More about viewing an evolution
When evolution is defined, BIRT Analytics internally creates a crosstab in which the categorical variable that you want to study is the row dimension (in the case of the example that accompanies this explanation, the product category) and the transition variable (the temporary variable) is the column dimension. The crosstab also includes the two or three defined measures (x, y, and measure).
The internal result of the crosstab is similar to
Table 4‑1.
Table 4‑1 Internal result of crosstab
| Scenario 1 | | Scenario 2 | | Scenario 3 |
Category 1 | x1 | y1 | measure1 | | x2 | y2 | measure2 | | x3 | y3 | measure3 |
Category 2 | x1 | y1 | measure1 | | x2 | y2 | measure2 | | x3 | y3 | measure3 |
The graphic representation of the evolution analysis consists of data extracted from each of the scenarios, displayed as spheres. The animation extracts data from each scenario and displays each category as a sphere in a particular color, positioning it in a way that takes into account the maximum and minimum values of the measures for other categories.
During execution, you can hover the cursor over one of the spheres to display the name of the category and the values for each of its measures for the current transition. A transition is the category of the transition variable that is displayed by an evolution.
As the transitions occur, the active scenario of the transition variable appears.
It is possible to modify the time between transitions by choosing the time icon and selecting the speed.
The analysis definition form also allows you to set the filters for the calculation.
Recommendations
To make this analysis useful, the number of discrete values of the categorical variable must be as low as possible.
The analysis is deemed to exceed the maximum if it occupies more than one page.
To learn more about internal calculations, consult the crosstab help utility.
For example, consider the question, “How do the sales of a product family change over the months?” To answer this question, create an evolution indicating the group of products as a categorical variable and the month when the order is placed as a transitional variable. Possible measures are the orders count (x-axis) and the average profit (y-axis), which enable you to see quickly the group of products that sells most over time, and the group of products that produces the most profit. You can convert this type of analysis into a crosstab analysis.
Video tutorial
