replace windows_with_hadoop, is_windows

R/pkg/R/install.R

@@ -267,7 +267,7 @@ hadoopVersionName <- function(hadoopVersion) {
 # The implementation refers to appdirs package: https://pypi.python.org/pypi/appdirs and
 # adapt to Spark context
 sparkCachePath <- function() {
-  if (.Platform$OS.type == "windows") {
+  if (is_windows()) {
     winAppPath <- Sys.getenv("LOCALAPPDATA", unset = NA)
     if (is.na(winAppPath)) {
       stop(paste("%LOCALAPPDATA% not found.",

R/pkg/R/utils.R

@@ -908,10 +908,6 @@ isAtomicLengthOne <- function(x) {
   is.atomic(x) && length(x) == 1
 }
 
-is_cran <- function() {
-  !identical(Sys.getenv("NOT_CRAN"), "true")
-}
-
 is_windows <- function() {
   .Platform$OS.type == "windows"
 }
@@ -920,6 +916,6 @@ hadoop_home_set <- function() {
   !identical(Sys.getenv("HADOOP_HOME"), "")
 }
 
-not_cran_or_windows_with_hadoop <- function() {
-  !is_cran() && (!is_windows() || hadoop_home_set())
+windows_with_hadoop <- function() {
+  !is_windows() || hadoop_home_set()
 }

R/pkg/tests/fulltests/test_Windows.R

@@ -17,7 +17,7 @@
 context("Windows-specific tests")
 
 test_that("sparkJars tag in SparkContext", {
-  if (.Platform$OS.type != "windows") {
+  if (!is_windows()) {
     skip("This test is only for Windows, skipped")
   }
 

R/pkg/tests/fulltests/test_mllib_classification.R

@@ -49,7 +49,7 @@ test_that("spark.svmLinear", {
   expect_equal(sort(as.list(take(select(prediction, "prediction"), 10))[[1]]), expected)
 
   # Test model save and load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-svm-linear", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -129,7 +129,7 @@ test_that("spark.logit", {
   expect_true(all(abs(setosaCoefs - setosaCoefs) < 0.1))
 
   # Test model save and load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-logit", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -246,7 +246,7 @@ test_that("spark.mlp", {
   expect_equal(head(mlpPredictions$prediction, 6), c("1.0", "0.0", "0.0", "0.0", "0.0", "0.0"))
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-mlp", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -359,7 +359,7 @@ test_that("spark.naiveBayes", {
                                "Yes", "Yes", "No", "No"))
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-naiveBayes", fileext = ".tmp")
     write.ml(m, modelPath)
     expect_error(write.ml(m, modelPath))

R/pkg/tests/fulltests/test_mllib_clustering.R

@@ -53,7 +53,7 @@ test_that("spark.bisectingKmeans", {
                c(0, 1, 2, 3))
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-bisectingkmeans", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -127,7 +127,7 @@ test_that("spark.gaussianMixture", {
   expect_equal(p$prediction, c(0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1))
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-gaussianMixture", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -175,7 +175,7 @@ test_that("spark.kmeans", {
   expect_true(class(summary.model$coefficients[1, ]) == "numeric")
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-kmeans", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -242,7 +242,7 @@ test_that("spark.lda with libsvm", {
   expect_true(logPrior <= 0 & !is.na(logPrior))
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-lda", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))

R/pkg/tests/fulltests/test_mllib_fpm.R

@@ -62,7 +62,7 @@ test_that("spark.fpGrowth", {
 
   expect_equivalent(expected_predictions, collect(predict(model, new_data)))
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-fpm", fileext = ".tmp")
     write.ml(model, modelPath, overwrite = TRUE)
     loaded_model <- read.ml(modelPath)

R/pkg/tests/fulltests/test_mllib_recommendation.R

@@ -37,7 +37,7 @@ test_that("spark.als", {
   tolerance = 1e-4)
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-als", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))

R/pkg/tests/fulltests/test_mllib_regression.R

@@ -389,7 +389,7 @@ test_that("spark.isoreg", {
   expect_equal(predict_result$prediction, c(7.0, 7.0, 6.0, 5.5, 5.0, 4.0, 1.0))
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-isoreg", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -440,7 +440,7 @@ test_that("spark.survreg", {
                2.390146, 2.891269, 2.891269), tolerance = 1e-4)
 
   # Test model save/load
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-survreg", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))

R/pkg/tests/fulltests/test_mllib_tree.R

@@ -44,7 +44,7 @@ test_that("spark.gbt", {
   expect_equal(stats$numFeatures, 6)
   expect_equal(length(stats$treeWeights), 20)
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-gbtRegression", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -78,7 +78,7 @@ test_that("spark.gbt", {
   expect_equal(length(grep("setosa", predictions)), 50)
   expect_equal(length(grep("versicolor", predictions)), 50)
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-gbtClassification", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -103,7 +103,7 @@ test_that("spark.gbt", {
   expect_equal(stats$maxDepth, 5)
 
   # spark.gbt classification can work on libsvm data
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     data <- read.df(absoluteSparkPath("data/mllib/sample_binary_classification_data.txt"),
                   source = "libsvm")
     model <- spark.gbt(data, label ~ features, "classification")
@@ -142,7 +142,7 @@ test_that("spark.randomForest", {
   expect_equal(stats$numTrees, 20)
   expect_equal(stats$maxDepth, 5)
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-randomForestRegression", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -176,7 +176,7 @@ test_that("spark.randomForest", {
   expect_equal(length(grep("setosa", predictions)), 50)
   expect_equal(length(grep("versicolor", predictions)), 50)
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-randomForestClassification", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -213,7 +213,7 @@ test_that("spark.randomForest", {
   expect_equal(length(grep("2.0", predictions)), 50)
 
   # spark.randomForest classification can work on libsvm data
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
                   source = "libsvm")
     model <- spark.randomForest(data, label ~ features, "classification")
@@ -238,7 +238,7 @@ test_that("spark.decisionTree", {
   expect_error(capture.output(stats), NA)
   expect_true(length(capture.output(stats)) > 6)
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-decisionTreeRegression", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -269,7 +269,7 @@ test_that("spark.decisionTree", {
   expect_equal(length(grep("setosa", predictions)), 50)
   expect_equal(length(grep("versicolor", predictions)), 50)
 
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     modelPath <- tempfile(pattern = "spark-decisionTreeClassification", fileext = ".tmp")
     write.ml(model, modelPath)
     expect_error(write.ml(model, modelPath))
@@ -305,7 +305,7 @@ test_that("spark.decisionTree", {
   expect_equal(length(grep("2.0", predictions)), 50)
 
   # spark.decisionTree classification can work on libsvm data
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     data <- read.df(absoluteSparkPath("data/mllib/sample_multiclass_classification_data.txt"),
                   source = "libsvm")
     model <- spark.decisionTree(data, label ~ features, "classification")

R/pkg/tests/fulltests/test_sparkSQL.R

@@ -61,7 +61,7 @@ unsetHiveContext <- function() {
 # Tests for SparkSQL functions in SparkR
 
 filesBefore <- list.files(path = sparkRDir, all.files = TRUE)
-sparkSession <- if (not_cran_or_windows_with_hadoop()) {
+sparkSession <- if (windows_with_hadoop()) {
     sparkR.session(master = sparkRTestMaster)
   } else {
     sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
@@ -100,7 +100,7 @@ mockLinesMapType <- c("{\"name\":\"Bob\",\"info\":{\"age\":16,\"height\":176.5}}
 mapTypeJsonPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
 writeLines(mockLinesMapType, mapTypeJsonPath)
 
-if (.Platform$OS.type == "windows") {
+if (is_windows()) {
   Sys.setenv(TZ = "GMT")
 }
 
@@ -320,7 +320,7 @@ test_that("createDataFrame uses files for large objects", {
 })
 
 test_that("read/write csv as DataFrame", {
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     csvPath <- tempfile(pattern = "sparkr-test", fileext = ".csv")
     mockLinesCsv <- c("year,make,model,comment,blank",
                      "\"2012\",\"Tesla\",\"S\",\"No comment\",",
@@ -589,7 +589,7 @@ test_that("Collect DataFrame with complex types", {
 })
 
 test_that("read/write json files", {
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     # Test read.df
     df <- read.df(jsonPath, "json")
     expect_is(df, "SparkDataFrame")
@@ -720,7 +720,7 @@ test_that("test cache, uncache and clearCache", {
 })
 
 test_that("insertInto() on a registered table", {
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     df <- read.df(jsonPath, "json")
     write.df(df, parquetPath, "parquet", "overwrite")
     dfParquet <- read.df(parquetPath, "parquet")
@@ -928,7 +928,7 @@ test_that("cache(), storageLevel(), persist(), and unpersist() on a DataFrame",
 })
 
 test_that("setCheckpointDir(), checkpoint() on a DataFrame", {
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     checkpointDir <- file.path(tempdir(), "cproot")
     expect_true(length(list.files(path = checkpointDir, all.files = TRUE)) == 0)
 
@@ -1305,7 +1305,7 @@ test_that("column calculation", {
 })
 
 test_that("test HiveContext", {
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     setHiveContext(sc)
 
     schema <- structType(structField("name", "string"), structField("age", "integer"),
@@ -2394,7 +2394,7 @@ test_that("read/write ORC files - compression option", {
 })
 
 test_that("read/write Parquet files", {
-  if (not_cran_or_windows_with_hadoop()) {
+  if (windows_with_hadoop()) {
     df <- read.df(jsonPath, "json")
     # Test write.df and read.df
     write.df(df, parquetPath, "parquet", mode = "overwrite")

R/pkg/tests/fulltests/test_streaming.R

@@ -24,7 +24,7 @@ context("Structured Streaming")
 sparkSession <- sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
 
 jsonSubDir <- file.path("sparkr-test", "json", "")
-if (.Platform$OS.type == "windows") {
+if (is_windows()) {
   # file.path removes the empty separator on Windows, adds it back
   jsonSubDir <- paste0(jsonSubDir, .Platform$file.sep)
 }

R/pkg/tests/run-all.R

@@ -21,7 +21,7 @@ library(SparkR)
 # Turn all warnings into errors
 options("warn" = 2)
 
-if (.Platform$OS.type == "windows") {
+if (is_windows()) {
   Sys.setenv(TZ = "GMT")
 }