[R] Reasons to Use R

Jorge Cornejo-Donoso
Mon, 09 Apr 2007 15:18:41 -0000

tha s9ze of db is an issue with R. We are still using SAS because R
can't handle own db, and of couse we don't want to sacrify resolution,
because the data collection is expensive (at least in fisheries and
oceagraphy), so.. I think that R need to improve the use of big DBs. Now
I only can use R for graph preparation and some data analisis, but we
can't do the main work on R, abd that is really sad.


-----Mensaje original-----
    De: "Wilfred Zegwaard" <wilfred.zegwaard en gmail.com
    Enviado: 08/04/07 21:47:29
    Para: "r-help en stat.math.ethz.ch" <r-help en
stat.math.ethz.ch>
    Asunto: Re: [R] Reasons to Use R

    Dear Johann and Gabor,

    It's what amounts to large datasets. There are hundreds of datasets
R
    can't handle, probably thousands or more. I noticed on my computer
    (which is nothing more that an average PC) that R breaks down after
250
    MB of memory. I also note that SPSS breaks down, Matlab, etc.

    I'm not a SAS user, but I have worked in the past with SAS. It's
very
    good as a remember, but it's ten years ago. And it's a "dollar
machine"
    I've been told: you add dollars to SAS as you add dollars to a
Porsche.
    I haven't got it and for most statistical applications it isn't
    necessary I've been told. R is sufficient for that. The datasets I
use
    are often not that big (the way I like it).
    About three years ago I spoke to somebody who has worked with it and
    said "it's database system is excellent and statistical
profound".
    Someone with a PhD, so probably he is right.

    Monte-Carlo simulations are computationally time-consuming, but
probably
    these can be done in R. I haven't seen any libaries for it (they
might
    be there). It has been done with S (the commercial counterpart of
R), so
    probably with R too. If you tie Monte Carlo simulaton with large
    datasets you probably run into problems with a conventional R
system.
    What I've been told in those instances is "buy a new computer"
/
"add
    memory and buy a new processor"... and don't smoke hashiesh.

    That wasn't a good advice because the guy who told me smoked
hashiesh
    like hell and drank Pastis (blue liqor) like water. I kicked him
out.
    But that's another story.

    Cheers,

    Wilfred

    (I drink wine and tailor made beer, and only on occasions. That's
why.
    His simulations were good I've been told.)

    ______________________________________________
    R-help en 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.

    __________ Informacisn de NOD32, revisisn 2174 (20070409) __________

    Este mensaje ha sido analizado con  NOD32 antivirus system
    http://www.nod32.com
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