Tag: SWITCH

Business Intelligence - BI, Data Visualisation, Power BI, Power BI Desktop, Power BI Service, Self-Service BI

Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI

Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI

I was working on a project a wee bit ago that the customer had conditional formatting requirement on a Column Chart.
They wanted to format the columns in the chart conditionally based on the average value based on the level of hierarchy you are at.
Here is the scenario, I have a Calendar hierarchy as below:

  • Calendar Hierarchy:
    • Year
    • Semester
    • Quarter
    • Month
    • Day

I use “Adventure Works DW2017, Internet Sales” Excel as my source in Power BI Desktop. If I want to visualise “Total Sales” over the above “Calendar Hierarchy” I get something like this:

Line Chart in Power BI, Total Sales by Year

Now I activate “Average Line” from “Analytics” tab of the Line chart.

Adding Average Line to Line Chart in Power BI

When I drill down in the line chart the Average line shows the average of that particular hierarchy level that I am in. This is quite cool that I get the average base on the level that I’m in code free.

Power BI, Drilling Donw in Line Chart

Easy, right?

Now, the requirement is to show the above behaviour in a “Column Chart” (yes! visualising time series with column chart, that’s what the customer wants) and highlight the columns with values below average amount in Orange and leave the rest in default theme colour.

So, I need to create Measures to conditionally format the column chart. I also need to add a bit of intelligent in the measures to:

  • Detect which hierarchy level I am in
  • Calculate the average of sales for that particular hierarchy level
  • Change the colour of the columns that are below the average amount

Let’s get it done!

Detecting Hierarchy Level with ISINSCOPE() DAX Function

Microsoft introduced ISINSCOPE() DAX function in the November 2018 release of Power BI Desktop. Soon after the announcement “Kasper de Jonge” wrote a concise blogpost about it.

So I try to keep it as simple as possible. Here is how is works, the ISINSCOPE() function returns “True” when a specified column is in a level of a hierarchy. As stated earlier, we have a “Calendar Hierarchy” including the following 5 levels:

  • Year
  • Semester
  • Quarter
  • Month
  • Day

So, to determine if we are in each of the above hierarchy levels we just need to create DAX measures like below:

ISINSCOPE Year		=	ISINSCOPE('Date'[Year])
ISINSCOPE Semester	=	ISINSCOPE('Date'[Semester])
ISINSCOPE Quarter	=	ISINSCOPE('Date'[Quarter])
ISINSCOPE Month		=	ISINSCOPE('Date'[Month])
ISINSCOPE Day		=	ISINSCOPE('Date'[Day])

Now let’s do an easy experiment.

  • Put a Matrix on the canvas
  • Put the “Calendar Hierarchy” to “Rows”
  • Put the above measures in “Values”
Detecting Year, Semester, Quarter, Month and Day hierarchy levels with ISINSCOPE in Power BI Desktop

As you see the “ISINSCOPE Year” shows “True” for the “Year” level. Let’s expand to the to the next level and see how the other measures work:

Continue reading “Highlighting Below Avg Sales per Hierarchy Level with SWITCH() and ISINSCOPE() DAX Functions in Power BI” →
DAX, Power BI, Power BI Desktop

Using “IN” Operator in DAX

IN operator in DAX

If you are a SQL guy I bet you’ve used “IN” operator zillions of times. You might also looked for the same functionality in DAX and I’m sure you’ve found fantastic blog posts showing you how to mimic the same functionality in DAX. The October release of Power BI Desktop is full of new analytics features such as Grouping, Binning and TOPN filtering. On top of that, one new awesome feature that is not documented at time of writing this article, or at least I haven’t find anything over the internet, is “IN” operator in DAX. In this post I show you how to use it in your DAX expressions.

Requirements

Note 1: You need to install SSMS2016 to be able to write DAX queries provided in this article. Alternatively, you can use DAX Studio . If for any reasons you cannot use SSMS 2016 or DAX Studio and you only have Power BI Desktop, don’t worry, I’ll provide some examples in Power BI Desktop as well.

Note 2: If you run previous versions of SQL Server it’s absolutely alright. There is nothing special in AdventureWorksDW2016CTP3 for this article that you don’t get in older versions of the sample database. But, keep in mind that SQL Server 2016 Developer Edition is now free and you can download it very easily. Check this out if you’re interested to see how.

Getting Started

After downloading the latest version of Power BI Desktop run it then

  • “Get Data” from SQL Server
  • From AdventureWorksDW2016CTP3 load “FactResellerSales”, “DimProduct”, “DimProductCategory”, “DimProductSubCategory” and “DimDate” to Power BI Desktop model
  • Find the local port of Power BI Desktop by opening “msmdsrv.port.txt” file from the following path:

“%UserProfile%\AppData\Local\Microsoft\Power BI Desktop\AnalysisServicesWorkspaces\AnalysisServicesWorkspaceXXXXXXXX\Data”

Note: The “XXXXXXXX” postfix is a random number. 

  • Open SSMS 2016 and connect to Power BI Desktop model as an Analysis Services local server. Do you want to learn more about how to connect your Power BI Desktop model from different software? Then check this out.

SSMS Connect to Power BI Desktop Model

  • Open an MDX new query
  • Run the following DAX query
EVALUATE
    SUMMARIZE('FactResellerSales'
                , DimDate[CalendarYear]
                , "Total Reseller Sales"
                , SUM('FactResellerSales'[SalesAmount])
                )

Here is the results:

Writing DAX in SSMS

Now we want to filter “CalendarYear” so that the query shows sales values for 2011 and 2012 only. One common scenario we had to do in prior versions of Power BI Desktop, Power Pivot or SSAS Tabular model was to use a logical OR operator “||” like below:

EVALUATE
FILTER(SUMMARIZE(FactResellerSales
                    , DimDate[CalendarYear]
                    , "Total Reseller Sales"
                    , sum(FactResellerSales[SalesAmount])
                    ) , DimDate[CalendarYear] = 2011 || DimDate[CalendarYear] = 2012
                    )

From now on we can write the above query using “IN” operator in DAX like below:

EVALUATE
    FILTER(
        SUMMARIZE(FactResellerSales
                    , DimDate[CalendarYear]
                    , "Total Reseller Sales"
                    , sum(FactResellerSales[SalesAmount])
                    ) 
            , DimDate[CalendarYear] 
                IN (2011, 2012)
            )

Here is the results:

IN operator in DAX

Continue reading “Using “IN” Operator in DAX” →