How to Choose the Right Colors for Your Data Visualizations

Color is a powerful tool for data visualizations. Used well, it can help communicate key findings; used poorly, it can distract from the insights you want the data to communicate.

Essentially, color imparts meaning to data visualizations. Using color strategically can draw attention to key metrics, helping the viewer readily absorb the information put before them. Misusing color, however, can slow down or distort the viewer’s analysis of the data.

Typically, the brain is hardwired to recognize and interpret patterns and anomalies in data when it’s presented in clear visualizations instead of tabular displays. As a creator of visualizations, your job is to find the right balance of visual attributes to make the data easier to process.

This is called visual encoding — the practice of assigning visual attributes, such as color and shape, to different types of data, which helps your audience translate data through a type of visual shorthand that the brain can easily decipher.

As data practitioners, understanding the role color plays in visual perception allows you to make more informed decisions when designing visualizations. Applying color in your visualizations successfully can make a world of difference in how decision-makers perceive the value of the data they’re being presented, and what actions they take based on that information.

So, how can you use and apply color in data visualizations for optimum success? Here are some rules and tips that will have you making intelligent and informed color choices in all your future designs.

Colors are extremely important in data visualization. They can help draw attention to key data points, establish relationships between different elements, and convey meaning and emotion. However, choosing the right colors can be tricky. A poor color palette can make your data viz hard to read, misleading, or just plain ugly.

In this comprehensive guide, I’ll walk you through the science and art of picking colors for data visualizations. Whether you’re a complete beginner or a seasoned data viz expert, you’ll learn techniques to create more beautiful, effective, and accessible data stories.

Why Color Matters in Data Viz

Before diving into the nitty gritty of color selection, it’s helpful to understand why color is so critical in data visualization.

Here are some key reasons:

Draws attention Vibrant colors naturally attract the human eye You can leverage this to highlight important data points or trends
Establishes relationships: Using the same colors consistently for the same variables allows readers to easily identify relationships in the data.
Adds meaning: Different colors provoke different emotional responses. Warm colors like red and orange feel energetic, while cool blues feel calm and relaxing.
Enhances aesthetics: Carefully chosen colors are simply more pleasant to look at than default software colors. This keeps your audience engaged.
Improves accessibility: Using colors of varying lightness and saturation makes your data viz readable for those with color blindness.

Now let’s look at how to select a color palette that achieves all these goals.

Understanding Color Theory Basics

While color choice often feels subjective, there is actually a lot of science behind it. Color theory provides a framework for creating color combinations that look pleasing together.

Here are a few key color theory concepts to understand:

Hue: The basic color – red, blue, green, etc.
Saturation: Intensity of the color. Higher saturation = bolder, richer color.
Lightness/Value: How light or dark the color is.
Color wheel: Colors organized in a circle by hue to show relationships. Complementary colors are opposite each other.
Color harmony: Combinations of colors that create a pleasing aesthetic. Examples are analogous, complementary, triadic, and more.
Temperature: Colors are either warm (red, orange, yellow) or cool (blue, purple, green). Warm colors stand out more.

Now let’s see how to apply this theory.

Choosing a Color Palette

Follow these steps to build a color palette that effectively conveys your data story:

1. Select a Key Color

First, choose one color as the star of your data viz. This will be used to highlight the most important element.

Some tips for picking a key color:

Match it to your brand colors if applicable. This creates cohesion.
Consider color psychology. Red conveys urgency, blue is calming, etc.
Make sure it has enough visual weight. Darker, richer colors stand out better than light pastels.
For accessibility, avoid very light colors, which can disappear against white backgrounds.

2. Build a Complementary Palette

Next, select complementary colors that support your key color. There are a few approaches:

Complementary palette: Use the color opposite your key color on the color wheel. This creates maximum contrast.

Analogous palette: Use colors adjacent to your key color for a soothing look.

Triadic palette: Select colors evenly spaced around the color wheel. Provides color variety while still being harmonious.

Monochromatic palette: Shades and tints of a single hue. Subtle but sophisticated.

Contrasting saturation: Pair vivid, saturated colors with soft pastels.

Contrasting lightness: Combine very light and very dark colors. Ensures accessibility.

3. Refine the Palette

You likely won’t nail the perfect palette on your first try. Here are some tips for refinement:

Adjust saturation and lightness of colors to create the look you want.
Make sure colors are distinct enough from each other when converted to grayscale.
Limit your palette to 2-4 colors, excluding neutrals like black and gray. Too many is chaotic.
Make sure colors pair well with your background color. Watch for insufficient or excessive contrast.
Use a color blindness simulator to check if any colors are indistinguishable.

Tools for Picking Colors

You don’t have to rely solely on your artistic eye to select colors. Here are some handy tools:

Color wheel: Built into most design programs or available online for experimenting with color harmonies.
Color picker: Extract colors from images and real life objects that you find visually appealing.
Online color palette generators: Input a key color and get complementary palettes instantly. Coolors and Adobe Color are popular options.
Accessibility checkers: Sites like Colorable and Color Oracle preview how your palette appears to those with color blindness.

No matter which tools you use, be sure to view your finished data viz on different displays to see how the colors render. Screen calibration can affect color appearance.

Special Considerations by Chart Type

The optimal color palette may vary based on what type of data visualization you are creating. Here are factors to consider for common chart types:

Line and bar charts: Use distinct colors of similar saturation and value for categories. Avoid colors that are too dull or eye-catching.

Scatterplots: Vivid, saturated colors work well to make points stand out against white space.

Pie charts: Use a monochromatic palette with contrasting shades for visual separation of slices.

Choropleth maps: Select a single hue and create a sequential color scale from light to dark. Darker = higher data values.

Heatmaps: Sequential color palettes are best for showing magnitude. Green-yellow-red is a popular heatmap scale.

Tips for Accessible Color Choices

Don’t forget about accessibility! Follow these tips to create color palettes that work for color blind readers:

Use colors with distinct lightness values even when converted to grayscale
Avoid color combinations that are problematic for common types of color blindness:
- Red & green (red-green color blindness)
- Yellow & blue (tritanopia)
- Green & red, green & brown, light green & yellow (deuteranomaly)
Include symbols along with color to encode data (e.g. stripes, polka dots, hashes)
Explicitly label colored elements rather than solely relying on a legend
Allow switching between color palettes and grayscale to improve readability

With a bit of knowledge and experimentation, you can learn to combine colors as beautifully as the work of Monet or Van Gogh. Follow this guide and your data viz will look like a true work of art.

When using colors in data visualization, remember to:

Select a bold key color to highlight the main element
Build a complementary palette with harmonious colors
Refine colors until distinct, accessible, and aesthetically pleasing
Use appropriate palettes for your specific chart types
Check for issues for color blind readers

Mastering color in data viz takes practice, but the payoff is worth it. Your data stories will resonate stronger and look more professional. So embrace your inner artist and get coloring!

how to choose color for data visualizations

Stick to a theme and be aware of brand colors

Keeping to the same theme or set of colors creates consistency and quicker association for viewers when seeing new charts.

Tying the theme to your company or client’s brand colors is also a smart move. People will be used to seeing presentations in the company’s colors and the charts will land more favorably with business leaders and employees. Be aware of the brand guidelines for use of color, particularly if designing data visualizations for a specific client. Most organizations will have recommendations for what colors can and can’t be used together and will have spent time and money on a team of people to devise those guidelines. Adhering to the brand’s own color guidelines will not only save you time but also save you from embarrassing faux pas if you — for example — mistakenly use a competitor’s brand colors or a color that is culturally inappropriate.

Likewise, be mindful that the brand’s color palette won’t necessarily have been designed with data visualizations in mind, so apply your own common sense when creating charts based on brand colors. You might only be able to use one or two of them to make the visualizations work.

Bad use of color:

Good use of color:

8 rules for using color in data visualization

Using one or two colors has more of an impact than peppering your charts with every color of the rainbow. Using fewer colors enables the brain to process the information more clearly and means your audience is more likely to remember the key data points. In fact, a good rule of thumb is to use neutral colors like gray for the majority of your charts, reserving bright colors only for the data points you want to direct your audience’s attention to or where you want a comparison made. Research from Xerox shows information can be located 70% faster if it’s in color — so use it wisely and assign it to the information you want to stand out.

Bad use of color:

Good use of color:

The best use of color:

How to select ideal colors for a data visualization

How do I choose a color scheme for data visualization?

The first step when choosing a color scheme for your data visualization is understanding the data that you’re working with. There are three main categories that matter when choosing color schemes for data: sequential, diverging, and qualitative color schemes.

What should a data visualization look like?

Shapes, lines and bars, probably. Some text elements, definitely. Figures, maybe. But most importantly: colors. Whether you choose a black and white design or a colorful one, the colors of any data visualization will have a non-negligible impact on the reader.

Can color theory make data visualization better?

This blog post explores the vibrant world of color theory for data visualization. It uses both timeline and graph visualization examples to demonstrate how color theory helps you design charts that look good, and make data more compelling. Colors can make or break your data visualization.

How do you use color in data visualizations?

Effectively using color in data visualizations involves more than selecting appealing shades. It encompasses: Communicating clearly: Ensuring data is easy to find, read, and comprehend without effort. Being consistent: Adopting a set palette to maintain consistent color meanings across visuals.