StatsModels: the first nail in R’s coffin

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In 2012, when I decided to write a book on evidence-based software engineering, R was the obvious system to use for data analysis. At the time, lots of new books had “using R” or “with R” added at the end of their titles; I chose “using R”.

When developers tell me they need to do some statistical analysis, and ask whether they should use Python or R, I tell them to use Python if statistics is a small part of the program, otherwise use R.

If I started work on the book today, I would till choose R. If I were starting five-years from now, I could be choosing Python.

To understand why I think Python will eventually take over the niche currently occupied by R, we need to understand the unique selling points of both systems.

R’s strengths are that it supports a way of thinking that is a good fit for doing data analysis and has an extensive collection of packages that simplify the task of applying a wide variety of analysis techniques to data.

Python also has packages supporting the commonly used data analysis techniques. But nearly all the Python packages provide a developer-mentality interface (i.e., they provide an API like any other package), R provides data-analysis-mentality interfaces. R supports a way of thinking that data analysts can identify with.

Python’s strengths, over R, are a much larger base of developers and language support for writing large programs (R is really a scripting language). Yes, Python has a package ecosystem supporting the full spectrum of application domains, this is not relevant for analysing a successful invasion of R’s niche market (but it is relevant for enticing new developers who are still making up their mind).

StatsModels is a Python package based around R’s data-analysis-mentality interface. When I discovered this package a few months ago, I realised the first nail had been hammered into R’s coffin.

Yes, today R has nearly all the best statistical analysis packages and a large chunk of the leading edge stuff. But packages can be reimplemented (C code can be copy-pasted, the R code mapped to Python); there is no magic involved. Leading edge has a short shelf life, and what proves to be useful can be duplicated; the market for leading edge code in a mature market (e.g., data analysis) is tiny.

A bunch of bright young academics looking to make a name for themselves will see the major trees in the R forest have been felled. The trees in the Python data-analysis-mentality forest are still standing; all it takes is a few people wanting to be known as the person who implemented the Python package that everybody uses for XYZ analysis.

A collection of packages supporting the commonly (and eventually not so commonly) used data analysis techniques, with a data-analysis-mentality interface, removes a major selling point for using R. Python is a bigger developer market with support for many other application domains.

The flow of developers starting out with R will slow down, casual R users will have nothing to lose from switching when the right project comes along. There will be groups where everybody uses R and will continue to use R because that is what everybody else in the group uses. Ten-Twenty years from now R, developers could be working in a ghost town.

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