Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/6928#discussion_r33059857
--- Diff: docs/sparkr.md ---
@@ -62,7 +62,9 @@ head(df)
SparkR supports operating on a variety of data sources through the
`DataFrame` 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 DataFrames from data sources is `read.df`.
This method takes in the `SQLContext`, the path for the file to load and the
type of data source. SparkR supports reading JSON and Parquet files natively
and through [Spark Packages](http://spark-packages.org/) you can find data
source connectors for popular file formats like
[CSV](http://spark-packages.org/package/databricks/spark-csv) and
[Avro](http://spark-packages.org/package/databricks/spark-avro).
+The general method for creating DataFrames from data sources is `read.df`.
This method takes in the `SQLContext`, the path for the file to load and the
type of data source. SparkR supports reading JSON and Parquet files natively
and through [Spark Packages](http://spark-packages.org/) you can find data
source connectors for popular file formats like
[CSV](http://spark-packages.org/package/databricks/spark-csv) and
[Avro](http://spark-packages.org/package/databricks/spark-avro). These packages
can either be added by
+specifying `--packages` with `sparm-submit` or `sparkR` commands, or if
creating context through `init`
--- End diff --
typo: `spark-submit`
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