Types of graphs: 16 different ways to visualize data

In the age of the information explosion, more and more data is accumulating. They are untidy and not very legible, so we need a correct visualization to help them to be easily understood and accepted. In today’s article, we’ll introduce the top 16 types of data visualization charts and discuss their application scenarios to help you quickly select the right one for your data characteristics.

Chart types for visualizing data

1. Column chart

Column charts, forgive redundancy, use vertical columns to show numerical comparisons between categories, and the number of them should not be too large. It takes advantage of the height of the column to reflect the difference in the data, and the human eye is sensitive to differences in height. The limitation is that it is only suitable for small and medium data sets .

Application scenario: comparison of classified data

2. Bar chart

Bar charts are similar to column charts, but the number of bars can be relatively large . Compared to the column chart, the positions of its two axes are modified.

Application scenario: data comparison (category name may be longer because there is more space on the Y-axis)

3. Line chart

A line chart is used to show data change over a continuous time interval or a period of time . It is characterized by a tendency to reflect things as they change over time or in ordered categories. It should be noted that the number of data records in the line chart must be greater than 2, which can be used to compare trends for a large volume of data. And it is better not to exceed 5 polylines in the same graph.

Application scenario: data volume trend over time, series trend comparison

4. Area chart

The area chart is formed on the basis of the line chart. Fills the area between the polyline and the axis on the line chart with color . This helps to better highlight trending information.

The fill color of the area chart should have some transparency. The latter can help the user to observe the overlapping relationship between different series. The non-transparent area will make the different series cover each other.

Application scenario: series relationship, time trend relationship

5. Pie chart

Pie charts are widely used in various fields to represent the proportion of different classifications and to compare various classifications by arc . This is not suitable for multiple data series, because as the series increases, each sector becomes smaller and eventually the size distinction is not obvious.

Application scenario: series ratio, series size comparison (rose diagram)

6. Scatter plot

The scatter plot shows two variables as points in a rectangular coordinate system . The position of the point is determined by the value of the variable. By looking at the distribution of the data points, we can infer the correlation between the variables. Making a scatter plot requires a lot of data, otherwise the correlation is not obvious.

Application scenario: correlation analysis, data distribution

7. Bubble chart

A bubble chart is a multivariate chart similar to a scatter chart. Except for the values ​​of the variables represented by the X and Y axes, the area of ​​each bubble represents the third value. We must bear in mind that the size of the bubble is limited and too many bubbles will make it difficult to read.

Application scenario: comparison of classified data, correlation analysis

8. Gauge chart

A gauge in data visualization is a kind of materialized chart . The scale represents the metric, the pointer represents the dimension, and the angle represents the value. You can visually represent the progress or actual status of an indicator.

The meter is suitable for comparison between intervals. It can also be made into a type of ring or tube, indicating the proportion.

Application scenario: clock, ratio display

9. Radar Chart

Radar charts are used to compare multiple quantized variables. In them you can see what variables have similar values ​​or if there are extreme values. They also help to see which variables in the data set have higher or lower values. Radar charts are suitable for demonstrating job performance.

The radar chart also has a stacked column style that can be used for two-way comparison between rank and series , while representing the ratio.

Application scenario: dimension analysis, series comparison, series weight analysis

10. Structure diagram

The structure diagram is a visual means of presenting the hierarchy in the form of a tree structure , clearly showing the hierarchical relationship.

Application scenario: hierarchy visualization, process visualization

11. Rectangular tree diagram

The rectangular tree diagram is suitable for presenting data with hierarchical relationships , which can visually reflect the comparison between the same levels. Compared with the traditional tree structure diagram, the rectangular tree diagram makes more efficient use of space and has the function of showing the proportion .

Rectangular tree diagrams are suitable for showing the hierarchy with weight relationships. If it is not necessary to reflect the proportion, the frame diagram may be clearer.

Application scenario: weighted data, proportion of data

12. Funnel chart

The funnel chart shows the ratio of each stage and visually reflects the size of each module . It is suitable for comparing rankings. At the same time, the funnel chart can also be used for comparison. If we place several funnel charts horizontally, the data contrast is also very clear.

Application scenario: data classification, proportion, standard value comparison

13. Word cloud chart

The word cloud is a visual representation of text data . It is used to display a large amount of data and can quickly help users to perceive the most prominent text. The word cloud graph requires a large amount of data and the degree of discrimination of the same is relatively large; otherwise, the effect is not obvious. It is not suitable for accurate analysis.

Application scenario: keyword search

14. Gantt chart

The Gantt chart visually shows the time of the mission, the actual progress and the comparison with the requirements, so that managers can easily understand the progress of a task (project).

Application scenario: project progress, state changes over time

15. Map

The map is divided into three types: regional map, point map, and flow map.

(1) Regional map

A regional map is a map that uses colors to represent the distribution of a certain range of values ​​in a map partition.

Application scenario: comparison and distribution of data

(2) Dot map

A dot map is a method of representing the geographic distribution of data by plotting dots of the same size on a geographic background. The point distribution makes it easier to understand the general distribution of data, but it is not suitable when you need to look at a single specific piece of data.

Application scenario: data distribution

(3) Flow map

The flow map shows the interaction data between the outflow area and the inflow area. It is usually expressed by the line that connects the geometric centers of gravity of the spatial elements. The width or color of the line indicates the value of the flow. Flow maps help illustrate the distribution of geographic migration, and the use of dynamic flow lines reduces visual clutter.

Application scenario: data flow, distribution and comparison

16. Heat map

The heat map is used to indicate the weight of each point in the geographic area . Color variation on a heat map generally refers to density.

Application scenario: regional visits, heat distribution, various things distribution

conclusion

We are done with the 16 types of charts commonly used in data visualization . Some people may think that basic graphics are too simple and primitive, and they tend to use more complicated graphics. However, the simpler it is, the easier it is to help people quickly understand the data. Isn’t that the most important purpose of data visualization? So don’t underestimate them and give them a try!