![]() ![]() 99.48%), and it’s unclear to me as to what the data is showing here. The first problem is that they’ve presented a volume metric (Total Users) as a ratio metric (i.e. The most problematic part of the graphic is the section shown above. Honestly I had to stare at this graphic for about 5 minutes before I understood what was happening, and I'm still not sure I get it. What this graphic is showing is the “State of Social Media Marketing in 2015”, which includes a range of stats related to social media network usage and behviour. I hate to name and shame, but seriously, if you’re going to tout infographic production as a core offering you need to understand the basic principles of data visualization and design. This graphic was created by a company named JBH, who by the way, create infographics for a living. Here’s an example of data visualization gone wrong, terribly wrong. Now that we’re warmed up let’s jump right into the deep end. In this case the horizontal bar chart was the right choice, but always remember to clearly and meaningfully label your chart or table axis and headers. count, sum of, % of, etc) so the reader can easily understand what was measured and how to interpret it. But either way, the column title should clearly state the unit of measure (e.g. ![]() I don’t know which because the graphic doesn’t tell me (and I couldn’t check because the journal article is behind a pay wall). The bar chart is either showing the total occurrences (in volume) or the frequency at which the symptom occurs, represented as a % of the total sample. Sure, there is a relationship between the symptom and the stressor, but labeling the column header as relationship is both confusing and misleading. The horizontal bar chart is showing the volume of something, in this case, the occurrence of each symptom relative to the workplace stressor. That said, there is a problem with the section shown above, particularly the column titled Relationship. Design wise I actually think the graphic looks ok, though it has a little too much copy for my liking. Of the 5 examples we’ll run through today this is probably the least sinful of the group. This is a snippet of a full graphic created by MPH Today, and is based on a recent peer-reviewed article which analyzed “79 studies on the effects of stress and the human body”. It’s as informative as it is amusing, and I thought it would be fun to take a look at a few recent WTF Viz submissions and break down what, exactly, makes them such a strain to both the eyes and the mind. From the deceptive to the confusing to the downright ugly monstrosities created in the name of statistics, sometimes it’s the lessons you learn from failure that are the most impactful.Įnter WTF Visualizations, a fabulous Tumblr blog that curates a collection of the most sinful dataviz blunders around. These are the kinds of charts and infographics that ignore every basic rule and design principle when it comes to visualising data. But sometimes, I also like to draw a little inspiration from the not-so-great examples of dataviz. But beyond their craft, they are also educators who advance a dialogue on best practices and principles for what I like to call empirical storytelling.įlowing Data and Info is Beautiful can be great sources of inspiration if you're on the lookout for beautiful, creative and cutting-edge data visualization. Yau and McCandless are both leaders in this field who create and curate some of the best examples of data visualization you can find on the web today. To get my creative juices flowing, I often look for inspiration in a few different places, including but not limited to Nathan Yau’s Flowing Data and David McCandless Information is Beautiful. I love my job because I get to spend a fair amount of my time thinking about creative ways to communicate through data. Recently, I’ve been thinking a lot about data visualization, information design principles and storytelling. ![]()
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