[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.

2 files changed, 12 insertions, 12 deletions

diff --git a/examples/src/main/r/data-manipulation.R b/examples/src/main/r/data-manipulation.R
index aa2336e300..594bf49d60 100644
--- a/examples/src/main/r/data-manipulation.R
+++ b/examples/src/main/r/data-manipulation.R
@@ -30,7 +30,7 @@ args <- commandArgs(trailing = TRUE)
 
 if (length(args) != 1) {
   print("Usage: data-manipulation.R <path-to-flights.csv")
-  print("The data can be downloaded from: http://s3-us-west-2.amazonaws.com/sparkr-data/flights.csv ")
+  print("The data can be downloaded from: http://s3-us-west-2.amazonaws.com/sparkr-data/flights.csv")
   q("no")
 }
 
@@ -49,33 +49,33 @@ flights_df$date <- as.Date(flights_df$date)
 ## Filter flights whose destination is San Francisco and write to a local data frame
 SFO_df <- flights_df[flights_df$dest == "SFO", ] 
 
-# Convert the local data frame into a SparkR DataFrame
+# Convert the local data frame into a SparkDataFrame
 SFO_DF <- createDataFrame(sqlContext, SFO_df)
 
-#  Directly create a SparkR DataFrame from the source data
+#  Directly create a SparkDataFrame from the source data
 flightsDF <- read.df(sqlContext, flightsCsvPath, source = "com.databricks.spark.csv", header = "true")
 
-# Print the schema of this Spark DataFrame
+# Print the schema of this SparkDataFrame
 printSchema(flightsDF)
 
-# Cache the DataFrame
+# Cache the SparkDataFrame
 cache(flightsDF)
 
-# Print the first 6 rows of the DataFrame
+# Print the first 6 rows of the SparkDataFrame
 showDF(flightsDF, numRows = 6) ## Or
 head(flightsDF)
 
-# Show the column names in the DataFrame
+# Show the column names in the SparkDataFrame
 columns(flightsDF)
 
-# Show the number of rows in the DataFrame
+# Show the number of rows in the SparkDataFrame
 count(flightsDF)
 
 # Select specific columns
 destDF <- select(flightsDF, "dest", "cancelled")
 
 # Using SQL to select columns of data
-# First, register the flights DataFrame as a table
+# First, register the flights SparkDataFrame as a table
 registerTempTable(flightsDF, "flightsTable")
 destDF <- sql(sqlContext, "SELECT dest, cancelled FROM flightsTable")
 
@@ -95,11 +95,11 @@ if("magrittr" %in% rownames(installed.packages())) {
   library(magrittr)
 
   # Group the flights by date and then find the average daily delay
-  # Write the result into a DataFrame
+  # Write the result into a SparkDataFrame
   groupBy(flightsDF, flightsDF$date) %>%
     summarize(avg(flightsDF$dep_delay), avg(flightsDF$arr_delay)) -> dailyDelayDF
 
-  # Print the computed data frame
+  # Print the computed SparkDataFrame
   head(dailyDelayDF)
 }
 
diff --git a/examples/src/main/r/dataframe.R b/examples/src/main/r/dataframe.R
index 62f60e57ee..436bac6aaf 100644
--- a/examples/src/main/r/dataframe.R
+++ b/examples/src/main/r/dataframe.R
@@ -24,7 +24,7 @@ sqlContext <- sparkRSQL.init(sc)
 # Create a simple local data.frame
 localDF <- data.frame(name=c("John", "Smith", "Sarah"), age=c(19, 23, 18))
 
-# Convert local data frame to a SparkR DataFrame
+# Convert local data frame to a SparkDataFrame
 df <- createDataFrame(sqlContext, localDF)
 
 # Print its schema