[R] vsize and nsize

[Tony Long]

Jim:

	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:

length(var)
94429
lenght(pow)
94429
tapply(pow,var,mean)
Error: heap memory (1953 Kb) exhausted [needed 737 more]....
var2 <- var[1:100]
pow2 <- pow[1:100]
tapply(pow2,var2,mean)
0	0.06

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
>>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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Tony Long
Ecology and Evolutionary Biology
Steinhaus Hall
University of California at Irvine
Irvine, CA
92697-2525

Tel:  (949) 824-2562   (office)          ****NOTE NEW AREA CODE****
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email:  tdlong at uci.edu 


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