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How can you make R visualizations more accessible?

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Data visualization is a powerful way to communicate insights, patterns, and stories from data. However, not everyone can access or understand the visual information in the same way. Some people may have visual impairments, cognitive disabilities, or different cultural backgrounds that affect how they perceive and interpret data. Therefore, as a data scientist, you should aim to make your R visualizations more accessible for a wider and more diverse audience. In this article, you will learn some practical tips and tools to achieve this goal.

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Gareth Hagger-Johnson

1 Use color wisely

Color is a key part of data visualization, but it can also create accessibility challenges. People with color blindness, low vision, or different color associations may find it difficult to distinguish or interpret color-coded data. To prevent this, you should consider some best practices when using color, such as choosing colors with enough contrast and brightness to be visible and readable, using color palettes that are friendly to color blindness, such as viridis, Okabe-Ito, or ColorBrewer, avoiding using color alone to convey information and adding other cues like labels, shapes, or patterns. Additionally, you should test your color choices with tools like Color Oracle, Vischeck, or WebAIM Color Contrast Checker.

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Rahim Rahimov

Data Scientist
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The essence of data visualization is not just to present numbers but to tell a story that is clear and accessible to everyone. It's about empathy and inclusivity, ensuring that our data narrative can be understood by a diverse audience with varying visual abilities. For instance, I imagine a color-blind person struggling to distinguish red-green variations in a chart; this is not merely an inconvenience but a barrier to understanding. By choosing accessible palettes, we're not just being considerate; we're enabling equal participation in the data-driven conversation.

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2 Add captions and alt text

Captions and alt text are textual descriptions that provide additional information and context for your R visualizations. They can assist those who use screen readers, have low literacy, or prefer text over visuals to access and understand the data. To add captions and alt text to your R visualizations, you can use the caption argument in ggplot2 for a summary of the main message, the fig.cap argument in R Markdown to explain the data source, variables, and methods, the alt argument in knitr to describe the visual elements such as title, axes, legends, and data points, or the alttext package to generate alt text automatically or interactively for your plot.

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Rahim Rahimov

Data Scientist
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Captions and alt text are crucial in making data visualizations not just seen but also heard and felt. They give voice to visual content, ensuring that every chart, graph, and image can be 'read' by screen readers, translated into braille, or understood through audio description. By providing descriptive detail and interpretive context, they empower those with visual impairments to engage with data on equal footing. This practice champions accessibility, inviting a broader audience to the table of understanding and insight.

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3 Simplify and clarify

To make your R visualizations more accessible, you can simplify and clarify your design and presentation. This can help people with cognitive disabilities, limited attention span, or different levels of data literacy to process and comprehend your data. To achieve this, use clear and concise titles, labels, and annotations that convey the purpose and meaning of your plot. Additionally, use appropriate and consistent scales, formats, and units that make your data easy to compare and interpret. Choose simple and familiar chart types that match your data and message, avoiding unnecessary or misleading effects such as 3D, pie charts, or dual axes. Moreover, utilize whitespace and layout to organize and highlight the most important information in your plot.

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4 Consider cultural diversity

Finally, when creating R visualizations, it is essential to consider the cultural diversity of your audience. Different cultures may have different preferences, expectations, and interpretations of data and visuals. To respect and accommodate the cultural diversity of your audience, you should do some research and testing to understand their needs and preferences. This can include using the scales package to format numbers, dates, and currencies according to the locale of your audience, the ggtext package to add support for right-to-left languages, the ggflags package to add country flags if relevant, or the ggmap package to add geographic maps with an awareness of their political and historical implications.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Gareth Hagger-Johnson
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If you use R, consider RShiny for interactive visualisations. This will allow you to export visualisations and their underlying data into a 'point and click' chart.

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How can you make R visualizations more accessible?

1

2

3

4

5

1 Use color wisely

2 Add captions and alt text

3 Simplify and clarify

4 Consider cultural diversity

5 Here’s what else to consider

Data Science

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How can you make R visualizations more accessible?

1

2

3

4

5

1 Use color wisely

2 Add captions and alt text

3 Simplify and clarify

4 Consider cultural diversity

5 Here’s what else to consider

Data Science

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