Are excellent data visualization charts just listing and summarizing data? of course not! The real value of data visualization is to design data presentations that can be easily understood by readers. Therefore, in the design process, every choice should ultimately be based on the reader's experience, not the individual chart maker.
Today, I will summarize 30 tips for data visualization production. By listing some common mistakes that are easily overlooked, you can quickly improve and consolidate your visualization production level.
1. Tips for making charts that you have to pay attention to
1. The baseline of the bar chart must start from zero
The principle of a bar chart is to compare the size of the value by comparing the length of the bar. When the baseline is changed, the visual effect is distorted.
2. Use Simple, Readable Fonts
Sometimes typography can elevate a visual, adding extra emotion and insight. But data visualization is not covered. Stick to simple sans-serif fonts (often the default in programs like Excel). Sans serif fonts are those fonts that do not have small feet around the edges of the text.
3. The width of the bar chart is moderate
The space between bars should be 1/2 column width.
4. Use 2D graphics
While they look cool, 3D shapes can distort perception, and therefore data. Adhere to 2D to ensure accurate data.
5. Use Tabular Number Fonts
Table spacing gives all numbers the same width so that they line up with each other, making comparisons easier. Most popular fonts have built-in tables. Not sure if the font is correct? Just look to see if the decimal point (or any number) is aligned.
6. A sense of unity
A sense of unity makes it easier for us to receive information: colours, images, styles, sources…
7. Don’t Get Overzealous About Pie Charts
Display the proportional size of multiple blocks, and the sum of all blocks (arcs) is equal to 100%. But it's best to avoid this chart because the naked eye is not sensitive to area size.
8. Use continuous lines in line charts
Dotted lines, dashed lines are easily distracting. On the contrary, using solid lines and colors, it is easy to distinguish the difference from each other.
9. Respect the proportion of the part to the whole
Overlap occurs on questions where people choose multiple choices, where the sum of the percentages for different options is greater than one. In order to avoid this situation, the ratio cannot be directly made into a statistical graph. Some diagrams focus more on showing the relationship of parts to the whole than on presenting numerical values.
10. Visualization of area and size
Differentiate the length, height or area of the same type of graphics (such as columns, rings, and spiders) to clearly express the comparison between the index values corresponding to different indicators. When making this kind of data visualization graphics, mathematical formulas are used to express accurate scale and proportion.
11. Use size to visualize values
Size can help emphasize important information and add contextual cues, and using size to represent values works well with maps. If you have multiple data points of the same size in your visualization, they will get mixed up and it will be difficult to distinguish values.
12. Use the same details
The more details (and numbers) you add, the longer it takes your brain to process. Think about what you want to communicate with your data, and what is the most effective way to do it.
13. Use base graphics
A good rule of thumb is that if you can't understand efficiently, your reader or listener probably won't understand either. So stick with the basics: histograms, bar charts, Venn diagrams, scatterplots, and line graphs.
14. Number of Views
Limit the number of views in your visualization to three or four. If you add too many views, the big picture becomes overwhelmed by the details.
2. About chart color matching, you can refer to 5 guidelines
1. Color depth
It is a common method of data visualization design to express the strength and size of the index value through the depth of the color. Users can see which part of the index's data value is more prominent at a glance.
2. Use the same color system
Too much color will add unbearable weight to the data. On the contrary, designers should use the same color system or analogous colors.
3. Avoid bright colors
Bright, vibrant colors are like capitalizing all the letters for emphasis, and your audience feels like you’re selling them loud. Monotone colors, on the contrary, work well for data visualization because they allow your readers to understand your data without being overwhelmed by it.
4. Labels are distinguished by different colors
In some cases, we may have measured different kinds of objects over a period of time or a range of values. For example, suppose we measure the weight of dogs and cats over a period of 6 months. At the end of the experiment, we want to plot the weight of each animal, distinguishing cats and dogs in blue and red respectively.
5. Number of colors
Do not use more than 6 colors in one image.
3. Standard visual charts must have annotations
1. Interpret encoding
Present the data through the combination of certain shapes, colors and geometric figures. In order for the reader to read it clearly, the graph designer needs to decode these graphs back to data values.
2. Axis labels
This may not seem necessary, or very helpful, but you can't imagine how many times you'll be asked what the x/y axis represents if your graph is a bit messy, or the person seeing the data isn't very familiar with it What. As per the previous two plot examples, if you want to set specific names for the axes.
3. Title
If we are going to present the data to a third party, another basic but crucial point is to use titles, which are very similar to the previous axis labels.
4. Annotate key elements
Often, just using scales on the left and right sides of the graph isn't very clear by itself. Labeling values on a graph is very useful for interpreting graphs.
5. Important View Position
Place the most important views at the top or upper left. The eyes usually notice this area first.
4. Excellent visual charts, 6 principles to follow
1. The data is sorted in an orderly manner
Data categories are sorted alphabetically, by size, or by value to guide readers through the data in a logical and intuitive manner.
2. Compare data
Comparisons are a great way to show differences in data, but if your readers can't easily see the difference, then your comparisons are meaningless. To ensure that all data are presented to the reader, select the most appropriate comparison method.
3. Do not distort data
Make sure all visualizations are accurate. For example, bubble chart sizes should be based on area expansion, not diameter.
4. Display data
Getting the reader to see the data is the point of visualization. Make sure no data is lost or by design. For example, when using a standard area chart, transparency can be added to ensure readers can see all the data.
5. Delete the variable
Many times, too much information will distract the reader, and it is a good idea to remove implicit information from the visualization, and in this case, I don't think we need to include the names of the variables in the axes.
6. Avoid Data Noise
Minimize or remove the unimportant. This includes weakening or removing graph lines, changing the color of axes, graph lines, and painting spreadsheet rows with a lighter gray. The "data ratio" can reach a very high level, and the audience will understand the data situation more easily.
V. Summary
Do you remember all the little details above? As the saying goes, practice makes perfect, think more about each data visualization production process, what details need to be paid attention to? Whether the handling of these details is reasonable, the data visualization master is just around the corner~