One way to achieve this is by using Power BI's query parameters and measures. You can create a parameter to allow the user to select a grouping option, such as a specific column. Then, you can create a measure that dynamically aggregates the data based on the user's selection. By referencing the parameter in the measure's calculation logic, Power BI will update the aggregation accordingly. This enables users to interactively group and aggregate data based on their preferences.

If you prefer a more advanced solution, you can explore Power BI's integration with R or Python. By leveraging the scripting capabilities of these languages, you can create dynamic grouping and aggregation logic based on user input. For example, you can use R or Python scripts to accept user input and generate custom code that performs the necessary grouping and aggregation operations. This approach provides flexibility and allows you to implement complex grouping and aggregation scenarios beyond what Power BI offers out of the box.

An alternative approach is to use the Power Query Editor's custom function feature. You can create a custom function that accepts a grouping parameter and applies the necessary aggregation steps. This way, you can dynamically group and aggregate data based on different user inputs. Additionally, you can leverage Power BI's visualization features, such as slicers, to allow users to select the desired grouping option directly in the report and see real-time changes in the displayed data.