it really depends on how you define "large dataset". In a corporate production environment, it is not unusual to do data manipulation for x-G dataset. In this case, SAS might be preferred from my personal experience.
On Sat, Jun 19, 2010 at 9:39 AM, skan <juanp...@gmail.com> wrote: > > Hello > > How do you compare R to SAS in terms of speed and management of large > datasets? > > What about Revolution R? > I've seen on their site, they claim that Revolution R is much faster than R > and it's multithread... > Can you really notice the difference?. What dissadvantage does it have? > I think it's based on R 2.10. but R already issued the version 2.12 > > > Regards > > > > What alternative to R would you use in order to merge asynchronus time > series?. SAS, Stata, eViews...? > -- > View this message in context: > http://r.789695.n4.nabble.com/R-vs-SAS-and-Revolution-R-tp2261149p2261149.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org 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. > -- ============================== WenSui Liu wens...@paypal.com statcompute.spaces.live.com ============================== ______________________________________________ R-help@r-project.org 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.
