Choosing the right font for your data visual
Typography is a fascinating domain. Fonts evoke emotions: there are very sophisticated fonts, playful fonts, attention-grabbing fonts, and elegant handwritten fonts. Using the wrong type of font can have a lot of impact. In data visualization the implications of typography are mainly focused on readability. Labels and annotations can easily become so small they get hard to read. Above all else, we should choose a font which is readable at small sizes.
What influences readability?
The readability of a font at small sizes is mainly determined by three aspects: the x-height, the counter, and the serifs.
The x-height of a font is the height of the lowercase letters compared to the height of the line itself. Fonts with a lower x-height are more difficult to read than fonts with a higher x-height.
The counter is the enclosed space inside a letter, such as in the letters ‘o’, ‘a’ or ‘e’. The larger the counter size, the easier it becomes to read a font at small sizes.
Finally, the serifs. Sans-serif fonts are generally easier to read at small sizes than serif fonts. The different serifs – the small lines and strokes attached to the end of individual letters – make longer texts easier to read, because our brains can more easily distinguish different letters from each other. That’s why almost any book is set in a serif font. But for small text, the serifs get in the way and sans-serif fonts are the way to go.
Where to find the perfect font?
If you’re struggling to find the perfect font, a good website like Google Fonts can help you out. With over 1300 free font families, there’s always something for every situation. You can enter your own text and immediately see how it will look in all of these different fonts. You can filter by category or you can look specifically for very bold fonts, wide fonts, etc. Once you’ve found the perfect font, all you have to do is download it and install it on your computer.
The best fonts for data visualization
If you still find looking for the perfect font a daunting task, try some of the fonts developed specifically for readability:
Assistant | Lato | Noto Sans | Roboto | Source Sans
If you want to know more about visualizing data in the right way, you can check out the other videos in this series. Or I invite you to read my book, Powerful Charts, that will give you actionable insights and practical guidelines to create data visuals that truly engage and inspire your audience.
Full video series
- 01. Why is data visualization so powerful?
- 02. Why is data visualization so challenging?
- 03. Navigating the landscape of powerful charts
- 04. A powerful chart tells a story
- 05. A powerful chart has a high signal-to-noise ratio
- 06. Making a data visual noise-free
- 07. 7 different goals for your chart
- 08. Three roles of colour in a data visual
- 09. Choosing the right font for your data visual
- 10. Can you use Excel to create a powerful chart? (coming on January 09, 2023)
Read more:
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Data visualization in a time of pandemic – #6: Viral scrollytelling
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Five steps towards improving your dashboard
Today I would like to share with you the five steps I usually follow when I analyze and improve dashboards. If you are planning to analyze and improve your own dashboard, or maybe the dashboard someone else created and you want to provide feedback on, you could follow these five steps as well.
Dear Data: Dataviz book review
Last February, on a cold and rainy day, I received the Dear Data book as part of a Dataviz Drawing workshop by Stefanie Posavec. A pretty large and heavy book, the kind you could put on your coffee table to show off (which I did!). Let's review it!
Data visualization tools: Datawrapper
If you are writing articles online and need to quickly insert beautiful, interactive charts, maps or tables, Datawrapper is the tool you are looking for.
Uncommon chart types: Waffle charts
If you thought a pie chart was the only food-related chart type out there, think again! Waffle charts are a great alternative.
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