Data visualization is a powerful weapon for communicating complex information. By visualizing information, our brains are better able to capture and preserve valid information, increasing the impression of information. However, if the data visualization is weak, it will bring negative effects. Misrepresentation can harm the dissemination of data, misinterpreting them completely.
So good data visualization relies on good design, not just choosing the right chart template. It is all about expressing information in a way that is more conducive to understanding and guidance, so as to reduce the cost of obtaining information for users as much as possible. Of course not all chart makers are good at this. Therefore, there are all kinds of ridiculous mistakes in the chart expression we see. Here are some examples of these mistakes that are easy to correct:
1. Improper order of
pie charts Pie charts are a very simple visualization tool, but they are often Overcomplicated. Shares should be sorted intuitively and should not exceed 5 segments. There are two sorting methods that allow your readers to grab the most important information quickly
Method 1: Place the part with the largest share in the direction of 12 o'clock, place the part with the second largest share counterclockwise, and so on.
Method 2: The largest part is placed at 12 o'clock, and then placed clockwise
2. Use dashed lines in linear diagrams
Dotted lines can be distracting, but solid lines with the right color are easier to distinguish from each other
3. Data placement is not intuitive
Your content should be logical and guide readers through the data in an intuitive way. Sort categories by alphabetical, number or numerical size
4. Data fuzzification
Make sure data is not lost or overwritten by design. For example, use transparency effects in area charts to ensure that users can see all the data.
5. Spend more energy on readers
Make the data easier to understand with auxiliary graphical elements, such as adding trendlines to a scatterplot.
6. Incorrect presentation of data
Make sure that any presentation is accurate, for example, the size of the bubble chart should be the same as the value, and don't label it casually.
7. Use different colors in the heatmap
Some colors stand out more than others, giving the data an unnecessarily heavy element. Instead you should use a single color and express it through the shades of the color.
8. The column is too wide or too narrow
The spacing between the pillars is best adjusted to 1/2 of the width.
9. Data comparison is difficult
Contrast is an effective way to show differences, but it's less effective if your readers can't easily compare. Make sure the data is presented consistently so your readers can compare.
10. Use 3D plots
尽管这些图看来让人振奋，但3D图也容易分散预期和扰乱数据，坚持2D是王道。
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