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DAX problem sum average
Hi, i have a issue about my dax measure. SUM( fac_compras[cantidad] ) * CALCULATE( [avgCosteUnidad]; SAMEPERIODLASTYEAR(dim_date[Date] ) ) all is right, but the total multipli...
try this
Measure = VAR __Summary = ADDCOLUMNS ( SUMMARIZE ( 'Table', 'Calendar'[YearMonth] ), "Quantity CY", CALCULATE ( SUM ( 'Table'[Quantity] ) ), "Avg Cost LY", [Average Cost LY] ) RETURN SUMX ( __Summary, [Quantity CY] * [Avg Cost LY] )the total is alignes with your logic here
Hi,
thanks overall
| Date | Quantity | Cost | Coste Total |
| 01/2018 | 5 | 10 | 50 |
| 01/2018 | 5 | 10 | 50 |
| 01/2018 | 3 | 20 | 60 |
| 01/2018 | 3 | 5 | 15 |
| 01/2018 | 5 | 5 | 25 |
| 01/2018 | 5 | 3 | 15 |
| 01/2018 | 5 | 4 | 20 |
| 01/2018 | 5 | 4 | 20 |
| 01/2018 | 5 | 5 | 25 |
| 01/2019 | 5 | 5 | 25 |
| 01/2019 | 56 | 0,5 | 28 |
| 01/2019 | 56 | 0,5 | 28 |
| 01/2019 | 5 | 10 | 50 |
| 01/2019 | 5 | 10 | 50 |
| 01/2019 | 3 | 20 | 60 |
| 01/2019 | 3 | 5 | 15 |
| 01/2019 | 5 | 5 | 25 |
| 01/2019 | 5 | 3 | 15 |
| 01/2019 | 5 | 4 | 20 |
| 01/2019 | 5 | 4 | 20 |
| 01/2019 | 5 | 5 | 25 |
| 01/2019 | 5 | 5 | 25 |
| 01/2019 | 56 | 0,5 | 28 |
| 02/2019 | 56 | 0,5 | 28 |
| 02/2019 | 5 | 0,5 | 2,5 |
| 02/2019 | 5 | 0,5 | 2,5 |
| 02/2019 | 5 | 10 | 50 |
| 02/2019 | 5 | 10 | 50 |
| 02/2019 | 3 | 20 | 60 |
| 02/2019 | 3 | 5 | 15 |
| 02/2019 | 5 | 5 | 25 |
| 02/2019 | 5 | 3 | 15 |
| 02/2019 | 5 | 4 | 20 |
| 02/2019 | 5 | 4 | 20 |
| 02/2019 | 5 | 5 | 25 |
| 02/2019 | 5 | 5 | 25 |
| 02/2019 | 56 | 0,5 | 28 |
| 02/2019 | 56 | 0,5 | 28 |
| 02/2019 | 5 | 0,5 | 2,5 |
| 02/2019 | 5 | 0,5 | 2,5 |
Then i need to calculate a 1 measure Average of Cost LY (01/2018)
Then i need to multiplie Quantity this year (01/2019) by Average of Cost LY (01/2018) an sum. This sum is error.
THanks
try this
Measure =
VAR __Summary =
ADDCOLUMNS (
SUMMARIZE ( 'Table', 'Calendar'[YearMonth] ),
"Quantity CY", CALCULATE ( SUM ( 'Table'[Quantity] ) ),
"Avg Cost LY", [Average Cost LY]
)
RETURN
SUMX ( __Summary, [Quantity CY] * [Avg Cost LY] )the total is alignes with your logic here
Thanks, this solution is perfect.