If a category is as important as other, colorful ones, don’t make it gray. By now, many of your readers are used to colorful visualizations that use gray for less important categories. Don’t use gray like other colors.Īs we’ve seen, gray is special and should be treated as such. These are the ones you should change from gray to a color like red, blue, or whatever else you fancy. Then hover over the data points in the preview and remember the interesting ones. To do so in Datawrapper, click on Customize Colors > Select all > Color picker and then choose gray. I actually recommend graying out all your colorful categories first. In short: Goals (“people should get this message”) and priorities (“this category is more important than that one”) lead to design decisions (“I’ll make the other categories gray”). Note the gray ⬤ labels for outliers North Korea, Eritrea, and Norway: They’re interesting but less important than India ⬤ and Pakistan ⬤. The last six recessions are all shown in the same gray ⬤ and same stroke width, making the current one stand out ⬤.īy making the average category a little harder to see, you help readers immediately notice the most important one. Look at the following example: The designer decided to leave the last six recessions gray and unlabeled, which helps the 2021-22 line really stand out: Washington Post, 2022. Gray is often used for categories like “miscellaneous,” “others,” “no data,” “no answer,” or “don’t know” - they’re almost always the least important.īy sacrificing some legibility for your gray categories, you can gain great clarity for the highlighted few. Here, all categories are important but one (“Don’t know”). Cooler regions take a back seat thanks to shades of gray ⬤ ⬤ ⬤. Readers can immediately spot regions with warmer temperatures highlighted in red ⬤ ⬤ ⬤ - just as the title promises. Note that the designer emphasizes the row not just with the use of orange, but also with a light blue background ⬤ and a bold label (“Bayern”). The distinct SPIEGEL orange ⬤ gets the most attention in this table. The reader’s eyes are first drawn to the “Offshore Gulf of Mexico” category, even though it’s at the bottom of the treemap. The Wall Street Journal demonstrates that in this chart, where our eyes are drawn to the smaller red areas ⬤ instead of the bigger gray ones ⬤: The Wall Street Journal, 2021. Then at the slightly less saturated colors. People look first at the highest-contrast, most saturated color in your chart. Gray is a storytelling tool: Against gray elements, colored ones will stick out. It can balance or even outweigh the effects of size - especially if we tone down everything else by making it gray: Color is our most powerful tool to control where the reader looks. Our eyes are immediately drawn to big things.īut you know what draws the eye even more? Color. The bigger and bolder you make something, the more attention your readers will give it. You’ll decide which categories are important to show and which ones can be grouped together or removed you’ll choose a visualization type that brings across your message you might even adjust the headline so that it states what you want to say you’ll decide where to place your chart on the page - and, yes, you change the visual design. You create a hierarchy: Your priorities will guide decisions about your visualization. Make everything gray except what’s important. It’s better for readers to take away your most important statement than none at all.ĭo you know your goals and priorities? Sweet! Let’s start designing. If everything is important and emphasized, nothing is - and readers might be so overwhelmed by your visualization that they stop looking at it. And you might personally really like the fact that life expectancy has increased faster in countries with a high GDP - but if you had to decide, you’d say the first two statements are more important for readers to understand. It might be less important, but still useful to show that life expectancy today is higher in countries with a high GDP. I also recommend prioritizing your goals: What’s most important to show - what should people definitely not miss? What point would be great, but not essential, for readers to spot? And what might be your personal darling data point or category, but is probably not of the highest importance to others?įor example, in a chart about life expectancy, your main priority might be to show that life expectancy improved in all countries over the last hundred years. I recommend writing down the answers if you’re just starting out. What should people take away from looking at your visualization? What do you want them to see and understand about your data? What question(s) should readers be able to answer? To emphasize or de-emphasize data points, you first need to decide what’s important. Start by thinking about which information is most important.
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