R vs. Python for Data Science

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 four R-related books, I've given keynote talks at useR! and other R conferences; I currently serve as Editor-in-Chief of the R Journal; etc. But I am also an enthusiastic Python coder, have been for many years. 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.

This is of particular interest to me, as an educator. I've taught a number of subjects -- math, stat, CS and even English As a Second Language -- and have given intense thought to the learning process for many, many years.

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. These libraries require a fair amount of computer systems sophistication.

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 for Data Science

Slight edge to R.

CRAN has over 14,000 packages. PyPI has over 183,000, but it seems thin on Data Science.

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 in CRAN to do this. By contrast, recently I tried to find nearest-neighbor code for Python and at least with my cursory search in PyPi, came up empty-handed; there was just one implementation that described itself as simple and straightforward, nothing fast.

The following (again, cursory) searches in PyPI turned up nothing: EM algorithm; log-linear model; Poisson regression; instrumental variables; spatial data; familywise error rate; etc.

This is not to say no Python libraries exist for these things; I am simply saying that they are not easily found in PyPI, whereas it is easy to find them in CRAN.

And the fact that R has a canonical package structure is a big advantage. When installing a new package, one knows exactly what to expect. Similarly, R's generic functions are an enormous plus for R. When I'm using a new package, I know that I can probably use print(), plot(), summary(), and so on, while I am exploring; All these form a "universal language" for packages.

Machine learning

Slight edge to Python here.

The R-vs.-Python debate is largely a statistics-vs.-CS debate, and since most research in neural networks has come from CS, available software for NNs is mostly in Python. RStudio has done some excellent work in developing a Keras implementation, but so far R is limited in this realm.

On the other hand, random forest research has been mainly pursued by the stat community, and in this realm I'd submit that R has the superior software. R also has excellent packages for gradient boosting.

I give the edge to Python here because for many people, machine learning means NNs.

Statistical sophistication

Big win for R.

In my book, the Art of R Programming, I made the statement, "R is written by statisticians, for statisticians," a line which I've been pleased to see used by others on occasion. It's important!

To be frank, 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 superb 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 multicore 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 and performance enhancement

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; indeed some would say Cython IS a C/C++ interface.

Object orientation, metaprogramming

Slight win for R.

For instance, though functions are objects in both languages, R takes that further than does Python. Whenever I work in Python, I'm annoyed by the fact that I cannot directly print a function to the terminal or edit it, which I do a lot in R.

Python has just one OOP paradigm. In R, you have your choice of several (S3, S4, R6 etc.), though some may debate whether this is a good thing.

Given R's magic metaprogramming features (code that produces code), computer scientists ought to be drooling over R. But most CS people are unaware of it.

Language unity

Sad 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. I, as a seasoned R programmer, cannot read Tidy code, as it calls numerous Tidyverse functions that I don't know. Conversely, as one person in the Twitter discussion of this document noted (approvingly), "One can code in the Tidyverse while knowing very little R."

I've been a skeptic on Tidyverse. For instance,I question the claim that it makes R more accessible to nonprogrammers.

Linked data structures

Win for Python.

Not a big issue in Data Science, but it does come up in some contexts.

Classical computer science data structures, e.g. binary trees, are easy to implement in Python. It is not part of base R, but can be done in various ways, e.g. the datastructures package, which wraps the widely-used Boost C++ library.

Online help

Big win for R.

To begin with, R's basic help() function is much more informative than Python's. It's nicely supplemented by example(). And most important, the custom of writing vignettes in R packages makes R a hands-down winner in this aspect.

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.[Source]-https://github.com/matloff/R-vs.-Python-for-Data-Science

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