![]() On top of what gung has correctly identified here, the biggest issue in the corporate world is legacy. I think #2 ignores several facts: there is some vetting that goes on with R, many of the main packages are written by some of the biggest names in statistics, and there have been studies that compare the accuracy of different statistical software & R has certainly been competitive. Personally, I only think #3 has any legitimate merit, although there are approaches to big data that have been developed with R. ![]() Thus, if your data approaches the limits of your memory, there will be problems. Big data: R performs operations with everything in memory, whereas SAS doesn't necessarily.Distrust of freeware: I've had several people say they aren't willing to accept results from R because you don't have a for-profit company vetting the code to ensure it gives correct results before it goes out to customers, lest they end up losing business.(Making it more difficult, the way you think in SAS and R is different.) This can apply to anyone who might have to send you code, or read / use your code, including managers and colleagues. Tradition / habit: people are used to SAS, and don't want to have to learn something new.I think there are several issues (in ascending order of possible validity):
0 Comments
Leave a Reply. |