topic Re: SUMX does not calculate at pivot aggregate level? in DAX Commands and Tips

SUMX does not calculate at pivot aggregate level?

riccardoprade93 — Mon, 21 Nov 2022 17:20:14 GMT

Hi everyone,

I am using the following formula, and I would like to calculate the ABS ERROR between the actuals and the forecast.

However, as you can see the it is not calculating at the pivot level. Shouldn't SUMX calculate the result at the aggregation provided in the Matrix? Why is not working?

Thanks a lot,

Riccardo

Re: SUMX does not calculate at pivot aggregate level?

AlexisOlson — Mon, 21 Nov 2022 17:58:43 GMT

The SUMX iterates over every row in the 'Dataset_Agg' table, not necessarily whatever rows you have showing in your pivot table. For each row in that table, it calculates the measure [Forecast] (converting the table row context to filter context via context transition) and takes the absolute difference between that measure and the table column 'Dataset_Agg'[Actuals].

How is the [Forecast] measure defined?

Re: SUMX does not calculate at pivot aggregate level?

tamerj1 — Mon, 21 Nov 2022 17:59:08 GMT

Hi riccardoprade93

the screenshot is not clear from my end. I guess I have a technical issue. Would you please copy/paste the code and write down the nsmes of the two columns used in the matrix visual? Are they from the same table?

Re: SUMX does not calculate at pivot aggregate level?

riccardoprade93 — Mon, 21 Nov 2022 18:09:53 GMT

Both Actuals and Forecast are two column of the table behind standing.

You can see below, in the second table the disaggregated data, that is why it calculate the ABS ERROR as 2,006.00

the DFU is the lowest disaggregation in my table.

is there a way to compute the ABS ERROR at aggregated level easily?

Riccardo

Re: SUMX does not calculate at pivot aggregate level?

riccardoprade93 — Mon, 21 Nov 2022 18:10:31 GMT

Both Actuals and Forecast are two column of the table behind standing.

You can see below, in the second table the disaggregated data, that is why it calculate the ABS ERROR as 2,006.00

the DFU is the lowest disaggregation in my table.

is there a way to compute the ABS ERROR at aggregated level easily?

Riccardo

Re: SUMX does not calculate at pivot aggregate level?

tamerj1 — Mon, 21 Nov 2022 18:31:03 GMT

riccardoprade93

This how I see the screenshot from my end. Would you please write down the highlied formula and the names of the highlighted columns?

Re: SUMX does not calculate at pivot aggregate level?

riccardoprade93 — Mon, 21 Nov 2022 20:02:22 GMT

Period	SHIP_FROM	Actuals	Forecast	ABSERROR V3 2
P09-22	EC9052A.61/1130	2006	2006	2006

ABSERR V3 2 = (SUMX('Dataset Agg', ABS('Dataset Agg'[Forecast]-'Dataset Agg'[Actuals])))

it does not sum to 0, because SHIP_FROM is an aggregation of the behind standing 'Dataset Agg' table.

Re: SUMX does not calculate at pivot aggregate level?

tamerj1 — Mon, 21 Nov 2022 20:08:07 GMT

riccardoprade93

Please try

ABSERR V3 2 =
SUMX (
SUMMARIZE ( 'Dataset Agg', 'Dataset Agg'[Period], 'Dataset Agg'[SHIP_FROM] ),
CALCULATE (
SUMX ( 'Dataset Agg', ABS ( 'Dataset Agg'[Forecast] - 'Dataset Agg'[Actuals] ) )
)
)

Re: SUMX does not calculate at pivot aggregate level?

riccardoprade93 — Mon, 21 Nov 2022 21:25:49 GMT

nah, not working. It also gives me 2006.

Because the SUMX it is summing at lower level. What I don't understand is how to sum at pivot aggregation....

Re: SUMX does not calculate at pivot aggregate level?

riccardoprade93 — Tue, 22 Nov 2022 16:01:04 GMT

I created a varation of yours and it works.

ABSERR CNS = SUMX(SUMMARIZE('Dataset Agg','Dataset Agg'[SHIP_FROM],'Dataset Agg'[SUB_DIV],'Dataset Agg'[Period]),[ABS CNS])

Re: SUMX does not calculate at pivot aggregate level?

Anonymous — Fri, 25 Nov 2022 03:08:35 GMT

Hi riccardoprade93

I am so glad to hear that your problem has been fixed , please consider Accept it as the solution to help the other members find it more quickly.

Best Regards,
Community Support Team _ Ailsa Tao
If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.