Issues with calculation filtering

[ad0pb]

I`m trying to calculate failure coefficient (cof on chart) that is equal to (number of errors)/(distance).

I would like to filter it by xxxxx slicer. It should calculate sum of errors (by xxxxx and by filter codes in visual filter that are chosen on chart) and divide by distance (total distance travled in period) that is sum of all vehicles form chosen location (xxxxx).

Current problem is if vehicle done some distance in month x and have no errors in that month distance for vehicle is not added to sum of distances for vehicles in chosen location.

I have working program (test). When I filter by types of errors then errors number is ok, but distance is only measured for IDs that have registered selected error in measured day. Is there a method to filter types of errors, and sum those while keeping distance for all vehicles from chosen location (filtered by xxxx) xxxxx- is location .

Choosing coefficient filtered in day shows ( at least i think so ) values that are used to calculate coefficient in chosen day/days.

Errors and Distance are calculated by ID numbers that are in columns in both files.

Attachments:

• test
• data source 1 (total distance traveled till day of log)
• data source 2 (failure log) 

[123abc]

Here are a few steps you might consider to address the issue:

1. Ensure Complete Distance Data: Make sure that your distance calculation considers all vehicles from the chosen location (xxxxx), regardless of whether they had errors or not in a given month. This means that the distance should be aggregated based on the location filter, not on whether errors were recorded.

2. Adjust Error Filtering: If filtering by error types causes distance data to be incomplete, you might need to decouple the error filtering from the distance calculation. Ensure that error filtering doesn't affect the aggregation of distance data.

3. Check Data Model Relationships: Ensure that your data model establishes the right relationships between the tables containing error data, distance data, and the location (xxxxx) slicer. This ensures that filtering by location doesn't inadvertently filter out distance data.

4. Consider DAX Measures: In Power BI or similar tools, consider using DAX measures to calculate the failure coefficient dynamically based on selected filters. This allows you to aggregate data based on slicer selections while keeping calculations independent of each other.

5. Test with Sample Data: Test your calculations with sample data to see how the filtering behaves in different scenarios. This can help identify any inconsistencies or unexpected behaviors in your calculations.

6. Consult Documentation or Forums: If you're using specific software or tools for your calculations, consult their documentation or online forums for insights and best practices related to data aggregation and filtering.

By carefully reviewing your data model, filtering mechanisms, and calculation logic, you should be able to address the issues you're facing with your failure coefficient calculation.

If this post helps, then please consider Accepting it as the solution to help the other members find it more quickly.

In case there is still a problem, please feel free and explain your issue in detail, It will be my pleasure to assist you in any way I can.

[ad0pb]

Thanks for the answer, but it does not resolve my problem. I've tried everything, and the data format is from the system, so I can't change it. By that, I mean errors will always be associated with the vehicle's location, and distance is in another file from a different vehicle system. I tried to use removefilters() and allexcept(), but it seems like filters removed inside the measurement are ineffective or not affecting the filters applied in the visual. For example, I want to show only errors that start with 'W,' divided by the total distance traveled by all vehicles in the chosen location, not only those that have errors starting with 'W' on their day.