[pypy-dev] Motivation for re-implementing R (GNU S) in PyPy (perhaps?)
santagada at gmail.com
Tue Apr 24 04:36:00 CEST 2007
Em 23/04/2007, às 21:47, Maurice Ling escreveu:
> Well, R is free and open sourced. It is based on S and Splus, which
> 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
> 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.
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
> I have to admit that my skills in this area (langauage
> interpreters/compilers) is rather limited to undergraduate level
> but I'm
> willing to learn more. So I'm currently reading codes on language
> interpreters for esoteric languages like BrainFuck simply because they
> are tiny but provides the relevant concepts.
Mine to, but there is a lot of really brainy people on the project...
and they really help (specially if you could go to some sprint)
> So, anyone like to support the idea of (re-)implementing R on PyPy?
> need some "emotional" strength to go on this route. Also willing to
> other radical options...
> Another thing of interest to me is implementing modeling/simulation
> languages in PyPy as it will help in my future career route but my
> knowledge of even the simulation languages are rather limited.
Making Numpy work as we are saying (making it really optimized and
cache friendly) is probably going to help your career even more.
> Please advice and comment.
My two cents.
santagada at gmail.com
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