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I'm finding the solution to reallocate amounts in time to smooth maximum sums. Is it possible to do this with DAX?
My data:
Start | End | Amount | |
Project 1 | 2018 | 2020 | 321 |
Project 2 | 2019 | 2021 | 141 |
Project 3 | 2018 | 2020 | 30 |
Project 4 | 2019 | 2022 | 220 |
… | … | … | … |
Total | 712 |
I equally allocated amounts like this
Start | End | 2018 | 2019 | 2020 | 2021 | 2022 | Total | |
Project 1 | 2018 | 2020 | 107 | 107 | 107 | 321 | ||
Project 2 | 2019 | 2021 | 47 | 47 | 47 | 141 | ||
Project 3 | 2018 | 2020 | 10 | 10 | 10 | 30 | ||
Project 4 | 2019 | 2022 | 55 | 55 | 55 | 55 | 220 | |
… | … | … | … | … | … | … | … | … |
Total | 117 | 219 | 219 | 102 | 55 | 712 |
Is it any solution to reallocate to nearly this
Start | End | 2018 | 2019 | 2020 | 2021 | 2022 | Total | |
Project 1 | 2018 | 2020 | 135 | 113 | 73 | 321 | ||
Project 2 | 2019 | 2021 | 18 | 30 | 93 | 141 | ||
Project 3 | 2018 | 2020 | 8 | 11 | 11 | 30 | ||
Project 4 | 2019 | 2022 | 1 | 29 | 50 | 140 | 220 | |
… | … | … | … | … | … | … | … | … |
Total | 143 | 143 | 143 | 143 | 140 | 712 |
HI @Sergun70,
You can refer to below formula to create a new calculated table with expand year ranges, then use this table to create matrix to achieve your requirement.
Expand Table = SELECTCOLUMNS ( FILTER ( CROSSJOIN ( 'Sample', GENERATESERIES ( MIN ( 'Sample'[Start] ), MAX ( 'Sample'[End] ), 1 ) ), [Value] >= [Start] && [Value] <= [End] ), "Project", [Project], "Start", [Start], "End", [End], "Amount", [Amount] / ( [End] - [Start] + 1 ), "Range", [Value] )
Regards,
Xiaoxin Sheng
Thanx Xiaoxin Sheng,
I've already managed to equally allocate amounts in time with dax formula.
I can't reallocate my data to smoth overlapping intervals as in my second table.
Is there any solution or algotithm to solve this?
Hi @Sergun70,
>>I can't reallocate my data to smoth overlapping intervals as in my second table.
Is there any offset or increase percent to change divided values? I need a additional column with percent or other similar offset values to achieve snapshot 3.
Regards,
Xiaoxin Sheng
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