Similar to RHadoop, SparkR is an R package that allows R users to use Spark APIs through the RDD class. For example, using SparkR, users can run jobs on Spark from RStudio. SparkR can be evoked from RStudio. To enable this, include the following lines in your .Rprofile file that R uses at startup to initialize the environments:

Sys.setenv(SPARK_HOME/.../spark-1.5.0-bin-hadoop2.6")
#provide the correct path where spark downloaded folder is kept for SPARK_HOME 
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"),""R",""lib"),".libPaths()))

Once this is done, start RStudio and enter the following commands to start using SparkR:

>library(SparkR)
>sc <- sparkR.init(master="local")

As mentioned, as of the latest version 1.5 when this chapter is in writing, SparkR supports limited functionalities of R. This mainly includes data slicing and dicing and summary stat functions. The current version does not support the use of contributed R packages; however, it is planned for a future release. On machine learning, currently SparkR supports the glm( ) function. We will do an example in the next section.