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On R versus SAS

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A short while ago there was a discussion on linkedin about the use of SAS versus R for the enterprise. I have thought a bit about the issue but, as I do not use Linkedin, I did not make any comments there.

Disclaimer: I did use SAS a lot between 1992 and 1997, mostly for genetic evaluation, heavily relying on BASE, STAT, IML and GRAPH. From that point on, I was a light SAS user (mostly STAT and IML) until 2009. The main reason I left SAS was that I started using ASReml in 1997 and, around two years ago asreml-R, the R package version of ASReml. Through my job I can access any statistical software; if the university does not have a license, I can buy an academic one without any issues.

I think it is important to make a distinction between enterprise use and huge datasets. Some companies have large datasets, but there probably are many companies that need to analyze large numbers of small to medium size datasets. If we accept this premise, there is room to use a diversity of statistical packages, including both SAS and R.

Another topic that often appears in the R vs. SAS discussion is cost. SAS licenses are not cheap, but for many large companies the cost of having expensive researchers with lower productivity while they learn another “free” system can be really high. Same issue applies if there are legacy programs: converting software to a new system can be expensive and time consuming. Of course this situation is changing: new graduates are being exposed much more to R than to SAS in many departments. We now use R in many courses and students may end up working in a small company that will be happy not to spend any money to pay for a SAS license.

The problem of openness versus closed source is, in my opinion, a bit of a red herring. Most users of statistical software will never have a look at statistical code (think of people driving software with menus). Most users will not be tempted about reading the source code. Most users will not need to recompile the software to make it work in some strange supercomputer. Besides the merits of “feeling good about oneself” for using open software, most users will not worry about losing access to SAS software, as the company has been on business for several decades (typical scenario put forward by open source advocates). After making clear the previous few points I should highlight why I choose R over SAS for both academic and commercial use:

Of course R has many drawbacks when compared to SAS; for example:

No, I did not mention technical support (which I find a non-issue), access to large data sets (it is possible to integrate R with databases and ongoing work to process data that can’t fit in memory) or documentation. Concerning the latter, it would be helpful to have better R documentation, but SAS would also benefit from better manuals. There has been a huge number of books using R published recently and the documentation gap is closing. R would benefit of having good canonical documentation, something that all users could see first as the default documentation. The documentation included with the system is, how to call it, Spartan, and sometimes plain useless and confusing. A gigantic link to a searchable version of the R users email list from the main R project page would be great.

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