Improving your graph: a case-study

Here’s the thing I love most about visual communication: there’s always room for improvement. As soon as you think a graph or data visualization is finished and perfect, someone else will come along with an idea to make it even better. Take, for example, the excellent series of blogposts “The little of visualization design” by (the amazing) Andy Kirk. How a minor detail can make a visualization so much more awesome!

If you have 15 minutes of spare time, I strongly encourage you to watch the following video, because it’s the perfect example of improving visualizations for dummies, and a great intro for this blog post: https://www.youtube.com/watch?v=IB7crD_paKQ

Summary: Alan Smith, Data Visualization Editor at the Financial Times, shows some great examples of converting ‘meh’ graphs from press releases to awesome visualizations that really tell a story. He also organizes crash courses to teach his fellow journalists how to do this in order to become more ‘data critical’.

In this blogpost, I’d like to do the same for a graph I encountered in my Twitter timeline a few days ago. To come straight to the point, here’s the graph we’ll redesign:

Lelijke grafiek

The graph is taken from a (publicly available) research paper on predicting depression from Instagram pictures. In short, the filter you use to prettify your Instagram pictures tells something about how you feel. Okay, to be more precise, there appears to be a correlation between the usage of certain filters and the mental health state of an Instagram user. You can see that in the graph above, but it’s not very clear. Let’s improve that!

Step 1: Readability

I’ve always learned that horizontal words are easier to read than vertical words. So, let’s turn all those words 90° to improve readability. In fact, why not rotate the entire graph? There’s no real added value in the current orientation.

graph v1

Hey, this just saved us from some serious neck injuries!

Examples of different instagram filters

Read more:

This chart is trying to trick you

The original chart in this example is trying to suggest a strong correlation between sugar intake and obesity in the US between 1980 and 2000. It does so by carefully choosing the vertical axis ranges and scaling so both lines nicely fall on top of each other.

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Books on a bookshelf - infographics resources

Research visuals: all the resources you’ll ever need!

If you want to start creating clear and attractive visuals about your research, but don't know where to start, this page is for you! Here's a complete overview of tools, resources and inspiration you can use as a starting point for your designs.

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Small datasets to practice your data visualization skills

When you're teaching data analysis or data visualization, or when you're learning new data visualization tools and techniques, you might be looking for datasets to practice with. Here are some great starting points.

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Graphical abstract for Ghent University PSYNC research

How to create a graphical abstract

Graphical abstracts are becoming more and more important. Journal publishers such as Elsevier encourage you to create a concise visual summary of the main findings of your research. But where to start? What steps should you follow to create the perfect graphical abstract for your article? What tools can you use?

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Behind the maps

In the 30-day Map Challenge, you are challenged to design a new map every day around a certain topic. I participated in November 2020, and wrote this post to share my thought processes, data sources, tools and results!

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Books on a bookshelf - infographics resources

Data visualization resources: all the links you’ll ever need!

You want to start creating clear and attractive data visuals, but don't know where to start? No worries, here's a complete overview of tools, resources and inspiration you can use as a starting point for your designs.

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