## What changes were proposed in this pull request?

Looks like `FlatMapGroupsInRExec.requiredChildDistribution` didn't consider empty grouping attributes. It should be a problem when running `EnsureRequirements` and `gapply` in R can't work on empty grouping columns.

## How was this patch tested?

Added test.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #19436 from viirya/fix-flatmapinr-distribution.

## What changes were proposed in this pull request?

Currently, we set lintr to jimhester/lintra769c0b (see [this](https://github.com/apache/spark/commit/7d1175011c976756efcd4e4e4f70a8fd6f287026) and [SPARK-14074](https://issues.apache.org/jira/browse/SPARK-14074)).

I first tested and checked lintr-1.0.1 but it looks many important fixes are missing (for example, checking 100 length). So, I instead tried the latest commit, https://github.com/jimhester/lintr/commit/5431140ffea65071f1327625d4a8de9688fa7e72, in my local and fixed the check failures.

It looks it has fixed many bugs and now finds many instances that I have observed and thought should be caught time to time, here I filed [the results](https://gist.github.com/HyukjinKwon/4f59ddcc7b6487a02da81800baca533c).

The downside looks it now takes about 7ish mins, (it was 2ish mins before) in my local.

## How was this patch tested?

Manually, `./dev/lint-r` after manually updating the lintr package.

Author: hyukjinkwon <gurwls223@gmail.com>
Author: zuotingbing <zuo.tingbing9@zte.com.cn>

Closes #19290 from HyukjinKwon/upgrade-r-lint.

[SPARK-22100][SQL] Make percentile_approx support date/timestamp type and change the output type to be the same as input type

## What changes were proposed in this pull request?

The `percentile_approx` function previously accepted numeric type input and output double type results.

But since all numeric types, date and timestamp types are represented as numerics internally, `percentile_approx` can support them easily.

After this PR, it supports date type, timestamp type and numeric types as input types. The result type is also changed to be the same as the input type, which is more reasonable for percentiles.

This change is also required when we generate equi-height histograms for these types.

## How was this patch tested?

Added a new test and modified some existing tests.

Author: Zhenhua Wang <wangzhenhua@huawei.com>

Closes #19321 from wzhfy/approx_percentile_support_types.

## What changes were proposed in this pull request?

This PR make `sample(...)` able to omit `withReplacement` defaulting to `FALSE`.

In short, the following examples are allowed:

```r
> df <- createDataFrame(as.list(seq(10)))
> count(sample(df, fraction=0.5, seed=3))
[1] 4
> count(sample(df, fraction=1.0))
[1] 10
```

In addition, this PR also adds some type checking logics as below:

```r
> sample(df, fraction = "a")
Error in sample(df, fraction = "a") :
  fraction must be numeric; however, got character
> sample(df, fraction = 1, seed = NULL)
Error in sample(df, fraction = 1, seed = NULL) :
  seed must not be NULL or NA; however, got NULL
> sample(df, list(1), 1.0)
Error in sample(df, list(1), 1) :
  withReplacement must be logical; however, got list
> sample(df, fraction = -1.0)
...
Error in sample : illegal argument - requirement failed: Sampling fraction (-1.0) must be on interval [0, 1] without replacement
```

## How was this patch tested?

Manually tested, unit tests added in `R/pkg/tests/fulltests/test_sparkSQL.R`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #19243 from HyukjinKwon/SPARK-21780.

[SPARK-21513][SQL][FOLLOWUP] Allow UDF to_json support converting MapType to json for PySpark and SparkR

## What changes were proposed in this pull request?
In previous work SPARK-21513, we has allowed `MapType` and `ArrayType` of `MapType`s convert to a json string but only for Scala API. In this follow-up PR, we will make SparkSQL support it for PySpark and SparkR, too. We also fix some little bugs and comments of the previous work in this follow-up PR.

### For PySpark
```
>>> data = [(1, {"name": "Alice"})]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'{"name":"Alice")']
>>> data = [(1, [{"name": "Alice"}, {"name": "Bob"}])]
>>> df = spark.createDataFrame(data, ("key", "value"))
>>> df.select(to_json(df.value).alias("json")).collect()
[Row(json=u'[{"name":"Alice"},{"name":"Bob"}]')]
```
### For SparkR
```
# Converts a map into a JSON object
df2 <- sql("SELECT map('name', 'Bob')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
# Converts an array of maps into a JSON array
df2 <- sql("SELECT array(map('name', 'Bob'), map('name', 'Alice')) as people")
df2 <- mutate(df2, people_json = to_json(df2$people))
```
## How was this patch tested?
Add unit test cases.

cc viirya HyukjinKwon

Author: goldmedal <liugs963@gmail.com>

Closes #19223 from goldmedal/SPARK-21513-fp-PySaprkAndSparkR.

## What changes were proposed in this pull request?

This PR proposes to add a wrapper for `unionByName` API to R and Python as well.

**Python**

```python
df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col0"])
df1.unionByName(df2).show()
```

```
+----+----+----+
|col0|col1|col3|
+----+----+----+
|   1|   2|   3|
|   6|   4|   5|
+----+----+----+
```

**R**

```R
df1 <- select(createDataFrame(mtcars), "carb", "am", "gear")
df2 <- select(createDataFrame(mtcars), "am", "gear", "carb")
head(unionByName(limit(df1, 2), limit(df2, 2)))
```

```
  carb am gear
1    4  1    4
2    4  1    4
3    4  1    4
4    4  1    4
```

## How was this patch tested?

Doctests for Python and unit test added in `test_sparkSQL.R` for R.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #19105 from HyukjinKwon/unionByName-r-python.

## What changes were proposed in this pull request?

SPARK-21100 introduced a new `summary` method to the Scala/Java Dataset API that included  expanded statistics (vs `describe`) and control over which statistics to compute. Currently in the R API `summary` acts as an alias for `describe`. This patch updates the R API to call the new `summary` method in the JVM that includes additional statistics and ability to select which to compute.

This does not break the current interface as the present `summary` method does not take additional arguments like `describe` and the output was never meant to be used programmatically.

## How was this patch tested?

Modified and additional unit tests.

Author: Andrew Ray <ray.andrew@gmail.com>

Closes #18786 from aray/summary-r.

## What changes were proposed in this pull request?

This PR adds `map_values` and `map_keys` to R API.

```r
> df <- createDataFrame(cbind(model = rownames(mtcars), mtcars))
> tmp <- mutate(df, v = create_map(df$model, df$cyl))
> head(select(tmp, map_keys(tmp$v)))
```
```
        map_keys(v)
1         Mazda RX4
2     Mazda RX4 Wag
3        Datsun 710
4    Hornet 4 Drive
5 Hornet Sportabout
6           Valiant
```
```r
> head(select(tmp, map_values(tmp$v)))
```
```
  map_values(v)
1             6
2             6
3             4
4             6
5             8
6             6
```

## How was this patch tested?

Manual tests and unit tests in `R/pkg/tests/fulltests/test_sparkSQL.R`

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18809 from HyukjinKwon/map-keys-values-r.

## What changes were proposed in this pull request?

This PR supports schema in a DDL formatted string for `from_json` in R/Python and `dapply` and `gapply` in R, which are commonly used and/or consistent with Scala APIs.

Additionally, this PR exposes `structType` in R to allow working around in other possible corner cases.

**Python**

`from_json`

```python
from pyspark.sql.functions import from_json

data = [(1, '''{"a": 1}''')]
df = spark.createDataFrame(data, ("key", "value"))
df.select(from_json(df.value, "a INT").alias("json")).show()
```

**R**

`from_json`

```R
df <- sql("SELECT named_struct('name', 'Bob') as people")
df <- mutate(df, people_json = to_json(df$people))
head(select(df, from_json(df$people_json, "name STRING")))
```

`structType.character`

```R
structType("a STRING, b INT")
```

`dapply`

```R
dapply(createDataFrame(list(list(1.0)), "a"), function(x) {x}, "a DOUBLE")
```

`gapply`

```R
gapply(createDataFrame(list(list(1.0)), "a"), "a", function(key, x) { x }, "a DOUBLE")
```

## How was this patch tested?

Doc tests for `from_json` in Python and unit tests `test_sparkSQL.R` in R.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18498 from HyukjinKwon/SPARK-21266.

## What changes were proposed in this pull request?

This PR proposes to support a DDL-formetted string as schema as below:

```r
mockLines <- c("{\"name\":\"Michael\"}",
               "{\"name\":\"Andy\", \"age\":30}",
               "{\"name\":\"Justin\", \"age\":19}")
jsonPath <- tempfile(pattern = "sparkr-test", fileext = ".tmp")
writeLines(mockLines, jsonPath)
df <- read.df(jsonPath, "json", "name STRING, age DOUBLE")
collect(df)
```

## How was this patch tested?

Tests added in `test_streaming.R` and `test_sparkSQL.R` and manual tests.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #18431 from HyukjinKwon/r-ddl-schema.

## What changes were proposed in this pull request?

Add SQL trunc function

## How was this patch tested?
standard test

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #18291 from actuaryzhang/sparkRTrunc2.

## What changes were proposed in this pull request?

clean up after big test move

## How was this patch tested?

unit tests, jenkins

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #18267 from felixcheung/rtestset2.

## What changes were proposed in this pull request?

Move all existing tests to non-installed directory so that it will never run by installing SparkR package

For a follow-up PR:
- remove all skip_on_cran() calls in tests
- clean up test timer
- improve or change basic tests that do run on CRAN (if anyone has suggestion)

It looks like `R CMD build pkg` will still put pkg\tests (ie. the full tests) into the source package but `R CMD INSTALL` on such source package does not install these tests (and so `R CMD check` does not run them)

## How was this patch tested?

- [x] unit tests, Jenkins
- [x] AppVeyor
- [x] make a source package, install it, `R CMD check` it - verify the full tests are not installed or run

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #18264 from felixcheung/rtestset.

## What changes were proposed in this pull request?

to investigate how long they run

## How was this patch tested?

Jenkins, AppVeyor

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #18104 from felixcheung/rtimetest.

## What changes were proposed in this pull request?

This change skips tests that use the Hadoop libraries while running
on CRAN check with Windows as the operating system. This is to handle
cases where the Hadoop winutils binaries are missing on the target
system. The skipped tests consist of
1. Tests that save, load a model in MLlib
2. Tests that save, load CSV, JSON and Parquet files in SQL
3. Hive tests

## How was this patch tested?

Tested by running on a local windows VM with HADOOP_HOME unset. Also testing with https://win-builder.r-project.org

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #17966 from shivaram/sparkr-windows-cran.

## What changes were proposed in this pull request?

- Adds R wrapper for `o.a.s.sql.functions.broadcast`.
- Renames `broadcast` to `broadcast_`.

## How was this patch tested?

Unit tests, check `check-cran.sh`.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17965 from zero323/SPARK-20726.

## What changes were proposed in this pull request?

- [x] need to test by running R CMD check --as-cran
- [x] sanity check vignettes

## How was this patch tested?

Jenkins

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17945 from felixcheung/rchangesforpackage.

## What changes were proposed in this pull request?

Change it to check for relative count like in this test https://github.com/apache/spark/blame/master/R/pkg/inst/tests/testthat/test_sparkSQL.R#L3355 for catalog APIs

## How was this patch tested?

unit tests, this needs to combine with another commit with SQL change to check

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17905 from felixcheung/rtabletests.

## What changes were proposed in this pull request?
Cleaning existing temp tables before running tableNames tests

## How was this patch tested?
SparkR Unit tests

Author: Hossein <hossein@databricks.com>

Closes #17903 from falaki/SPARK-20661.

## What changes were proposed in this pull request?

set timezone on windows

## How was this patch tested?

unit test, AppVeyor

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17892 from felixcheung/rtimestamptest.

## What changes were proposed in this pull request?

- Add SparkR wrapper for `Dataset.alias`.
- Adjust roxygen annotations for `functions.alias` (including example usage).

## How was this patch tested?

Unit tests, `check_cran.sh`.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17825 from zero323/SPARK-20550.

## What changes were proposed in this pull request?

add environment

## How was this patch tested?

wait for appveyor run

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17878 from felixcheung/appveyorrcran.

## What changes were proposed in this pull request?

Adds wrapper for `o.a.s.sql.functions.input_file_name`

## How was this patch tested?

Existing unit tests, additional unit tests, `check-cran.sh`.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17818 from zero323/SPARK-20544.

## What changes were proposed in this pull request?

Adds support for generic hints on `SparkDataFrame`

## How was this patch tested?

Unit tests, `check-cran.sh`

Author: zero323 <zero323@users.noreply.github.com>

Closes #17851 from zero323/SPARK-20585.

## What changes were proposed in this pull request?

General rule on skip or not:
skip if
- RDD tests
- tests could run long or complicated (streaming, hivecontext)
- tests on error conditions
- tests won't likely change/break

## How was this patch tested?

unit tests, `R CMD check --as-cran`, `R CMD check`

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17817 from felixcheung/rskiptest.

## What changes were proposed in this pull request?

Adds R wrappers for:

- `o.a.s.sql.functions.grouping` as `o.a.s.sql.functions.is_grouping` (to avoid shading `base::grouping`
- `o.a.s.sql.functions.grouping_id`

## How was this patch tested?

Existing unit tests, additional unit tests. `check-cran.sh`.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17807 from zero323/SPARK-20532.

## What changes were proposed in this pull request?

- Add null-safe equality operator `%<=>%` (sames as `o.a.s.sql.Column.eqNullSafe`, `o.a.s.sql.Column.<=>`)
- Add boolean negation operator `!` and function `not `.

## How was this patch tested?

Existing unit tests, additional unit tests, `check-cran.sh`.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17783 from zero323/SPARK-20490.

## What changes were proposed in this pull request?

Ad R wrappers for

- `o.a.s.sql.functions.explode_outer`
- `o.a.s.sql.functions.posexplode_outer`

## How was this patch tested?

Additional unit tests, manual testing.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17809 from zero323/SPARK-20535.

## What changes were proposed in this pull request?

It seems we are using `SQLUtils.getSQLDataType` for type string in structField. It looks we can replace this with `CatalystSqlParser.parseDataType`.

They look similar DDL-like type definitions as below:

```scala
scala> Seq(Tuple1(Tuple1("a"))).toDF.show()
```
```
+---+
| _1|
+---+
|[a]|
+---+
```

```scala
scala> Seq(Tuple1(Tuple1("a"))).toDF.select($"_1".cast("struct<_1:string>")).show()
```
```
+---+
| _1|
+---+
|[a]|
+---+
```

Such type strings looks identical when R’s one as below:

```R
> write.df(sql("SELECT named_struct('_1', 'a') as struct"), "/tmp/aa", "parquet")
> collect(read.df("/tmp/aa", "parquet", structType(structField("struct", "struct<_1:string>"))))
  struct
1      a
```

R’s one is stricter because we are checking the types via regular expressions in R side ahead.

Actual logics there look a bit different but as we check it ahead in R side, it looks replacing it would not introduce (I think) no behaviour changes. To make this sure, the tests dedicated for it were added in SPARK-20105. (It looks `structField` is the only place that calls this method).

## How was this patch tested?

Existing tests - https://github.com/apache/spark/blob/master/R/pkg/inst/tests/testthat/test_sparkSQL.R#L143-L194 should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17785 from HyukjinKwon/SPARK-20493.

## What changes were proposed in this pull request?

- Add `rollup` and `cube` methods and corresponding generics.
- Add short description to the vignette.

## How was this patch tested?

- Existing unit tests.
- Additional unit tests covering new features.
- `check-cran.sh`.

Author: zero323 <zero323@users.noreply.github.com>

Closes #17728 from zero323/SPARK-20437.

## What changes were proposed in this pull request?

Add wrappers for `o.a.s.sql.functions`:

- `split` as `split_string`
- `repeat` as `repeat_string`

## How was this patch tested?

Existing tests, additional unit tests, `check-cran.sh`

Author: zero323 <zero323@users.noreply.github.com>

Closes #17729 from zero323/SPARK-20438.

## What changes were proposed in this pull request?

Adds wrappers for `collect_list` and `collect_set`.

## How was this patch tested?

Unit tests, `check-cran.sh`

Author: zero323 <zero323@users.noreply.github.com>

Closes #17672 from zero323/SPARK-20371.

## What changes were proposed in this pull request?

Adds wrappers for `o.a.s.sql.functions.array` and `o.a.s.sql.functions.map`

## How was this patch tested?

Unit tests, `check-cran.sh`

Author: zero323 <zero323@users.noreply.github.com>

Closes #17674 from zero323/SPARK-20375.

## What changes were proposed in this pull request?

This was suggested to be `as.json.array` at the first place in the PR to SPARK-19828 but we could not do this as the lint check emits an error for multiple dots in the variable names.

After SPARK-20278, now we are able to use `multiple.dots.in.names`. `asJsonArray` in `from_json` function is still able to be changed as 2.2 is not released yet.

So, this PR proposes to rename `asJsonArray` to `as.json.array`.

## How was this patch tested?

Jenkins tests, local tests with `./R/run-tests.sh` and manual `./dev/lint-r`. Existing tests should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17653 from HyukjinKwon/SPARK-19828-followup.

## What changes were proposed in this pull request?

Fixed spelling of "charactor"

## How was this patch tested?

Spelling change only

Author: Brendan Dwyer <brendan.dwyer@ibm.com>

Closes #17611 from bdwyer2/SPARK-20298.

## What changes were proposed in this pull request?

Following up on #17483, add createTable (which is new in 2.2.0) and deprecate createExternalTable, plus a number of minor fixes

## How was this patch tested?

manual, unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17511 from felixcheung/rceatetable.

## What changes were proposed in this pull request?

Add a set of catalog API in R

```
"currentDatabase",
"listColumns",
"listDatabases",
"listFunctions",
"listTables",
"recoverPartitions",
"refreshByPath",
"refreshTable",
"setCurrentDatabase",
```
https://github.com/apache/spark/pull/17483/files#diff-6929e6c5e59017ff954e110df20ed7ff

## How was this patch tested?

manual tests, unit tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17483 from felixcheung/rcatalog.

## What changes were proposed in this pull request?

It seems `checkType` and the type string in `structField` are not being tested closely. This string format currently seems SparkR-specific (see https://github.com/apache/spark/blob/d1f6c64c4b763c05d6d79ae5497f298dc3835f3e/sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala#L93-L131) but resembles SQL type definition.

Therefore, it seems nicer if we test positive/negative cases in R side.

## How was this patch tested?

Unit tests in `test_sparkSQL.R`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17439 from HyukjinKwon/r-typestring-tests.

## What changes were proposed in this pull request?

Currently JSON and CSV have exactly the same logic about handling bad records, this PR tries to abstract it and put it in a upper level to reduce code duplication.

The overall idea is, we make the JSON and CSV parser to throw a BadRecordException, then the upper level, FailureSafeParser, handles bad records according to the parse mode.

Behavior changes:
1. with PERMISSIVE mode, if the number of tokens doesn't match the schema, previously CSV parser will treat it as a legal record and parse as many tokens as possible. After this PR, we treat it as an illegal record, and put the raw record string in a special column, but we still parse as many tokens as possible.
2. all logging is removed as they are not very useful in practice.

## How was this patch tested?

existing tests

Author: Wenchen Fan <wenchen@databricks.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Wenchen Fan <cloud0fan@gmail.com>

Closes #17315 from cloud-fan/bad-record2.

## What changes were proposed in this pull request?

Add checkpoint, setCheckpointDir API to R

## How was this patch tested?

unit tests, manual tests

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #17351 from felixcheung/rdfcheckpoint.