On 12/31/06, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote: > R is the free version of the S language. S-PLUS is a commercial version. > Both are targeted at statisticians per se. Their strengths are in > exploratory data analysis (in my opinion). > > SAS has many statistical featues, and is phenomenally well-documented and > supported. One of its great strengths is the robustness of its data model > -- very well suited to large sizes, repetitive inputs, industrial-strength > data processing with a statistics slant. Well over 200 SAS books,for > example. > > I think of SAS and R as being like airliners and helicopters -- airlines get > the job done, and well, as long as it's well-defined and nearly the same job > all the time. Helicopters can go anywhere, do anything, but a moment's > inattention leads to a crash. > --
inattention leading to a crash? I don't get it. I used SAS for about 3 or 4 years, and have used S-Plus and then R for 10 years (R for 8 years now). I've never noticed inattention leading to a crash. I've noticed I cannot get away in R without a careful definition of what I want (which is good), and the immediate interactivity of R is very helpful with mistakes. And of course, programming in R is, well, programming in a reasonable language. Programming in SAS is ... well, programming in SAS (which is about as fun as programming in SPSS). (Another email somehow suggested that the stability/instability analogy of airplanes vs. helicopters does apply to SAS vs. R. Again, I don't really get it. Sure, SAS is very stable. But so is R ---one common complaint is getting seg faults because package whatever has memory leaks, but that is not R's fault, but rather the package's fault). But then, this might start looking a lot like a flame war, which is actually rather off-topic for this list. Anyway, for a Python programmer, picking up R should be fairly easy. And rpy is really a great way of getting R and Python to talk to each other. We do this sort of thing quite a bit on our applications. And yes, R is definitely available for both Linux and Windows (and Mac), has excellent support from several editors in those platforms (e.g., emacs + ess, tinn-R, etc), and seems to be becoming a de facto standard at least in statistical research and is extremely popular in bioinformatics and among statisticians who do bioinformatics (look at bioconductor.org). Ramon -- Ramon Diaz-Uriarte Statistical Computing Team Structural Biology and Biocomputing Programme Spanish National Cancer Centre (CNIO) http://ligarto.org/rdiaz -- http://mail.python.org/mailman/listinfo/python-list
