Philip Seamark shows us a way of splitting strings into words in DAX:

Here is a technique you might consider if you need to split text down to individual words. This could be used to help count, rank or otherwise aggregate the words in some longer text. The approach detailed here uses spaces as a delimiter and will not be tripped up if multiple spaces are used between words.

There is no SPLIT function in DAX, so this approach uses the MID function to help find words.

The PBIX file used for the blog can be downloaded here.

[Updated 14th Oct, 2018]

A slightly updated version that uses UNICHAR/UNICODE to preserve the case (“A” versus “a”) of each letter can be downloaded here. The reason for this is DAX stores a dictionary of unique values for every column. It is the first instance of any value that is added to the dictionary and assigned a new ID. Subsequent values that are considered the same “A” and “a” are considered the same are assigned the same ID. Using the UNICHAR/UNICODE version helps preserve the original case of each letter.

It’s an interesting approach and reminded me a bit of using a tally table to split strings in T-SQL.

Marco Russo shows us how to get the Nth element in a list using DAX:

The complexity of the calculation is in the Nth-Product Name Single and Nth-Product Sales Amount Single measures. These two measures are identical. The only difference is the RETURN statement in the last line, which chooses the return value between the NthProduct and NthAmount variables.

Unfortunately, DAX does not offer a universal way to share the code generating tables between different measures. Analysis Services Tabular provides access to DETAILROWS as a workaround, but this feature cannot be defined in a Power BI or Power Pivot data model as of now.

Indeed, the code of the two measures is nearly identical.

Read on for code and explanation.

Marco Russo announces a new site:

**What is DAX Guide?**DAX Guide is a website offering a complete reference to the DAX language. Every function is presented with its complete syntax, a short description, and links to related functions and articles.**Is DAX Guide a tutorial to learn DAX?**No, DAX Guide is not designed as a learning tool. The goal of DAX Guide is to provide a quick reference with accurate information. The only commitment is “quality first”.**What are some unique features of DAX Guide?**DAX Guide is updated automatically through the monitoring of new versions of Microsoft products. Every DAX function comes with a compatibility matrix describing in which Microsoft products and versions the function may be available. Additional attributes highlight which functions perform a context transition, which arguments are executed within a row context, and which functions are obsolete or deprecated – in our opinion.

If that sounds interesting to you, check it out.

Dustin Ryan shows us how to calculate quartiles using DAX:

To calculate the quartile, we’re going to use the PERCENTILEX.INC DAX function. The PERCENTILEX.INC function returns the number at the specified percentile. So for example, if I had numbers 0 and 100 in my data set, the 25th percentile value would be 25. The 50th percentile value would be 50 and the 75th percentile value would be 75, and you can figure out what the 100th percentile value would be.

Dustin shares an example with his NFL data set and also walks us through a couple of tricky situations.

Matthew Brice walks us through filters and calculations in DAX:

CALCULATEis somewhat unique in that it evaluates the 2nd, 3rd, …nth parameter first, and evaluates the first parameter last using values from my Filter Context Box. I think it is extremely helpful to list briefly the stepsCALCULATEperforms whenever it is invoked.(So maybe we are not at 10,000 feet, but 5,000?)The

CALCULATEfunction performs the following operations:

Create a new filter context by cloning the existing one.

**(***Important visual step!***)**Move rows in the row context to the new clone filter context box one by one replacing filters if it references the same column.

*(We will ignore this step for this post)*Evaluate each filter argument to

**CALCULATE**in the**old**filter context and then add column filters to the new clone filter context box one by one, replacing column filters if it references the same column.Evaluate the first argument in the newly constructed filter context.

Destroy this newly created, cloned filter context box before moving on to calculating the next “cell.”

If you’re interested in getting started with DAX, this is a good place to begin.

Marco Russo shows us a way of improving performance on conditional statements:

Consider the following measure.

12345`Margin :=`

`IF`

`(`

`[Sales Amount] > 0 && [Total Cost] > 0,`

`[Sales Amount] - [Total Cost]`

`)`

The basic idea is that the difference between Sales Amount and Total Cost should be evaluated only whether both measures are greater than zero. In such a condition, the DAX engine produces a query plan that evaluates each measure twice. This is visible in the storage engine requests generated for the following query.

Read on to see how Marco avoids this performance issue.

Matt Allington explains what the TREATAS function does:

The TREATAS function can be used to detect filters from your visual (filter context) and then apply these filters to a disconnected table in your data model.

It takes a source table (first parameter) and applies the values from that table to columns in a target table (second and subsequent parameters).

You can use a function like VALUES as the first parameter to detect the initial filter context in a visual and hence TREATAS can propagate filter context to the target table.

You do not need to have a physical relationship between the source table and the target table. It therefore means that TREATAS can be used as a virtual many to many relationship.

You can pass multiple filters (columns) from the source table to the target table. TREATAS can therefore can be used to apply multiple relationships (ie on more than one column) between tables.

Read on for a good example of how this works.

This post shows how you can generate

optimized multi-value DAX parameters in SSRSand achieve greater performance compared to the DAX PathContains function. This will be a short post that provides the SSRS expression to convert multiple SSRS parameters into a double-pipe delimited string for use in a DAX query. In other words, the goal is to use the DAX OR operator (||) instead of the PathContains function. I’m assuming the reader has experience with SSRS, so not all steps will be shown.

Read on for the example, which ended up being a 16X performance improvement.

Matt Allington shows us how to use variables in DAX:

Variables in DAX is a relatively new feature and is available in

- Power BI Desktop
- Excel 2016
- SSAS Tabular 2016
Variables are not available in Excel 2013 or Excel 2010.

Click through to see how to assign and use variables. It’s interesting to see how they’re local to a measure, so at this point at least, you can’t share variables between measures. Given what DAX is supposed to be, that’s probably the right choice.

Reid Havens explains why he teaches DAX in his Power BI courses:

The Check Formula button is an easily overlooked feature. However, before I hit ok and save my DAX Measure I ALWAYS press this button first!But what exactly does this button do? Well it’s checking yourDAX syntaxand making sure everything is written correctly. Now you COULD simply hit OK after writing your DAX and see if it errors, this is true. However when doing that your data model is actuallyattempting to calculatethe DAX measure in the background as well. Not a big deal with a few thousand rows, but if you’re working with a model that hasmillions of rowsthen that could take a long time for it to calculate, and then error!

The smart thing to do is to check your DAX syntax using the Check Formula button BEFORE hitting ok.Checking your DAX syntax doesn’t run your calculation and returns a rewardingNo errors in formulaoutput if everything was written correctly. Such a simple thing that can save you SO MUCH TIME! I highly recommend as a best practice toalwaysuse this before hitting ok and saving your measures, you’ll thank me later.

It makes for interesting reading.

Kevin Feasel

2018-10-15

DAX

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