Now you’re in graphic designers’ comfort zone. If you’re an analyst or in a more technical field, this is probably unfamiliar territory. You can do a lot with a combination of code and out-of-the-box visualization tools, but the resulting data graphics almost always have that look of something that was automatically generated. Maybe labels are out of place or a legend feels cluttered. For analyses, this is usually fine—you know what you’re looking at.

However, when you make graphics for a presentation, a report, or a publication, more polished data graphics are usually appropriate so that people can clearly see the story you’re telling.

For example, Figure 3-19 is the raw output from R. It shows views and comments on FlowingData for 100 popular posts. Posts are separated by category such as Mapping. The brighter the green, the more comments on that post, and the larger the rectangle, the more views. You wouldn’t know that from the original, but when I was looking at the numbers, I knew what I was looking at, because I’m the one who wrote the code in R.

Figure 3-22 is a revised version. The labels have been adjusted so that they’re all readable; lead-in copy has been added on the top so that readers know what they’re looking at; and the red portion of the color legend was removed because there is no such thing as a post having a negative number of comments. I also changed the background to white from gray just because I think it looks better.

I could have edited the ...

Get Visualize This: The FlowingData Guide to Design, Visualization, and Statistics now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.