Grouping data
When working with an analysis, it might be beneficial to group the data in various ways to serve different purposes.
- Tags
- You can group data rows exactly as you wish by attaching tags to data rows.
- Creating intervals of numerical data
- You can group numerical data into intervals that are then handled as categories.
- Grouping categories
- You can group categorical values into a new categorical value. This can be useful when a data column contains categorical values that are not important to display separately.
- Tags
The attachment of tags to data rows is a method to distinguish and group rows exactly as you wish. It could be any data table rows that you consider have something in common, and you would find it beneficial to be able to identify them quickly in different visualization contexts. For example, you might want to assign a 'Top 10' tag to the highest ten values of a measure, or tag rows with 'Good', 'OK', or 'Bad'. Rows with the same tag, and also the rows that are untagged, can then easily be identified in a visualization. - Creating intervals of numerical data
Numerical data can be grouped into intervals. The intervals are named bins and can be handled as categories in an analysis. - Grouping categories
Sometimes a column in your data contains many categorical values, but some of them are not important to display as separate values. Then you can group these values into a new categorical value.