What is the benefit of using Rpy? I'm familiar with Python as a high-level compiled language. Python was very popular here (Iowa State, Statistics) a few years ago to vastly speed up S+ simulation studies. One could recode into Python a lot more quickly than recoding into C++. Both Python and C++ were much faster than S+; I don't know how Python compares with native R. Thanks, Philip Dixon
Why use Rpy?
2 messages · Dixon, Philip M [STAT], Gregory, Matthew
Philip Dixon wrote:
What is the benefit of using Rpy? I'm familiar with Python as a high-level compiled language. Python was very popular here (Iowa State, Statistics) a few years ago to vastly speed up S+ simulation studies. One could recode into Python a lot more quickly than recoding into C++. Both Python and C++ were much faster than S+; I don't know how Python compares with native R.
I guess since I think I was the one who brought it up, I should probably explain my rationale. The really glib answer (for me) is that I know Python and wasn't willing to learn another programming language for the small bit that I needed from R (probably not a very popular opinion on an R listserve ...). RPy provides that relatively seamless link into R and given that most of our codebase is in Python, this was the path of least resistance for me. There is also the RSPython package which has similar functionality to RPy. I can only speak to the benefits that I've found from Python, which isn't to say they don't exist in R - I'm just not fully aware of them. The main strength of Python for me is the vast array of amazing packages that are written for it. This includes: - Numpy/Scipy/matplotlib for array handling, scientific computing and graphing - bindings for GDAL for abstract spatial translation and projection support - PIL for image processing - numerous other packages that have nothing to do with statistical computing I realize that most of this functionality already exists in R, so probably not worth getting into Python if R fits your needs. As for speed, I can't speak to the R vs. Python question, although colleagues that are R users do complain about memory limitations and speed when running large spatial models. Python will be slower than C/C++, but the things that *need* to be fast can be coded in C/C++ and be bound to Python using SWIG. I'm sure that someone on this list probably has much more experience with both languages than I do and will provide a better answer. Matt Gregory Faculty Research Assistant Department of Forest Science Oregon State University