Nima Hejazi

4 minute read

In the past several weeks, something of a debate has emerged regarding whether, in fact, there is a superior plotting system in R. First, a bit of history. The arguments on plotting all began with an off-hand comment about the plotting preferences of statistician and JHU Professor Jeff Leek, on the “Not So Standard Deviations” podcast of Hilary Parker and Roger Peng. The comment, as I remember it, had to do with why anyone would ever bother to use base graphics in R when a tool like ggplot2 exists.

In response to this, Jeff Leek provided a blog post about the advantages and relative utility of base graphics; many others have made posts expressing their views on the ggplot2 vs. base graphics debate. One such post argues the other side.

I happen to think that ggplot2 is a fantastic tool, which incorporates a number of benefits that were ignored in Jeff Leek’s post. Here are a few responses loosely organized around the main points from his blog post.

I - Exploratory graphs

The main point defending base graphics here simply seems to be that exploratory graphics need not be pretty. While technically true, this point ignores the many benefits that emerge when exploratory visualizations actually are pretty. For one, when making scatterplots, histograms, and other such visualizations, it helps to share these with collaborators; and, while base graphics can generate decent looking plots quickly, the graphs themselves are far surpassed in quality by those created by ggplot2. What’s more, the code generating base plots is unintelligible; by contrast, comparable ggplot2 code is nearly human-readable. This clarity of syntax means that plots made with ggplot2 can be easily modified upon requests by collaborators, shared with students, and even easily dissected at later times when the intricacies of using base graphics have long been forgotten. The bit on teaching leads nicely to a rebuttal of Jeff Leek’s point on grading the work of students in data science courses.

II - Teaching / Grading students

In regard to teaching students how to properly do data science, the point made in Jeff’s post seems to be that ggplot2 in effect makes plotting too easy. While having to think about how to generate a plot may be useful when creating very complex plots – the syntax of base graphics necessitates thinking – this alone by no means makes base graphics better suited to teaching students. Often, students learning the principles of applied statistics and data science for the first time would benefit far more from thinking critically about why a particular plot is useful in communicating an important finding, as opposed to struggling with some of the terrible syntax that one runs into when using base graphics. Teaching students to think critically and develop good habits for scientific communication is much more important than having them struggle with complex syntax for generating elementary plots. As far as teaching goes, the use of base graphics is, without a doubt, a bad practice, and this is exacerbated when teaching students at a beginner level (how many students new to statistics and programming want to learn that base’s cex parameter scales text on a plot?).

III - Literate programming

We now live in an era of science where openness and reproducibility are highly valued, and rightly so. Sharing not only data but the code used to build models and visualizations is an important part of working in science. This ideal is not well served by the use of tools whose properties run in contrast to the requirements of accessibility. Base graphics and comparable systems in other languages (e.g., basically anything done in MATLAB) have challenging syntax (to put it nicely), which makes sharing code challenging. What’s more, writing code is often a very subjective process – that is, it is hard to share code between individuals, often simply because of stylistic differences – and systems that make sharing code easier ought to be highly valued, if truly open science is ever to stand a chance. The ggplot2 system is an implementation of the grammar of graphics, which attempts to unpack the components of a visualization in a sensible manner. This means that using ggplot2 generates code that is not only human readable, but can be dissected for the purposes of teaching and explaining methods to collaborators not well-versed in computational science. As far as visualization of data is concerned, ggplot2 is an important addition to the toolbox of a computational scientist aiming to follow the ideals of open and reproducible science. In fact, aiming to build accessible work puts the use of base graphics squarely in the realm of worst practices, and worst practices should be hard (which, to its credit, the base graphics system certainly is).

Anyway, cheers for now – I think I mostly covered everything I had hoped to talk about from Jeff Leek’s post, but, really, who knows, there might be a follow-up post at some point…

last updated: 04 May 2016

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