Visualization of Data 1: Parallel Structure

In my experience, there are many people who are well-spoken and who write well but couldn't name many specific grammatical or literary devices. They could probably identify nouns, verbs, pronouns, and even prepositions. But if I were to say, "great use of subjunctive mood, dude!," I'd get a perplexed look (and not just because that's a weird thing to point out.) Parallel structure is one of those literary devices. If it's done correctly, nobody notices, but if it's done incorrectly, the "intuitive grammarians" flinch. Here's what I mean:

In this blog post, I plan to:

  • explain parallel structure
  • apply the concept of parallel structure to visualization of data
  • helping my readers become better science writers.

OUCH! See? For most of you, that last bullet was cringe-worthy. That's because the bullets all follow on the preposition "to," which should be placed with "explain," "apply," and "helping." You'd never say "to helping," so it just sounds wrong.

The idea applies to data. For example, consider these two graphs of the same data:

Can you see which graph is a better data visualization?

The graph on the right is more correct, and not just because the trend in the data is more visually evident. On the left, the height ranges are different (one foot, six inches, six inches, one foot), while the data on the right are broken into the same "bin sizes."

Here's another example:

What's wrong here? When readers examine separate graphs within the same report, they tend to assume that the samples are listed in the same order. Otherwise, it is very difficult to move from graph to graph and draw conclusions. 

Next Wednesday, we'll continue looking at data visualization with another common mistake: selecting the wrong format for the job.

Have you ever run into these parallel structure issues in your own writing or another writer's? What is your biggest struggle when writing with data?