Best practices to choose colors in data viz

a data visualization color palette on a table

The problem of picking a color in data visualization is well expressed by a quote from statistician and professor Edward Tufte “avoiding catastrophe becomes the first principle in bringing color to information: above all, do no harm” (Tufte, 1997).

And that’s because the color is probably one of the most abused tools in data visualization due to the temptation to sprinkle some color within the chart to make it look more eye-catching.

Let’s understand how to properly use color in data visualization and some best practices for selecting effective color palettes.

Why is color important in data visualization?

Color plays a vital role in the world we live in. It has the power to influence our everyday lives at different levels by swaying thinking, evoking emotions, causing reactions, or even changing actions. In short, color is a prominent medium for communicating information even louder than mere words.

Even in data visualization, color is crucial. It has not to be considered just an aesthetic choice but a strategic and functional tool for conveying specific information of your data story to your audience.

Judicious use of color in data visualization can help you speak to your audience in many different ways, and it can also affect the way people perceive your data visually. Not by case, when color is well used, it can enhance a visualization; on the contrary, when used poorly, it makes the presentation of data ambiguous and challenging to interpret.

Being able to master color in data visualization properly, especially if you’re not a designer and you’re not into it yet, will help readers focus their attention on the most critical data and information presented within the chart.

Color theory principles in data visualization

Color theory refers to both the science and art of using color. It deals with how colors to mix, match or contrast with each other. Moreover, in color theory, colors are arranged on a color wheel and grouped in the following categories: primary, secondary, and tertiary colors.

Knowing the basics of color theory can have a real practical value for everyone working in data visualization.
We can not mention some of the color’s main components: hue, saturation, and lightness.

Hue: it is the color in its purest form. Primary and secondary colors are hues, such as yellow, orange, violet, blue, and green. On a color wheel, we plot shades on the circumference of the wheel.

Hues are helpful to represent different values or categories within your data visualization.

Saturation: it’s the intensity of a color. It goes from a range from pure color (100% saturated) to gray (0% saturated). A very saturated tint has a bright and full color; as the saturation decreases, the color becomes softer and gray.

Don’t exceed saturation in your chart or graphs, or you will risk making other information hard to interpret.

Lightness: refers to the degree of black or white in a given hue. The more white you add, the lighter the color is; on the other hand, the more black you add, the darker the color is.

The role of color harmony and the color scheme for visualizing data

Once color’s main components are precise, we can introduce the concept of color harmony, which is the process of combining colors in such a harmonious way as to provoke, in the observer, a sensation of pleasure. These color combinations create balanced contrast and cohesion within your charts.

Understanding these concepts will help you make better palette decisions and more impactful data visualization if you work with data. When choosing colors for your visualization, you can rely on the color scheme, a selection of colors used in different artistic contexts, even in the data visualization one. Depending on the data story you want to convey, you can choose among monochromatic, analogous, complementary, and triadic colors.

Monochromatic palette

It’s based on shades of a single hue, and although it lacks contrast, it will give the chart a clean look. A monochromatic palette works well for sequential data when creating high contrast within charts or graphs isn’t the primary choice.

The various following examples are made with Adobe colors and based on BStreams colors.

bstreams monochromatic color palette

Analogous colors

An analogous color palette consists of groups of three colors plotted next to each other on the color wheel, composed of one key color (primary or secondary) and a tertiary color, a mix of the first two colors. Red, orange, and red-orange are analogous colors, just to make an example.

In data visualization, they help perceive that data are closely related but differ in some points.

bstreams analogous color palette

Complementary color scheme

A complementary color scheme uses two opposite on the color wheel. It’s the primary choice when to highlight a high level of contrast in data visualization. For example, to represent positive and negative data values like gain or losses. Be careful that it doesn’t assume a negative meaning when you want to use your brand color in your complementary color palette.

For example, let’s suppose you want to show on a chart the profits and losses of BStreams; you will use the blue BStreams to represent profits, and dark yellow, which is its complementary color to represent losses.

bstreams complementary color palette

Triadic colors

When it comes to triadic colors, we refer to colors equally plotted on the color wheel. They work well with a qualitative palette, of which we will talk in a bit and create high contrast between each color. A triadic color scheme can be used with bar charts or pie charts to create a good contrast to make comparisons.

tridiac color palette

How to choose your data visualization color palette

There are three primary color palettes in data visualization best known as:

  1. qualitative palette
  2. sequential palette
  3. diverging palette

Choosing among the palettes above depends on the type of data you want to show in your visualization.

Qualitative: in this color palette, each color differs, so they are basically spread around the color wheel. It works well for charts or graphs showing categorical data because they are unrelated to one another. Hence, for using a qualitative color palette, you will need as many hues as the data in your chart. However, it’s still advisable to opt for a small dataset to not overbroad the chart with colors. It would require an extra cognitive load for your audience, and your visualization would be hard to interpret.

Sequential: it requires a monochromatic or single hue in an increasing intensity or saturation of colors. The lowest data value is always represented with a hue that almost matches the chart’s background. In contrast, the highest data value matches the key color with a 100% saturation and a 50% lightness. It’s best to opt for a sequential color palette when the variable is numeric or possesses ordered values.

Diverging: if your data includes negative and positive numeric values and a significant center value, opt for a dichromatic or two-hues color palette. Use a key color for positive values and its complementary color for negative values, and a neutral color for the midpoint to make the extremes pop.

Best practice to use color in data visualization

Color inspires creativity, but when it comes to data visualization, color has to be considered not an aesthetic tool, but a functional tool to convey the data information adequately within the chart to the target audience. That’s why we can list some of the best practices to use color in data visualization.

1.Use color within charts with a purpose

The golden rule of data visualization is to use color as a strategic tool; in fact, not by case it’s recommended to limit your color palette to 10 or even a few colors. And that’s because the more color you add, the more compelling your data visualization will be.

Use color with the purpose to communicate and convey certain information of your data story, and not just for aesthetics if there’s no need to add more than a color into your data visualization, limit to highlighting only the data you want to focus the attention to.

A poor use of color is one of the most common data visualization mistakes. Read our previous article to learn more about it.

charts with a bad use of color in data visualization

2. Be coherent when using color in data visualization

Using an extensive color palette and continuously changing color within the chart to keep the attention alive isn’t the best choice. If you think your audience will get annoyed by reading your visualization, you should probably review your storytelling with data rather than the color palette. Changing color frequently helps to communicate a variation within the visualization that needs to be explained. If there is none of all this, you will just create confusion.

3. Pay attention to colors emotive impact

No doubt that color evokes emotions like strength, fear, anxiety, and confidence. Hence, when creating a chart, assign a tone of voice to your data storytelling and pick the colors that will best spark a specific feeling in your readers.
Given that humans associate colors with emotions, using red, for example, to communicate an increment and green to indicate a loss, would be counter-productive. Finally, it is crucial to consider the color connotation in a given culture when it comes to an international audience. David McCandless has written a book about this topic titled The Visual Miscellaneum: A Colorful Guide to the World’s Most Consequential Trivia (2012).

4. Use intuitive colors

Make sure to use light colors for low values and dark colors for high values so that your visualization will be most intuitive. Moreover, consider using colors that readers will easily associate with your data; for example, if you’re showing a chart regarding sustainability, use green or sharing data regarding the pollution of the oceans, use blue or a light blue.

the use of color in data visualization within a chart

5. Choose an alternative to the stereotypical colors

Stray away from the classic blue-pink color scheme when displaying gender data. A good alternative that will not look confusing for your audience would be using dark colors for men like purple or black and light colors for women like yellow, orange, or light green.

pie chart with alternatives to stereotypical colors

Conclusion

In data visualization, color plays a huge role in conveying strategic information to the target audience. Knowing the basic principles of the color theory applied to data visualization will help you make better decisions when creating a chart by avoiding the temptation of choosing some colors just because they are trendy and would make your chart attractive.

For making impactful data visualization and focusing your audience’s attention on particular data, don’t underestimate the value of picking the right strategic colors.

Did you know BStreams has three ways of picking colors within the chart? Find out more in our user documentation.

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