>> Well, R is free and open sourced. It is based on S and Splus, which the >> developers decided to close-source after a while (after achieving >> success). R is also known as GNU S for that matter. >> >> IMHO, R itself is a simple language and a relatively simple >> platform. It >> is a language that manipulates data but philosophically, it is not a >> statistical or mathematical language. The R core team seems to >> adhere to >> the view that R is a programming language where (by chance and >> intent) a >> lot of statistical tools are built on. Hence, most of the statistical >> libraries for R are written in R. I believe that the entire >> Bioconductor >> is written in R. >> >> Hence, the motivation for considering implementing or re- implementing R >> in PyPy is "natively" to bring in the strength of R (huge amounts of >> statistical libraries and data sets) into Python. > > > So, if the language is open source, quite simple and the only > advantage would be to have huge amounts of libraries... Why not just > make a code conversion from R to Numpy + Python, a full featured > language, used in a lot more places. Then you can get the libraries > and also a bigger comunity.
This could be a nice idea - make R codes run on Numpy + Python. My line of thought is that since there is an implementation of Numpy (I do not know how complete it is) in PyPy already (pypy/rpython/numpy), implementing a means to get R codes to run in RPython will be just as equally good, since RPython are essentially complete Python programs. Kinda killing 2 birds with a stone, or so. > > Just remember that making an interpreter of R using pypy will not > bring anything to python, as they would be diferent interpreters that > just happen to be inplemented on the same platform. It would keep the > same distance as R as it is today (implemented in C I suppose) and > Python (also made in C if you consider CPython). So I would probably > say, do the Numpy work on pypy than make a translator R2Py so you can > run your favorite R library on top of your optimized Numpy > implementation. I think this line of argument can be made against every language interpreter implemented in PyPy, essentially everything can be interfaced at C. IMHO, what PyPy offers is the language-optimization-platform modularization. This may be loafy goal but sure worth the effort. I am not sure what making an interpreter of X (where X can be any language, such as javascript or prolog) using PyPy will really bring anything to Python than implementing X interpreter directly on Python itself (without using PyPy). Please enlighten me on this. Cheers maurice _______________________________________________ [email protected] http://codespeak.net/mailman/listinfo/pypy-dev
