Forum Discussion
Under Over representation
@vikash_1108 - Funny, I thought this is where this was going, so he actually cooked it that way last night before I went to bed. I didn't publish it because this version of the measure actually returns the exact same numbers as if you take the first measure, add it to the visual, and then choose Show values as . Percentage of the total column. I just wanted to do it to prove I could do it. But, here it is with updated PBIX attached, same page 9, Table 9. The old code is lines with // comments. Finally, remember me when you answer, I almost lost track of this thread!!
Measure 9 Column % of Row % = VAR __Age = MAX([Age - Category]) VAR __Table = ADDCOLUMNS(SUMMARIZE(FILTER(ALL('Table (9)'),[Age - Category]=__Age),[District - Category]),"__Measure",[Measure 9 Row %]) // VAR __All = SUMX(__Table,[__Measure]) VAR __All = IF(HASONEVALUE('Table (9)'[District - Category]),CALCULATE([Measure 9 Row %],ALLEXCEPT('Table (9)','Table (9)'[Age - Category])),[Measure 9 Row %]) VAR __Cell = [Measure 9 Row %] RETURN //IF(HASONEVALUE('Table (9)'[District - Category]),DIVIDE(__Cell,__All,0),[Measure 9 Row %]) DIVIDE(__Cell,__All,0)@vikash_1108 - Oh, yes, I got it, duh. Updated PBIX, Page 17, Table (17). The second measure is identical to the reference changes in the measure and table names.
Measure 17 Row % = VAR __Age = MAX([Age - Category]) VAR __District = MAX([District - Category]) VAR __Cell = SUM([Member Count]) VAR __All = IF(HASONEVALUE('Table (17)'[District - Category]),SUMX(FILTER(ALL('Table (17)'),[District - Category] = __District),[Member Count]),SUMX(ALL('Table (17)'),[Member Count])) RETURN DIVIDE(__Cell,__All,0) Measure 17 Column % of Row % = VAR __Age = MAX([Age - Category]) VAR __Table = ADDCOLUMNS(SUMMARIZE(FILTER(ALL('Table (17)'),[Age - Category]=__Age),[District - Category]),"__Measure",[Measure 17 Row %]) // VAR __All = SUMX(__Table,[__Measure]) VAR __All = IF(HASONEVALUE('Table (17)'[District - Category]),CALCULATE([Measure 17 Row %],ALLEXCEPT('Table (17)','Table (17)'[Age - Category])),[Measure 17 Row %]) VAR __Cell = [Measure 17 Row %] RETURN //IF(HASONEVALUE('Table (17)'[District - Category]),DIVIDE(__Cell,__All,0),[Measure 17 Row %]) DIVIDE(__Cell,__All,0)
Member ID | Age - Category | District - Category |
| A | 23 | z |
| b | 23 | z |
| c | 23 | x |
| d | 29 | s |
| e | 29 | s |
| f | 29 | z |
| g | 36 | z |
| h | 36 | x |
| i | 36 | x |
| j | 36 | z |
| k | 67 | z |
Hi greg
I have included sample data.
1. So in first instance you will have pivot of District (row) over Age (coloum), counting members.
2. Then at each district level you will find proportion of member count
3. then as final calc you will get the proportion of member count in (2) divided by total proprtion of member count for that column (i.e. Age)
Hope this will help you derive the formular
Thanks Vikash
Anonymous - OK, I did it like this, not sure if it is correct or what you are looking for. First, I created a measure to calculate the % Total of Row since that is what your original image showed (hence why you have 100% in the row totals).
Measure 9 Row % =
VAR __Age = MAX([Age - Category])
VAR __District = MAX([District - Category])
VAR __Cell = COUNT([Member ID])
VAR __All = IF(HASONEVALUE('Table (9)'[District - Category]),COUNTROWS(FILTER(ALL('Table (9)'),[District - Category] = __District)),COUNTROWS(ALL('Table (9)')))
RETURN
DIVIDE(__Cell,__All,0)
Then I calculated the % column total of that like this:
Measure 9 Column % of Row % =
VAR __Age = MAX([Age - Category])
VAR __Table = ADDCOLUMNS(SUMMARIZE(FILTER(ALL('Table (9)'),[Age - Category]=__Age),[District - Category]),"__Measure",[Measure 9 Row %])
VAR __All = SUMX(__Table,[__Measure])
VAR __Cell = [Measure 9 Row %]
RETURN
IF(HASONEVALUE('Table (9)'[District - Category]),DIVIDE(__Cell,__All,0),[Measure 9 Row %])
However, note that if you put the first measure in the matrix and then do the thing I mentioned originally with the Show value as | Percent of Column total, you get a different result so not sure which you want.
Anyway, PBIX file is attached below sig. Page 9, Table (9).
Hi Greg,
Thanks for your endurance in doing this. The first calculation you have done is correct. However, the second calculation is not giving the desired outcome. Following is what the 1st and then the 2nd calculation should give as outcome when we pivot it.
1st Calculation Age Category 23 29 36 67 District Category s 0% 100% 0% 0% 100% x 33% 0% 67% 0% 100% z 33% 17% 33% 17% 100% Grand Total 27% 27% 36% 9% 100% 2nd Calculation Age Category 23 29 36 67 District Category s 0.00 3.67 0.00 0.00 1.00 x 1.22 0.00 1.83 0.00 1.00 z 1.22 0.61 0.92 1.83 1.00 1.00 1.00 1.00 1.00 1.00 Thanks in advance. I think we are almost there.
Regards
Vikash
@vikash_1108 - Funny, I thought this is where this was going, so he actually cooked it that way last night before I went to bed. I didn't publish it because this version of the measure actually returns the exact same numbers as if you take the first measure, add it to the visual, and then choose Show values as . Percentage of the total column. I just wanted to do it to prove I could do it. But, here it is with updated PBIX attached, same page 9, Table 9. The old code is lines with // comments. Finally, remember me when you answer, I almost lost track of this thread!!
Measure 9 Column % of Row % = VAR __Age = MAX([Age - Category]) VAR __Table = ADDCOLUMNS(SUMMARIZE(FILTER(ALL('Table (9)'),[Age - Category]=__Age),[District - Category]),"__Measure",[Measure 9 Row %]) // VAR __All = SUMX(__Table,[__Measure]) VAR __All = IF(HASONEVALUE('Table (9)'[District - Category]),CALCULATE([Measure 9 Row %],ALLEXCEPT('Table (9)','Table (9)'[Age - Category])),[Measure 9 Row %]) VAR __Cell = [Measure 9 Row %] RETURN //IF(HASONEVALUE('Table (9)'[District - Category]),DIVIDE(__Cell,__All,0),[Measure 9 Row %]) DIVIDE(__Cell,__All,0)Hi @Greg_Deckler @Ashish_Mathur
Thank you so much. This is exacktly what I wanted.
I have one more questions though. What would be the code for the 2 calculations if the data is already summarised. Below are the details of the summarised Raw table and what final results would be expected ..
Member Count Age - Category District - Category 2 23 z 6 23 z 5 23 x 4 29 s 1 29 s 2 29 z 9 36 z 10 36 x 24 36 x 9 36 z 27 67 z Raw Pivot Row Labels 23 29 36 67 Grand Total s 5 5 x 5 34 39 z 8 2 18 27 55 Grand Total 13 7 52 27 99 1st Calculation Age Category 23 29 36 67 District Category s 0% 100% 0% 0% 100% x 13% 0% 87% 0% 100% z 15% 4% 33% 49% 100% Grand Total 13% 7% 53% 27% 100% 2nd Calculation Age Category 23 29 36 67 District Category s 0.00 14.14 0.00 0.00 1.00 x 0.98 0.00 1.66 0.00 1.00 z 1.11 0.51 0.62 1.80 1.00 1.00 1.00 1.00 1.00 1.00 Thanks a Million 🙂