I want to talk about some similarities between writing and statistical graphics. Just about everybody knows something about writing, and I’d like to help transfer some of this expertise to thinking about statistical graphics.
The story begins with some ugly pie charts I noticed the other day. I wascommenting on them and suddenly realized . . . the graphs weren’t as bad as I thought they were! To be more precise, the graphs had a lot of failings, but the sum total of all these problems wasn’t so bad.
Here are the actual charts:
As I wrote earlier, these graphs have lots of obviously-fixable problems, most notably that the wedges aren’t labeled directly. Instead, the reader has to go back and forth, back and forth, between the chart and the legend. On the other hand, the information is conveyed unambiguously.
I’d like to make the analogy to sloppy writing–misspellings, grammatical errors, sentence fragments and run-ons, garden-path sentences, distracting cliches, and all the rest. (All these “errors” can be used to good effect. No rule is absolute. For sure, baby. Much of the time, though, I think these really are mistakes rather than intentional use for )emphasis or clarity.)
Why is sloppy writing a bad thing? For example, what’s wrong with using “it’s” instead of “its,” or messing up subject-verb agreement, or losing track of an adverb’s pointer, in a setting where the meaning is clear? The problem is that it creates work for your readers, who often have to double back to figure out the meaning. If you’re Ezra Pound writing a poem, maybe you want to have that effect, but I don’t think it’s the goal of most journalists, news bloggers, etc.
OK, back to the pie charts. They could be worse, but they require a lot of work to read. Arguably, this criticism could be thrown at any graph: for example, I love line plots, but if you’ve never seen a line plot before, you’ll struggle with it. The difference is that you can learn to read line plots, but you’ll never be able to quickly read the pie charts shown above: no matter what, you have to back and forth between the pie, the legend, the pie, the legend, and so forth, to keep it all in your mind at once.
To push the analogy further, I’m recommending what might be called the George Orwell approach to statistical graphics: the goal is to be clear as a window pane. This isn’t the only option, though. There’s the Chris Ware style: graphs that are tiny and nearly impossible to read, but if you stare at them for a long time you realize they actually make sense. Or the Martin Amis style: flashy gimmicks that make the graph fun to read even if you don’t care so much about the subject. Or the Veronica Geng style: playing it straight while going over the top at the same time. And so forth.
I think some of the confusion that has arisen from Ed Tufte’s work is that people read his book and then want to go make cool graphs of their own. But cool like Amis, not cool like Orwell. We each have our own styles, and I’m not trying to tell you what to do, just to help you look at your own writing and graphics so you can think harder about what you want your style to be.
P.S. Yes, yes, I’m sure I have various usage, grammatical, and stylistic errors above. Give me a break, man! It’s just a blog entry. More to the point . . . by now you should trust me enough to think, when you see something discordant, that maybe I’ve done it on purpose!
P.P.S. Another issue is cost or effort. It wasn’t necessarily worth it for Tom Schaller to learn a bunch of new graphical tools just to make his blog entry slightly easier to read. In my discussion above, I’m ignoring the investment in time required to think in terms of graphics and to learn the relevant software.