Hello, I’m Peter
Data is often presented visually. Here are 30 tips for improving your visualization skills by highlighting some of the most commonly overlooked mistakes you can make (from DataHunter)
1. Chart making tips you have to pay attention to
Bar chart baselines must start from zero
The idea behind bar charts is to compare values by comparing the length of the bars. When the baseline is changed, the visuals are distorted.
2. Use fonts that are easy to read
Sometimes typography can enhance visual effects and add extra emotion and insight. But data visualization is not included. Stick to a simple sans serif font (usually the default font in programs like Excel). Sans serif fonts are those that do not have small legs around the edges of the text.
3. Moderate width of the bar chart
Spacing bar charts should be 1/2 column width.
4. Use 2D graphics
As cool as they look, 3D shapes can distort perception, but appear to distort data. Insist on doing a 2 dimensional, ensure accurate data, good!
5. Use tabular digital fonts
Table spacing gives all numbers the same width, allowing them to align with each other when arranged, making comparisons easier. Most popular fonts have tables built in. Not sure if the font is correct? It just depends on whether the decimal points (or any number) line up.
6. Sense of unity
A sense of unity makes it easier for us to receive information: color, image, style, source…
7. Don’t go overboard with pie charts
Shows the scale of multiple blocks, with the sum of all blocks (arcs) equal to 100%. But it’s best to avoid using this chart because the naked eye is insensitive to area size.
They seem to have made this problem ðŸ˜
8. Use continuous lines in line charts
Dashed lines are distracting. By contrast, using solid lines and colors makes it easier to tell the difference.
Respect part of the proportion of the whole
There is an overlap in proportions where people choose more than one question, with the percentages of different choices adding up to more than one. To avoid this, do not draw a statistical graph of the scale directly. Rather than showing numbers, some diagrams focus more on showing the relationship between parts and the whole.
10. Visualization of area and size
Distinguish the length, height or area of the same type of graph (such as column, ring and spider graph) to clearly express the comparison between the corresponding index values of different indicators. When making such data visualizations, mathematical formulas are used to express the exact scale and scale.
Visualize values using size
Size can help highlight important information and add context cues, and it works well to represent values 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 basic graphics
A good rule of thumb is that if you don’t understand it effectively, your readers or listeners probably won’t understand it either. Therefore, stick to basic graphs: histograms, bar charts, Venn charts, scatter charts, and line charts.
Number of views
Limit the number of views in your visualization to three or four. If you add too many views, the big picture gets lost in detail.
Here are 5 guidelines you can follow when it comes to graphic color matching
1, color depth
It is a common method of data visualization design to express the strength and size of index values through the depth of color. Users can see which part of index data values are more prominent at a glance.
2. Use the same color scheme
Using too many colors can add an unbearable amount of weight to the data. Instead, designers should use the same color scheme, or similar colors.
3. Avoid bright colors
Bright colors are like capitalizing all the letters for emphasis, and your audience feels like you’re yelling at them. Flat colors, on the other hand, are great for data visualization because they allow your readers to understand your data without being overwhelmed by it.
4. Use different colors for labels
In some cases, over a period of time or a series of values, we may have measured different kinds of objects. For example, suppose we measure the weight of dogs and cats for six months. At the end of the experiment, we wanted to draw the weight of each animal, distinguishing cats and dogs in blue and red.
5, the number of colors
Don’t use more than 6 colors in one image; Remember remember remember
Standard visualizations must be annotated
1. Explain the coding
Data is presented through a combination of shapes, colors and geometric shapes. In order for the reader to read it clearly, the graphic designer decodes the shapes back to the data values.
2, axis label
This may seem unnecessary, or not very helpful, but you can’t imagine how many times you’ll be asked what the X /y axis represents if your chart is a bit confusing, or if the person looking at the data isn’t very familiar with it. Following the previous two drawing examples, if you want to set a specific name for the axis.
3, the title,
Another basic but critical point if we’re going to present data to a third party is to use a header, which is very similar to the previous axis tag.
4. Comment on key elements
Often, it is not very clear to use the scale itself just on the left and right sides of the chart. Annotating values on diagrams is useful for interpreting diagrams.
5. Important view location
Place the most important view at the top or upper left corner. The eye is usually the first to notice this area.
Four, good visual diagrams, follow the six principles
1. The data is ordered
Data categories are sorted alphabetically, in size order, or by value to guide the reader through the data in a logical and intuitive way.
2. Compare data
Comparisons are a great way to show differences in data, but if your readers can’t easily see the differences, then your comparisons are meaningless. Make sure all the data is presented to the reader and choose the most appropriate comparison method.
3. Do not distort data
Make sure all visualizations are accurate. For example, the bubble map size should expand by region, not by diameter.
4. Present data
Let the reader see the data, that’s the point of visualization. Ensure that no data is lost or engineered. For example, when using standard area maps, you can add transparency to ensure that the reader can see all the data.
5. Delete variables
Many times, too much information can get in the way of the reader’s attention, and it’s a good idea to remove the implied information from the visualization, in which case I don’t think we need to include variable names in the axis.
6. Avoid data noise
Minimize or eliminate unimportant items. This includes weakening or removing graph lines, changing axes, color of graph lines, and painting spreadsheet rows in light gray. The “data ratio” can be achieved at a high level, so that the audience can understand the data more easily.
Five, the summary
Did you remember all the little details? As the saying goes, practice makes perfect. Think about it in each data visualization process. What details should you pay attention to? If these details are properly handled, the data visualization gods are just around the corner.
I’ve written a lot of visualization tools like Plotly, Pyecharts, Matplotlib, Tableau, and Pyg2plot. Which one do you like best?