Forum Discussion

jeggen's avatar
jeggen
Helper II
2 years ago
Solved

Sum Columns based on Filter

I am using a visualization (sanka chart) that only allows one value but the chart compares time periods. I would like the user to be able to select which values are being used on the visualization by using a slicer.

 

Sample data would be:

CustomerYear 1Year 2Year 3Year 4Sum*
11010203070
2100102050180

 

I envisoned adding the column "Sum" above and using that for the value on my visualization. Then ideally having a slicer where the user can select which of the other columns are included in this, e.g., if they select just year 1 the visual shows values based on year 1 data, if they select year 2, just year 2 data etc. Is it possible to do something like this with a column who's data is derived from a variable combination of other columns based on a filter?

  • Anonymous's avatar
    Anonymous
    2 years ago

    Hi jeggen ,

    You said that your chart only allows one value and plan to create a column 'Sum' to display the data.I think I could use unpivot.

    The Table data is shown below:

    Please follow these steps:

    1.Change table structure.

    2.Use the following DAX expression to create a measure

     

    Sum = SUM('Table'[Value])

     

    3.Final output

    Best Regards,
    Wenbin Zhou
    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

     

2 Replies

  • Anonymous's avatar
    Anonymous
    Not applicable

    Hi jeggen ,

    You said that your chart only allows one value and plan to create a column 'Sum' to display the data.I think I could use unpivot.

    The Table data is shown below:

    Please follow these steps:

    1.Change table structure.

    2.Use the following DAX expression to create a measure

     

    Sum = SUM('Table'[Value])

     

    3.Final output

    Best Regards,
    Wenbin Zhou
    If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.