CountIf-Type Function in DAX

[Anonymous]

Schedule table

Schedule date	Lot number
10252021	12345
10252021	23456

Production table

Production start date	Lot number
10252021	12345
10262021	23456

We want to count the number of lot numbers in the production table only if the start date for the production table matches the date in the schedule date table.

For example, we would only count 1 for Lot number 12345 because the production start date matches the date in the schedule date table. The lot number actually ran on the day that it was scheduled.

So far we've tried this:

CountIf = COUNTROWS(FILTER(production_data,production_data[new_plot_no]=schedule_date,schedule_date[new_slot_no]))

Based on this starting point from class:

Countrows(filter(schedule data, lot_no_new(#####)=

[VahidDM · Accepted solution]

Hi @Anonymous 

Try this measure:

Measure = 
VAR _A =
    FILTER (
        ADDCOLUMNS (
            production_data,
            "Sche-Date",
                LOOKUPVALUE (
                    schedule_date[Schedule date],
                    schedule_date[Lot number], production_data[Lot number]
                )
        ),
        [Production start date] = [Sche-Date]
    )
RETURN
    COUNTROWS ( _A )

Output:

If this post helps, please consider accepting it as the solution to help the other members find it more quickly.

Appreciate your Kudos!!

[Anonymous]

Hello again! Is there a way to extract those lot numbers that were counted? So instead of counting the lots I would be able to see which lots were run?

Here is another column of the data I'm using. The end goal is to create card visualizations counting the number of lots based on type. "count of type A lots that actually ran on the date they were supposed to" "count of type B lots that actually ran on the date they were supposed to", etc

Schedule table

Schedule date	Lot number	Lot Type
10252021	12345	A
10252021	23456	B

Production table

Production start date	Lot number	Lot type
10252021	12345	A
10262021	23456	B

[Anonymous]

This is great, thank you so much!