In the article titled What is a Pivot Table? How is it Used?, I provided a definition and key considerations regarding pivot tables, and demonstrated step-by-step, with images, how to create a basic pivot table using Microsoft Excel and Google Sheets.
In this article, I will discuss how we can transfer our data into a pivot table using the Looker Studio interface, and what precautions we should take during this process.
You can find support for Looker Studio here.
Looker Studio and Pivot Table Operations
Among the chart options in Data Studio, there are also bar and heatmap pivot table options. Of course, these customizations can be performed by you under Pivot Table > Style. During the data application process (metrics and dimensions), up to two row dimensions, two column dimensions, and ten metrics can be used. In the standard table layout, data is presented only as rows and columns, without grouping under metrics and dimensions, and up to 10 dimensions and 20 metrics can be used. Certainly, for both table types, it is important that dimensions and metrics carry as many values as possible and that data is defined in a standardized way. While data from standard fields is unlikely to cause confusion, caution is advised when using custom metrics and dimensions, calculated fields, and data from external sources.
In the Totals section, it is also possible to include both row and column grand totals within the table. After importing the data into the table, you can perform filtering operations using the Pivot table filter and display summarized data by segments, reflecting the data in a summary table.
In the example table above, the Google Analytics data does not include any calculated fields, filters, or segments. The row dimensions are Day of Week and Source / Medium, while the column dimensions are Gender and User Type. The metrics we evaluate in relation to these dimensions are Pages per Session and Average Time on Page.
By reviewing the table overall, we can analyze the new or returning visitors by gender, along with the average number of pages viewed per session and the average time spent on each page, grouped by day of the week and source. When you move your mouse over a row or column, you can see the current section of the table highlighted in the row-column context.
Things to Keep in Mind
As I mentioned under the heading, Looker Studio provides us with the opportunity to use 10 different metrics when creating a pivot table. Thus, we are able to perform numerous evaluations within the relevant dimension framework. However, you should not forget that with the number of relevant metrics, the table’s structure will become more complex, and that some of the metrics might carry null (empty) values. Particularly, when using custom metrics and dimensions, you should pay close attention to ensuring that the relevant data is properly acquired. You must also verify that operations are correctly executed in calculated fields and filters, and then incorporate them into the visualization and reporting process.