You’ve probably seen “smoothed” or “curvy” line charts like this before. Some people like them and others definitely don’t. In this blog post, I explain when I think it’s okay to use smoothed line charts and when they should be avoided.
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You're looking at a chart and it contains a date labeled “10-02-2007.” What day is that, exactly? Feb. 10, 2007? Or October 2, 2007? How about “10-Feb-07”? Is that Feb. 10, 2007, or Feb. 7, 2010? To ensure that your chart readers know what the dates in your chart actually are, choose a date format in which:
The month is alphabetic (e.g., “Aug” instead of “08”)
The year is four digits (e.g., "2011" instead of "11")
As long as the date format that you choose checks both of those boxes, your readers will always interpret the dates in your charts correctly.
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When creating charts for “everyday” reports and presentations, it's generally best to stick to chart types that you know are familiar to the audience. Sometimes, though, you can’t use a familiar chart type, either because there aren’t any familiar charts that can accurately represent the type of data to be shown, or because there aren’t any that can communicate the specific insights that you need to communicate about the data.
In these situations, you might have to use a chart type that you know is unfamiliar to the audience. For example, you might have to use a scatterplot or step chart, even though you suspect (or know) that the audience is unfamiliar with that chart type. What to do?
There are three techniques that I use to quickly teach audiences how to read an unfamiliar chart type:
Gentle reveal
Bait-and-switch
Duh insights
What, exactly, are these techniques? Let’s see some examples, starting with…
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There are six formatting mistakes that I commonly see in pie charts “in the wild.” This article discusses how to recognize and avoid those formatting mistakes in your own pie charts.
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If you haven’t been using step charts, there’s a good chance that you’ve been misrepresenting at least some of your data to your audience. That’s why I suggest that, if you can, try to start using step charts whenever you need to show irregular, persistent time series values, and start getting your audiences used to seeing this chart type.
Don’t know what a step chart is? Read this post!
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While less common nowadays, it’s still possible that you might need to create a chart that will be printed in black and white. For charts that were originally designed in color, just printing the color chart on a black and white device will often make it uninterpretable, so it must be redesigned. Redesigning a chart so that it only uses black and white can be tricky, though, and different design changes are needed depending on the chart type, number of data series shown, and a variety of other factors.
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Connected scatterplots are sometimes used to show how two variables are related over time. In my latest article for Nightingale (the journal of the Data Visualization Society), I argue that connected scatterplot are virtually never the best choice, and alternatives like stacked line charts and indexed line charts can communicate the same insights as connected scatterplots, but are much easier to read and less prone to misinterpretation.
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There are about a dozen chart types that I don’t recommend using for “everyday” charts in reports and presentations, such as bullet graphs and box plots. Some people are uncomfortable with the idea of saying that a given chart type is never the most effective choice, however. In this blog post, I argue that there’s no reason to think that, for every chart type, there must be situations in which it’s the most effective choice, and that it’s entirely possible that some chart types are never the most effective choice.
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Occasionally, I come across a claim on social media that, in order to become truly competent at dataviz, you need to know about theoretical concepts such as graphical objects, encoding channels, and preattentive attributes of visual perception.
Is that true, though? Or can you still create effective charts without knowing about those theoretical concepts?
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Many chart creators assume that choosing a chart type is an easy decision that can be made using simple rules of thumb like, "Use line charts to show data over time." Unfortunately, these rules of thumb are too simplistic and often lead to poor chart type choices, and making a good chart type choice usually requires taking at least six or eight factors into consideration.
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As pretty much everyone and their robot dog is now aware, there are jaw-dropping breakthroughs happening in artificial intelligence (AI) on an almost daily basis. To those of us in the data visualization field, this begs the obvious question: Will AIs be able to create expert-level charts without any human intervention, and, if so, when might that happen?
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Unfortunately, there seems to be a widespread belief that a lack of data storytelling is always the main—or possibly only—reason why charts and data presentations don’t go over well with audiences, and that turning charts and data presentations into “data stories” virtually guarantees that they’ll be effective and well received. Even though that view isn’t promoted by most data storytelling thought leaders, it seems to have taken hold among many data professionals anyway.
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Thoughts on a draft manuscript of Alberto Cairo’s upcoming book, The Art of Insight.
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Chart creators often try to make unclear charts clearer by labelling all the values in the chart. This sounds like a good solution in theory, but, in practice, charts that rely mostly or entirely on reading textual value labels are far slower and more effortful to read than charts that communicate key insights primarily through graphics (bars, lines, color shades, etc.)
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After teaching many data professionals about bullet graphs and using them in many dashboards, I started to notice that they had a fair number of downsides. A few years ago, I started using an alternative called “action dots” that, I believe, are more informative, easier to understand, faster to visually scan, more compact, easier to implement, and don’t have any of the downsides of bullet graphs.
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