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Benefits of Python versus R

I’m refactoring someone else’s code that’s been written half in Python and half in R. The core purpose of the code is discriminate analysis. Of course, I think it would be clearly beneficial for performance if only a single language was used, and I’m trying to make an argument one way or another.

The two common areas of debate between R and Python are speed and differences in algorithms (and thus mathematical accuracy). In addition, it’s my understanding that R was chosen from the belief that the validity of the analysis would be more defensible (scientifically) if it were done in R.

So, I’d love some input on two ideas:

1.      In a (specifically) discriminate analysis environment, would R be more defensible (and why)?
2.      In a (specifically) discriminate analysis environment, in what analytical/mathematical ways is R better than Python?

I really appreciate anyone taking the time to respond to this.  

Best!
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Mike R.
Asked:
Mike R.
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1 Solution
 
d-glitchCommented:
Presumably you mean discriminant not discriminate.
     http://www.jmp.com/support/help/Discriminant_Analysis_2.shtml#65479

This topic is covered extensively on line.  This is just one of the threads from Googling "Python vs R"
     http://www.quora.com/Which-is-better-for-data-analysis-R-or-Python

Python is a general purpose interpreted language.
R is also an interpreted language which is targeted at statistical analysis (your application).

I wouldn't worry much about numerical accuracy.  The respective communities are sure to be vigilant.
Speed is apt to be a toss-up as well.  Check the published bench marks for each language.

Community acceptance might dictate a choice of R over Python.  Check with the editors of Journals in your field or scan some recent issues.  And even that decision could be wrong by next year.

One last (frustratingly unhelpful) note:  The hybrid combination of R and Python may be the right choice.
     http://www.r-bloggers.com/python-compliments-rs-shortcomings/
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d-glitchCommented:
Several criteria can be used to justify the choice of a particular language:

Exigency:  If the project is incomplete, and needs to be completed soon, use the language you know best.

Authority:  If external authorities require a particular language, use it even if there is a learning curve.

Expediency:  Use a combination of languages if that gets the job done fastest.
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