Data visualization makes data intuitive and makes it easy to discover the secrets behind the data. Data visualization tools even provides a shortcut to integrate and visualize big data. However, there are so many data visualization types.

How do you make the right choice to create better data visualizations and display your data correctly? For the most common used charts, are you sure you know how to design it to maximize its value?

In this article, I review 7 most common data visualization types and use the visualization way to help you improve your design for your data visualization.

1. Bar Chart
2. Pie Chart
3. Line Chart
4. Area Chart
5. Scatter Plot
6. Bubble Chart
7. Heat Map

Understand Your Data

I can’t emphasize this enough:

Before choosing the data visualization types, it is important to know your data, especially data relationships are shown below.

Choose the right data visualization types and design it

Now, I assume that you understand your data and have the right data visualization tool. Let’s move on, choosing the very best types of data visualization for your project, and designing it impressively.

1) Bar Chart

When to use bar charts?

Bar charts are mostly used for: Data changes over time, comparison of different data, and the relationship between parts and whole.

Vertical bar charts：best for showing chronological data

Stacked bar charts: can be used when comparing relationships between parts and whole, and can be applied to discrete data and continuous data.

Horizontal bar charts: you can use it when there are many data categories and the label is long

100% Stacked: use it when only paying attention to the partial-whole relationship while the total value of each group is not important

How to design it?

1.Use horizontal text labels: Do not use horizontal or vertical lines of text to make sure the labels are easy to read.

2. Column spacing should be appropriate：Column spacing should be 1/2 the width of the column.

3. The numerical value of the Y coordinate should start from 0: If set the origin of coordinates beyond zero, you can not express the whole value accurately.

4. Keep the color scheme consistent: It is better to use the same color. If you need to emphasize the data, you can use another striking color to highlight it.

5. Arrange the category properly: Sort by initial of the word.

2) Pie Chart

When to use pie charts?

Pie charts can easily express the relationship between parts and the whole, which is suitable for discrete data and continuous data. This approach is most attractive and understandable when the amount of data is small.

Pie chart:

Ring pie chart: you can put the most important element in the middle.

How to design it?

1.Position the slices correctly： there are two design ideas.

Plan A: Place the largest part at 12 o ‘clock in a clockwise direction. Then, place the second largest at 12 o ‘clock in an anticlockwise direction. Arrange the rest counterclockwise as shown above.

Plan B: Place the largest part at 12 o ‘clock in a clockwise direction. Arrange the rest clockwise as shown above.

2. Better no more than five categories：It is difficult to distinguish regions on the chart when the data percentage is too small. If there are too many categories, put the unimportant ones in ‘other’ category.

3. Don’t use multiple pie charts to show comparison relationship: Use bar charts rather than pie charts to compare data.

4. Make sure the percentages add up to 100%

3) Line Chart

When to use line charts?

Line charts are used to show time series relationships and persistent data. It’s a good indicator of trends, accumulations, decreases, and changes.

How to design it?

1.No more than four lines: 

2. Only use full lines: dotted lines are distracting.

3. Coordinate axes should include zero reference lines: Although the line chart does not need to start with a zero baseline, the chart should try to include it. If some small ranges are meaningful, you can shorten the ratio to highlight them.

4. Display text labels at the end of the line directly：

5. The height of the line should be similar to the scale of the chart: The maximum height of the line chart should be 2/3 of the Y-axis.

4) Area Chart

When to use area charts?

The area charts can show the time series relation of the data, and different from the line charts, the area charts can show the quantity clearly.

Stacked area chart:It is used to visualize the relationship between the part and the whole, and to show the contribution of the part to the total amount.

100% stacked: It is used to show the relationship between parts and whole. especially when the specific value of the whole quantity is not important.

How to design it?

1.Be readable: In stacked area charts, put the most variable data at the top, the least variable data at the bottom.

2. The Y-axis starts at 0：The data would be more precise.

3. Don’t display discrete data：Display the stable data like temperature rather than unstable data.

4. Don’t show more than 4 groups of data categories：Too many data categories will make charts difficult to read.

5. Use transparent colors flexibly: Try to make sure you don’t use overlap. If the overlap is unavoidable, you can use transparent colors

5) Scatter Plot

When to use scatter plots?

A scatter plot shows the relationship between two sets of variables. Correlation can be shown when data volume is large.

How to design it?

1.The Y-axis starts at 0: 

2. Include multiple groups of variables: Use size and color to add variable

3. Use trend line: The trendline can display the trend and correlation.

4. No more than two trendlines:

6) Bubble Chart

When to use bubble charts?

When you want to show comparisons and rankings.

Bubble scatter plot: to display additional variables

Bubble map: to visualize the regional data

How to design it?

1.Ensure the text label clear :

2. The bubble size should be appropriate:

3. Don’t use strange shapes

7) Heat Map

When to use heat maps?

Heat maps can display classified data, using a strong sense of color contrast to represent geographic areas or data lists.

How to design it?

1.Use simple map outline: Distinct outline is distracting.

2. Select the appropriate data range：The data range should be between 3 and 5 groups. The data that out of range is denoted by +/-.

3. The pattern should be simple :

4. Use appropriate color: Intense color will lead to vision burden. Use monochrome, and adjust the shades to distinguish the regions is better.

These are the entry-levl data visualization types. You can express your data correctly and meaningfuly with these basic charts.

However, when your data gets big, and data relationships get complicated, you need more advanced data visualization types. But keep in mind, the same thing is that you need to understand your data.