Libraries of sparkR need to be created in `$SPARK_HOME/R/lib`. This can be done by running the script `$SPARK_HOME/R/install-dev.sh`.
By default the above script uses the system wide installation of R. However, this can be changed to any user installed location of R by setting the environment variable `R_HOME` the full path of the base directory where R is installed, before running install-dev.sh script.
Build Spark with [Maven](http://spark.apache.org/docs/latest/building-spark.html#building-with-buildmvn) and include the `-Psparkr` profile to build the R package. For example to use the default Hadoop versions you can run
You can start using SparkR by launching the SparkR shell with
./bin/sparkR
The `sparkR` script automatically creates a SparkContext with Spark by default in
local mode. To specify the Spark master of a cluster for the automatically created
SparkContext, you can run
./bin/sparkR --master "local[2]"
To set other options like driver memory, executor memory etc. you can pass in the [spark-submit](http://spark.apache.org/docs/latest/submitting-applications.html) arguments to `./bin/sparkR`
If you wish to use SparkR from RStudio or other R frontends you will need to set some environment variables which point SparkR to your Spark installation. For example
The SparkR documentation (Rd files and HTML files) are not a part of the source repository. To generate them you can run the script `R/create-docs.sh`. This script uses `devtools` and `knitr` to generate the docs and these packages need to be installed on the machine before using the script. Also, you may need to install these [prerequisites](https://github.com/apache/spark/tree/master/docs#prerequisites). See also, `R/DOCUMENTATION.md`
You can run R unit tests by following the instructions under [Running R Tests](http://spark.apache.org/docs/latest/building-spark.html#running-r-tests).
The `./bin/spark-submit` can also be used to submit jobs to YARN clusters. You will need to set YARN conf dir before doing so. For example on CDH you can run