SAS versus R - The longest discussion on Linkedin I have ever seen
Six months ago Oleg Okun asked the posed the following question to the Advanced Business Analytics, Data Mining and Predictive Modeling Group on Linkedin:
SAS versus R
Did anyone have to justify to a prospect/customer why R is better than SAS? What arguments did you provide? Did your prospect/customer agree with them? Why do you think, despite being free and having a lot of packages, R is still not a favorite in Data Mining/Predictive Analytics in the corporate world?
I responded to the original question and engaged in a some discussion as time has gone by. It has been fun and interesting. The range and breath of this discussion thread, and the number of participants is amazing. It has never gone stale and there are new contributor every day. The most recent topics include Oracle R Enterprise which was not even in existence when the question was originally posed by Oleg.
Here is a sample of the Discussion:
John Charnes • @Daniel Lieb -- Hope all's well with you, Dan. Yes, the dig on R has been limits on the size of data sets that it can handle. However, I listened to a webcast last week that described Oracle R Enterprise. The speakers described analyses of terabytes of data by running existing R scripts directly against data stored in Oracle Database 11g. I haven't used it yet myself, but is definitely worth checking out.
David Tussey • A side comment, I see Python and the various packages (SciPy, NumPy) as the emerging winner in this battle. Python is much easier to program than R, and more "data friendly" when it comes to file and database manipulation.
Alfredo Roccato • In the real world (I'm speaking of large commercial organizations) where 80%-90% of the time is spent in large scale data processing, SAS has proven to be a very efficient and flexible tool. In an academic contest, where most of time is spent in analysis, mainly dealing with toys data, no doubt that R is the preferred software. In my opinion these packages do not compete each other, even if there is a considerable overlap for statistical methodologies. Rather, a better communication would benefit both: you can use SAS for complex data manipulation and R for all the analyses written by the moltitude of its contributors.
If you are on Linkedin join the group and the "SAS versus R" thread.