[SPARK-19399][SPARKR] Add R coalesce API for DataFrame and Column

## What changes were proposed in this pull request?

Add coalesce on DataFrame for down partitioning without shuffle and coalesce on Column

## How was this patch tested?

manual, unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #16739 from felixcheung/rcoalesce.

2 files changed, 28 insertions, 6 deletions

diff --git a/R/pkg/inst/tests/testthat/test_rdd.R b/R/pkg/inst/tests/testthat/test_rdd.R
index ceb31bd896..787ef51c50 100644
--- a/R/pkg/inst/tests/testthat/test_rdd.R
+++ b/R/pkg/inst/tests/testthat/test_rdd.R
@@ -315,7 +315,7 @@ test_that("repartition/coalesce on RDDs", {
   expect_true(count >= 0 && count <= 4)
 
   # coalesce
-  r3 <- coalesce(rdd, 1)
+  r3 <- coalesceRDD(rdd, 1)
   expect_equal(getNumPartitionsRDD(r3), 1L)
   count <- length(collectPartition(r3, 0L))
   expect_equal(count, 20)
diff --git a/R/pkg/inst/tests/testthat/test_sparkSQL.R b/R/pkg/inst/tests/testthat/test_sparkSQL.R
index 1494ebb3de..199eb2057f 100644
--- a/R/pkg/inst/tests/testthat/test_sparkSQL.R
+++ b/R/pkg/inst/tests/testthat/test_sparkSQL.R
@@ -725,7 +725,7 @@ test_that("objectFile() works with row serialization", {
   objectPath <- tempfile(pattern = "spark-test", fileext = ".tmp")
   df <- read.json(jsonPath)
   dfRDD <- toRDD(df)
-  saveAsObjectFile(coalesce(dfRDD, 1L), objectPath)
+  saveAsObjectFile(coalesceRDD(dfRDD, 1L), objectPath)
   objectIn <- objectFile(sc, objectPath)
 
   expect_is(objectIn, "RDD")
@@ -1236,7 +1236,7 @@ test_that("column functions", {
   c16 <- is.nan(c) + isnan(c) + isNaN(c)
   c17 <- cov(c, c1) + cov("c", "c1") + covar_samp(c, c1) + covar_samp("c", "c1")
   c18 <- covar_pop(c, c1) + covar_pop("c", "c1")
-  c19 <- spark_partition_id()
+  c19 <- spark_partition_id() + coalesce(c) + coalesce(c1, c2, c3)
   c20 <- to_timestamp(c) + to_timestamp(c, "yyyy") + to_date(c, "yyyy")
 
   # Test if base::is.nan() is exposed
@@ -2491,15 +2491,18 @@ test_that("repartition by columns on DataFrame", {
     ("Please, specify the number of partitions and/or a column\\(s\\)", retError), TRUE)
 
   # repartition by column and number of partitions
-  actual <- repartition(df, 3L, col = df$"a")
+  actual <- repartition(df, 3, col = df$"a")
 
-  # since we cannot access the number of partitions from dataframe, checking
-  # that at least the dimensions are identical
+  # Checking that at least the dimensions are identical
   expect_identical(dim(df), dim(actual))
+  expect_equal(getNumPartitions(actual), 3L)
 
   # repartition by number of partitions
   actual <- repartition(df, 13L)
   expect_identical(dim(df), dim(actual))
+  expect_equal(getNumPartitions(actual), 13L)
+
+  expect_equal(getNumPartitions(coalesce(actual, 1L)), 1L)
 
   # a test case with a column and dapply
   schema <-  structType(structField("a", "integer"), structField("avg", "double"))
@@ -2515,6 +2518,25 @@ test_that("repartition by columns on DataFrame", {
   expect_equal(nrow(df1), 2)
 })
 
+test_that("coalesce, repartition, numPartitions", {
+  df <- as.DataFrame(cars, numPartitions = 5)
+  expect_equal(getNumPartitions(df), 5)
+  expect_equal(getNumPartitions(coalesce(df, 3)), 3)
+  expect_equal(getNumPartitions(coalesce(df, 6)), 5)
+
+  df1 <- coalesce(df, 3)
+  expect_equal(getNumPartitions(df1), 3)
+  expect_equal(getNumPartitions(coalesce(df1, 6)), 5)
+  expect_equal(getNumPartitions(coalesce(df1, 4)), 4)
+  expect_equal(getNumPartitions(coalesce(df1, 2)), 2)
+
+  df2 <- repartition(df1, 10)
+  expect_equal(getNumPartitions(df2), 10)
+  expect_equal(getNumPartitions(coalesce(df2, 13)), 5)
+  expect_equal(getNumPartitions(coalesce(df2, 7)), 5)
+  expect_equal(getNumPartitions(coalesce(df2, 3)), 3)
+})
+
 test_that("gapply() and gapplyCollect() on a DataFrame", {
   df <- createDataFrame (
     list(list(1L, 1, "1", 0.1), list(1L, 2, "1", 0.2), list(3L, 3, "3", 0.3)),