Before we build our first chart type, you should know about the two major ways that Tableau classifies every field in a dataset. The first way is still the cornerstone of how I create every visualization in Tableau: dimension versus measure.
Using Measures
By default, Tableau classifies quantitative fields as measures. Measures are considered dependent because they tell us very little on their own. Consider the bar chart in Figure 5-1, showing the sum of the Profit measure across all the rows in the Sample – Superstore dataset.
You may feel very financially comfortable if this $286K value represents your annual salary, or a bit stressed out if this value represents your credit card debt—you just don’t know!
Figure 5-1. Tableau bar chart showing the sum of the Profit measure
Without details about the measure value—including its name, the time range that the values span, the way the values are being aggregated (discussed in Chapter 7), which category we are analyzing, and so forth—this number is all but meaningless. Measures are dependent on the context that is provided by combining numerical values with dimensions.
Using Dimensions
By default, Tableau classifies qualitative fields and dates as dimensions. Dimensions are considered independent because some information about them is inherent. For example, the Category dimension in the Sample ...
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