3D scatter plots are used to plot data points on three axes in the attempt to show the relationship between three variables. Each row in the data table is represented by a marker whose position depends on its values in the columns set on the X, Y, and Z axes.

More variables can be set to correspond to the color, size or shape of the markers, thereby adding more dimensions to the plot.

The relationship between different variables is called correlation. If the markers are close to making a straight line in any direction in the three-dimensional space of the 3D scatter plot, the correlation between the corresponding variables is high. If the markers are equally distributed in the 3D scatter plot, the correlation is low, or zero. However, even if it looks like a correlation is present, this might not always be the case. The variables could be related to some fourth variable, which could explain their variation, or pure coincidence might make it look like there is a correlation even if it is not.

You can change how the 3D scatter plot is viewed by zooming in and out as well as rotating it by using the navigation controls located in the top right part of the visualization.

You can adjust the scales and scale labels, as well as other axis settings, from the visualization properties for each axis, and you can add features such as gridlines, zoom sliders or error bars, and so on.

Example 1: A 3d scatter plot and trellising

In the 3D scatter plot below, sales, cost, and year are plotted against each other for a number of different products (colored by product).

Each category can be shown separately using trellising. In the example below, the markers are colored by product, and trellised by category:

Example 2: Aggregated markers and labels

The 3D scatter plot can also be used together with aggregation (for example, Sum or Average) by using the visualization properties setting Marker by. In this case, the values for a certain category are bundled together to show one marker for each category, as seen in the example below. The aggregated markers can also be sized by the count of items within each category, or by any other column.

Labels can be used in visualizations to identify and describe markers and the data associated with them.

Example 3: Markers sized by sales

You can change the shape and size of markers, and also make the size depend on the value for a specific column or hierarchy.

In the example below, the 3d scatter plot is showing one marker per row and each marker is sized by how large the value of sales was in each transaction.

All visualizations can be configured to show data limited by one or more markings in other visualizations only (details visualizations). 3D scatter plots can also be limited by one or more filterings. Another alternative is to set up a 3D scatter plot without any filtering at all. See Adding data limitations for a visualization for more information.

You can show data from multiple data tables in the same visualization if a proper data table matching is available. For more information, see Multiple data tables in one visualization and Column matches.