Introducing the free Microsoft R Client

July 11, 2016

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

Over the years, we've shared several posts on using the ScaleR package to importprocess, visualize and analyze large data sets with R. Until now, you needed to have access to a Microsoft R Server license to take advantage of the package. Now, you can use all of the capabilities of ScaleR free of charge with Microsoft R Client for Windows, which is available for download now.

Microsoft R Client is a free, community-supported, data science tool for high performance analytics. R Client is built on top of Microsoft R Open so you can use any open source R packages to build your analytics. Additionally, R Client introduces the powerful ScaleR technology and its proprietary functions to benefit from parallelization and remote computing.

R Client allows you to work with production data locally using the full set of ScaleR functions, but there are some constraints. On its own, the data to be processed must fit in local memory, and processing is limited up to two threads for ScaleR functions. To benefit from disk scalability, performance and speed, you can push the compute context to a production instance of Microsoft R Server such as SQL Server R Services and R Server for Hadoop.

Here's a short video that explains the capabilities of Microsoft R Client for local data processing, and how you can push computations to a remote Microsoft R Server for even greater power. 


If you're new to big-data functions in Microsoft R, a great place to start is the The RevoScaleR Getting Started Guide. If you want to dive deeper, the full documentation set is available on MSDN. To get started with Microsoft R Client, follow the instructions in the installation guide linked below.

MSDN: Install Microsoft R Client on Windows


To leave a comment for the author, please follow the link and comment on their blog: Revolutions. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)