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Here is a Example of what the new graph would be.
Essentially subtracting the actual_hours with all the values in the previous rows. This will provide a measure to show the difference of change for each sequence.
Any suggestions?
Thank you!
Solved! Go to Solution.
=SUM(Table[[Total Actual hours])-CALCULATE(SUM(Table[[Total Actual hours]),TOPN(1,FILTER(ALLSELECTED(Table[SEQ]),Table[SEQ]<MAX(Table[SEQ])),Table[SEQ]))
=SUM(Table[[Total Actual hours])-CALCULATE(SUM(Table[[Total Actual hours]),TOPN(1,FILTER(ALLSELECTED(Table[SEQ]),Table[SEQ]<MAX(Table[SEQ])),Table[SEQ]))
@wdx223_Daniel
This works! Thank you!
I have one additional question if you are able,
I have a function [Gaussian_Hours] which calculates a forecast of values which by a normal Distribution, though for some reason this new measure is incredible show (to measure the increase by SEQ.
Do you have any ideas for why the performance is slow?
Thank you!
performance optimization is hard work, it involves many aspects. you can try this code, maybe it won't work or not.
SeqChange=[Gaussian_Hours]-CALCULATE([Gaussian_Hours],OFFSET(-1,,ORDERBY(PROJ_FRIDAY_SEQ[SEQ#])))
This unfortunately doesn't work for my table, perhaps it is due to the fact that there are many values of SEQ, each corresponding due a different _ID? With your previous code is just repeats actual_hours in Diff.
Apologies, here is a better example.
ID | SEQ | ACTUAL_HOURS | DIFF |
Job1 | 1 | 5 | 5 |
Job1 | 2 | 10 | 5 |
Job1 | 3 | 20 | 10 |
Job2 | 1 | 3 | 3 |
Job2 | 2 | 10 | 7 |
Job2 | 3 | 15 | 5 |
Job2 | 4 | 25 | 10 |
Hi @CJBoelt ,
Please find below solution
Best Regards,
Shreya
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