![]() Beyond the familiar scatterplot form with two continuous variables, dot plots adapt to many situations: with any combination of continuous and categorical variables, with univariate data, with multivariate data and with small or medium sized data sets. However, in one situation the dot plot invariably gets replaced with a bar chart, even at the expense of obscuring or distorting the data: when there is one continuous variable, one categorical variable with one data value per combination. After seeing yet another truncated bar chart, I wondered why on Twitter and found similar thinking from data visualization educator and consultant Nick Desbarats of Practical Reporting. We decided to team up on this article, and in this next section, Nick frames the issue from his teaching experiences. One of the chart types that I cover when teaching Stephen Few’s Show Me the Numbers course is the Cleveland dot plot (named after pioneering data visualization researcher William S. If you’re like most of my workshop participants, though, you won’t recognize this chart type. When I ask participants if they’ve seen one before, somewhere between 0% and 10% of hands typically go up. Immediately following the few hands that do go up, a much larger number of eyebrows invariably go down. The dominant emotion on the faces of participants is unmistakable: it’s confusion. If you’re confused too, let me explain: The chart above is essentially a horizontal bar chart with seven bars, but with the end of each bar indicated by a dot and the bars removed. Why would I use dots instead of bars? Because, if I used bars, my chart would look like this:Ī bar chart such as this would be misleading because it looks like, for example, Brazil’s record is about twice that of Indonesia since its bar is about twice as long. Looking at the value scale below the bars tells us that that’s not even close to true, however.
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