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:

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:

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Storytelling with Data: Dataviz book review

The Storytelling with Data book has been on my wishlist as long as I can remember, because so many people recommend it as one of the must read dataviz books. So let's see what the fuzz is all about - here's my review!

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Uncommon chart types: Slopegraphs

Slopegraphs appear in 'serious' newspapers, but they are very easy to create yourself. Use them if you want to compare how values have changed between two different points in time!

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Data visualization in a time of pandemic – #6: Viral scrollytelling

In this final chapter, we’ll dive deeper into some of the insightful stories which have been published about the novel coronavirus and the COVID-19 pandemic. Rather than looking at single charts, we’ll highlight some long-form stories about the origin of the virus, how it works, and how it spread.

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