Open Thoughts on Analytics
MAY 1, 2012 9:10am ET

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SAS vs. R: Statistical Modeling Rivalry Renewed


I met up with my old analytics friend a few weeks back. Throughout the ‘90s, we shared statistical war stories from our respective SAS consulting customers. Around 2000, I made the software leap to first S+ and then R, while he remained loyal to SAS. Since that time, we’ve stayed close, but there’s generally a bit of a SAS-R edge to our statistical discussions.

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Comments (5)
Hello Steve -

I ordinarily avoid posting comments that are focused on product capabilities, but since your post is specifically about SAS... I agree that you should definitely take a new look at SAS. Just a few of the things you will find:

- In addition to the graphic samples that you link to, we now have SAS Visual Analytics, a high-performance, in-memory solution for working with massive amounts of data very quickly. SAS Visual Analytics provides a very compelling visual representation that includes autocharting, geographic visualizations, etc., that is capable of visually representing billions of rows of data in seconds - This offering includes visualization support for mobile devices like the ipad.

- SAS Visual Analytics is part of a broader high performance analytics offering that SAS includes grid technology, in-DB processing and in-memory processing. SAS has offered these capabilities for many years and has constantly expanded the functionality to include additional in-memory capabilities. The latest is the SAS LASR Server, which is an in-memory analytics engine that forms the foundation for the SAS Visual Analytics. In addition to this solution that provides high concurrency support for many users doing rich visualization work, SAS offers high performance analytics capabilities that allow organizations to solve complex problems using big data and sophisticated analytics. This is done via a distributed, in-memory architecture that is not limited by physical memory limits and provides multiple deployment patterns - from MPP database implementations on EMC Greenplum and Teradata, as well as Hadoop-based implementations. For more information on our high performance analytics capabilities, go here:

- SAS Information Management provides a complete set of capabilities to help oversee the entire data to decision lifecycle. As you mentioned, this includes extensive data preparation capabilities, but also includes the ability to manage a large number of complex analytical models PLUS the ability to integrate the analytical results directly in operational systems. As part of the SAS Information Management, we have support for Hadoop - this includes the ability to work with Hadoop data in a SAS environment using the SAS/ACCESS module for Hadoop, that is based on Hive. It also includes the ability to interact with Hadoop processing via HDFS commands, MapReduce code and Pig, which can be authored graphically in the SAS Data Integration Studio environment. It is possible to build a job flow that intermingles SAS processing and Hadoop code all in the same job, making it easy to leverage the best execution environment for the task at hand. In addition, just as SAS has moved to run processing in and alongside the MPP databases, SAS will soon provide the ability to run an embedded SAS process directly on a Hadoop node. For more information on our Hadoop support, please read here:

I would be happy to provide you with a more extensive briefing on these and other SAS capabilities.

Thanks, Mark Troester IT/CIO Thought Leader & Strategist Twitter: @mtroester Blog:

Posted by Mark T | Tuesday, May 01 2012 at 12:11PM ET
For the statistical user, there are two kinds statistical graphics in SAS. (1) Graphics that are created AUTOMATICALLY by SAS procedures as part of the analysis. For an overview, see

(2) Graphics that are created manually from data sets. These include the SGPLOT and SGPANEL procedures. There is a statistical graphics blog at

Posted by Rick W | Wednesday, May 02 2012 at 9:28AM ET
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