When I started to create analyses and visualizations I tried to find out which chart / style is the best for the current task/problem. I found 3 way to choose the right way and create something to make everybody to understand my point. First, the “5-questions method“:
1) Do you want to compare values?
Charts are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:
2) Do you want to show the composition of something?
Use this type of chart to show how individual parts make up the whole of something, such as the device type used for mobile visitors to your website or total sales broken down by sales rep. To show composition, use these charts:
- Treemap&Bubble Chart
- Stacked Column
3) Do you want to understand the distribution of your data?
Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values. Use these charts to show distribution:
- Scatter Plot
- Box-and-whisker plot
4) Are you interested in analyzing trends in your data set?
If you want to know more information about how a data set performed during a specific time period, there are specific chart types that do extremely well. You should choose a:
- Dual-Axis Line
5) Do you want to better understand the relationship between value sets?
Relationship charts are suited to showing how one variable relates to one or numerous different variables. You could use this to show how something positively effects, has no effect, or negatively effects another variable.
When trying to establish the relationship between things, use these charts:
- Scatter Plot
- Highlight Table
További diagrammok felhasználási területei:
Gantt chart – Use Gantt charts to show the duration of events or activities. In a Gantt chart, each separate mark (usually a bar) shows a duration. For example, you might use a Gantt chart to display average delivery time for a range of products.
Map– There are many reasons to put your data on a map. Perhaps you have some location data in your data source? Or maybe you think a map could really make your data pop? Both of those are good enough reasons to create a map visualization, but it’s important to keep in mind that maps, like any other type of visualization, serve a particular purpose: they answer spatial questions.