Large dataset problem

This question came to me via email. I don’t have a good answer so I’m posting it here in case others can help:

I have been using R for sometime now and trying to push it in my organization as a viable alternative/parallel analytical workbench to SAS. I have created a process using Sweave to create pdf files of univariate exploratory analysis of datasets, where every column will be graphed, summary statistics collected, etc and all of this put in a pdf document.

My problem is that the datasets I deal with currently are small (few hundred MB) whereas I have been asked to execute this process on datasets upwards of 10 GB at times.

I know R is limited by the RAM in the system, and there are a few packages(filehash, bigmemory) to name a few that provide solutions to this problem, but they are not extensively tested. Creating SQL servers and having R connect with it is also an option, but I am not sure if such a solution can be used to gather summary statistics by calling each column from the DB.

I have also looked at the possibility of using R on a 64 bit Linux box with 8 GB of RAM but the cost involved is prohibiting that implementation and still does not take care of datasets upwards of 10 GB in size.

Is there a method by which I can create a swap space or a temporary disk space like SAS does during execution? Are you aware of possible solutions to this problem?