[R] vsize and nsize
tdlong at uci.edu
Tue May 18 17:41:07 CEST 1999
So now I am confused. I have two vectors: var and pow. var has a
dozen or so "levels". (I can not apply factor to var either) Here is a
sample of my R session:
Error: heap memory (1953 Kb) exhausted [needed 737 more]....
var2 <- var[1:100]
pow2 <- pow[1:100]
As I stated in previous messages the version of R I have ( 0.63.2 ) could
not increase heap memory, Brian Ripley suggested I upgrade to a newer
version (0.64.1, which I haven't done yet) -- so I can not tell you if this
solves the problem or not...
>> Tony Long <tdlong at 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
>> > 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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