition of an RDD +#' +#' Return a new RDD by applying a function to each partition of this RDD. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each partition. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' partitionSum <- lapplyPartition(rdd, function(part) { Reduce("+", part) }) +#' collect(partitionSum) # 15, 40 +#'} +#' @rdname lapplyPartition +#' @aliases lapplyPartition,RDD,function-method +#' @noRd setMethod("lapplyPartition", signature(X = "RDD", FUN = "function"), function(X, FUN) { lapplyPartitionsWithIndex(X, function(s, part) { FUN(part) }) }) -# mapPartitions is the same as lapplyPartition. -# -# @rdname lapplyPartition -# @aliases mapPartitions,RDD,function-method +#' mapPartitions is the same as lapplyPartition. +#' +#' @rdname lapplyPartition +#' @aliases mapPartitions,RDD,function-method +#' @noRd setMethod("mapPartitions", signature(X = "RDD", FUN = "function"), function(X, FUN) { lapplyPartition(X, FUN) }) -# Return a new RDD by applying a function to each partition of this RDD, while -# tracking the index of the original partition. -# -# @param X The RDD to apply the transformation. -# @param FUN the transformation to apply on each partition; takes the partition -# index and a list of elements in the particular partition. -# @return a new RDD created by the transformation. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10, 5L) -# prod <- lapplyPartitionsWithIndex(rdd, function(partIndex, part) { -# partIndex * Reduce("+", part) }) -# collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76 -#} -# @rdname lapplyPartitionsWithIndex -# @aliases lapplyPartitionsWithIndex,RDD,function-method +#' Return a new RDD by applying a function to each partition of this RDD, while +#' tracking the index of the original partition. +#' +#' @param X The RDD to apply the transformation. +#' @param FUN the transformation to apply on each partition; takes the partition +#' index and a list of elements in the particular partition. +#' @return a new RDD created by the transformation. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10, 5L) +#' prod <- lapplyPartitionsWithIndex(rdd, function(partIndex, part) { +#' partIndex * Reduce("+", part) }) +#' collect(prod, flatten = FALSE) # 0, 7, 22, 45, 76 +#'} +#' @rdname lapplyPartitionsWithIndex +#' @aliases lapplyPartitionsWithIndex,RDD,function-method +#' @noRd setMethod("lapplyPartitionsWithIndex", signature(X = "RDD", FUN = "function"), function(X, FUN) { PipelinedRDD(X, FUN) }) -# @rdname lapplyPartitionsWithIndex -# @aliases mapPartitionsWithIndex,RDD,function-method +#' @rdname lapplyPartitionsWithIndex +#' @aliases mapPartitionsWithIndex,RDD,function-method +#' @noRd setMethod("mapPartitionsWithIndex", signature(X = "RDD", FUN = "function"), function(X, FUN) { lapplyPartitionsWithIndex(X, FUN) }) -# This function returns a new RDD containing only the elements that satisfy -# a predicate (i.e. returning TRUE in a given logical function). -# The same as `filter()' in Spark. -# -# @param x The RDD to be filtered. -# @param f A unary predicate function. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2) -#} -# @rdname filterRDD -# @aliases filterRDD,RDD,function-method +#' This function returns a new RDD containing only the elements that satisfy +#' a predicate (i.e. returning TRUE in a given logical function). +#' The same as `filter()' in Spark. +#' +#' @param x The RDD to be filtered. +#' @param f A unary predicate function. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' unlist(collect(filterRDD(rdd, function (x) { x < 3 }))) # c(1, 2) +#'} +#' @rdname filterRDD +#' @aliases filterRDD,RDD,function-method +#' @noRd setMethod("filterRDD", signature(x = "RDD", f = "function"), function(x, f) { @@ -587,30 +603,32 @@ setMethod("filterRDD", lapplyPartition(x, filter.func) }) -# @rdname filterRDD -# @aliases Filter +#' @rdname filterRDD +#' @aliases Filter +#' @noRd setMethod("Filter", signature(f = "function", x = "RDD"), function(f, x) { filterRDD(x, f) }) -# Reduce across elements of an RDD. -# -# This function reduces the elements of this RDD using the -# specified commutative and associative binary operator. -# -# @param x The RDD to reduce -# @param func Commutative and associative function to apply on elements -# of the RDD. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# reduce(rdd, "+") # 55 -#} -# @rdname reduce -# @aliases reduce,RDD,ANY-method +#' Reduce across elements of an RDD. +#' +#' This function reduces the elements of this RDD using the +#' specified commutative and associative binary operator. +#' +#' @param x The RDD to reduce +#' @param func Commutative and associative function to apply on elements +#' of the RDD. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' reduce(rdd, "+") # 55 +#'} +#' @rdname reduce +#' @aliases reduce,RDD,ANY-method +#' @noRd setMethod("reduce", signature(x = "RDD", func = "ANY"), function(x, func) { @@ -624,70 +642,74 @@ setMethod("reduce", Reduce(func, partitionList) }) -# Get the maximum element of an RDD. -# -# @param x The RDD to get the maximum element from -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# maximum(rdd) # 10 -#} -# @rdname maximum -# @aliases maximum,RDD +#' Get the maximum element of an RDD. +#' +#' @param x The RDD to get the maximum element from +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' maximum(rdd) # 10 +#'} +#' @rdname maximum +#' @aliases maximum,RDD +#' @noRd setMethod("maximum", signature(x = "RDD"), function(x) { reduce(x, max) }) -# Get the minimum element of an RDD. -# -# @param x The RDD to get the minimum element from -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# minimum(rdd) # 1 -#} -# @rdname minimum -# @aliases minimum,RDD +#' Get the minimum element of an RDD. +#' +#' @param x The RDD to get the minimum element from +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' minimum(rdd) # 1 +#'} +#' @rdname minimum +#' @aliases minimum,RDD +#' @noRd setMethod("minimum", signature(x = "RDD"), function(x) { reduce(x, min) }) -# Add up the elements in an RDD. -# -# @param x The RDD to add up the elements in -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# sumRDD(rdd) # 55 -#} -# @rdname sumRDD -# @aliases sumRDD,RDD +#' Add up the elements in an RDD. +#' +#' @param x The RDD to add up the elements in +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' sumRDD(rdd) # 55 +#'} +#' @rdname sumRDD +#' @aliases sumRDD,RDD +#' @noRd setMethod("sumRDD", signature(x = "RDD"), function(x) { reduce(x, "+") }) -# Applies a function to all elements in an RDD, and force evaluation. -# -# @param x The RDD to apply the function -# @param func The function to be applied. -# @return invisible NULL. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# foreach(rdd, function(x) { save(x, file=...) }) -#} -# @rdname foreach -# @aliases foreach,RDD,function-method +#' Applies a function to all elements in an RDD, and force evaluation. +#' +#' @param x The RDD to apply the function +#' @param func The function to be applied. +#' @return invisible NULL. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' foreach(rdd, function(x) { save(x, file=...) }) +#'} +#' @rdname foreach +#' @aliases foreach,RDD,function-method +#' @noRd setMethod("foreach", signature(x = "RDD", func = "function"), function(x, func) { @@ -698,37 +720,39 @@ setMethod("foreach", invisible(collect(mapPartitions(x, partition.func))) }) -# Applies a function to each partition in an RDD, and force evaluation. -# -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# foreachPartition(rdd, function(part) { save(part, file=...); NULL }) -#} -# @rdname foreach -# @aliases foreachPartition,RDD,function-method +#' Applies a function to each partition in an RDD, and force evaluation. +#' +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' foreachPartition(rdd, function(part) { save(part, file=...); NULL }) +#'} +#' @rdname foreach +#' @aliases foreachPartition,RDD,function-method +#' @noRd setMethod("foreachPartition", signature(x = "RDD", func = "function"), function(x, func) { invisible(collect(mapPartitions(x, func))) }) -# Take elements from an RDD. -# -# This function takes the first NUM elements in the RDD and -# returns them in a list. -# -# @param x The RDD to take elements from -# @param num Number of elements to take -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# take(rdd, 2L) # list(1, 2) -#} -# @rdname take -# @aliases take,RDD,numeric-method +#' Take elements from an RDD. +#' +#' This function takes the first NUM elements in the RDD and +#' returns them in a list. +#' +#' @param x The RDD to take elements from +#' @param num Number of elements to take +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' take(rdd, 2L) # list(1, 2) +#'} +#' @rdname take +#' @aliases take,RDD,numeric-method +#' @noRd setMethod("take", signature(x = "RDD", num = "numeric"), function(x, num) { @@ -763,39 +787,40 @@ setMethod("take", }) -# First -# -# Return the first element of an RDD -# -# @rdname first -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# first(rdd) -# } +#' First +#' +#' Return the first element of an RDD +#' +#' @rdname first +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' first(rdd) +#' } +#' @noRd setMethod("first", signature(x = "RDD"), function(x) { take(x, 1)[[1]] }) -# Removes the duplicates from RDD. -# -# This function returns a new RDD containing the distinct elements in the -# given RDD. The same as `distinct()' in Spark. -# -# @param x The RDD to remove duplicates from. -# @param numPartitions Number of partitions to create. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, c(1,2,2,3,3,3)) -# sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3) -#} -# @rdname distinct -# @aliases distinct,RDD-method +#' Removes the duplicates from RDD. +#' +#' This function returns a new RDD containing the distinct elements in the +#' given RDD. The same as `distinct()' in Spark. +#' +#' @param x The RDD to remove duplicates from. +#' @param numPartitions Number of partitions to create. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, c(1,2,2,3,3,3)) +#' sort(unlist(collect(distinct(rdd)))) # c(1, 2, 3) +#'} +#' @rdname distinct +#' @aliases distinct,RDD-method +#' @noRd setMethod("distinct", signature(x = "RDD"), function(x, numPartitions = SparkR:::numPartitions(x)) { @@ -807,24 +832,25 @@ setMethod("distinct", resRDD }) -# Return an RDD that is a sampled subset of the given RDD. -# -# The same as `sample()' in Spark. (We rename it due to signature -# inconsistencies with the `sample()' function in R's base package.) -# -# @param x The RDD to sample elements from -# @param withReplacement Sampling with replacement or not -# @param fraction The (rough) sample target fraction -# @param seed Randomness seed value -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements -# collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates -#} -# @rdname sampleRDD -# @aliases sampleRDD,RDD +#' Return an RDD that is a sampled subset of the given RDD. +#' +#' The same as `sample()' in Spark. (We rename it due to signature +#' inconsistencies with the `sample()' function in R's base package.) +#' +#' @param x The RDD to sample elements from +#' @param withReplacement Sampling with replacement or not +#' @param fraction The (rough) sample target fraction +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' collect(sampleRDD(rdd, FALSE, 0.5, 1618L)) # ~5 distinct elements +#' collect(sampleRDD(rdd, TRUE, 0.5, 9L)) # ~5 elements possibly with duplicates +#'} +#' @rdname sampleRDD +#' @aliases sampleRDD,RDD +#' @noRd setMethod("sampleRDD", signature(x = "RDD", withReplacement = "logical", fraction = "numeric", seed = "integer"), @@ -868,23 +894,24 @@ setMethod("sampleRDD", lapplyPartitionsWithIndex(x, samplingFunc) }) -# Return a list of the elements that are a sampled subset of the given RDD. -# -# @param x The RDD to sample elements from -# @param withReplacement Sampling with replacement or not -# @param num Number of elements to return -# @param seed Randomness seed value -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:100) -# # exactly 5 elements sampled, which may not be distinct -# takeSample(rdd, TRUE, 5L, 1618L) -# # exactly 5 distinct elements sampled -# takeSample(rdd, FALSE, 5L, 16181618L) -#} -# @rdname takeSample -# @aliases takeSample,RDD +#' Return a list of the elements that are a sampled subset of the given RDD. +#' +#' @param x The RDD to sample elements from +#' @param withReplacement Sampling with replacement or not +#' @param num Number of elements to return +#' @param seed Randomness seed value +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:100) +#' # exactly 5 elements sampled, which may not be distinct +#' takeSample(rdd, TRUE, 5L, 1618L) +#' # exactly 5 distinct elements sampled +#' takeSample(rdd, FALSE, 5L, 16181618L) +#'} +#' @rdname takeSample +#' @aliases takeSample,RDD +#' @noRd setMethod("takeSample", signature(x = "RDD", withReplacement = "logical", num = "integer", seed = "integer"), function(x, withReplacement, num, seed) { @@ -931,18 +958,19 @@ setMethod("takeSample", signature(x = "RDD", withReplacement = "logical", base::sample(samples)[1:total] }) -# Creates tuples of the elements in this RDD by applying a function. -# -# @param x The RDD. -# @param func The function to be applied. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1, 2, 3)) -# collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3)) -#} -# @rdname keyBy -# @aliases keyBy,RDD +#' Creates tuples of the elements in this RDD by applying a function. +#' +#' @param x The RDD. +#' @param func The function to be applied. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3)) +#' collect(keyBy(rdd, function(x) { x*x })) # list(list(1, 1), list(4, 2), list(9, 3)) +#'} +#' @rdname keyBy +#' @aliases keyBy,RDD +#' @noRd setMethod("keyBy", signature(x = "RDD", func = "function"), function(x, func) { @@ -952,44 +980,46 @@ setMethod("keyBy", lapply(x, apply.func) }) -# Return a new RDD that has exactly numPartitions partitions. -# Can increase or decrease the level of parallelism in this RDD. Internally, -# this uses a shuffle to redistribute data. -# If you are decreasing the number of partitions in this RDD, consider using -# coalesce, which can avoid performing a shuffle. -# -# @param x The RDD. -# @param numPartitions Number of partitions to create. -# @seealso coalesce -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1, 2, 3, 4, 5, 6, 7), 4L) -# numPartitions(rdd) # 4 -# numPartitions(repartition(rdd, 2L)) # 2 -#} -# @rdname repartition -# @aliases repartition,RDD +#' Return a new RDD that has exactly numPartitions partitions. +#' Can increase or decrease the level of parallelism in this RDD. Internally, +#' this uses a shuffle to redistribute data. +#' If you are decreasing the number of partitions in this RDD, consider using +#' coalesce, which can avoid performing a shuffle. +#' +#' @param x The RDD. +#' @param numPartitions Number of partitions to create. +#' @seealso coalesce +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5, 6, 7), 4L) +#' numPartitions(rdd) # 4 +#' numPartitions(repartition(rdd, 2L)) # 2 +#'} +#' @rdname repartition +#' @aliases repartition,RDD +#' @noRd setMethod("repartition", signature(x = "RDD", numPartitions = "numeric"), function(x, numPartitions) { coalesce(x, numPartitions, TRUE) }) -# Return a new RDD that is reduced into numPartitions partitions. -# -# @param x The RDD. -# @param numPartitions Number of partitions to create. -# @seealso repartition -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1, 2, 3, 4, 5), 3L) -# numPartitions(rdd) # 3 -# numPartitions(coalesce(rdd, 1L)) # 1 -#} -# @rdname coalesce -# @aliases coalesce,RDD +#' Return a new RDD that is reduced into numPartitions partitions. +#' +#' @param x The RDD. +#' @param numPartitions Number of partitions to create. +#' @seealso repartition +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5), 3L) +#' numPartitions(rdd) # 3 +#' numPartitions(coalesce(rdd, 1L)) # 1 +#'} +#' @rdname coalesce +#' @aliases coalesce,RDD +#' @noRd setMethod("coalesce", signature(x = "RDD", numPartitions = "numeric"), function(x, numPartitions, shuffle = FALSE) { @@ -1013,19 +1043,20 @@ setMethod("coalesce", } }) -# Save this RDD as a SequenceFile of serialized objects. -# -# @param x The RDD to save -# @param path The directory where the file is saved -# @seealso objectFile -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:3) -# saveAsObjectFile(rdd, "/tmp/sparkR-tmp") -#} -# @rdname saveAsObjectFile -# @aliases saveAsObjectFile,RDD +#' Save this RDD as a SequenceFile of serialized objects. +#' +#' @param x The RDD to save +#' @param path The directory where the file is saved +#' @seealso objectFile +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' saveAsObjectFile(rdd, "/tmp/sparkR-tmp") +#'} +#' @rdname saveAsObjectFile +#' @aliases saveAsObjectFile,RDD +#' @noRd setMethod("saveAsObjectFile", signature(x = "RDD", path = "character"), function(x, path) { @@ -1038,18 +1069,19 @@ setMethod("saveAsObjectFile", invisible(callJMethod(getJRDD(x), "saveAsObjectFile", path)) }) -# Save this RDD as a text file, using string representations of elements. -# -# @param x The RDD to save -# @param path The directory where the partitions of the text file are saved -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:3) -# saveAsTextFile(rdd, "/tmp/sparkR-tmp") -#} -# @rdname saveAsTextFile -# @aliases saveAsTextFile,RDD +#' Save this RDD as a text file, using string representations of elements. +#' +#' @param x The RDD to save +#' @param path The directory where the partitions of the text file are saved +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' saveAsTextFile(rdd, "/tmp/sparkR-tmp") +#'} +#' @rdname saveAsTextFile +#' @aliases saveAsTextFile,RDD +#' @noRd setMethod("saveAsTextFile", signature(x = "RDD", path = "character"), function(x, path) { @@ -1062,21 +1094,22 @@ setMethod("saveAsTextFile", callJMethod(getJRDD(stringRdd, serializedMode = "string"), "saveAsTextFile", path)) }) -# Sort an RDD by the given key function. -# -# @param x An RDD to be sorted. -# @param func A function used to compute the sort key for each element. -# @param ascending A flag to indicate whether the sorting is ascending or descending. -# @param numPartitions Number of partitions to create. -# @return An RDD where all elements are sorted. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(3, 2, 1)) -# collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3) -#} -# @rdname sortBy -# @aliases sortBy,RDD,RDD-method +#' Sort an RDD by the given key function. +#' +#' @param x An RDD to be sorted. +#' @param func A function used to compute the sort key for each element. +#' @param ascending A flag to indicate whether the sorting is ascending or descending. +#' @param numPartitions Number of partitions to create. +#' @return An RDD where all elements are sorted. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(3, 2, 1)) +#' collect(sortBy(rdd, function(x) { x })) # list (1, 2, 3) +#'} +#' @rdname sortBy +#' @aliases sortBy,RDD,RDD-method +#' @noRd setMethod("sortBy", signature(x = "RDD", func = "function"), function(x, func, ascending = TRUE, numPartitions = SparkR:::numPartitions(x)) { @@ -1138,97 +1171,95 @@ takeOrderedElem <- function(x, num, ascending = TRUE) { resList } -# Returns the first N elements from an RDD in ascending order. -# -# @param x An RDD. -# @param num Number of elements to return. -# @return The first N elements from the RDD in ascending order. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) -# takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6) -#} -# @rdname takeOrdered -# @aliases takeOrdered,RDD,RDD-method +#' Returns the first N elements from an RDD in ascending order. +#' +#' @param x An RDD. +#' @param num Number of elements to return. +#' @return The first N elements from the RDD in ascending order. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) +#' takeOrdered(rdd, 6L) # list(1, 2, 3, 4, 5, 6) +#'} +#' @rdname takeOrdered +#' @aliases takeOrdered,RDD,RDD-method +#' @noRd setMethod("takeOrdered", signature(x = "RDD", num = "integer"), function(x, num) { takeOrderedElem(x, num) }) -# Returns the top N elements from an RDD. -# -# @param x An RDD. -# @param num Number of elements to return. -# @return The top N elements from the RDD. -# @rdname top -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) -# top(rdd, 6L) # list(10, 9, 7, 6, 5, 4) -#} -# @rdname top -# @aliases top,RDD,RDD-method +#' Returns the top N elements from an RDD. +#' +#' @param x An RDD. +#' @param num Number of elements to return. +#' @return The top N elements from the RDD. +#' @rdname top +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(10, 1, 2, 9, 3, 4, 5, 6, 7)) +#' top(rdd, 6L) # list(10, 9, 7, 6, 5, 4) +#'} +#' @aliases top,RDD,RDD-method +#' @noRd setMethod("top", signature(x = "RDD", num = "integer"), function(x, num) { takeOrderedElem(x, num, FALSE) }) -# Fold an RDD using a given associative function and a neutral "zero value". -# -# Aggregate the elements of each partition, and then the results for all the -# partitions, using a given associative function and a neutral "zero value". -# -# @param x An RDD. -# @param zeroValue A neutral "zero value". -# @param op An associative function for the folding operation. -# @return The folding result. -# @rdname fold -# @seealso reduce -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1, 2, 3, 4, 5)) -# fold(rdd, 0, "+") # 15 -#} -# @rdname fold -# @aliases fold,RDD,RDD-method +#' Fold an RDD using a given associative function and a neutral "zero value". +#' +#' Aggregate the elements of each partition, and then the results for all the +#' partitions, using a given associative function and a neutral "zero value". +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param op An associative function for the folding operation. +#' @return The folding result. +#' @rdname fold +#' @seealso reduce +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4, 5)) +#' fold(rdd, 0, "+") # 15 +#'} +#' @aliases fold,RDD,RDD-method +#' @noRd setMethod("fold", signature(x = "RDD", zeroValue = "ANY", op = "ANY"), function(x, zeroValue, op) { aggregateRDD(x, zeroValue, op, op) }) -# Aggregate an RDD using the given combine functions and a neutral "zero value". -# -# Aggregate the elements of each partition, and then the results for all the -# partitions, using given combine functions and a neutral "zero value". -# -# @param x An RDD. -# @param zeroValue A neutral "zero value". -# @param seqOp A function to aggregate the RDD elements. It may return a different -# result type from the type of the RDD elements. -# @param combOp A function to aggregate results of seqOp. -# @return The aggregation result. -# @rdname aggregateRDD -# @seealso reduce -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1, 2, 3, 4)) -# zeroValue <- list(0, 0) -# seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } -# combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } -# aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4) -#} -# @rdname aggregateRDD -# @aliases aggregateRDD,RDD,RDD-method +#' Aggregate an RDD using the given combine functions and a neutral "zero value". +#' +#' Aggregate the elements of each partition, and then the results for all the +#' partitions, using given combine functions and a neutral "zero value". +#' +#' @param x An RDD. +#' @param zeroValue A neutral "zero value". +#' @param seqOp A function to aggregate the RDD elements. It may return a different +#' result type from the type of the RDD elements. +#' @param combOp A function to aggregate results of seqOp. +#' @return The aggregation result. +#' @rdname aggregateRDD +#' @seealso reduce +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1, 2, 3, 4)) +#' zeroValue <- list(0, 0) +#' seqOp <- function(x, y) { list(x[[1]] + y, x[[2]] + 1) } +#' combOp <- function(x, y) { list(x[[1]] + y[[1]], x[[2]] + y[[2]]) } +#' aggregateRDD(rdd, zeroValue, seqOp, combOp) # list(10, 4) +#'} +#' @aliases aggregateRDD,RDD,RDD-method +#' @noRd setMethod("aggregateRDD", signature(x = "RDD", zeroValue = "ANY", seqOp = "ANY", combOp = "ANY"), function(x, zeroValue, seqOp, combOp) { @@ -1241,25 +1272,24 @@ setMethod("aggregateRDD", Reduce(combOp, partitionList, zeroValue) }) -# Pipes elements to a forked external process. -# -# The same as 'pipe()' in Spark. -# -# @param x The RDD whose elements are piped to the forked external process. -# @param command The command to fork an external process. -# @param env A named list to set environment variables of the external process. -# @return A new RDD created by piping all elements to a forked external process. -# @rdname pipeRDD -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:10) -# collect(pipeRDD(rdd, "more") -# Output: c("1", "2", ..., "10") -#} -# @rdname pipeRDD -# @aliases pipeRDD,RDD,character-method +#' Pipes elements to a forked external process. +#' +#' The same as 'pipe()' in Spark. +#' +#' @param x The RDD whose elements are piped to the forked external process. +#' @param command The command to fork an external process. +#' @param env A named list to set environment variables of the external process. +#' @return A new RDD created by piping all elements to a forked external process. +#' @rdname pipeRDD +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:10) +#' collect(pipeRDD(rdd, "more") +#' Output: c("1", "2", ..., "10") +#'} +#' @aliases pipeRDD,RDD,character-method +#' @noRd setMethod("pipeRDD", signature(x = "RDD", command = "character"), function(x, command, env = list()) { @@ -1274,42 +1304,40 @@ setMethod("pipeRDD", lapplyPartition(x, func) }) -# TODO: Consider caching the name in the RDD's environment -# Return an RDD's name. -# -# @param x The RDD whose name is returned. -# @rdname name -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1,2,3)) -# name(rdd) # NULL (if not set before) -#} -# @rdname name -# @aliases name,RDD +#' TODO: Consider caching the name in the RDD's environment +#' Return an RDD's name. +#' +#' @param x The RDD whose name is returned. +#' @rdname name +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1,2,3)) +#' name(rdd) # NULL (if not set before) +#'} +#' @aliases name,RDD +#' @noRd setMethod("name", signature(x = "RDD"), function(x) { callJMethod(getJRDD(x), "name") }) -# Set an RDD's name. -# -# @param x The RDD whose name is to be set. -# @param name The RDD name to be set. -# @return a new RDD renamed. -# @rdname setName -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list(1,2,3)) -# setName(rdd, "myRDD") -# name(rdd) # "myRDD" -#} -# @rdname setName -# @aliases setName,RDD +#' Set an RDD's name. +#' +#' @param x The RDD whose name is to be set. +#' @param name The RDD name to be set. +#' @return a new RDD renamed. +#' @rdname setName +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list(1,2,3)) +#' setName(rdd, "myRDD") +#' name(rdd) # "myRDD" +#'} +#' @aliases setName,RDD +#' @noRd setMethod("setName", signature(x = "RDD", name = "character"), function(x, name) { @@ -1317,25 +1345,26 @@ setMethod("setName", x }) -# Zip an RDD with generated unique Long IDs. -# -# Items in the kth partition will get ids k, n+k, 2*n+k, ..., where -# n is the number of partitions. So there may exist gaps, but this -# method won't trigger a spark job, which is different from -# zipWithIndex. -# -# @param x An RDD to be zipped. -# @return An RDD with zipped items. -# @seealso zipWithIndex -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) -# collect(zipWithUniqueId(rdd)) -# # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2)) -#} -# @rdname zipWithUniqueId -# @aliases zipWithUniqueId,RDD +#' Zip an RDD with generated unique Long IDs. +#' +#' Items in the kth partition will get ids k, n+k, 2*n+k, ..., where +#' n is the number of partitions. So there may exist gaps, but this +#' method won't trigger a spark job, which is different from +#' zipWithIndex. +#' +#' @param x An RDD to be zipped. +#' @return An RDD with zipped items. +#' @seealso zipWithIndex +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) +#' collect(zipWithUniqueId(rdd)) +#' # list(list("a", 0), list("b", 3), list("c", 1), list("d", 4), list("e", 2)) +#'} +#' @rdname zipWithUniqueId +#' @aliases zipWithUniqueId,RDD +#' @noRd setMethod("zipWithUniqueId", signature(x = "RDD"), function(x) { @@ -1354,28 +1383,29 @@ setMethod("zipWithUniqueId", lapplyPartitionsWithIndex(x, partitionFunc) }) -# Zip an RDD with its element indices. -# -# The ordering is first based on the partition index and then the -# ordering of items within each partition. So the first item in -# the first partition gets index 0, and the last item in the last -# partition receives the largest index. -# -# This method needs to trigger a Spark job when this RDD contains -# more than one partition. -# -# @param x An RDD to be zipped. -# @return An RDD with zipped items. -# @seealso zipWithUniqueId -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) -# collect(zipWithIndex(rdd)) -# # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) -#} -# @rdname zipWithIndex -# @aliases zipWithIndex,RDD +#' Zip an RDD with its element indices. +#' +#' The ordering is first based on the partition index and then the +#' ordering of items within each partition. So the first item in +#' the first partition gets index 0, and the last item in the last +#' partition receives the largest index. +#' +#' This method needs to trigger a Spark job when this RDD contains +#' more than one partition. +#' +#' @param x An RDD to be zipped. +#' @return An RDD with zipped items. +#' @seealso zipWithUniqueId +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, list("a", "b", "c", "d", "e"), 3L) +#' collect(zipWithIndex(rdd)) +#' # list(list("a", 0), list("b", 1), list("c", 2), list("d", 3), list("e", 4)) +#'} +#' @rdname zipWithIndex +#' @aliases zipWithIndex,RDD +#' @noRd setMethod("zipWithIndex", signature(x = "RDD"), function(x) { @@ -1407,20 +1437,21 @@ setMethod("zipWithIndex", lapplyPartitionsWithIndex(x, partitionFunc) }) -# Coalesce all elements within each partition of an RDD into a list. -# -# @param x An RDD. -# @return An RDD created by coalescing all elements within -# each partition into a list. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, as.list(1:4), 2L) -# collect(glom(rdd)) -# # list(list(1, 2), list(3, 4)) -#} -# @rdname glom -# @aliases glom,RDD +#' Coalesce all elements within each partition of an RDD into a list. +#' +#' @param x An RDD. +#' @return An RDD created by coalescing all elements within +#' each partition into a list. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, as.list(1:4), 2L) +#' collect(glom(rdd)) +#' # list(list(1, 2), list(3, 4)) +#'} +#' @rdname glom +#' @aliases glom,RDD +#' @noRd setMethod("glom", signature(x = "RDD"), function(x) { @@ -1433,21 +1464,22 @@ setMethod("glom", ############ Binary Functions ############# -# Return the union RDD of two RDDs. -# The same as union() in Spark. -# -# @param x An RDD. -# @param y An RDD. -# @return a new RDD created by performing the simple union (witout removing -# duplicates) of two input RDDs. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:3) -# unionRDD(rdd, rdd) # 1, 2, 3, 1, 2, 3 -#} -# @rdname unionRDD -# @aliases unionRDD,RDD,RDD-method +#' Return the union RDD of two RDDs. +#' The same as union() in Spark. +#' +#' @param x An RDD. +#' @param y An RDD. +#' @return a new RDD created by performing the simple union (witout removing +#' duplicates) of two input RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:3) +#' unionRDD(rdd, rdd) # 1, 2, 3, 1, 2, 3 +#'} +#' @rdname unionRDD +#' @aliases unionRDD,RDD,RDD-method +#' @noRd setMethod("unionRDD", signature(x = "RDD", y = "RDD"), function(x, y) { @@ -1464,27 +1496,28 @@ setMethod("unionRDD", union.rdd }) -# Zip an RDD with another RDD. -# -# Zips this RDD with another one, returning key-value pairs with the -# first element in each RDD second element in each RDD, etc. Assumes -# that the two RDDs have the same number of partitions and the same -# number of elements in each partition (e.g. one was made through -# a map on the other). -# -# @param x An RDD to be zipped. -# @param other Another RDD to be zipped. -# @return An RDD zipped from the two RDDs. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd1 <- parallelize(sc, 0:4) -# rdd2 <- parallelize(sc, 1000:1004) -# collect(zipRDD(rdd1, rdd2)) -# # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004)) -#} -# @rdname zipRDD -# @aliases zipRDD,RDD +#' Zip an RDD with another RDD. +#' +#' Zips this RDD with another one, returning key-value pairs with the +#' first element in each RDD second element in each RDD, etc. Assumes +#' that the two RDDs have the same number of partitions and the same +#' number of elements in each partition (e.g. one was made through +#' a map on the other). +#' +#' @param x An RDD to be zipped. +#' @param other Another RDD to be zipped. +#' @return An RDD zipped from the two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, 0:4) +#' rdd2 <- parallelize(sc, 1000:1004) +#' collect(zipRDD(rdd1, rdd2)) +#' # list(list(0, 1000), list(1, 1001), list(2, 1002), list(3, 1003), list(4, 1004)) +#'} +#' @rdname zipRDD +#' @aliases zipRDD,RDD +#' @noRd setMethod("zipRDD", signature(x = "RDD", other = "RDD"), function(x, other) { @@ -1503,24 +1536,25 @@ setMethod("zipRDD", mergePartitions(rdd, TRUE) }) -# Cartesian product of this RDD and another one. -# -# Return the Cartesian product of this RDD and another one, -# that is, the RDD of all pairs of elements (a, b) where a -# is in this and b is in other. -# -# @param x An RDD. -# @param other An RDD. -# @return A new RDD which is the Cartesian product of these two RDDs. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd <- parallelize(sc, 1:2) -# sortByKey(cartesian(rdd, rdd)) -# # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2)) -#} -# @rdname cartesian -# @aliases cartesian,RDD,RDD-method +#' Cartesian product of this RDD and another one. +#' +#' Return the Cartesian product of this RDD and another one, +#' that is, the RDD of all pairs of elements (a, b) where a +#' is in this and b is in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @return A new RDD which is the Cartesian product of these two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd <- parallelize(sc, 1:2) +#' sortByKey(cartesian(rdd, rdd)) +#' # list(list(1, 1), list(1, 2), list(2, 1), list(2, 2)) +#'} +#' @rdname cartesian +#' @aliases cartesian,RDD,RDD-method +#' @noRd setMethod("cartesian", signature(x = "RDD", other = "RDD"), function(x, other) { @@ -1533,24 +1567,25 @@ setMethod("cartesian", mergePartitions(rdd, FALSE) }) -# Subtract an RDD with another RDD. -# -# Return an RDD with the elements from this that are not in other. -# -# @param x An RDD. -# @param other An RDD. -# @param numPartitions Number of the partitions in the result RDD. -# @return An RDD with the elements from this that are not in other. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4)) -# rdd2 <- parallelize(sc, list(2, 4)) -# collect(subtract(rdd1, rdd2)) -# # list(1, 1, 3) -#} -# @rdname subtract -# @aliases subtract,RDD +#' Subtract an RDD with another RDD. +#' +#' Return an RDD with the elements from this that are not in other. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions Number of the partitions in the result RDD. +#' @return An RDD with the elements from this that are not in other. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(1, 1, 2, 2, 3, 4)) +#' rdd2 <- parallelize(sc, list(2, 4)) +#' collect(subtract(rdd1, rdd2)) +#' # list(1, 1, 3) +#'} +#' @rdname subtract +#' @aliases subtract,RDD +#' @noRd setMethod("subtract", signature(x = "RDD", other = "RDD"), function(x, other, numPartitions = SparkR:::numPartitions(x)) { @@ -1560,28 +1595,29 @@ setMethod("subtract", keys(subtractByKey(rdd1, rdd2, numPartitions)) }) -# Intersection of this RDD and another one. -# -# Return the intersection of this RDD and another one. -# The output will not contain any duplicate elements, -# even if the input RDDs did. Performs a hash partition -# across the cluster. -# Note that this method performs a shuffle internally. -# -# @param x An RDD. -# @param other An RDD. -# @param numPartitions The number of partitions in the result RDD. -# @return An RDD which is the intersection of these two RDDs. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5)) -# rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8)) -# collect(sortBy(intersection(rdd1, rdd2), function(x) { x })) -# # list(1, 2, 3) -#} -# @rdname intersection -# @aliases intersection,RDD +#' Intersection of this RDD and another one. +#' +#' Return the intersection of this RDD and another one. +#' The output will not contain any duplicate elements, +#' even if the input RDDs did. Performs a hash partition +#' across the cluster. +#' Note that this method performs a shuffle internally. +#' +#' @param x An RDD. +#' @param other An RDD. +#' @param numPartitions The number of partitions in the result RDD. +#' @return An RDD which is the intersection of these two RDDs. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, list(1, 10, 2, 3, 4, 5)) +#' rdd2 <- parallelize(sc, list(1, 6, 2, 3, 7, 8)) +#' collect(sortBy(intersection(rdd1, rdd2), function(x) { x })) +#' # list(1, 2, 3) +#'} +#' @rdname intersection +#' @aliases intersection,RDD +#' @noRd setMethod("intersection", signature(x = "RDD", other = "RDD"), function(x, other, numPartitions = SparkR:::numPartitions(x)) { @@ -1597,26 +1633,27 @@ setMethod("intersection", keys(filterRDD(cogroup(rdd1, rdd2, numPartitions = numPartitions), filterFunction)) }) -# Zips an RDD's partitions with one (or more) RDD(s). -# Same as zipPartitions in Spark. -# -# @param ... RDDs to be zipped. -# @param func A function to transform zipped partitions. -# @return A new RDD by applying a function to the zipped partitions. -# Assumes that all the RDDs have the *same number of partitions*, but -# does *not* require them to have the same number of elements in each partition. -# @examples -#\dontrun{ -# sc <- sparkR.init() -# rdd1 <- parallelize(sc, 1:2, 2L) # 1, 2 -# rdd2 <- parallelize(sc, 1:4, 2L) # 1:2, 3:4 -# rdd3 <- parallelize(sc, 1:6, 2L) # 1:3, 4:6 -# collect(zipPartitions(rdd1, rdd2, rdd3, -# func = function(x, y, z) { list(list(x, y, z))} )) -# # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6))) -#} -# @rdname zipRDD -# @aliases zipPartitions,RDD +#' Zips an RDD's partitions with one (or more) RDD(s). +#' Same as zipPartitions in Spark. +#' +#' @param ... RDDs to be zipped. +#' @param func A function to transform zipped partitions. +#' @return A new RDD by applying a function to the zipped partitions. +#' Assumes that all the RDDs have the *same number of partitions*, but +#' does *not* require them to have the same number of elements in each partition. +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' rdd1 <- parallelize(sc, 1:2, 2L) # 1, 2 +#' rdd2 <- parallelize(sc, 1:4, 2L) # 1:2, 3:4 +#' rdd3 <- parallelize(sc, 1:6, 2L) # 1:3, 4:6 +#' collect(zipPartitions(rdd1, rdd2, rdd3, +#' func = function(x, y, z) { list(list(x, y, z))} )) +#' # list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6))) +#'} +#' @rdname zipRDD +#' @aliases zipPartitions,RDD +#' @noRd setMethod("zipPartitions", "RDD", function(..., func) { http://git-wip-us.apache.org/repos/asf/spark/blob/ea9f7c58/R/pkg/R/SQLContext.R ---------------------------------------------------------------------- diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R index 1bf025c..fd013fd 100644 --- a/R/pkg/R/SQLContext.R +++ b/R/pkg/R/SQLContext.R @@ -144,7 +144,6 @@ createDataFrame <- function(sqlContext, data, schema = NULL, samplingRatio = 1.0 } stopifnot(class(schema) == "structType") - # schemaString <- tojson(schema) jrdd <- getJRDD(lapply(rdd, function(x) x), "row") srdd <- callJMethod(jrdd, "rdd") @@ -160,22 +159,21 @@ as.DataFrame <- function(sqlContext, data, schema = NULL, samplingRatio = 1.0) { createDataFrame(sqlContext, data, schema, samplingRatio) } -# toDF -# -# Converts an RDD to a DataFrame by infer the types. -# -# @param x An RDD -# -# @rdname DataFrame -# @export -# @examples -#\dontrun{ -# sc <- sparkR.init() -# sqlContext <- 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 +#' @noRd +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlContext <- 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") }) setMethod("toDF", signature(x = "RDD"), @@ -217,23 +215,23 @@ jsonFile <- function(sqlContext, path) { } -# JSON RDD -# -# Loads an RDD storing one JSON object per string as a DataFrame. -# -# @param sqlContext 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() -# sqlContext <- sparkRSQL.init(sc) -# rdd <- texFile(sc, "path/to/json") -# df <- jsonRDD(sqlContext, rdd) -# } +#' JSON RDD +#' +#' Loads an RDD storing one JSON object per string as a DataFrame. +#' +#' @param sqlContext 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 +#' @noRd +#' @examples +#'\dontrun{ +#' sc <- sparkR.init() +#' sqlContext <- sparkRSQL.init(sc) +#' rdd <- texFile(sc, "path/to/json") +#' df <- jsonRDD(sqlContext, rdd) +#'} # TODO: support schema jsonRDD <- function(sqlContext, rdd, schema = NULL, samplingRatio = 1.0) { --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
