Re-posting interesting article from
R-vs.-Python-for-Data-Science
R vs. Python for Data Science
Norm Matloff, Prof. of Computer Science, UC Davis; my bio
Hello! This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. As a professional computer scientist and statistician, I hope to shed some useful light on the topic. I have potential bias — I've written 4 R-related books, and currently serve as Editor-in-Chief of the R Journal — but I hope this analysis will be considered fair and helpful.
Elegance
Clear win for Python.
This is subjective, of course, but having written (and taught) in many different programming languages, I really appreciate Python's greatly reduced use of parentheses and braces:
if x > y:
z = 5
w = 8
vs.
if (x > y)
{
z = 5
w = 8
}
Python is sleek!
Learning curve
Huge win for R.
To even get started in Data Science with Python, one must learn a lot of material not in base Python, e.g., NumPy, Pandas and matplotlib.
By contrast, matrix types and basic graphics are built-in to base R. The novice can be doing simple data analyses within minutes. Python libraries can be tricky to configure, even for the systems-savvy, while most R packages run right out of the box.
Available libraries
Call it a tie.
For example, I once needed code to do fast calculation of nearest-neighbors of a given data point. (NOT code using that to do classification.) I was able to immediately find not one but two packages to do this. By contrast, just now I tried to find nearest-neighbor code for Python and at least with my cursory search, came up empty-handed; there was just one implementation that described itself as simple and straightforward, nothing fast.
The following searches in PyPI turned up nothing: log-linear model; Poisson regression; instrumental variables; spatial data; familywise error rate; etc.
Machine learning
Slight edge to Python here.
The Pythonistas would point to a number of very finely-tuned libraries, e.g. AlexNet, for image recognition. Good, but R versions easily could be developed. The Python libraries' power comes from setting certain image-smoothing ops, which easily could be implemented in R's Keras wrapper, and for that matter, a pure-R version of TensorFlow could be developed. Meanwhile, I would claim that R's package availabity for random forests and gradient boosting are outstandng.
Statisical correctness
Big win for R.
In my book, the Art of R Programming, I made the statement, "R is written by statisticians, for statisticians," which I'm pleased to see pop up here and there on occasion. It's important!
To be blunt, I find the machine learning people, who mostly advocate Python, often have a poor understanding of, and in some cases even a disdain for, the statistical issues in ML. I was shocked recently, for instance, to see one of the most prominent ML people, state in his otherwise outstanding book that standardizing the data to mean-0, variance-1 means one is assuming the data are Gaussian — absolutely false and misleading.
Parallel computation
Let's call it a tie.
Neither the base version of R nor Python have good support for multicore computation. Threads in Python are nice for I/O, but parallel computation using them is impossible, due to the infamous Global Interpreter Lock. Python's multiprocessing package is not a good workaround, nor is R's 'parallel' package. External libraries supporting cluster computation are OK in both languages.
Currently Python has better interfaces to GPUs.
C/C++ interface
Slight win for R.
Though there are tools like swig etc. for interfacing Python to C/C++, as far is I know there is nothing remotely as powerful as R's Rcpp for this at present. The Pybind11 package is being developed.
In addition, R's new ALTREP idea has great potential for enhancing performance and usability.
On the other hand, the Cython and PyPy variants of Python can in some cases obviate the need for explicit C/C++ interface in the first place.
Object orientation, metaprogramming
Slight win for R.
For instance, though functions are objects in both languages, R takes that more seriously than does Python. Whenever I work in Python, I'm annoyed by the fact that I cannot print a function to the terminal, which I do a lot in R.
Python has just one OOP paradigm. In R, you have your choice of several, though some may debate that this is a good thing.
Given R's magic metaprogramming features (code that produces code), computer scientists ought to be drooling over R.
Language unity
Horrible loss for R.
Python is currently undergoing a transition from version 2.7 to 3.x. This will cause some disruption, but nothing too elaborate.
By contrast, R is rapidly devolving into two mutually unintelligible dialects, ordinary R and the Tidyverse. Sadly, this is a conscious effort by a commercial entity that has come to dominate the R world, RStudio. I know and admire the people at RStudio, but a commercial entity should not have such undue influence on an open-source project.
It might be more acceptable if the Tidyverse were superior to ordinary R, but in my opinion it is not. It makes things more difficult for beginners. E.g. the Tidyverse has so many functions, some complex, that must be learned to do what are very simple operations in base R. Pipes, apparently meant to help beginners learn R, actually make it more difficult, I believe. And the Tidyverse is of questionable value for advanced users.
Linked data structures
Likely win for Python.
Classical computer science data structures, e.g. binary trees, are easy to implement in Python. While this can be done in R using its 'list' class, I'd guess that it is slow.
R/Python interoperability
RStudio is to be commended for developing the reticulate package, to serve as a bridge between Python and R. It's an outstanding effort, and works well for pure computation. But as far as I can tell, it does not solve the knotty problems that arise in Python, e.g. virtual environments and the like.
At present, I do not recommend writing mixed Python/R code.
