> (1)Institutions (not only academia) using R http://www.r-project.org/useR-2006/participants.html
> (2)Hardware requirements, possibly benchmarks Since you mention huge data sets, GNU/Linux running on 64-bit machines with as much RAM as your budget allows. > (3)R & clusters, R & multiple CPU machines, > R performance on different hardware. OpenMosix, Quantian for clusters; the archive for multiple CPUs (this was asked quite a few times). It may be best to measure R performance on different hardware by yourself, using your own data and code. > (4)finally, a list of the advantages for using R over > commercial statistical packages. I'd say it's not R vs. commercial packages, but S vs. the rest of the world. Check http://www.insightful.com/ , much of what they say is applicable to R. Make the case that S is vastly superior directly, not just through a list of reasons: take a few data sets and show how they can be analyzed with S compared to other choices. Both R and S-Plus are likely to significantly outperform most other software, depending on the kind of work that needs to be done. > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Lorenzo Isella > Sent: Thursday, April 05, 2007 11:02 AM > To: [email protected] > 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 > > ______________________________________________ > [email protected] 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. > ______________________________________________ [email protected] 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.
