“Number One, I order you to take… a number two.”
-Captain Jean-Luc Butthead
Before I get started, do you have any idea how hard it is to find cartoon images from the pre-Internet era? I just lost ninety minutes, first looking for the Far Side cartoon of “Custer’s Last Group Photo” (where everyone is holding up the “we’re number 1!” finger – get it?) and then struggling to find a version of the Beavis and Butthead image above at a better size. No dice. OK, enough of that…
DAX v1 has no RANK() function
That’s right. If you want to write a pivot measure that tells you where a particular store, or product, or customer ranks according to another measure, and according to current filter context, well, a RANK() function would be nice. But there wasn’t time to implement it in PowerPivot v1.
Never fear, there’s a workaround…
OK, yes, there’s a workaround – the FAQ says so right [link removed due to 404] here.
But “never fear” might be an overstatement. In fact, I should pause right here and say…
PowerPivot is simple, this technique is not
I’ve been doing this blog since October. I’ve covered a lot of ground. If you read this post and fear that you have to understand it in order to get great results with PowerPivot, well, then I’ve done you a great disservice.
Because in many ways, PowerPivot is actually easier than Excel itself. If you are new here, I strongly recommend going back to the Great Football Project and starting from the beginning.
OK, back to the power technique
I don’t know about you, but personally, my first impression of the formula referenced in the FAQ sounded a lot like “double yoo tee eff.” Check it out:
&& DimCustomer[CustomerName] <>
Explaining how it works
That formula has been bothering me for a long time. I knew it wasn’t going to be easy to break it down, but on our little DAX adventure together, we must leave no stone unturned, even the really heavy ones.
So today, I dove in. And whenever I get uncomfortable, I run to familiar ground. Which means, of course, oh yes…
The Great Football Project Returns!
Quick! Someone get me some early-2000’s running backs!
Phew, I feel better already. OK, let’s dig in. A long time ago I defined a [RushingYards] measure using the CALCULATE function. Let’s park that in a pivot:
First step: convert the sample formula over to my table/column names
When I substitute my football workbook’s names into that formula, I get:
<[Rushing Yards] && Players[FullName]<>
I won’t bother pretty-printing that monster just yet, because I’m going to dissect it step by step.
Outside functions: COUNTROWS + 1
The formula is basically:
COUNTROWS(all kinds of crazy stuff) + 1
Which starts to make a little bit of sense, if you’re feeling sleuthy. Really, what this is doing is:
COUNTROWS(# of Player Rows where Player has more yards) + 1
Make sense? Find how many players outrank the current player, add one, and that’s your rank. OK, so how does the middle stuff determine the number of players that outrank the current player?
Inside functions – FILTER and ALL
[Rushing Yards](Values(Players[FullName]))<[Rushing Yards]
Recall that the syntax for FILTER is:
FILTER(Table Expression, Filter Clause)
In this case, our table expression is:
Which makes sense. Remember, on a particular row of our pivot above, the Players table is going to only contain the row from that player, for purposes of measure calculation.
Ranking Edgerrin James against himself, for instance, isn’t very interesting, unless you want to compare the Colts version to the Cards version, or heaven forbid the Seahawks version. (The wear and tear on RB’s really is astounding).
So in order to have any players to rank the current player against, we need a table where the filter has been cleared. That’s why we use ALL.
That leaves us with the Filter Clause, and it deserves its own section.
Inside Functions – the “Filter Clause” parameter to FILTER
OK, so we have a table that consists of all player names. And we want to filter that down to just those players that have more Rushing Yards than the current player.
The FILTER function goes through that table of player names, one by one, and for each row, evaluates the filter clause to determine whether that row is “in” or “out.”
Our filter clause is really two clauses connected by the logical “and” operator – &&. So a row must meet BOTH criteria to be included.
[Rushing Yards](Values(Players[FullName]))<[Rushing Yards]
And the FILTER function is going to step through our table of all player names, at right. (There are about 4,000 players in my players table). So, FILTER starts with Rabih Abdullah.
The right side of the clause above, [Rushing Yards], is the [Rushing Yards] measure, evaluated in the context of this source row, Rabih Abdullah. Well, he totaled 172 yards in his rushing career.
But what about the left side of the clause:
That uses the context from the *pivot*, not the source table. the VALUES function returns the list of valid values of the specified column, according to current pivot context. Since the specified column is on the rows axis of our pivot, it only returns one value in this case, which is Edgerrin James.
Edge has 9,842 rushing yards. So the clause becomes:
9842 < 172
Which is of course FALSE. Rabih’s row gets excluded by the FILTER function.
Wash rinse, repeat. And then repeat.
FILTER then moves on to the next source row: Khalid Abdullah. The pivot context remains the same (Edge James), so the left side of the comparison is still 9842. Khalid has less than that, so again, excluded.
Well, in my data set, no one has more yards than Edge, he’s the top dog in my data set. So when FILTER finishes its pass, there are no rows left included. COUNTROWS then returns 0, the + 1 from the end of the formula gets added, and Edge gets his proper rank of 1.
And then FILTER starts over again, running through the entire Players table again, row by row, starting with our friend Rabih.
Two loops tucked inside each other. Wow.
The other half of the filter clause
That just makes sure we don’t count the player against himself for purposes of rank. Off the top of my head, I’m not sure that’s 100% necessary. I’ll have to noodle on that a bit.
OK, let’s see it in action
I add my new measure to the pivot, and I get:
What the heck? Is my measure broken?
No, it’s not. I’ll explain what’s going on in my next post, since this one has run so long already.