![]() ![]() It says, “if animal’s class is in monotremes, marsupials, and placental, then it gets the label of mammal”. It exits out of the first one that’s true.Īnd so, in this case, we go from the general. What SWITCH TRUE statement does is that it goes through each of the conditions. We’ve got a SWITCH TRUEstatement that is built from the general to the specific, and I want to show you why this doesn’t work. Fixing Power BI Matrix Totals With SWITCH TRUE Logic But we’ve got a better construct in Power BI that makes it a lot easier to see the logic, which is the SWITCH TRUE statement. In many cases, where you’re looking at multiple conditions, you could do this as a nested I F statement. To solve this, we’ve got to build some additional logic here that tells Power BI what to do in the row totals, the column totals, and the grand totals. It doesn’t have any idea what those totals should be. And oftentimes, that guess is wrong, and you’ve got to fix the totals.īut in this case, Power BI just gives the equivalent of the shrug. In many cases, when you have totals, subtotals, and grand totals, Power BI will at least take a guess. Now if we take that Spread Revenue and drop it into the field for the matrix, this is what happens (see below).Īs you can see, the base rows calculate properly, but the total rows don’t calculate it. This is just an example of a way that totals can break. The Total Opportunity Revenue is just a simple SUM measure, while the Lookup Scaling Factor is a more complicated, kind of a multi-criteria Lookup that we do base on a series of filter conditions. ![]() First, we have a measure called Spread Revenue, which is the branch of Total Opportunity Revenue and Lookup Scaling Factor. Let’s look at the context of the measures in this example. Fixing Power BI Matrix Totals With SWITCH TRUE Logic. ![]()
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