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For decades, chart creators have disagreed on whether pie charts should be used, but I think there are a few important arguments that have been missing from the debate and that might—just might—help to bring it to some kind of conclusion.
Find out what those arguments are in my latest article for Nightingale, the Journal of the Data Visualization Society.
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.
Read moreIf 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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Looking forward to the release of the Practical Charts book on November 15th? Interested in getting a free chapter right now, reading an advance review copy weeks before the release date, and in helping others to discover the book? Want to pick my brain during invite-only “Ask Me Anything” Zoom calls?
Awesome! To get all of these benefits and other goodies, and to help promote the book, I’d love it if you were to sign up as a Practical Charts VIP Reader!
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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At embarrassingly long last, I’m relieved to announce that the Practical Charts book is finished! The book will be released on November 15th, with pre-orders beginning in a week or two.
The goal of the book is as ambitious as the course on which it’s based: to teach chart creators of any experience level to create expert-level charts in just a few days. Given how many common data visualization challenges and mistakes need to be tackled to accomplish that, it was a tall order to keep the book down to 300 pages, but, flipping through the advance copies that I just giddily unwrapped, I think it might actually deliver. You tell me, though, when you read the book (you’re gonna read it, right?). More details about the book can be found on the official Practical Charts book page.
There will be more book-related announcements in the coming weeks, so make sure you’re subscribed to my email list to be notified when…
Pre-orders can be placed so you get the book shipped to you automatically on the release date (you might even get an extra goodie or two from me if you let me know that you pre-ordered it).
Bulk orders for team leaders and educators are available (which will be before November 15th).
Free advance reviewer copies can be requested (we’ll be sending out a limited number of free advance reviewer copies so that there are reviews on Amazon when the book is released).
Sneak peeks of content from the book are released.
Also, make sure you're subscribed to my email list if you’re interested in becoming a “VIP Reader” of the book. WTH is a “VIP Reader”? Well, VIP Readers will receive perks such as preferential consideration when requests for free advance reviewer copies of the book are being considered, and access to private “ask me anything” Zoom group calls just for VIP Readers. In exchange, I’ll be asking VIP Readers to pre-order the book (so that Amazon’s algorithms quickly learn who to show the book to), then post a review on Amazon and chat up the book on their social media channels when the book is released in November. Interested? Awesome! I expect to put out the call for VIP Readers in one to two weeks via my email list.
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.
Read moreThere 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.
Read moreOccasionally, 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?
Read moreMany 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.
Read moreAs 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?
Read moreUnfortunately, 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.
Read moreThoughts on a draft manuscript of Alberto Cairo’s upcoming book, The Art of Insight.
Read moreChart 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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I recently asked dataviz Twitter if there were situations in which a vertical line chart was the truly best choice. I was skeptical at first, but they changed my mind, so I wrote up the thought process as a Nightingale article for those who may be considering using vertical line charts in their own work.
Read moreWhen I show line charts in my Practical Charts workshops, participants sometimes express concern that line charts “hide values that occur in between the points on the line”. In this article, I argue that this is a misconception and that line charts of summary values (e.g., monthly totals) don’t hide meaningful information.
Read moreChoosing how many bins to include in a histogram can be a tricky design decision. There are many articles out there that recommend algorithms or rules of thumb for calculating the “optimal” number of bins, however, I don’t think that any calculation can do this reliably. In this post, I argue that the “optimal” number of bins depends mostly on the specific insight that needs to be communicated about the data, and not on the nature of the data (number of values, standard deviation of the values, etc.)
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I love data visualization but, TBH, the field is a bit of a mess and can be overwhelming and unapproachable, especially for beginners. In an effort to bring a bit of order to the chaos, I've been working on a "map" that brings many dataviz-related terms, types of visuals, skills, software products and books together into a single graphic that attempts to show how they all relate to one another, and to make the field more approachable.