[R] Need advice on using R with large datasets

[Thomas Lumley]

On Tue, 13 Apr 2004, Roger D. Peng wrote:

> As far as I know, R does compile on AMD Opterons and runs as a
> 64-bit application.  So it can store objects larger than 4GB.
> However, I don't think R gets tested very often on 64-bit
> machines with such large objects so there may be yet undiscovered
> bugs.

Using more than 4Gb memory is reasonably tested now.  Single objects of
that size may not be -- I think you still can't have a vector whose
length() is more than 2^31, for example.

	-thomas


>
> -roger
>
> Sunny Ho wrote:
>
> > Hello everyone,
> >
> > I would like to get some advices on using R with some really large datasets.
> >
> > I'm using RH9 Linux R 1.8.1 for a research with a lot of numerical data. The datasets total to around 200Mb (shown by memory.size). During my data manipulation, the system memory usage grew to 1.5Gb, and this caused a lot of swapping activities on my 1Gb PC. This is just a small-scale experiment, the full-scale one will be using data 30 times as large (on a 4Gb machine). I can see that I'll need to deal with memory usage problem very soon.
> >
> > I notice that R keeps all datasets in memory at all times. I wonder whether there is any way to instruct R to push some of the less-frequently-used data tables out of main memory, so as to free up memory for those that are actively in used. It'll be even better if R can keep only part of a table in memory only when that part is needed. Using save & load could help, but I just wonder whether R is intelligent enough to do this by itself, so I don't need to keep track of memory usage at all times.
> >
> > Another thought is to use a 64-bit machine (AMD64). I find there is a pre-compiled R for Fedora Linux on AMD64. Anyone knows whether this version of R runs as 64-bit? If so, then will R be able to go beyond the 32-bit 4Gb memory limit?
> >
> > Also, from the manual, I find that the RPgSQL package (for PostgreSQL database) supports a feature "proxy data frame". Does anyone have experience with this? Can "proxy data frame" handle memory efficiently for very large datasets? Say, if I have a 6Gb database table defined as a proxy data frame, will R & RPgSQL be able to handle it with just 4Gb of memory?
> >
> > Any comments will be useful. Many thanks.
> >
> > Sunny Ho
> > (Hong Kong University of Science & Technology)
> >
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle