Fairly Represent Your Data with Tableau TreeMaps
A treemap is a graphical representation of data that uses nested rectangles to represent the hierarchical structure of the data. In Tableau, you can create a treemap using the “TreeMap” mark type in the “Show Me” menu, or by manually setting up the chart using a packed bubbles chart and customizing the appearance.
A treemap in Tableau is a type of data visualization that displays hierarchical data as nested rectangles. Each rectangle represents a node in the hierarchy, with the size of the rectangle proportional to the quantity of the data it represents. The rectangles are nested within each other to show the hierarchical relationship between the data.
In Tableau, treemaps can be created by connecting to a data source and dragging a dimension field to the Rows shelf and a measure field to the Columns shelf. The dimension field will be used to create the hierarchy, while the measure field will determine the size of the rectangles.
The color of the rectangles can also be used to represent additional data, such as a category or status. Treemaps are useful for showing the distribution and proportion of data, as well as for identifying patterns and outliers in large datasets.
To create a treemap in Tableau, follow these steps:
- Select the “TreeMap” mark type from the “Show Me” menu.
- Drag a measure to the “Size” shelf. This will be the value that the treemap displays.
- Drag a dimension to the “Color” shelf. This will be used to differentiate the different elements in the treemap.
- Drag a dimension to the “Labels” shelf. This will be used to label the different elements in the treemap.
You can customize the appearance of the chart by right-clicking on the chart and selecting “Format.” From here, you can change the color scheme, font, and other visual properties of the chart.
Treemaps are often used to display hierarchical data, such as the structure of a company or the categories of a product catalog. They can be useful for showing the relative sizes of different elements within a hierarchy, and to identify patterns and trends in the data.