Forum Discussion
Total Overall Value and Individual Value using the same Measure. How to divide them
Hi All.
I'm kind of stuck here. Need your help on this.
My problem is the measure used to get defect count and total defect count is the same, to differentiate it is using Plant (a column). I want to calculate defect count weightage as per formula below:
Defect count weightage = defect count / total defect countThe total defect count is the row that I highlighted in the screenshot. How to get that value if it's using the same measure?
Here is the measure used to calculate the defect count.
DefectCount = SUMX(FILTER(Joined_FactAIVC,Joined_FactAIVC[DefectClass]<> "Produced Glove" && Joined_FactAIVC[DefectClass]<> "Good Glove" && Joined_FactAIVC[DefectClass]<> "Empty Link" && Joined_FactAIVC[DefectClass]<> "System Status"),[Value])
Kindly help.
Not sure where you are going with this. Here is the standard approach
But it feels like you need something different?
Yaa I think this solved it. I chose different type of ALL previously.
DIVIDE(CALCULATE([DefectCount]),CALCULATE([DefectCount],ALLSELECTED(TableABC[ID])))
5 Replies
Please provide sample data that covers your issue or question completely, in a usable format (not as a screenshot).
Do not include sensitive information or anything not related to the issue or question.
If you are unsure how to upload data please refer to https://community.fabric.microsoft.com/t5/Community-Blog/How-to-provide-sample-data-in-the-Power-BI-Forum/ba-p/963216
Please show the expected outcome based on the sample data you provided.
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Defect Count
Defect Count Weightage
A
767,212
0.3848
B
504,089
0.2528
C
722,384
0.3623
Total
1,993,685
Above is the sample data that can be used.
My objective is to get the correct DAX to calculate defect count weightage.
Here is the formula
Defect count weightage = defect count / total defect count
Example,
for ID A : 767,212 / 1,993,685 = 0.3848
My current DAX,
DIVIDE(CALCULATE([DefectCount]),CALCULATE([DefectCount],ALL(Joined_FactAIVC))
which resulting to wrong output as below. The expected output is the same as the sample data above.
Not sure where you are going with this. Here is the standard approach
But it feels like you need something different?