Chart Design Principles
Although not a science, data visualization comes with a set of rules, principles, and best practices that create a basis for clear and eloquent charts. Some of those rules are less rigid than others, but prior to “breaking” them, it is important to establish why they are important.
Before you begin, ask yourself: Do I really need a chart to tell this data story? Or would a table or text alone do a better job? Making a good chart takes time and effort, so make sure it enhances your story.
Deconstructing a Chart
Let’s take a look at Figure 5.1. It shows basic chart components that are shared among most chart types.
A title is perhaps the most important element of any chart. A good title is short, clear, and tells a story on its own. For example, “Black and Asian Population More Likely to Die of Covid-19”, or “Millions of Tons of Plastic Enter the Ocean Every Year” are both clear titles.
Sometimes a more “dry” and “technical” title is preferred. Our two titles can then be changed to “Covid-19 Deaths by Race in New York City, March 2020” and “Tons of Plastic Entering the Ocean, 1950–2020”, respectively.
Often these two styles are combined into a title (“story”) and a subtitle (“technical”), like that:
Black and Asian Population More Likely to Die of Covid-19 Covid-19 Deaths by Race in New York City, March 2020
Make sure your subtitle is less prominent than the title. You can achieve this by decreasing font size, or changing font color (or both).
Horizontal (x) and vertical (y) axes define the scale and units of measure.
A data series is a collection of observations, which is usually a row or a column of numbers, or data points, in your dataset.
Labels and annotations are often used across the chart to give more context. For example, a line chart showing US unemployment levels between 1900 and 2020 can have a “Great Depression” annotation around 1930s, and “Covid-19 Impact” annotation for 2020, both representing spikes in unemployment. You might also choose to label items directly instead of relying on axes, which is common with bar charts. In that case, a relevant axis can be hidden and the chart will look less cluttered.
A legend shows symbology, such as colors and shapes used in the chart, and their meaning (usually values that they represent).
You should add any Notes, Data Sources, and Credits underneath the chart to give more context about where the data came from, how it was processed and analyzed, and who created the visualization. Remember that being open about these things helps build credibility and accountability.
In interactive charts, a tooltip is often used to provide more data or context once a user clicks or hovers over a data point or a data series. Tooltips are great for complex visualizations with multiple layers of data, because they declutter the chart. But because tooltips are harder to interact with on smaller screens, such as phones and tablets, and are invisible when the chart is printed, only rely on them to convey additional, nice-to-have information. Make sure all essential information is visible without any user interaction.
Some Rules are More Important than Others
Although the vast majority of rules in data visualization are open to interpretation, there are some that are hard to bend.
Bar charts must start at zero
Bar charts use length to represent value, therefore their value axis must start at zero. That applies to column and area charts as well. This is to ensure that a bar twice the length of another bar represents twice its value. The Figure 5.2 shows a good and a bad example.
Starting y-axis at anything other than zero is a common trick used by some media and politicians to exaggerate differences in surveys and election results. Learn more about how to detect bias in data stories in chapter 12.
Pie Charts Represent 100%
Pie charts is one of the most contentious issues in data visualization. Most dataviz practitioners will recommend avoiding them entirely, saying that people are bad at accurately estimating sizes of different slices. We take a less dramatic stance, as long as you adhere to the recommendations we give in the next section.
But the one and only thing in data visualization that every single professional will agree on is that pie charts represent 100% of the quantity. If slices sum up to anything other than 100%, it is a crime. If you design a survey titled Are you a cat or a dog person? and include I am both as the third option, forget about putting the results into a pie chart.
Remember that you create a chart to help the reader understand the story, not to confuse them. Decide if you want to show absolute numbers, percentages, or percent changes, and do the math for your readers.
Avoid chart junk
Start with a white background and add elements as you see appropriate. You should be able to justify each element you add. To do so, ask yourself: Does this element improve the chart, or can I drop it without decreasing readability? This way you won’t end up with so-called “chart junk” as shown in Figure 5.3, which includes 3D perspectives, shadows, and unnecessary elements. They might have looked cool in early versions of Microsoft Office, but let’s stay away from them today. Chart junk distracts the viewer and reduces chart readability and comprehension. It also looks unprofessional and doesn’t add credibility to you as a storyteller.
Do not use shadows or thick outlines with bar charts, because the reader might think that decorative elements are part of the chart, and thus misread the values that bars represent.
The only justification for using three dimensions is to plot three-dimensional data, which has x, y, and z values. For example, you can build a three-dimensional map of population density, where x and y values represent latitude and longitude. In most cases, however, three dimensions are best represented in a bubble chart, or a scatterplot with varying shapes and/or colors.
Beware of pie charts
Remember that pie charts only show part-to-whole relationship, so all slices need to add up to 100%. Generally, the fewer slices—the better. Arrange slices from largest to smallest, clockwise, and put the largest slice at 12 o’clock. Figure 5.4 illustrates that.
If your pie chart has more than five slices, consider showing your data in a bar chart, either stacked or separated, like Figure 5.5 shows.
Don’t make people turn their heads to read labels
When your column chart has long x-axis labels that have to be rotated (often 90 degrees) to fit, consider turning the chart 90 degrees so that it becomes a horizontal bar chart. Take a look at Figure 5.6 to see how much easier it is to read horizontally-oriented labels.
Arrange elements logically
If your bar chart shows different categories, consider ordering them, like is shown in Figure 5.7. You might want to sort them alphabetically, which can be useful if you want the reader to be able to quickly look up an item, such as their town. Ordering categories by value is another common technique that makes comparisons possible. If your columns represent a value of something at a particular time, they have to be ordered sequentially, of course.
Do not overload your chart
When labelling axes, choose natural increments that space equally, such as [0, 20, 40, 60, 80, 100], or [1, 10, 100, 1000] for a logarithmic scale. Do not overload your scales.
Keep your typography simple, and use (but do not overuse) bolding to highlight major insights.
Consider using commas as thousands separators for readability (
1,000,000 is much easier to read than
Be careful with the colors
The use of color is a complex topic, and there are plenty of books and research devoted to it. But some principles are fairly universal. First, do not use colors just for the sake of it, most charts are fine being monochromatic. Second, remember that colors come with some meaning attached, which can vary among cultures. In the world of business, red is conventionally used to represent loss, and it would be unwise to use this color to show profit. Make sure you avoid random colors.
Whatever colors you end up choosing, they need to be distinguishable (otherwise what is the point?). Do not use colors that are too similar in hue (for example, various shades of green––leave them for choropleth maps). Certain color combinations are hard to interpret for color-blind people, like green/red or yellow/blue, so be very careful with those. Figure 5.8 shows some good and bad examples of color use.
If you follow the advice, you should end up with a de-cluttered chart as shown in Figure 5.9. Notice how your eyes are drawn to the bars and their corresponding values, not bright colors or secondary components like the axes lines.