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Danny-Happy
Helper I
Helper I

Calculate average FTE 2020

Hi All,

 

I like to calculate the average FTE per month for 2020.
I already calculate the FTE per month with this DAX:

 

Count FTE =

CALCULATE(

   SUM( 'Dim - employees'[FTE] ) ,

   FILTER(

       VALUES( 'Dim - employees '[date hired] ) , 'Dim - employees '[date hired] <= MAX( 'Dim - time'[Day] )) ,

       FILTER(

           VALUES( 'Dim - employees'[date leave] ) , OR( 'Dim - employees'[date leave]>= MIN( 'Dim - time'[Day] ) , ISBLANK( 'Dim - employees'[date leave] ))

 

But what I now like to know is; what is the average FTE 2020 until September 2020.

I tried to solve this with DAX; Average but then it just calculate the unique FTE for the whole year.

Thank you!

 

 

 

 

1 ACCEPTED SOLUTION
amitchandak
Super User
Super User

@Danny-Happy , Try like this and divide my month

Count FTE =
sumx(Values('Dim - time'[Month Year])
CALCULATE(
SUM( 'Dim - employees'[FTE] ) ,
FILTER('Dim - employees '[date hired] , 'Dim - employees '[date hired] <= MAX( 'Dim - time'[Day] )) ,
FILTER('Dim - employees'[date leave] , OR( 'Dim - employees'[date leave]>= MIN( 'Dim - time'[Day] ) , ISBLANK( 'Dim - employees'[date leave] ))
)
))

View solution in original post

3 REPLIES 3
smb711
Helper I
Helper I

This is really useful, I am getting an error when using the first calculation for determining actual FTE over months = A circular dependency was detected: PBIPND_vwPosition_Private[SUM of FTE], PBIPND_vwPosition_Private[Column], PBIPND_vwPosition_Private[SUM of FTE].

 

Any suggestions would be greatly appreciated?

amitchandak
Super User
Super User

@Danny-Happy , Try like this and divide my month

Count FTE =
sumx(Values('Dim - time'[Month Year])
CALCULATE(
SUM( 'Dim - employees'[FTE] ) ,
FILTER('Dim - employees '[date hired] , 'Dim - employees '[date hired] <= MAX( 'Dim - time'[Day] )) ,
FILTER('Dim - employees'[date leave] , OR( 'Dim - employees'[date leave]>= MIN( 'Dim - time'[Day] ) , ISBLANK( 'Dim - employees'[date leave] ))
)
))

Works perfect.

Thank you so much!

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