1. Understand the Purpose and Audience
Start by reflecting on the context of your visualization. Think about these questions: Who are you speaking to? Are they technical experts, general users, or executives? The complexity of your color choices can fluctuate based on your audience.
What message would you like to convey? Different visualizations—like categorical, sequential, or diverging—require unique color schemes. Are you emphasizing relationships, differences, or trends?
Remember to consider the devices or formats your audience will use. Colors can appear quite different on screens than in print and vary between mobile devices and desktops, too!
2. Choose a Color Scheme by Data Type
Categorical Data (Discrete)
It's important to use a qualitative color scheme for data divided into different categories, like regions, departments, or products. Each category should be distinguishable, but remember, the colors don’t have to show a progression—it’s all about making your data easy to understand!
Choose Distinct Colors: Use differentiated colors, ideally no more than 5-7 categories. If you have more categories, consider using patterns or other cues.
Avoid Over-reliance on Colors: For clarity, use direct labeling of each category, and ensure that no two adjacent colors are too similar.
Sequential Data (Ordered)
You need a sequential color scheme when visualizing data that follows a natural progression (e.g., increasing sales, temperature changes). This involves using one color in varying intensities to represent different values.
Single-Hue Gradients: Use one color that gradually gets lighter or darker to represent a range of values (e.g., shades of blue from light to dark).
Ensure Legibility: Ensure the darkest and lightest shades provide enough contrast for users to differentiate between values easily.
Diverging Data (Emphasizing Two Extremes)
Use a diverging color scheme for data with two opposing extremes (e.g., profit vs. loss, positive vs. negative sentiment). This typically uses two distinct colors with a neutral color in the center to show the balance point.
Balance Colors: Use contrasting colors at the ends of the spectrum (e.g., red for negative values, blue for positive values), with a neutral color (e.g., white or gray) in the middle.
Test for Accessibility: Be mindful of how your extremes appear to users with color vision deficiencies. It’s essential to select contrasting colors that remain distinguishable.
3. Choose Accessible Colors
After selecting a color scheme based on the data type, you must ensure that the colors you choose are accessible to everyone, including people with visual impairments like color blindness.
Consider Color Blindness
Avoid Relying on Red and Green: Around 8% of men and 0.5% of women have some form of red-green color blindness, so avoid using red and green together for critical information.
Use Simulations: Use tools like Color Oracle to simulate how your colors will appear to people with various types of color blindness.
Check Contrast
Ensure enough contrast between your text and background colors and between different visual elements.
Test Contrast Ratios: Use tools like the Contrast Checker or Accessible Colors to ensure your colors meet the WCAG contrast ratio of at least 4.5:1 for standard text and 3:1 for larger text.
4. Limit the Number of Colors
Using too many colors in a visualization can confuse the reader and dilute the effectiveness of your message. Here are some general guidelines:
For Categorical Data: Use at most 5-7 colors. If more categories are necessary, group similar categories and use patterns or shades to distinguish them.
For Sequential or Diverging Data: Limit to 5-9 shades or gradient stops to make it easier to discern values.
5. Use Color Tools and Generators
Several tools can help you create practical, accessible, and visually appealing color palettes:
ColorBrewer: One of the best tools for generating qualitative, sequential, and diverging color palettes, with built-in accessibility checks.
Adobe Color: great for generating custom color schemes and testing them in different scenarios, such as print, digital, or accessible contexts.
Coolors.co: This tool helps you generate color schemes and tweak them easily for web or data visualization.
Chroma.js Color Palette Helper: This JavaScript library can programmatically generate color scales, particularly for data visualizations.
6. Test Your Palette in Context
Once you have chosen your color palette, try it out in your visualization. Colors that look nice alone may clash or become unclear when used together in a chart or graph.
Look for Clarity: Ensure that your data remains legible and that important information stands out. If specific data points blend into the background or are difficult to distinguish, tweak the colors.
Check for Cognitive Load: Make sure your color choices don’t overwhelm the viewer. Simpler is often better in data visualizations, which aim to convey information quickly.
7. Refine Based on Feedback
After designing your visualization, collect feedback from a varied group of users. Ensure it’s easy to understand for people with different skills and visual abilities. This feedback will help you improve your design.