On Tue, Feb 17, 2009 at 6:05 PM, Barry Rowlingson <b.rowling...@lancaster.ac.uk> wrote: > 2009/2/17 Esmail Bonakdarian <esmail...@gmail.com>:
> When I need to use the two together, it's easiest with 'rpy'. This > lets you call R functions from python, so you can do: > > from rpy import r > r.hist(z) wow .. that is pretty straight forward, I'll have to check out rpy for sure. > to get a histogram of the values in a python list 'z'. There are some > complications converting structured data types between the two but > they can be overcome, and apparently are handled better with the next > generation Rpy2 (which I've not got into yet). Google for rpy for > info. Will do! >> Is there much of a performance hit either way? (as both are interpreted >> languages) > > Not sure what you mean here. Do you mean is: > > R> sum(x) > > faster than > > Python> sum(x) > > and how much worse is: > > Python> from rpy import r > Python> r.sum(x) > Well, I have a program written in R which already takes quite a while to run. I was just wondering if I were to rewrite most of the logic in Python - the main thing I use in R are its regression facilities - if it would speed things up. I suspect not since both of them are interpreted, and the bulk of the time is taken up by R's regression calls. Esmail ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
