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An R programmer looks at Julia

r-bloggers.com

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Re: An R programmer looks at Julia

#21

Post by chimeracoder »

keithflower wrote: post #7
chimeracoder wrote: post #4

I have to come to love R (for what I use it it for), but reading this makes me realize how unusual my R-workflow must be, because most of the 'advantages' of Julia over R don't really come up in my daily workflow anymore - it seems that's likely because I've adapted to the shortcomings of R and have twisted other tools to my needs. I'll add Julia to my list of languages to check out in more detail, because perhaps Ju…

I tend to use Python and Num/SciPy for as much as I can, then reach for RPy: http://rpy.sourceforge.net/

Haha, that's really interesting - I can't believe I've never seen that. I'll see if this may solve my problems....

Re: An R programmer looks at Julia

#22

Post by drunkpotato »

chimeracoder wrote: post #14
drunkpotato wrote: post #8

Earlier quoted context omitted.

That is very interesting. I use Python to pre-process data for Matlab, and have been giving serious thought lately to learning R for its free license and easy(?) integration with Hadoop. Can you briefly comment on the advantages or R over Matlab aside from licensing?

If you're already used to Matlab, then you may not find my comments as relevant. If you were already proficient in both, then they're both interchangeable for many tasks (which is in fact why I always recommend learning R over learning Matlab). Licensing isn't just a minor thing - getting Matlab to run on non-Debian Linux is a painful ordeal. I never actually got it working, because I never bothered to debug its cryp…

Thank you, that's a good list! Dealing with cell arrays and text vs. numerics is why I do my pre-processing in Python. Matlab's job is to read in the data, run it through various algorithms, collect accuracy statistics, and show plots.

For us the issue is not so much Matlab as a programming language, but rather availability of new algorithms and ease of parallel processing. The licensing issues involved in getting the parallel toolbox running on multiple workstations seems like a headache, which is part of what is motivating us to look at R.

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