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What chart types does Narratives for Tableau support?

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Narratives for Tableau current generates the follow types of narratives: 

  • Continuous analysis - You would typically see this reflected in a line chart
  • Discrete analysis - You would typically see this reflected in a bar, column, map or treemap chart
  • Percent of Whole analysis - You would see this reflected in a pie chart
  • Scatter plot analysis

It is important to note that how you structure your visualizations and data in Tableau will impact your options for generating narratives and how those narratives are produced. The following explanation describes the way Narratives for Tableau assumes data will be presented in order to generate an accurate narrative.

When you initially open the Narratives for Tableau extension to generate a narrative, you will be presented with a prompt to select your measures and dimensions.

For example, this chart:

Will produce a selection screen that looks like this:

For narrative generation, a dimension can be a text value or a number, either continuous or discrete. A measure is a numerical value. 

Narratives for Tableau supports the following conditions:

  • For continuous analysis, there can be either:
    • One dimension and multiple measures
    • One measure and a maximum of two dimensions
  • For discrete analysis, there can be either:
    • One dimension and multiple measures
    • One measure and a maximum of two dimensions
  • For percent of whole analysis, there can be only:
    • One measure and one dimension
  • For scatter plot analysis, there can be only:
    • One dimension and either two or three measures

In Tableau, you are able to view your data for any sheet object in tabular form:

When you view the data for this visualization, it looks like this:

The two dimensions are "Month" and "Region". This means that for every unique combination of month and region, there must be a measure value for the narrative to generate.

For objects with a single dimension, such as this bar chart example:

The data looks like this:

As more measures are added, maintaining the unique combinations of dimension values to measures is straightforward.

 

 

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