[SPARK-14883][DOCS] Fix wrong R examples and make them up-to-date

## What changes were proposed in this pull request?

This issue aims to fix some errors in R examples and make them up-to-date in docs and example modules.

- Remove the wrong usage of `map`. We need to use `lapply` in `sparkR` if needed. However, `lapply` is private so far. The corrected example will be added later.
- Fix the wrong example in Section `Generic Load/Save Functions` of `docs/sql-programming-guide.md` for consistency
- Fix datatypes in `sparkr.md`.
- Update a data result in `sparkr.md`.
- Replace deprecated functions to remove warnings: jsonFile -> read.json, parquetFile -> read.parquet
- Use up-to-date R-like functions: loadDF -> read.df, saveDF -> write.df, saveAsParquetFile -> write.parquet
- Replace `SparkR DataFrame` with `SparkDataFrame` in `dataframe.R` and `data-manipulation.R`.
- Other minor syntax fixes and a typo.

## How was this patch tested?

Manual.

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #12649 from dongjoon-hyun/SPARK-14883.

1 files changed, 13 insertions, 17 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 77887f4ca3..9a3db9c3f9 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -173,7 +173,7 @@ df.show()
 {% highlight r %}
 sqlContext <- SQLContext(sc)
 
-df <- jsonFile(sqlContext, "examples/src/main/resources/people.json")
+df <- read.json(sqlContext, "examples/src/main/resources/people.json")
 
 # Displays the content of the DataFrame to stdout
 showDF(df)
@@ -366,7 +366,7 @@ In addition to simple column references and expressions, DataFrames also have a
 sqlContext <- sparkRSQL.init(sc)
 
 # Create the DataFrame
-df <- jsonFile(sqlContext, "examples/src/main/resources/people.json")
+df <- read.json(sqlContext, "examples/src/main/resources/people.json")
 
 # Show the content of the DataFrame
 showDF(df)
@@ -889,8 +889,8 @@ df.select("name", "favorite_color").write.save("namesAndFavColors.parquet")
 <div data-lang="r"  markdown="1">
 
 {% highlight r %}
-df <- loadDF(sqlContext, "people.parquet")
-saveDF(select(df, "name", "age"), "namesAndAges.parquet")
+df <- read.df(sqlContext, "examples/src/main/resources/users.parquet")
+write.df(select(df, "name", "favorite_color"), "namesAndFavColors.parquet")
 {% endhighlight %}
 
 </div>
@@ -939,8 +939,8 @@ df.select("name", "age").write.save("namesAndAges.parquet", format="parquet")
 
 {% highlight r %}
 
-df <- loadDF(sqlContext, "people.json", "json")
-saveDF(select(df, "name", "age"), "namesAndAges.parquet", "parquet")
+df <- read.df(sqlContext, "examples/src/main/resources/people.json", "json")
+write.df(select(df, "name", "age"), "namesAndAges.parquet", "parquet")
 
 {% endhighlight %}
 
@@ -1138,19 +1138,15 @@ for teenName in teenNames.collect():
 schemaPeople # The DataFrame from the previous example.
 
 # DataFrames can be saved as Parquet files, maintaining the schema information.
-saveAsParquetFile(schemaPeople, "people.parquet")
+write.parquet(schemaPeople, "people.parquet")
 
 # Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved.
 # The result of loading a parquet file is also a DataFrame.
-parquetFile <- parquetFile(sqlContext, "people.parquet")
+parquetFile <- read.parquet(sqlContext, "people.parquet")
 
 # Parquet files can also be registered as tables and then used in SQL statements.
-registerTempTable(parquetFile, "parquetFile");
+registerTempTable(parquetFile, "parquetFile")
 teenagers <- sql(sqlContext, "SELECT name FROM parquetFile WHERE age >= 13 AND age <= 19")
-teenNames <- map(teenagers, function(p) { paste("Name:", p$name)})
-for (teenName in collect(teenNames)) {
-  cat(teenName, "\n")
-}
 {% endhighlight %}
 
 </div>
@@ -1318,14 +1314,14 @@ df3.printSchema()
 # sqlContext from the previous example is used in this example.
 
 # Create a simple DataFrame, stored into a partition directory
-saveDF(df1, "data/test_table/key=1", "parquet", "overwrite")
+write.df(df1, "data/test_table/key=1", "parquet", "overwrite")
 
 # Create another DataFrame in a new partition directory,
 # adding a new column and dropping an existing column
-saveDF(df2, "data/test_table/key=2", "parquet", "overwrite")
+write.df(df2, "data/test_table/key=2", "parquet", "overwrite")
 
 # Read the partitioned table
-df3 <- loadDF(sqlContext, "data/test_table", "parquet", mergeSchema="true")
+df3 <- read.df(sqlContext, "data/test_table", "parquet", mergeSchema="true")
 printSchema(df3)
 
 # The final schema consists of all 3 columns in the Parquet files together
@@ -1612,7 +1608,7 @@ sqlContext <- sparkRSQL.init(sc)
 # The path can be either a single text file or a directory storing text files.
 path <- "examples/src/main/resources/people.json"
 # Create a DataFrame from the file(s) pointed to by path
-people <- jsonFile(sqlContext, path)
+people <- read.json(sqlContext, path)
 
 # The inferred schema can be visualized using the printSchema() method.
 printSchema(people)