15 juni 2022 Aggregating data in Qlik Sense with the Aggr() function Deel dit bericht Every Friday at Bitmetric we’re posting a new Qlik certification practice question to our LinkedIn company page. Last Friday we asked the following Qlik Data Architect certification practice question about aggregations in Qlik Sense with the Aggr() function: The correct answer is C: Avg(Aggr(Sum(NumberOfProducts * ProductPrice), OrderID)) To explain the answer let’s see what it is that is exactly wanted. We have a straight table with an order overview, in which we can see the PartnerID, how many orders they have placed, the lowest order value and the highest order value. Now the request is to add the average order value to this as well. Going trough the possible answers we can see that while answer A will work for the first four partners, it will quickly run into troubles calculating the average if there are more then two orders. And this is where the Aggr() function comes into play. To properly use the Aggr() function we need to have a look at the data model from which we can determine that the Fact table will look like this: So even without knowing the true contents of the table in this question, we can get an idea of the contents and what to do next. To be able to calculate the average order value per PartnerID, we need to calculate the total value of each OrderID first. This is done by multiplying the NumberOfProducts and the ProductPrice. Then if we total those values per OrderID we know what the total value of each OrderID is. To finalize we can then get the average of all these OrderID totals. And this is exactly what Avg(Aggr(Sum(NumberOfProducts * ProductPrice), OrderID)) does. The Aggr() function syntax is: Aggr({SetExpression}[DISTINCT] [NODISTINCT ] expr, StructuredParameter{, StructuredParameter}) So you will aggregate an expression, based on a StructuredParameter. The StructuredParameter is the dimension on which you would like to aggregate the expression. In our current example this is OrderID. To brake it down: We first calculate the value of the products: Then calculate the aggregated value of the products per OrderID: This basically creates an in memory table containing each OrderID and the total value of the OrderID. And now we can finally use the average function to calculate the average over the OrderID’s: Some other things to keep in mind: It is possible to aggregate on more then one dimension. And it is also possible to sort these. So if you use MonthYear as dimension for example, it is possible to sort these ascending or descending however it is needed. As seen in the Syntax, Aggr() can also use set expressions. So for example: {<Year = {2022}>} can be added to the Aggr() function. The standard calculation of the Aggr() syntax is a distinct aggregation. So for each distinct value of the dimension you would like to aggregate on, it will give the result. However if you have a repeating value in the dimension you can add NODISTINCT to the function. That’s it for this week. See you next Friday? More from the Bitmetric team Qlik Cloud Backup Protect your investment in Qlik with daily incremental backups stored in an encrypted environment with redundant storage. Available for as little as 2 Euro per day. Learn more. Join the team! Do you want to work within a highly-skilled, informal team where craftsmanship, ingenuity, knowledge sharing and personal development are valued and encouraged? Check out our job openings. Friday Qlik Test Prep Functions Performance Solution Hoe kunnen we je ondersteunen? Barry beschikt over meer dan 20 jaar ervaring als architect, developer, trainer en auteur op het gebied van Data & Analytics. Hij is bereid om je te helpen met al je vragen. Bel ons Mail ons 16 april 2025 The Cost of Bad Data: What Is It Really Doing to Your Business? Inaccurate or outdated data doesn’t just cause small hiccups. This can severely impact your bottom line. It slows down your teams, leads to expensive errors, and creates serious compliance risks. The good news is that these challenges are avoidable. TimeXtender 3 april 2025 Rethinking Pop-Ups in Qlik Cloud: A Simple Yet Effective Solution Struggling with the lack of native pop up support in Qlik Cloud? Learn how we created a smooth and user friendly alternative using guided sheet transitions with only native Qlik functionality. No extensions or layout issues involved. Qlik Solution Visualization 10 maart 2025 Qlik Data Flow: Simplifying Data Transformation Without Code Qlik Data Flow simplifies data transformation with a visual editor. Clean, join, and reshape data using drag and drop, no coding needed. Automatically generate Qlik script as you build. Learn how it works, see a step-by-step example, and compare it to Qlik Data Manager and Qlik Script. ETL No-Code Qlik Script
16 april 2025 The Cost of Bad Data: What Is It Really Doing to Your Business? Inaccurate or outdated data doesn’t just cause small hiccups. This can severely impact your bottom line. It slows down your teams, leads to expensive errors, and creates serious compliance risks. The good news is that these challenges are avoidable. TimeXtender
3 april 2025 Rethinking Pop-Ups in Qlik Cloud: A Simple Yet Effective Solution Struggling with the lack of native pop up support in Qlik Cloud? Learn how we created a smooth and user friendly alternative using guided sheet transitions with only native Qlik functionality. No extensions or layout issues involved. Qlik Solution Visualization
10 maart 2025 Qlik Data Flow: Simplifying Data Transformation Without Code Qlik Data Flow simplifies data transformation with a visual editor. Clean, join, and reshape data using drag and drop, no coding needed. Automatically generate Qlik script as you build. Learn how it works, see a step-by-step example, and compare it to Qlik Data Manager and Qlik Script. ETL No-Code Qlik Script