BEP wrote:
> Hello all,
>
> I am working with a very large data set into R, and I have no interest in
> reviving my SAS skills. To do this, I will need to drop unwanted variables
> given the size of the data file. The most common strategy seems to be
> subsetting the data after it is read into R. Unfortunately, given the size
> of the data set, I can't get the file read and then subsquently do the
> subset procedure. I would be appreciative of help on the following:
>
> 1. What are the possibilities of reading in just a small set of variables
> during the <read.table> statement (or another 'read' statement)? That is,
> is it possible specify just the variables that I want to keep?
>
> 2. Can I randomly select a set of observations during the 'read' statement?
>
>
> I have searched various R resources for this information, so if I am simply
> overlooking a key resource on this issue, pointing that out to me would be
> greatly appreciated.
>
> Thanks in advance.
>
> Brian
Check this for input of specific columns from a large data matrix:
mysubsetdata<-do.call("cbind",scan(file='location and name of your
file',what=list(NULL,NULL,NULL,NULL,NULL,NULL,NULL,NULL,0,NULL,0,NULL,NULL),flush=TRUE))
This will input only columns 10 and 11 into 'mysubsetdata'.
With this method you can work out the way to select random columns.
HTH
Rubén
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