Categorising into groups using a result from a calculated measure to use in a visual.

[TFTF_BI]

Hi Guys, Im stuck on the below. 

So I have a measure which gives me a total average percentage of increase or decrease.

Example:

(Im comparing students attendance against 2 time frames. 'this part works fine' )

Time frame 1 = 70% average over 20 students 

Time frame 2 = 90% average over the same 20 students

giving me an average increase of 28.5% 

My measure called (attandance_variance) gives my 28.5% when comparing the dynamic time frames selected.

I also have now got a working measure that tells me how many students within this measure have improved. shown below: 

Count_Students_Attendance_Improved =

VAR Count_Improved = COUNTX(

FILTER(

VALUES(Students[school_student_id]),

'Measures'[Difference_Attendance_Percentage]>0),

Students[school_student_id])

Return Count_Improved

This is working great and tells me that 7/20 students have improved.

What I am struggling with is if I want to see this result in a visual like a pie chart in catorgories for example: 

7/20 Improved (>0.05)

3/20 Did not significantly change (>-0.05 & <0.05)

10/20 Did not improve (<-0.05)

I deally I would like to drop a measure into a visual and it will break it down into the 3 catorgories for me. 

I would love some help in solving this problem.

[Anonymous · Accepted solution]

Hi  @TFTF_BI ,

Here are the steps you can follow：

1. Create calculated column.

Flag =
SWITCH(
    TRUE(),
    'Table'[Count_Students_Attendance_Improved]=0.15,"Did not significantly change (>-0.05 & <0.05)",
   'Table'[Count_Students_Attendance_Improved]=0.5,"Did not improve (<-0.05)",
    'Table'[Count_Students_Attendance_Improved]=0.35,"Improved (>0.05)")

2. Create measure.

Improved =
COUNTX(FILTER( 'Table','Table'[Flag]="Improved (>0.05)"),[Flag])

Did not improve =
COUNTX(FILTER( 'Table','Table'[Flag]="Did not improve (<-0.05)"),[Flag])

Did not significantly change =
COUNTX(FILTER( 'Table','Table'[Flag]="Did not significantly change (>-0.05 & <0.05)"),[Flag])

3. Result:

Best Regards,

Liu Yang

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