Skip to content
master
Switch branches/tags
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Jun 12, 2019
Sep 3, 2021

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 have a foot in both camps, and 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. 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 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 not allow shared memory. (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.

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.

R's metaprogramming features (code that produces code) are wonderful.

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.

Language unity

Sad loss for R.

Python recently underwent a major transition from version 2.7 to 3.x. All 2.7 code was rendered invalid. This caused 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."

The tidyverse was developed with the express goal of redefining R. This was done without collaborating with the R Core Team, the official body that develops R. Thus it was inevitable that a bifurcation of the language would ensue. RStudio, a commercial entity with large financial resources, has aggressively promoted the tidyverse, making the bifurcation a reality.

I've been a skeptic on tidyverse. In particular, I strongly dispute the claim that the tidyverse makes R more accessible to nonprogrammers. I believe the opposite is the case. It ought to be obvious, for instance, that one shouldn't force non-coder R learners to use functional programming instead of loops, when they are still struggling to learn functions. I've actually seen R learners apologize for using a loop. This is craziness, folks.

As a lifelong teacher and student of the human learning process, I regard the aggressive promotion of the tidyverse to non-coder learners of R as tragic.

The R Core Team offered an olive branch by including a "pipe" capability to base R. I've yet to see this gesture reciprocated by RStudio, but "hope springs eternal."

Attitudes

There is a conscious aim in the R community for inclusiveness. This is not the case for Python.

The R community generally has a positive view of Python, and RStudio has reached out to the Pythonistas by developing the reticulate package. Though Python's Wes McKinney participated somewhat in the latter, I frequently find that the Python community, and for that matter CS in general, look down on R.

Actually, given R's magical metaprogramming tools, computer scientists ought to be drooling over R. But most CS people are unaware of it.

I hope this situation improves in the coming years.

Learning R and Python

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.

Thanks

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

About

No description, website, or topics provided.

Resources

Releases

No releases published

Packages

No packages published