Color Considerations in Graphs
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How to Choose a Color Palette
Effective data visualization uses color purposefully to highlight important information and guide the viewer’s eye. Using too many colors can overwhelm the viewer, while poorly contrasted colors can impact clarity and readability. A basic understanding of the color wheel can help you make good color decisions for your data visualization.
Monochromatic color series are effective in depicting quantitative variations in the same variable (e.g. heat maps).
Analogous colors are effective in differentiating multiple groups without creating distracting color differences.
Complementary colors are effective in highlighting important details or key results. This strategic use of color should be implemented sparingly. Remember: Highlighting everything means you are highlighting nothing.
All of BioRender Graph's color presets are thoughtfully chosen and colorblind friendly. Learn more today.
Using Color Strategically
Color is a powerful tool that can very easily be misused. Poor color considerations can cause confusion and distraction, whereas strategic use of color can dramatically enhance the clarity and effectiveness of your data visualization story.
Consider these tips when making color choices for your graphs:
1. Use consistent colors for the same groups across multiple charts. This ensures cohesive storytelling and allows viewers to easily follow the “main characters” across their entire data journey. On the flip side, different groups should be differentiate with different colors. These can be analogous colors for a more harmonious graph, or completely distinct for better differentiation (with some restraint to avoid clashing or distracting combinations).
2. Avoid using the same or very similar color values (lightness/darkness), particularly for elements that appear next to each other. Colors with very similar values are difficult to distinguish from each other. After selecting colors, try viewing your graph in black and white to see if adjacent elements are sufficiently contrasted.
3. Avoid using colors that are highly saturated (very colorful) or “pure” (e.g. pure red, or pure blue). Saturated and pure colours are too visually intense and can easily clash with each other. Reducing the saturation/intensity of a color, or adjusting it’s value (lightness/darkness) can make it much more pleasing for the eye.
4. Some colors carry cultural sentiments, often subconsciously. For instance, warm colors like red and orange are associated with inflammation, heat, or decreased effect, while cool colors like blue or green often signify health, cooling, or increased effect. These associations can be used strategically to enhance a data visualization story. But it’s equally important to avoid making unintended associations.
5. To ensure your graph is color-blind friendly, avoid using color combinations such as red-green. Common alternatives are red-blue or purple-green, but to be sure, you can install one of many free browser add-ons that allow you to simulate various color-blind perceptions (e.g. Colorblindly).
Using Color to Show Quantitative Data
Our perception of color can be subject to distortion and visual illusion. For example, two squares that are the exact same color can appear to be different if surrounded by lighter vs. darker colors. For this reason, it’s typically not ideal to represent quantitative data using color scales or gradients.
By necessity, heat-maps use color scales to represent quantitative data. In this case, ensure the color scale is intuitive and clearly labeled. You can also leverage color scales that have been optimized for representing quantitative data. These color scales are designed to be:
- Colorful: utilize a broad palette to clearly distinguish differences.
- Perceptually uniform: ensure the differences in colors are proportional to the differences in values, consistently across the entire range.
- Robust to colorblindness: maintain clarity and differentiation for people with colorblindness and in grayscale printing.
BioRender Graph Tip
BioRender Graph makes it easy to select color themes that are optimized for effective data visualization, visually pleasing, and created with visual impairment accessibility in mind. Try out some of these themes under the “Appearance” category of the left formatting panel!
Regardless of whether you choose one of our curated color themes or decide to create your own, you can double-check the contrast of your graph elements by viewing it in black and white. To do this, click on the greyscale icon in the right panel. Clicking this icon again reverts back to the color view.
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References
- The Wall Street Journal Guide to Information Graphics: The Do's And Don'ts Of Presenting Data Facts And Figures by
- Research Data Visualization and Scientific Graphics: for Papers, Presentations and Proposals by Martins Zaumanis
- Data Visualization: a practical guide to producing effective visualizations for research communication by Rebecca Wolfe (2014. RESYST Consortium)
