R Relative to Statistical Packages
- statistics can mean “give me a p-value less than 5%” or “what is the data saying?”
- R is good for the latter, and overkill for the former
- R can be harder to learn — because it does more
- R is a language, and is meant to be used interactively
The document above was written years ago, and some of its links have gone stale.
UCLA has deleted the technical report from their site, but Bob Muenchen has exposed a copy of “Strategically using General Purpose Statistics Packages: A Look at Stata, SAS and SPSS” by Michael Mitchell.
What Bob Muenchen previously titled: “Comparison of SAS and SPSS Products with R Packages and Functions” is located at: http://r4stats.com/articles/add-ons/.
The McCullough articles are now on Talyor and Francis (behind a paywall).
In 2006 a discussion broke out on the R-help mailing list about a technical report put out by the statistical computing group at UCLA. The report in question talked mainly about SAS, SPSS and Stata. It talked briefly — and not especially positively — about R. You can find that thread with a web search like:
"A comment about R" 2006
The linked document was a comment on the technical report that derived much of its content from the R-help thread.
Times have changed. The technical report is no longer at its original location, instead there are substantial resources about R at the UCLA site.
The blog post: “On the acceptance of R”.
Another point, which I repeatedly make to students, is that R is free and will continue to exist. Nothing can make it go away. Once you learn it, you are no longer subject to price increases (e.g., from zero, when, as a grad student, you use your advisor’s copy of SAS, to several hundred dollars or more after you leave). You can take it with you wherever you go. The investment in learning thus has a long-term payoff.
— Jonathan Baron