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Get fresh perspectives and insights into the actionable approaches needed to build back smarter after inflation. Be inspired to transform your organisation while delivering profits.

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Insights | Interviews

Tips for visualising data like The Economist

The importance of colour, form and layout in chart-making 

February 27th 2024

In data visualisation, looks matter. When creating a chart from a data set, its form, colour and layout are tools for making your message clearer. But how do you make charts that are attractive yet not overcomplicated?

We asked Elizabeth Lees, a visual data journalist at The Economist, for her advice, which features in a video in Economist Education’s popular course on data storytelling and visualisation. What follows is a written extract from that, edited for length and clarity.

If you are interested in developing a data mindset and making better charts, you can find the full course prospectus here.

Economist Education: The Economist’s journalists prize simplicity and often use basic charts to visualise data. But they use fancier ones too: which types, and why?

Elizabeth Lees: Bubble charts are one example. There are disadvantages to them: for instance, the human eye can’t accurately compare the size of the circles. However, bubble charts are visually engaging and good for displaying huge ranges in data. You can also use labels to show the exact values of data points. This same information could have been presented in a table, but this is more interesting and the steel data point jumps out immediately:

Forecast cumulative demand for global decarbonization by 2050

Thermometer charts are another type of visualisation. They work well when you want to compare two categories, such as men and women or the same data point across two different years. This saves space and makes it easy to see the difference between categories:

Percentage of households with dogs and children in the US, 2021

Economist Education: How do you go about choosing the colour palettes of charts?

Elizabeth Lees: We typically keep to a limited colour palette. This makes it easy for readers to identify your work and keeps a cohesive style across your charts. It also removes the need to make colour decisions every time you make a chart, saving time. At The Economist we have consistently used one main colour—dark blue—and another, secondary colour—light blue. These have a high contrast so you can see the difference between them easily. 

Our purposely limited palette lets us use up to six colours very quickly. Beyond six, your reader might struggle to see the differences between your data points. If you do have a lot of series that you want to show, consider using colour to emphasise your main indicator:

Real hour prices for G7 countries

Most of the time, using colours extensively is unnecessary and can detract from the story you’re trying to tell in the chart. But an exception, when we might stray from our colour palette, is for well-known political parties, which a lot of people associate with a certain colour:

Line graph pf general-election voting intention in Britain

You can use colour to highlight your data, whether through bolding text, using a tint or using one strong colour to highlight the key message of your data. This can be very effective and eye-catching. 

In the course video The Economist’s data journalists go on to discuss the characteristics of bad charts and provide practical hints on how not to make your visualisations too complex. Our full course covers these subjects and many more: for further information download the prospectus here.

If you're interested in exploring Economist Education's data storytelling course, click here.

Find out more on this topic in our course...

Data storytelling and visualisation

Do you need to review, analyse or prepare reports and presentations using data, or use numbers for business? Designed by the award-winning data-journalism team at The Economist, this two-week online course will show you how to harness data to make better decisions. You’ll explore data visualisation, develop a “data mindset” and gain the tools to use numbers effectively to solve real problems.