Re: [R] Using huge datasets

[Roger D. Peng]

By my calculation, your dataset should occupy less than 
400MB of RAM, so this is not a terribly large dataset (these 
days).  But that's not including any possible attributes 
(like row names) which often also take up a lot of memory. 
Considering that a function like read.csv() makes a copy of 
the dataset your actual requirements are ~800MB, which for a 
1GB machine may be too big depending on what else the 
computer is doing.  I have successfully loaded *much* bigger 
datasets into R (2-4GB) without a problem.

Some possible solutions are

1. Buy more RAM
2. Use scan(), which doesn't make a copy of the dataset
3. Use a 64-bit machine and buy even more RAM.
Using a cluster of computers doesn't really help in this 
situation because there's no easy way to spread a dataset 
across multiple machines.  So you will still be limited by 
the memory on a single machine.

As far as I know, R does not have a "memory limitation" -- 
the only limit is the memory installed on your computer.

-roger

Fabien Fivaz wrote:
Hi,

Here is what I want to do. I have a dataset containing 4.2 *million* 
rows and about 10 columns and want to do some statistics with it, mainly 
using it as a prediction set for GAM and GLM models. I tried to load it 
from a csv file but, after filling up memory and part of the swap (1 gb 
each), I get a segmentation fault and R stops. I use R under Linux. Here 
are my questions :

1) Has anyone ever tried to use such a big dataset?
2) Do you think that it would possible on a more powerfull machine, such 
as a cluster of computers?
3) Finaly, does R has some "memory limitation" or does it just depend on 
the machine I'm using?

Best wishes

Fabien Fivaz

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