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This graph builds on the previous one, replacing the nine dots with nine football shapes.

On Decoration and Design

Before I started Data Soapbox, I was a practicing researcher. I designed research studies and collected, analyzed, and reported the data.

As time went on, I got more and more interested in the reporting part of my job, to the point that colleagues would sometimes ask me for help with their presentations and reports. I’d say that at least half the time, the request was phrased as some version of, “Can you make these slides pretty?”

Record scratch.

Picture this: You’ve just given a presentation to a room full of people. The lights come up, and the audience streams into the hallway for the mid-afternoon snack break. Is your goal really for them to gush, “WOW, that presentation was so…PRETTY”?

Pretty is a term for decoration. Your makeup is pretty. Her necklace is pretty. That cake is pretty. Awww, who’s a pretty kitty?*

Graphic design, on the other hand, prioritizes function. It’s the use of images and text to convey a message that achieves an objective.

Let’s walk through an example of decoration:

I created a small dataset of Penn State’s 2020 football games. Among other variables, it included the point spread (calculated by subtracting the opponent’s score from Penn State’s score) and the approximate temperature at kickoff (based on historical weather data from the nearest airport).

Here’s a basic scatterplot of those data:

This is a scatterplot graph. The y (vertical axis) is temperature at kickoff, approximate degrees fehrenheit, and it runs from twenty to eighty. The x (horizontal) axis is point spread, running from Penn State University losing by 30 points to Penn State winning by 40 points. There are nine dots, and they generally slope downward from left to right.

OK, friends! Let’s start decorating!

First, let’s substitute the dots for cute little footballs:

This graph builds on the previous one, replacing the nine dots with nine football shapes.

Then let’s add some clip art representing hot and cold temperatures:

The graph builds on the previous one, with a sweating emoji near eighty degrees and a penguin in a scarf and hat near twenty degrees.

While we’re talking about hot and cold, let’s add a red-to-blue color gradient in the background:

This graph builds on the previous one, adding a color gradient that runs from red at the top of the graph to blue at the bottom of the graph.

And let’s add some cute snowflakes to highlight the freezing mark:

This graph builds on the previous one by adding a row of snowflake shapes at the thirty-two degree line.

And, finally, what Penn State graph would be complete without a blue-and-white-striped background?

This graph builds on the previous one by adding a blue and white striped background.

The result? A tragic mess that doesn’t tell us anything more than the original graph. In fact, the visual clutter makes this version harder to understand.

Don’t get me wrong; decoration can be beautiful in a way that this example clearly isn’t. The distinction between decoration and design is really about why you are including particular visual elements. Decoration is about adding elements because they’re pretty or cute or interesting.**

Now, let’s attack this same example using a design framework.

As a reminder, here’s our original (with the blue dots changed to black) :

This is a scatterplot graph. The y (vertical axis) is temperature at kickoff, approximate degrees fehrenheit, and it runs from twenty to eighty. The x (horizontal) axis is point spread, running from Penn State University losing by 30 points to Penn State winning by 40 points. There are nine dots, and they generally slope downward from left to right.

The most important gridline in this graph is the vertical line at the zero point spread. This line represents a tie, and it separates the wins from the losses. So, let’s darken that line to draw attention to its conceptual importance:

This graph builds on the previous one by darkening the vertical gridline at a zero point spread.

Next, we can emphasize and label the hottest and coldest games. The large dots help make the downward data trend a little more obvious. The labels flesh out two example data points, which can help readers check that they’ve interpreted the graph correctly.

This graph builds on the previous one by making the upper right data point large and labeling it 72 degrees, P S U versus Maryland, nineteen to thirty-five. It labels the lower right data point twenty-eight degrees, P S U versus Illinois, fifty-six to twenty-one.

Color can be really helpful in data visualizations. (Our decoration example just took that to an unfortunate extreme.) By coloring the hottest data point red and the coldest one blue, it taps into cultural associations with color. Many readers will suspect that we’re showing temperature extremes without having to read a word.

The graph builds on the previous one by making the upper left dot red and the lower right dot blue.

Finally, no good data visualization is complete without a strong, informative title:***

This graph adds to the previous one by adding a title. In twenty twenty, Penn State football won bigger when the weather was colder.

If you’re working with a make-up artist or a jeweler or a baker, keep asking for pretty. The world needs beautiful things! If you’re working with a research communication designer, let’s focus on what you want your audience to be exclaiming en route to the refreshment table.

*Actual quote of me cooing to our female cat while I scratch behind her ears.

**Decoration makes me think back to my college marketing class group projects, circa 1999-2001. We’d pick out particular PowerPoint templates and fonts because we’d never seen anyone use them before.

***The “boring scientist” version of this title would be “Association between point spread and temperature at kick-off for Penn State football, 2020 season.” Ugh.

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