Re:R for Data Science
by xaa on Friday September 16, 2016 @08:09PM
(#12518331)
I'm in bioinformatics. R is huge, Python is a distant second. So I am forced to use both.
R is a terrible programming language. It's slow and syntactically inconsistent. For interactive statistical analysis it can be OK, but anything beyond a small program becomes unmanageable quickly. In particular, if you want to manipulate strings or hierarchical data structures quickly in R, good luck.
For ML, scikit-learn is almost always sufficient. For statistics, statsmodels is OK but very underdeveloped compared to what is available in R. IMO plotting is equally painful in both Python and R (seaborn is a good Python library to ease the pain if you haven't seen it).
Personally, I write everything in Python and call out to R as infrequently as possible using rpy2, usually only for specific statistical routines or bioinformatics-specific libraries.
R is a terrible programming language. It's slow and syntactically inconsistent. For interactive statistical analysis it can be OK, but anything beyond a small program becomes unmanageable quickly. In particular, if you want to manipulate strings or hierarchical data structures quickly in R, good luck.
For ML, scikit-learn is almost always sufficient. For statistics, statsmodels is OK but very underdeveloped compared to what is available in R. IMO plotting is equally painful in both Python and R (seaborn is a good Python library to ease the pain if you haven't seen it).
Personally, I write everything in Python and call out to R as infrequently as possible using rpy2, usually only for specific statistical routines or bioinformatics-specific libraries.