The problem of openness versus closed source is, in my opinion, a bit of a red herring. 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. Of course this situation is changing: new graduates are being exposed much more to R than to SAS in many departments.
Same issue applies if there are legacy programs: converting software to a new system can be expensive and time consuming. 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. If we accept this premise, there is room to use a diversity of statistical packages, including both SAS and R.Īnother topic that often appears in the R vs. Some companies have large datasets, but there probably are many companies that need to analyze large numbers of small to medium size datasets. I think it is important to make a distinction between enterprise use and huge datasets. 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. 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. From that point on, I was a light SAS user (mostly STAT and IML) until 2009. I have thought a bit about the issue but, as I do not use Linkedin, I did not make any comments there.ĭisclaimer: I did use SAS a lot between 19, mostly for genetic evaluation, heavily relying on BASE, STAT, IML and GRAPH. A short while ago there was a discussion on linkedin about the use of SAS versus R for the enterprise.