https://issues.apache.org/jira/browse/SPARK-16445
## What changes were proposed in this pull request?
Create Multilayer Perceptron Classifier wrapper in SparkR
## How was this patch tested?
Tested manually on local machine
Author: Xin Ren <iamshrek@126.com>
Closes#14447 from keypointt/SPARK-16445.
## What changes were proposed in this pull request?
refactor, cleanup, reformat, fix deprecation in test
## How was this patch tested?
unit tests, manual tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14735 from felixcheung/rmllibutil.
## What changes were proposed in this pull request?
#14551 fixed off-by-one bug in ```randomizeInPlace``` and some test failure caused by this fix.
But for SparkR ```spark.gaussianMixture``` test case, the fix is inappropriate. It only changed the output result of native R which should be compared by SparkR, however, it did not change the R code in annotation which is used for reproducing the result in native R. It will confuse users who can not reproduce the same result in native R. This PR sends a more robust test case which can produce same result between SparkR and native R.
## How was this patch tested?
Unit test update.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14730 from yanboliang/spark-16961-followup.
JIRA issue link:
https://issues.apache.org/jira/browse/SPARK-16961
Changed one line of Utils.randomizeInPlace to allow elements to stay in place.
Created a unit test that runs a Pearson's chi squared test to determine whether the output diverges significantly from a uniform distribution.
Author: Nick Lavers <nick.lavers@videoamp.com>
Closes#14551 from nicklavers/SPARK-16961-randomizeInPlace.
## What changes were proposed in this pull request?
Add LDA Wrapper in SparkR with the following interfaces:
- spark.lda(data, ...)
- spark.posterior(object, newData, ...)
- spark.perplexity(object, ...)
- summary(object)
- write.ml(object)
- read.ml(path)
## How was this patch tested?
Test with SparkR unit test.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#14229 from yinxusen/SPARK-16447.
## What changes were proposed in this pull request?
Gaussian Mixture Model wrapper in SparkR, similarly to R's ```mvnormalmixEM```.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14392 from yanboliang/spark-16446.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
Add Isotonic Regression wrapper in SparkR
Wrappers in R and Scala are added.
Unit tests
Documentation
## How was this patch tested?
Manually tested with sudo ./R/run-tests.sh
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#14182 from wangmiao1981/isoR.
## What changes were proposed in this pull request?
Rename RDD functions for now to avoid CRAN check warnings.
Some RDD functions are sharing generics with DataFrame functions (hence the problem) so after the renames we need to add new generics, for now.
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14626 from felixcheung/rrddfunctions.
## What changes were proposed in this pull request?
Add an install_spark function to the SparkR package. User can run `install_spark()` to install Spark to a local directory within R.
Updates:
Several changes have been made:
- `install.spark()`
- check existence of tar file in the cache folder, and download only if not found
- trial priority of mirror_url look-up: user-provided -> preferred mirror site from apache website -> hardcoded backup option
- use 2.0.0
- `sparkR.session()`
- can install spark when not found in `SPARK_HOME`
## How was this patch tested?
Manual tests, running the check-cran.sh script added in #14173.
Author: Junyang Qian <junyangq@databricks.com>
Closes#14258 from junyangq/SPARK-16579.
## What changes were proposed in this pull request?
Training GLMs on weighted dataset is very important use cases, but it is not supported by SparkR currently. Users can pass argument ```weights``` to specify the weights vector in native R. For ```spark.glm```, we can pass in the ```weightCol``` which is consistent with MLlib.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#14346 from yanboliang/spark-16710.
## What changes were proposed in this pull request?
This change moves the include jar test from R to SparkSubmitSuite and uses a dynamically compiled jar. This helps us remove the binary jar from the R package and solves both the CRAN warnings and the lack of source being available for this jar.
## How was this patch tested?
SparkR unit tests, SparkSubmitSuite, check-cran.sh
Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>
Closes#14243 from shivaram/sparkr-jar-move.
## What changes were proposed in this pull request?
Fix R SparkSession init/stop, and warnings of reusing existing Spark Context
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14177 from felixcheung/rsessiontest.
## What changes were proposed in this pull request?
More tests
I don't think this is critical for Spark 2.0.0 RC, maybe Spark 2.0.1 or 2.1.0.
## How was this patch tested?
unit tests
shivaram dongjoon-hyun
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14206 from felixcheung/rroutetests.
## What changes were proposed in this pull request?
Fix function routing to work with and without namespace operator `SparkR::createDataFrame`
## How was this patch tested?
manual, unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14195 from felixcheung/rroutedefault.
## What changes were proposed in this pull request?
Rename window.partitionBy and window.orderBy to windowPartitionBy and windowOrderBy to pass CRAN package check.
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#14192 from sun-rui/SPARK-16509.
## What changes were proposed in this pull request?
Minor documentation update for code example, code style, and missed reference to "sparkR.init"
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14178 from felixcheung/rcsvprogrammingguide.
## What changes were proposed in this pull request?
Minor example updates
## How was this patch tested?
manual
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#14171 from felixcheung/rexample.
## What changes were proposed in this pull request?
This PR prevents ERRORs when `summary(df)` is called for `SparkDataFrame` with not-numeric columns. This failure happens only in `SparkR`.
**Before**
```r
> df <- createDataFrame(faithful)
> df <- withColumn(df, "boolean", df$waiting==79)
> summary(df)
16/07/07 14:15:16 ERROR RBackendHandler: describe on 34 failed
Error in invokeJava(isStatic = FALSE, objId$id, methodName, ...) :
org.apache.spark.sql.AnalysisException: cannot resolve 'avg(`boolean`)' due to data type mismatch: function average requires numeric types, not BooleanType;
```
**After**
```r
> df <- createDataFrame(faithful)
> df <- withColumn(df, "boolean", df$waiting==79)
> summary(df)
SparkDataFrame[summary:string, eruptions:string, waiting:string]
```
## How was this patch tested?
Pass the Jenkins with a updated testcase.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14096 from dongjoon-hyun/SPARK-16425.
## What changes were proposed in this pull request?
Apply default "NA" as null string for R, like R read.csv na.string parameter.
https://stat.ethz.ch/R-manual/R-devel/library/utils/html/read.table.html
na.strings = "NA"
An user passing a csv file with NA value should get the same behavior with SparkR read.df(... source = "csv")
(couldn't open JIRA, will do that later)
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13984 from felixcheung/rcsvnastring.
## What changes were proposed in this pull request?
ORC test should be enabled only when HiveContext is available.
## How was this patch tested?
Manual.
```
$ R/run-tests.sh
...
1. create DataFrame from RDD (test_sparkSQL.R#200) - Hive is not build with SparkSQL, skipped
2. test HiveContext (test_sparkSQL.R#1021) - Hive is not build with SparkSQL, skipped
3. read/write ORC files (test_sparkSQL.R#1728) - Hive is not build with SparkSQL, skipped
4. enableHiveSupport on SparkSession (test_sparkSQL.R#2448) - Hive is not build with SparkSQL, skipped
5. sparkJars tag in SparkContext (test_Windows.R#21) - This test is only for Windows, skipped
DONE ===========================================================================
Tests passed.
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#14019 from dongjoon-hyun/SPARK-16233.
## What changes were proposed in this pull request?
gapplyCollect() does gapply() on a SparkDataFrame and collect the result back to R. Compared to gapply() + collect(), gapplyCollect() offers performance optimization as well as programming convenience, as no schema is needed to be provided.
This is similar to dapplyCollect().
## How was this patch tested?
Added test cases for gapplyCollect similar to dapplyCollect
Author: Narine Kokhlikyan <narine@slice.com>
Closes#13760 from NarineK/gapplyCollect.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
Add unit tests for csv data for SPARKR
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13904 from felixcheung/rcsv.
## What changes were proposed in this pull request?
Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise.
## How was this patch tested?
Existing tests. + 1 new test in DataFrameSuite.
For SparkR and pyspark, existing tests and manual testing.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#13839 from ScrapCodes/add_truncateTo_DF.show.
## What changes were proposed in this pull request?
Add `conf` method to get Runtime Config from SparkSession
## How was this patch tested?
unit tests, manual tests
This is how it works in sparkR shell:
```
SparkSession available as 'spark'.
> conf()
$hive.metastore.warehouse.dir
[1] "file:/opt/spark-2.0.0-bin-hadoop2.6/R/spark-warehouse"
$spark.app.id
[1] "local-1466749575523"
$spark.app.name
[1] "SparkR"
$spark.driver.host
[1] "10.0.2.1"
$spark.driver.port
[1] "45629"
$spark.executorEnv.LD_LIBRARY_PATH
[1] "$LD_LIBRARY_PATH:/usr/lib/R/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/jre/lib/amd64/server"
$spark.executor.id
[1] "driver"
$spark.home
[1] "/opt/spark-2.0.0-bin-hadoop2.6"
$spark.master
[1] "local[*]"
$spark.sql.catalogImplementation
[1] "hive"
$spark.submit.deployMode
[1] "client"
> conf("spark.master")
$spark.master
[1] "local[*]"
```
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13885 from felixcheung/rconf.
## What changes were proposed in this pull request?
Updated setJobGroup, cancelJobGroup, clearJobGroup to not require sc/SparkContext as parameter.
Also updated roxygen2 doc and R programming guide on deprecations.
## How was this patch tested?
unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13838 from felixcheung/rjobgroup.
## What changes were proposed in this pull request?
add union and deprecate unionAll, separate roxygen2 doc for rbind (since their usage and parameter lists are quite different)
`explode` is also deprecated - but seems like replacement is a combination of calls; not sure if we should deprecate it in SparkR, yet.
## How was this patch tested?
unit tests, manual checks for r doc
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13805 from felixcheung/runion.
## What changes were proposed in this pull request?
This PR is a subset of #13023 by yanboliang to make SparkR model param names and default values consistent with MLlib. I tried to avoid other changes from #13023 to keep this PR minimal. I will send a follow-up PR to improve the documentation.
Main changes:
* `spark.glm`: epsilon -> tol, maxit -> maxIter
* `spark.kmeans`: default k -> 2, default maxIter -> 20, default initMode -> "k-means||"
* `spark.naiveBayes`: laplace -> smoothing, default 1.0
## How was this patch tested?
Existing unit tests.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13801 from mengxr/SPARK-15177.1.
## What changes were proposed in this pull request?
This PR adds `pivot` function to SparkR for API parity. Since this PR is based on https://github.com/apache/spark/pull/13295 , mhnatiuk should be credited for the work he did.
## How was this patch tested?
Pass the Jenkins tests (including new testcase.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13786 from dongjoon-hyun/SPARK-15294.
## What changes were proposed in this pull request?
This PR adds `spark_partition_id` virtual column function in SparkR for API parity.
The following is just an example to illustrate a SparkR usage on a partitioned parquet table created by `spark.range(10).write.mode("overwrite").parquet("/tmp/t1")`.
```r
> collect(select(read.parquet('/tmp/t1'), c('id', spark_partition_id())))
id SPARK_PARTITION_ID()
1 3 0
2 4 0
3 8 1
4 9 1
5 0 2
6 1 3
7 2 4
8 5 5
9 6 6
10 7 7
```
## How was this patch tested?
Pass the Jenkins tests (including new testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13768 from dongjoon-hyun/SPARK-16053.
## What changes were proposed in this pull request?
spark.lapply and setLogLevel
## How was this patch tested?
unit test
shivaram thunterdb
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13752 from felixcheung/rlapply.
## What changes were proposed in this pull request?
This issue adds `read.orc/write.orc` to SparkR for API parity.
## How was this patch tested?
Pass the Jenkins tests (with new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13763 from dongjoon-hyun/SPARK-16051.
## What changes were proposed in this pull request?
Add dropTempView and deprecate dropTempTable
## How was this patch tested?
unit tests
shivaram liancheng
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13753 from felixcheung/rdroptempview.
## What changes were proposed in this pull request?
This PR adds `monotonically_increasing_id` column function in SparkR for API parity.
After this PR, SparkR supports the followings.
```r
> df <- read.json("examples/src/main/resources/people.json")
> collect(select(df, monotonically_increasing_id(), df$name, df$age))
monotonically_increasing_id() name age
1 0 Michael NA
2 1 Andy 30
3 2 Justin 19
```
## How was this patch tested?
Pass the Jenkins tests (with added testcase).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13774 from dongjoon-hyun/SPARK-16059.
## What changes were proposed in this pull request?
This PR introduces the new SparkSession API for SparkR.
`sparkR.session.getOrCreate()` and `sparkR.session.stop()`
"getOrCreate" is a bit unusual in R but it's important to name this clearly.
SparkR implementation should
- SparkSession is the main entrypoint (vs SparkContext; due to limited functionality supported with SparkContext in SparkR)
- SparkSession replaces SQLContext and HiveContext (both a wrapper around SparkSession, and because of API changes, supporting all 3 would be a lot more work)
- Changes to SparkSession is mostly transparent to users due to SPARK-10903
- Full backward compatibility is expected - users should be able to initialize everything just in Spark 1.6.1 (`sparkR.init()`), but with deprecation warning
- Mostly cosmetic changes to parameter list - users should be able to move to `sparkR.session.getOrCreate()` easily
- An advanced syntax with named parameters (aka varargs aka "...") is supported; that should be closer to the Builder syntax that is in Scala/Python (which unfortunately does not work in R because it will look like this: `enableHiveSupport(config(config(master(appName(builder(), "foo"), "local"), "first", "value"), "next, "value"))`
- Updating config on an existing SparkSession is supported, the behavior is the same as Python, in which config is applied to both SparkContext and SparkSession
- Some SparkSession changes are not matched in SparkR, mostly because it would be breaking API change: `catalog` object, `createOrReplaceTempView`
- Other SQLContext workarounds are replicated in SparkR, eg. `tables`, `tableNames`
- `sparkR` shell is updated to use the SparkSession entrypoint (`sqlContext` is removed, just like with Scale/Python)
- All tests are updated to use the SparkSession entrypoint
- A bug in `read.jdbc` is fixed
TODO
- [x] Add more tests
- [ ] Separate PR - update all roxygen2 doc coding example
- [ ] Separate PR - update SparkR programming guide
## How was this patch tested?
unit tests, manual tests
shivaram sun-rui rxin
Author: Felix Cheung <felixcheung_m@hotmail.com>
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#13635 from felixcheung/rsparksession.
## What changes were proposed in this pull request?
This PR adds `randomSplit` to SparkR for API parity.
## How was this patch tested?
Pass the Jenkins tests (with new testcase.)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13721 from dongjoon-hyun/SPARK-16005.
## What changes were proposed in this pull request?
Add registerTempTable to DataFrame with Deprecate
## How was this patch tested?
unit tests
shivaram liancheng
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13722 from felixcheung/rregistertemptable.
## What changes were proposed in this pull request?
This PR adds varargs-type `dropDuplicates` function to SparkR for API parity.
Refer to https://issues.apache.org/jira/browse/SPARK-15807, too.
## How was this patch tested?
Pass the Jenkins tests with new testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13684 from dongjoon-hyun/SPARK-15908.
## What changes were proposed in this pull request?
gapply() applies an R function on groups grouped by one or more columns of a DataFrame, and returns a DataFrame. It is like GroupedDataSet.flatMapGroups() in the Dataset API.
Please, let me know what do you think and if you have any ideas to improve it.
Thank you!
## How was this patch tested?
Unit tests.
1. Primitive test with different column types
2. Add a boolean column
3. Compute average by a group
Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com>
Author: NarineK <narine.kokhlikyan@us.ibm.com>
Closes#12836 from NarineK/gapply2.
## What changes were proposed in this pull request?
Because of the fix in SPARK-15684, this exclusion is no longer necessary.
## How was this patch tested?
unit tests
shivaram
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#13636 from felixcheung/rendswith.
## What changes were proposed in this pull request?
This PR replaces `registerTempTable` with `createOrReplaceTempView` as a follow-up task of #12945.
## How was this patch tested?
Existing SparkR tests.
Author: Cheng Lian <lian@databricks.com>
Closes#13644 from liancheng/spark-15925-temp-view-for-r.
## What changes were proposed in this pull request?
In R 3.3.0, startsWith and endsWith are added. In this PR, I make the two work in SparkR.
1. Remove signature in generic.R
2. Add setMethod in column.R
3. Add unit tests
## How was this patch tested?
Manually test it through SparkR shell for both column data and string data, which are added into the unit test file.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#13476 from wangmiao1981/start.
## What changes were proposed in this pull request?
Change version check in R tests
## How was this patch tested?
R tests
shivaram
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#13369 from felixcheung/rversioncheck.
## What changes were proposed in this pull request?
This PR corrects SparkR to use `shell()` instead of `system2()` on Windows.
Using `system2(...)` on Windows does not process windows file separator `\`. `shell(tralsate = TRUE, ...)` can treat this problem. So, this was changed to be chosen according to OS.
Existing tests were failed on Windows due to this problem. For example, those were failed.
```
8. Failure: sparkJars tag in SparkContext (test_includeJAR.R#34)
9. Failure: sparkJars tag in SparkContext (test_includeJAR.R#36)
```
The cases above were due to using of `system2`.
In addition, this PR also fixes some tests failed on Windows.
```
5. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#128)
6. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#131)
7. Failure: sparkJars sparkPackages as comma-separated strings (test_context.R#134)
```
The cases above were due to a weird behaviour of `normalizePath()`. On Linux, if the path does not exist, it just prints out the input but it prints out including the current path on Windows.
```r
# On Linus
path <- normalizePath("aa")
print(path)
[1] "aa"
# On Windows
path <- normalizePath("aa")
print(path)
[1] "C:\\Users\\aa"
```
## How was this patch tested?
Jenkins tests and manually tested in a Window machine as below:
Here is the [stdout](https://gist.github.com/HyukjinKwon/4bf35184f3a30f3bce987a58ec2bbbab) of testing.
Closes#7025
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Author: Prakash PC <prakash.chinnu@gmail.com>
Closes#13165 from HyukjinKwon/pr/7025.
Eliminate the need to pass sqlContext to method since it is a singleton - and we don't want to support multiple contexts in a R session.
Changes are done in a back compat way with deprecation warning added. Method signature for S3 methods are added in a concise, clean approach such that in the next release the deprecated signature can be taken out easily/cleanly (just delete a few lines per method).
Custom method dispatch is implemented to allow for multiple JVM reference types that are all 'jobj' in R and to avoid having to add 30 new exports.
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#9192 from felixcheung/rsqlcontext.
## What changes were proposed in this pull request?
(Please fill in changes proposed in this fix)
There are some failures when running SparkR unit tests.
In this PR, I fixed two of these failures in test_context.R and test_sparkSQL.R
The first one is due to different masked name. I added missed names in the expected arrays.
The second one is because one PR removed the logic of a previous fix of missing subset method.
The file privilege issue is still there. I am debugging it. SparkR shell can run the test case successfully.
test_that("pipeRDD() on RDDs", {
actual <- collect(pipeRDD(rdd, "more"))
When using run-test script, it complains no such directories as below:
cannot open file '/tmp/Rtmp4FQbah/filee2273f9d47f7': No such file or directory
## How was this patch tested?
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
Manually test it
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#13284 from wangmiao1981/R.
## What changes were proposed in this pull request?
in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.
## How was this patch tested?
tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#13186 from adrian-wang/locate.
## What changes were proposed in this pull request?
dapplyCollect() applies an R function on each partition of a SparkDataFrame and collects the result back to R as a data.frame.
```
dapplyCollect(df, function(ldf) {...})
```
## How was this patch tested?
SparkR unit tests.
Author: Sun Rui <sunrui2016@gmail.com>
Closes#12989 from sun-rui/SPARK-15202.
