Categorical analysis is the foundation of data visualization. It is the first and most frequent type of data visualization that data analysts use. Categorical analysis takes a dimension (for example, [Regions]) and breaks it apart by a measure (for example, [Sales]). A dimension is typically a categorical value; these do not get aggregated. They are likely used to create data headers or to generate filters. A measure is a (usually numerical) value that can be aggregated using mathematical functions (like sum, average, or median). Measures create unbroken axes, those that extend from one end of a range to the other.
This type of analysis aids in answering common business questions such as these:
How does A compare to B?
How is X measure distributed across Y categories?
How much do A, B, and C contribute to the total?
How does X measure change over time (where time is the dimension)?
Categorical analysis is usually presented as bar charts. Bar charts use height or length as visual encoding to express a measure. Visual encoding refers to the techniques used to display data in charts; Figure 1-1 shows some examples. Encoding data in bar charts is effective because humans can quickly analyze the variation among the size of the bars; they are also easy to understand and label.
Figure 1-1. This illustration shows the various ways that data can ...
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