I am going to be undertaking some logfile analyses in R (unless I can\'t do it in R), and I understand that my data needs to fit in RAM (unless I use some kind of fix like
R is well suited for big datasets, either using out-of-the-box solutions like bigmemory
or the ff package (especially read.csv.ffdf
) or processing your stuff in chunks using your own scripts. In almost all cases a little programming makes processing large datasets (>> memory, say 100 Gb) very possible. Doing this kind of programming yourself takes some time to learn (I don't know your level), but makes you really flexible. If this is your cup of tea, or if you need to run depends on the time you want to invest in learning these skills. But once you have them, they will make your life as a data analyst much easier.
In regard to analyzing logfiles, I know that stats pages generated from Call of Duty 4 (computer multiplayer game) work by parsing the log file iteratively into a database, and then retrieving the statsistics per user from the database. See here for an example of the interface. The iterative (in chunks) approach means that logfile size is (almost) unlimited. However, getting good performance is not trivial.
A lot of the stuff you can do in R, you can do in Python or Matlab, even C++ or Fortran. But only if that tool has out-of-the-box support for what you want, I could see a distinct advantage of that tool over R. For processing large data see the HPC Task view. See also an earlier answer of min for reading a very large text file in chunks. Other related links that might be interesting for you:
In regard to choosing R or some other tool, I'd say if it's good enough for Google it is good enough for me ;).