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(2011) The Visual Display Of Quantitative Information.pdf
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How to Use (2011) The Visual Display Of Quantitative Information.pdf to Improve Your Data Visualization Skills
Data visualization is the art and science of presenting data in a clear, effective and engaging way. It can help you communicate your findings, persuade your audience and reveal insights that might otherwise be hidden. But how do you create data visualizations that stand out from the crowd
One of the best resources to learn from is (2011) The Visual Display Of Quantitative Information.pdf, a classic book by Edward Tufte, a pioneer and expert in the field of data visualization. This book covers the principles, techniques and examples of how to design and display quantitative information in various formats, such as charts, graphs, maps and tables.
In this article, we will summarize some of the key lessons from (2011) The Visual Display Of Quantitative Information.pdf and show you how to apply them to your own data visualization projects.
Lesson 1: Maximize the Data-Ink Ratio
Data-ink is the ink used to display the data in a graphic. Tufte argues that the data-ink ratio, which is the proportion of data-ink to the total ink used in the graphic, should be as high as possible. This means that you should eliminate any unnecessary or redundant elements that do not convey information, such as grid lines, borders, backgrounds, labels and decorations. By doing so, you can reduce clutter and noise and make your data stand out more clearly.
For example, look at the following bar chart:
This chart has a lot of non-data ink, such as the 3D effect, the gradient fill, the axis lines and labels, and the legend. These elements distract from the main message of the chart, which is the comparison of sales across different regions. A better way to display this data would be:
This chart has a much higher data-ink ratio, as it only uses ink to show the data values and their names. It also uses horizontal bars instead of vertical ones, which makes it easier to read and compare the labels. The result is a simpler, cleaner and more effective chart.
Lesson 2: Avoid Chartjunk
Chartjunk is any element in a graphic that does not enhance or support the data presentation, but rather distracts or confuses the viewer. Chartjunk can include unnecessary or misleading features such as moirà patterns, hatching, shading, embellishments, icons, pictures and 3D effects. Chartjunk can also include inappropriate or inaccurate scales, axes and legends that distort or obscure the data.
Tufte advises us to avoid chartjunk and instead use clear and simple graphics that show the data accurately and honestly. He also warns us against using graphics that are designed to manipulate or deceive the viewer by exaggerating or hiding certain aspects of the data.
For example, look at the following pie chart:
This chart has several problems that make it an example of chartjunk. First of all, it uses a 3D effect that adds no value but creates visual distortion. The perspective makes some slices appear larger or smaller than they actually are. Second, it uses colors that are too similar or too contrasting, making it hard to distinguish between different categories. Third, it uses icons that are irrelevant and distracting. They do not match the labels or the proportions of the slices. Fourth, it uses a legend that is separated from the chart, forcing the viewer to look back and forth between them.
A better way to display this data would be:
This chart has none of the problems of the previous one. It uses a 2D effect that shows the data accurately and consistently. It uses colors that are distinct and harmonious,
making it easy to identify different categories. It does ec8f644aee