
> 
> Tony Long <tdlong@uci.edu> wrote:
> 
> > 	I agree that R is not "designed" for large calculations.  On the
> > other hand it is nice to have one statistical package to use for all
> > calculations.  I mostly deal with Drosophila and DNA, as such I  am an
> > amateur statistician and would like to avoid learning a number of
> > statistical languages.  With a big Linux box, I can often power through
> > things.  In the past I have found it frustrating to do a bunch of stuff in
> > SAS only to hit a snag and then have to write (time consuming) "C" code to
> > finish the job.  So although not designed for large calculations, R is so
> > flexible and logical that it is very attractive to use it for such...I
> > think that many other people may be similarly attracted to the language.  I
> > would appreciate dialog, as I think that much more may have been
> > accomplished in R than was intended by the founders.
> 
> R's poor handling of large datasets is half the reason I have
> not moved more of my work from S(plus) to R (the other half
> being the absence of trellis).  I love its lexical closures, but
> they're not worth the memory penalty if you have huge datasets.

I am wondering what you mean by "R's poor handling of large datasets".
How large is large? I have often been working simultaneously with a
fair number of vectors of say 40,000 using my libraries (data objects
and functions) with no problems. They use the R scoping rules. On the
other hand, if you use dataframes and/or standard functions like glm,
then you are restricted to extremely small (toy) data sets. But then
maybe you are thinking of gigabytes of data.
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