Here are a couple more thougts to add to what you have already received: You mentioned that price is not at issue, but there are other costs than money that you may want to look at. On my work machine I have R, S-PLUS, SAS, SPSS, and a couple of other stats programs; on my laptop and home computers I have R installed. So, if a deadline is looming and I am working on a project mainly in R, it is easy to work on it on the bus or at home (or in a boring meeting), the same does not work for a SAS or SPSS project (Hmm, thinking about this now, maybe I need to do less in R :-).
R and S-PLUS are very flexible/customizable, if you have a certain plot that you make often you can write your own function/script to do it automatically, most other programs will give you their standard, then you have to modify it to meet your specifications. With sweave (and the odf and html extensions) you can automate whole reports, very useful for things that you do month after month. And what I think is the biggest advantage of R and S-PLUS is that they strongly encourage you to think about your data. Other programs (at least that I am familiar with) tend to have 1 specific way of treating your data, and expect you to modify your data to fit that programs model. These models can be overrestrictive (force you to restructure your data to fit their model) or underrestrictive (allow things that should really be separate data objects to be combined into a single "dataset") and sometimes both. S on the other hand allows many different ways to store and work with your data, and as you analyze the data, different branches of new analysis open up depending on early results rather than just getting stock output for a procedure. If all you want is a black box where data goes in one end and a specific answer comes out the other, then most programs will work; but if you want to really understand what your data has to tell you, then R/S-PLUS makes this easy and natural. Hope this helps, -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare [EMAIL PROTECTED] (801) 408-8111 > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Lorenzo Isella > Sent: Thursday, April 05, 2007 9:02 AM > To: r-help@stat.math.ethz.ch > Subject: [R] Reasons to Use R > > Dear All, > The institute I work for is organizing an internal workshop > for High Performance Computing (HPC). > I am planning to attend it and talk a bit about fluid > dynamics, but there is also quite a lot of interest devoted > to data post-processing and management of huge data sets. > A lot of people are interested in image processing/pattern > recognition and statistic applied to geography/ecology, but I > would like not to post this on too many lists. > The final aim of the workshop is understanding hardware > requirements and drafting a list of the equipment we would > like to buy. I think this could be the venue to talk about R as well. > Therefore, even if it is not exactly a typical mailing list > question, I would like to have suggestions about where to > collect info about: > (1)Institutions (not only academia) using R (2)Hardware > requirements, possibly benchmarks (3)R & clusters, R & > multiple CPU machines, R performance on different hardware. > (4)finally, a list of the advantages for using R over > commercial statistical packages. The money-saving in itself > is not a reason good enough and some people are scared by the > lack of professional support, though this mailing list is > simply wonderful. > > Kind Regards > > Lorenzo Isella > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.