[R] read large amount of data

Thomas Lumley tlumley at u.washington.edu
Mon Jul 18 17:53:56 CEST 2005

On Mon, 18 Jul 2005, Weiwei Shi wrote:

> Hi,
> I have a dataset with 2194651x135, in which all the numbers are 0,1,2,
> and is bar-delimited.
> I used the following approach which can handle 100,000 lines:
> t<-scan('fv', sep='|', nlines=100000)
> t1<-matrix(t, nrow=135, ncol=100000)
> t2<-t(t1)
> t3<-as.data.frame(t2)
> I changed my plan into using stratified sampling with replacement (col
> 2 is my class variable: 1 or 2). The class distr is like:
> awk -F\| '{print $2}' fv | sort | uniq -c
> 2162792 1
>  31859 2
> Is it possible to use R to read the whole dataset and do the
> stratified sampling? Is it really dependent on my memory size?

You may well not be able to read the whole data set into memory at once: 
it would take a bit more than 2Gb memory even to store it.

You can use readLines to read it in chunks of, say, 10000 lines.

To do stratified sampling I would suggest bernoulli sampling of slightly 
more than you want. Eg if you want 10000 from class 1, keeping each 
elements with probability 10500/2162792 will get you Poisson(10500) 
elements, which will be more than 10000 elements with better than 99.999% 
probability. You can then choose 10000 at random from these. I can't think 
of an approach that it is guaranteed to work in one pass over the data, 
but 99.999% is pretty close.


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