Hello! This Web page is aimed at shedding some light on the perennial R-vs.-Python debates in the Data Science community. This is largely (though not exclusively) a debate between the Statistics (R) and Computer Science (Python) fields. Since I have a foot in both camps (I was a founding member of both the Statistics and Computer Science Departments at UC Davis), 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 have served as Editor-in-Chief of the R Journal; etc. But I am also an enthusiastic Python coder, have been for many years, and am the author of a popular Python tutorial. I hope this analysis will be considered fair and helpful.

Again, please note: The emphasis here is on Data Science, not Computer Science. Note too that other than references to specific packages, ``R'' here means base R, not the Tidyverse, of which I have been a critic.

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.

Python libraries can be tricky to configure, even for the systems-savvy, while most R packages run right out of the box.

Huge win for R.

In my book, The Art of R Programmming, I wrote "R is written by statisticians, for statisticians," a line I've been pleased to see quoted by others. One could update that to read "R is written by data scientists, for data scientists," and it is of crucial importance in our discussion here.

Matrix types, data frames, missing-value handling, basic graphics, data-and-time processing, linear models, basic statistics, contingency tables and so on are built-in to base R. The novice can be doing simple data analyses with these tools within minutes.

Generally an R data science function will be richer in coverage than its Python counterpart. For instance, R's histogram plot function, hist(), offers many advanced options, not the case for Python.

All this is the result of the fact that, indeed, "R is written by data scientists, for data scientists."

Slight edge to R.

CRAN has over 14,000 packages. PyPI has over 183,000 (both numbers are growing), 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 indeed, such libraries are more likely to be in CRAN but not PyPI. Once again, this reflects the difference in orientation, Data Science for R versus Computer Science for Python.

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, without checking the documentation. These form a "universal language" for packages.

Win for R

Unlike Python, base R itself has sophisticated graphics utilities built in, and there are two outstanding graphics packages available, ggplot2 and lattice. The former is so widely used that many probably perceive it as being part of base R.

But it goes far beyond that. As noted, a major built-in function in R is plot(). It is polymorphic, meaning that its role is different for each use case it has been written for. This is a fancy term whose practical meaning is that the objects returned by R functions are typically paired with a visualization, which we can invoke simply by calling the generic plot().

Slight (or more) edge to Python.

As noted, 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. To many in CS, machine learning means neural networks (NNs).

RStudio/Posit has done some excellent work in developing a Keras implementation, and there are R interfaces to PyTorch and so on, but so far R is limited in this realm. Again, if one's view is that Data Science = NNs, then Python is the language of choice.

On the other hand, random forest research originated in the Statistics community, and most research in this field has been conducted there. In this realm I'd submit that R has the superior software. The grf package, for instance, allows linear interpolation within tree leaves, crucial for removing bias near the edges of the data. R also has excellent packages for gradient boosting, another field originally invented in Statistics.

Big win for R.

As noted, I use the slogan, "R is written by statisticians, for statisticians." 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. And, sadly, I often see ignorance. 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.

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 difficult, due to the infamous Global Interpreter Lock. Python's multiprocessing package is much better than before, but still clunky. R's parallel package does allows shared memory for Macs or Linux, but not on Windows platforms.

(See my Rdsm package if you wish to use shared memory at the R level.)

External libraries supporting cluster computation are OK in both languages.

Currently Python has better interfaces to GPUs, but again, only for NNs.

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.

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. (This goes back to the polymorphic nature of R's print() function etc.)

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.

R's metaprogramming features (code that produces code) are wonderful, arguably as powerful as, or more powerful than, those of Python. In both languages, these are only for experts, but the point is that R is competitive with Python even in this highly CS-ish aspect.

Win for Python.

This is 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. This can be done in R in various ways, e.g. with the datastructures package, which wraps the widely-used Boost C++ library, but it is not base-R.

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.

Hopefully I've made a strong case above for using R in data science, in analytics, visualization and so on. It is widely used in business and industry. In a very significant move, Python pandas creator Wes McKinney recently joined RStudio/Posit as a principal architect (see reticulate. below).

On the other hand, Python is my preferred tool in some applications. An example is OMSI, an online examination tool that my students and I developed. Python's built-in threading made our work much easier in that project.

I thus very strongly recommend that those considering a data science career not only learn both languages, but also use them, thus developing expertise.

For similar reasons, some user apps may be best developed as a mixture of R and Python. Here is the current status:

RStudio/Posit is to be commended for developing the reticulate package, to serve as a bridge between Python and R. The package enables calling Python from R code. For the opposite direction, calling R from Python, I recommend RPpy2, which is the approach we take in our dsld package.

The reticulate package is 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. The RPy2 library has similar issues.

At present, computer systems expertise--skill with environment variables, search paths and general coding ability-- is required for developing mixed R/Python apps.

I have a quick tutorial on R for non-programmers, an evolving project. I also have a book, the Art of R Programming, NSP, 2011.

I have a tutorial on Python, for those with a strong programming background.

This document has benefited from various reader comments, notably from Dirk Eddelbuettel, as well as Paul Hewson, Bob Muenchen and Inaki Ucar.

Updated December 17, 2023