The Basics Of SparkR

Kevin Feasel


R, Spark

Yanbo Liang has an introductory article on what SparkR is and why you might want to use it:

However, data analysis using R is limited by the amount of memory available on a single machine and further as R is single threaded it is often impractical to use R on large datasets. To address R’s scalability issue, the Spark community developed SparkR package which is based on a distributed data frame that enables structured data processing with a syntax familiar to R users. Spark provides distributed processing engine, data source, off-memory data structures. R provides a dynamic environment, interactivity, packages, visualization. SparkR combines the advantages of both Spark and R.

In the following section, we will illustrate how to integrate SparkR with R to solve some typical data science problems from a traditional R users’ perspective.

This is a fairly introductory article, but gives an idea of what SparkR can accomplish.

Related Posts


Hadley Wickham announces dbplyr version 1.1.0: Since you’ve read this far, I also wanted to touch on RStudio’s vision for databases. Many analysts have most of their data in databases, and making it as easy as possible to get data out of the database and into R makes a huge difference. Thanks to the community, […]

Read More

Neural Nets On Spark

Nisha Muktewar and Seth Hendrickson show how to use Deeplearning4j to build deep learning models on Hadoop and Spark: Modern convolutional networks can have several hundred million parameters. One of the top-performing neural networks in the Large Scale Visual Recognition Challenge (also known as “ImageNet”), has 140 million parameters to train! These networks not only […]

Read More


April 2017
« Mar May »