2014-01-12 R or Python
Should you use R or Python? I won't give a precise answer except a reference to this blog post: Python Displacing R As The Programming Language For Data Science. To summarize, if you are a statistician, you are already using R. However, if you are not a statistician but you need statistics, you are probably wondering if you should use R and another language or just another language. R is not very well designed as a programming language and is not very suitable to manipulate files, create a web server or games... Using Python for everything avoids switching to another language. It avoids converting the data into various formats between the two languages.
With pandas, numpy, scipy, scikit-learn, matplotlib, IPython, many common statistics routines are available in Python. In the last two years, it became a really strong alternative to R. In the next years, SAS should less and less used (see Forecast Update: Will 2014 be the Beginning of the End for SAS and SPSS?). Computers speed and memory are not an issue anymore with others alternatives. Plus, it is expensive. I would also look at Julia (+ Julia Studio) which seems to be a promising language. I discovered at MCMSki IV. But maybe the future will be dedicated languages such as BUGS for bayesian models.
Finally, some articles about R and Python:
- Python Displacing R As The Programming Language For Data Science
- Why are pandas merges in python faster than data.table merges in R?
- Pros and Cons of R vs Python Sci-kit learn
- The Statistics Software Signal
- THE HOMOGENIZATION OF SCIENTIFIC COMPUTING, OR WHY PYTHON IS STEADILY EATING OTHER LANGUAGES’ LUNCH
- Data Science Wars: Python vs. R
- On the growth of R and Python for data science
- R vs Python: Practical Data Analysis (Nonlinear Regression)
2014/06/30: I recommend reading Numeric matrix manipulation, The cheat sheet for MATLAB, Python NumPy, R, and Julia
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Xavier Dupré
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