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:
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.
Hey, this just saved us from some serious neck injuries!
Read more:
Making a data visual noise-free
Removing noise from a data visual is not only about taking things away such as gridlines, axes or legends. That’s just one part of it, which we could call removing physical noise. Improving the signal-to-noise ratio is often also about adding little things that help our audience better understand the visual. We are helping them by removing mental noise, or mental barriers.
Three tips to create powerful charts in Excel
Creating charts in Excel can be a very powerful tool for making sense of complex data sets, and for visualizing them. But the default options are not always the most pretty or effective ones. Here are our top three tips to create better Excel charts.
A powerful chart has a high signal-to-noise ratio
‘Less is more’. It’s a crucial principle in most of our communication, and in data visualization in particular. Because of my background as a physicist, I prefer to talk about the ‘signal-to-noise ratio’. The message - our signal - should be amplified as much as possible, giving it all of the attention. Everything that can distract from our message - the noise - should be removed.
A powerful chart tells a story
A powerful chart has a clear message. It should be short and meaningful, and obvious in the blink of an eye. If there’s only one thing our audience remembers at the end of the day, this should be it.
Navigating the landscape of powerful charts
Once we’ve decided to create a data visual or infographic, there are a lot of questions we should ask ourselves to determine the most suitable format. These considerations could include size, readability, possible interactive functionality, and the level of detail we need.
How powerful charts can boost your career
An American accountant managed - as the youngest ever! - to win a crucial election in Los Angeles - thanks to the power of data visualization. And you, too, can use powerful charts to boost your career!
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