Reporting dimensions are all fields that you can see in the Fields menu, which are used to populate reports. Basically, these are the columns in the table model. Also, they can be Calculated Columns or Measures. In some earlier “recipes” I talked about how to create them in PowerPivot model, and now I will show you how to do perform this in PowerBI Desktop. Also, I will mention another interesting novelty: Calculated Tables!
Purpose of PowerBI Desktop application is to create reports on a local machine and publish them in the cloud for users who can see them anytime, at any moment, by Internet browser on a PC or a mobile device. Managers need to get clear, simple and visually nice reports. It seems that PowerBI meets all given criteria. In a text that follows I will describe various ways to create reports in PowerBI Desktop.
By dragging the dimensions with numeric values into the Values field, the automatic aggregation of data is performed. Applied mathematical operation can be: SUM, COUNT, AVERAGE … Sometimes there is a need to see a percentage of the value in relation to the total sum, a cumulative value increase (Running Total) or a different view instead of simple aggregation. The following text will be about how to change the Value Field Settings.
Pivot tables have built-in options for applying autofilter to display data relative to rows, columns, and filters. By opening the autofilter menu, data sorting can be performed according to the specified reporting dimension, or it can narrow down the set of displayed values in the table by setting the conditions that relate to the description or value of the data displayed in the report. More about filtering Pivot tables can be found in the text that follows …
Pivot tables (Pivot table reports) are a tool for creating interactive reports based on the range of selected data or structured tables. Columns from source tables are used as reporting dimensions by aggregation of data (measures) or using them to analyze in rows, columns, as filters (dimensions) … Once made, the Pivot tables are easily changed by adding or removing dimensions, giving us more different look at the unique original dataset.