Merge branch 'master' into SPARK-13576

R/pkg/DESCRIPTION

@@ -18,10 +18,10 @@ Collate:
     'schema.R'
     'generics.R'
     'jobj.R'
-    'RDD.R'
-    'pairRDD.R'
     'column.R'
     'group.R'
+    'RDD.R'
+    'pairRDD.R'
     'DataFrame.R'
     'SQLContext.R'
     'backend.R'
@@ -36,3 +36,4 @@ Collate:
     'stats.R'
     'types.R'
     'utils.R'
+RoxygenNote: 5.0.1

R/pkg/R/DataFrame.R

@@ -321,7 +321,7 @@ setMethod("colnames<-",
             }
 
             # Check if the column names have . in it
-            if (any(regexec(".", value, fixed=TRUE)[[1]][1] != -1)) {
+            if (any(regexec(".", value, fixed = TRUE)[[1]][1] != -1)) {
               stop("Colum names cannot contain the '.' symbol.")
             }
 
@@ -351,7 +351,7 @@ setMethod("coltypes",
             types <- sapply(dtypes(x), function(x) {x[[2]]})
 
             # Map Spark data types into R's data types using DATA_TYPES environment
-            rTypes <- sapply(types, USE.NAMES=F, FUN=function(x) {
+            rTypes <- sapply(types, USE.NAMES = F, FUN = function(x) {
               # Check for primitive types
               type <- PRIMITIVE_TYPES[[x]]
 
@@ -1779,7 +1779,7 @@ setMethod("merge",
           signature(x = "DataFrame", y = "DataFrame"),
           function(x, y, by = intersect(names(x), names(y)), by.x = by, by.y = by,
                    all = FALSE, all.x = all, all.y = all,
-                   sort = TRUE, suffixes = c("_x","_y"), ... ) {
+                   sort = TRUE, suffixes = c("_x", "_y"), ... ) {
 
             if (length(suffixes) != 2) {
               stop("suffixes must have length 2")
@@ -2299,7 +2299,7 @@ setMethod("as.data.frame",
           function(x, ...) {
             # Check if additional parameters have been passed
             if (length(list(...)) > 0) {
-              stop(paste("Unused argument(s): ", paste(list(...), collapse=", ")))
+              stop(paste("Unused argument(s): ", paste(list(...), collapse = ", ")))
             }
             collect(x)
           })
@@ -2395,13 +2395,13 @@ setMethod("str",
                 # Get the first elements for each column
 
                 firstElements <- if (types[i] == "character") {
-                  paste(paste0("\"", localDF[,i], "\""), collapse = " ")
+                  paste(paste0("\"", localDF[, i], "\""), collapse = " ")
                 } else {
-                  paste(localDF[,i], collapse = " ")
+                  paste(localDF[, i], collapse = " ")
                 }
 
                 # Add the corresponding number of spaces for alignment
-                spaces <- paste(rep(" ", max(nchar(names) - nchar(names[i]))), collapse="")
+                spaces <- paste(rep(" ", max(nchar(names) - nchar(names[i]))), collapse = "")
 
                 # Get the short type. For 'character', it would be 'chr';
                 # 'for numeric', it's 'num', etc.
@@ -2413,7 +2413,7 @@ setMethod("str",
                 # Concatenate the colnames, coltypes, and first
                 # elements of each column
                 line <- paste0(" $ ", names[i], spaces, ": ",
-                               dataType, " ",firstElements)
+                               dataType, " ", firstElements)
 
                 # Chop off extra characters if this is too long
                 cat(substr(line, 1, MAX_CHAR_PER_ROW))

R/pkg/R/RDD.R

@@ -67,7 +67,7 @@ setMethod("initialize", "RDD", function(.Object, jrdd, serializedMode,
 
 setMethod("show", "RDD",
           function(object) {
-              cat(paste(callJMethod(getJRDD(object), "toString"), "\n", sep=""))
+              cat(paste(callJMethod(getJRDD(object), "toString"), "\n", sep = ""))
           })
 
 setMethod("initialize", "PipelinedRDD", function(.Object, prev, func, jrdd_val) {

R/pkg/R/context.R

@@ -103,7 +103,10 @@ parallelize <- function(sc, coll, numSlices = 1) {
   # TODO: bound/safeguard numSlices
   # TODO: unit tests for if the split works for all primitives
   # TODO: support matrix, data frame, etc
+  # nolint start
+  # suppress lintr warning: Place a space before left parenthesis, except in a function call.
   if ((!is.list(coll) && !is.vector(coll)) || is.data.frame(coll)) {
+  # nolint end
     if (is.data.frame(coll)) {
       message(paste("context.R: A data frame is parallelized by columns."))
     } else {

R/pkg/R/deserialize.R

@@ -186,7 +186,7 @@ readMultipleObjects <- function(inputCon) {
   # of the objects, so the number of objects varies, we try to read
   # all objects in a loop until the end of the stream.
   data <- list()
-  while(TRUE) {
+  while (TRUE) {
     # If reaching the end of the stream, type returned should be "".
     type <- readType(inputCon)
     if (type == "") {

R/pkg/R/generics.R

@@ -607,15 +607,15 @@ setGeneric("selectExpr", function(x, expr, ...) { standardGeneric("selectExpr")
 
 #' @rdname showDF
 #' @export
-setGeneric("showDF", function(x,...) { standardGeneric("showDF") })
+setGeneric("showDF", function(x, ...) { standardGeneric("showDF") })
 
 # @rdname subset
 # @export
 setGeneric("subset", function(x, ...) { standardGeneric("subset") })
 
 #' @rdname agg
 #' @export
-setGeneric("summarize", function(x,...) { standardGeneric("summarize") })
+setGeneric("summarize", function(x, ...) { standardGeneric("summarize") })
 
 #' @rdname summary
 #' @export

R/pkg/R/mllib.R

@@ -54,7 +54,7 @@ setMethod("glm", signature(formula = "formula", family = "ANY", data = "DataFram
           function(formula, family = c("gaussian", "binomial"), data, lambda = 0, alpha = 0,
             standardize = TRUE, solver = "auto") {
             family <- match.arg(family)
-            formula <- paste(deparse(formula), collapse="")
+            formula <- paste(deparse(formula), collapse = "")
             model <- callJStatic("org.apache.spark.ml.api.r.SparkRWrappers",
                                  "fitRModelFormula", formula, data@sdf, family, lambda,
                                  alpha, standardize, solver)

R/pkg/R/serialize.R

@@ -100,7 +100,7 @@ writeJobj <- function(con, value) {
 writeString <- function(con, value) {
   utfVal <- enc2utf8(value)
   writeInt(con, as.integer(nchar(utfVal, type = "bytes") + 1))
-  writeBin(utfVal, con, endian = "big", useBytes=TRUE)
+  writeBin(utfVal, con, endian = "big", useBytes = TRUE)
 }
 
 writeInt <- function(con, value) {

R/pkg/R/sparkR.R

@@ -153,7 +153,7 @@ sparkR.init <- function(
     if (!file.exists(path)) {
       stop("JVM is not ready after 10 seconds")
     }
-    f <- file(path, open="rb")
+    f <- file(path, open = "rb")
     backendPort <- readInt(f)
     monitorPort <- readInt(f)
     rLibPath <- readString(f)
@@ -185,9 +185,9 @@ sparkR.init <- function(
   }
 
   sparkExecutorEnvMap <- convertNamedListToEnv(sparkExecutorEnv)
-  if(is.null(sparkExecutorEnvMap$LD_LIBRARY_PATH)) {
+  if (is.null(sparkExecutorEnvMap$LD_LIBRARY_PATH)) {
     sparkExecutorEnvMap[["LD_LIBRARY_PATH"]] <-
-      paste0("$LD_LIBRARY_PATH:",Sys.getenv("LD_LIBRARY_PATH"))
+      paste0("$LD_LIBRARY_PATH:", Sys.getenv("LD_LIBRARY_PATH"))
   }
 
   # Classpath separator is ";" on Windows

R/pkg/R/utils.R

@@ -158,7 +158,7 @@ wrapInt <- function(value) {
 # Multiply `val` by 31 and add `addVal` to the result. Ensures that
 # integer-overflows are handled at every step.
 mult31AndAdd <- function(val, addVal) {
-  vec <- c(bitwShiftL(val, c(4,3,2,1,0)), addVal)
+  vec <- c(bitwShiftL(val, c(4, 3, 2, 1, 0)), addVal)
   Reduce(function(a, b) {
           wrapInt(as.numeric(a) + as.numeric(b))
          },
@@ -202,7 +202,7 @@ serializeToString <- function(rdd) {
 # This function amortizes the allocation cost by doubling
 # the size of the list every time it fills up.
 addItemToAccumulator <- function(acc, item) {
-  if(acc$counter == acc$size) {
+  if (acc$counter == acc$size) {
     acc$size <- acc$size * 2
     length(acc$data) <- acc$size
   }

R/pkg/inst/profile/general.R

@@ -19,5 +19,5 @@
   packageDir <- Sys.getenv("SPARKR_PACKAGE_DIR")
   dirs <- strsplit(packageDir, ",")[[1]]
   .libPaths(c(dirs, .libPaths()))
-  Sys.setenv(NOAWT=1)
+  Sys.setenv(NOAWT = 1)
 }

R/pkg/inst/tests/testthat/packageInAJarTest.R

@@ -25,6 +25,6 @@ run2 <- myfunc(-4L)
 
 sparkR.stop()
 
-if(run1 != 6) quit(save = "no", status = 1)
+if (run1 != 6) quit(save = "no", status = 1)
 
-if(run2 != -3) quit(save = "no", status = 1)
+if (run2 != -3) quit(save = "no", status = 1)

R/pkg/inst/tests/testthat/test_binaryFile.R

@@ -23,8 +23,8 @@ sc <- sparkR.init()
 mockFile <- c("Spark is pretty.", "Spark is awesome.")
 
 test_that("saveAsObjectFile()/objectFile() following textFile() works", {
-  fileName1 <- tempfile(pattern="spark-test", fileext=".tmp")
-  fileName2 <- tempfile(pattern="spark-test", fileext=".tmp")
+  fileName1 <- tempfile(pattern = "spark-test", fileext = ".tmp")
+  fileName2 <- tempfile(pattern = "spark-test", fileext = ".tmp")
   writeLines(mockFile, fileName1)
 
   rdd <- textFile(sc, fileName1, 1)
@@ -37,7 +37,7 @@ test_that("saveAsObjectFile()/objectFile() following textFile() works", {
 })
 
 test_that("saveAsObjectFile()/objectFile() works on a parallelized list", {
-  fileName <- tempfile(pattern="spark-test", fileext=".tmp")
+  fileName <- tempfile(pattern = "spark-test", fileext = ".tmp")
 
   l <- list(1, 2, 3)
   rdd <- parallelize(sc, l, 1)
@@ -49,8 +49,8 @@ test_that("saveAsObjectFile()/objectFile() works on a parallelized list", {
 })
 
 test_that("saveAsObjectFile()/objectFile() following RDD transformations works", {
-  fileName1 <- tempfile(pattern="spark-test", fileext=".tmp")
-  fileName2 <- tempfile(pattern="spark-test", fileext=".tmp")
+  fileName1 <- tempfile(pattern = "spark-test", fileext = ".tmp")
+  fileName2 <- tempfile(pattern = "spark-test", fileext = ".tmp")
   writeLines(mockFile, fileName1)
 
   rdd <- textFile(sc, fileName1)
@@ -73,8 +73,8 @@ test_that("saveAsObjectFile()/objectFile() following RDD transformations works",
 })
 
 test_that("saveAsObjectFile()/objectFile() works with multiple paths", {
-  fileName1 <- tempfile(pattern="spark-test", fileext=".tmp")
-  fileName2 <- tempfile(pattern="spark-test", fileext=".tmp")
+  fileName1 <- tempfile(pattern = "spark-test", fileext = ".tmp")
+  fileName2 <- tempfile(pattern = "spark-test", fileext = ".tmp")
 
   rdd1 <- parallelize(sc, "Spark is pretty.")
   saveAsObjectFile(rdd1, fileName1)

R/pkg/inst/tests/testthat/test_binary_function.R

@@ -31,7 +31,7 @@ test_that("union on two RDDs", {
   actual <- collect(unionRDD(rdd, rdd))
   expect_equal(actual, as.list(rep(nums, 2)))
 
-  fileName <- tempfile(pattern="spark-test", fileext=".tmp")
+  fileName <- tempfile(pattern = "spark-test", fileext = ".tmp")
   writeLines(mockFile, fileName)
 
   text.rdd <- textFile(sc, fileName)
@@ -74,10 +74,10 @@ test_that("zipPartitions() on RDDs", {
   actual <- collect(zipPartitions(rdd1, rdd2, rdd3,
                                   func = function(x, y, z) { list(list(x, y, z))} ))
   expect_equal(actual,
-               list(list(1, c(1,2), c(1,2,3)), list(2, c(3,4), c(4,5,6))))
+               list(list(1, c(1, 2), c(1, 2, 3)), list(2, c(3, 4), c(4, 5, 6))))
 
   mockFile <- c("Spark is pretty.", "Spark is awesome.")
-  fileName <- tempfile(pattern="spark-test", fileext=".tmp")
+  fileName <- tempfile(pattern = "spark-test", fileext = ".tmp")
   writeLines(mockFile, fileName)
 
   rdd <- textFile(sc, fileName, 1)

R/pkg/inst/tests/testthat/test_broadcast.R

@@ -25,7 +25,7 @@ nums <- 1:2
 rrdd <- parallelize(sc, nums, 2L)
 
 test_that("using broadcast variable", {
-  randomMat <- matrix(nrow=10, ncol=10, data=rnorm(100))
+  randomMat <- matrix(nrow = 10, ncol = 10, data = rnorm(100))
   randomMatBr <- broadcast(sc, randomMat)
 
   useBroadcast <- function(x) {
@@ -37,7 +37,7 @@ test_that("using broadcast variable", {
 })
 
 test_that("without using broadcast variable", {
-  randomMat <- matrix(nrow=10, ncol=10, data=rnorm(100))
+  randomMat <- matrix(nrow = 10, ncol = 10, data = rnorm(100))
 
   useBroadcast <- function(x) {
     sum(randomMat * x)

R/pkg/inst/tests/testthat/test_mllib.R

@@ -96,9 +96,9 @@ test_that("summary coefficients match with native glm of family 'binomial'", {
   training <- filter(df, df$Species != "setosa")
   stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
     family = "binomial"))
-  coefs <- as.vector(stats$coefficients[,1])
+  coefs <- as.vector(stats$coefficients[, 1])
 
-  rTraining <- iris[iris$Species %in% c("versicolor","virginica"),]
+  rTraining <- iris[iris$Species %in% c("versicolor", "virginica"), ]
   rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
     family = binomial(link = "logit"))))