Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/14178#discussion_r70704632
--- Diff: docs/sparkr.md ---
@@ -111,19 +111,17 @@ head(df)
SparkR supports operating on a variety of data sources through the
`SparkDataFrame` interface. This section describes the general methods for
loading and saving data using Data Sources. You can check the Spark SQL
programming guide for more [specific
options](sql-programming-guide.html#manually-specifying-options) that are
available for the built-in data sources.
The general method for creating SparkDataFrames from data sources is
`read.df`. This method takes in the path for the file to load and the type of
data source, and the currently active SparkSession will be used automatically.
SparkR supports reading JSON, CSV and Parquet files natively and through [Spark
Packages](http://spark-packages.org/) you can find data source connectors for
popular file formats like
[Avro](http://spark-packages.org/package/databricks/spark-avro). These packages
can either be added by
-specifying `--packages` with `spark-submit` or `sparkR` commands, or if
creating context through `init`
-you can specify the packages with the `packages` argument.
+specifying `--packages` with `spark-submit` or `sparkR` commands, or if
initializing SparkSession with `sparkPackages` parameter.
--- End diff --
Related to #14179 the sparkPackages flag only works when used from an
interactive R shell. It might be good to say that here as well.
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