You can group data rows exactly as you wish by attaching tags to data rows. You can tag any in-memory data table rows that you consider have something in common, and you can use the tags to easily identify the rows in different visualizations.

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.

You can view and manage tags in a panel that opens when you select View > Tags from the menu bar. The tag panel can also be moved to other positions by drag-and-drop operation. The image below shows an example of the Tags panel. When some rows have been tagged, you simply double-click a tag in the panel to mark all its rows in the visualizations.

The tags used to group the data rows form a tag collection, and the tag collection becomes a new data column in the data table. Its different tags are the column values. The column is treated in the same way as any other column.

You attach the tags to rows that have been marked. The active visualization defines which marking to get the marked rows from. Each row can only contain a single tag from each tag collection. Tags can only be attached to rows from a single data table, but the same tag collection and tag names can be used for multiple data tables.

Tags are similar to Lists, but Tags are specific to the current analysis, while with Lists you work with the same collection of lists all the time, from one session to the next. Combining the functionality from Tags and Lists can be very useful. You can create lists from tag collections, and you can create tag collections from lists. This means that Lists can be a way to transfer knowledge from one analysis to another, while Tags can be a way to use lists within an analysis. See Lists for more information.