7 July 2022 Bucket analysis in Qlik Sense with the Class() function Share this message 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 creating buckets (or intervals) in Qlik using the Class() function: The correct answer is A: Class(Age, 5) Judging by the amount of reactions this week this must be a well-known function! And it is great to say that all answers were right. Something which is not strange since the Class() function is a great way to quickly create buckets to help in doing analysis. The class() function can be used as a script or as a chart function and by looking at the syntax below, should minimally consist of the expression and the interval. In the expression we enter the numeric value to evaluate. This can be a fixed number, but also a field name, seeing as we used the Age field in the question. class(expression, interval [ , label [ , offset ]]) The interval determines the range on which the values will be classed. So in the example we used 5 as the interval, meaning we use increments of 5 to class all values in. So for example from 0 to 5, from 5 to 10 etc, etc. The class() function standardly creates these classes shown as below: lower limit <= x (label) < higher limit This brings us to the possibility of the label. Normally the class is depicted as shown above, so the class from 0 to 5 will be shown as the value: 0 <= x < 5 (see fig 1.), having an x as label in the value. This can be changed by adding a label to the syntax, bring it towards something more understandable (see fig 2.) or using the replace function (Replace(Class(Age, 5), ‘<= x <’, ‘ to ‘) to fully customize the result (shown in fig 3.). The final possibility in the syntax is to use an offset. For example, if you want increments of 10, but only start at number 21, you can use Class(Age, 5, ‘age’, 21) as syntax to start at 21. Keep in mind that in this case you need to give a label, otherwise the offset will be seen as the label. Also if you have results below the given offset, this function will take that into account and count back as seen in figure 4. Some other things to keep in mind If there are no values, there is no class created. For example there are no 5 to 10, or 10 to 15 classes, since there are no values over there. The classes are not customizable per class. It is always the same increment as given. In this example the data set only contains from age 18 and onwards. The 7 values which are below that are values with 0, but due to the classes which are created it is difficult to determine data quality issues. The classes are not customizable. The increments are fixed according to the interval given. If you want to have more control, use classes of various lengths or add some information to control data quality, it is worth looking at nested if statements or IntervalMatch solutions in the script. That’s it for this week. See you next Friday? More from the Bitmetric team Take your Qlik skills to the next level! Since 2013, the Masters Summit for Qlik is the premier advanced training for Qlik. Join us in Vienna and take your Qlik skills to the next level. 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 Solution Hoe kunnen we helpen? Barry heeft meer dan 20 jaar ervaring als Data & Analytics architect, developer, trainer en auteur. Hij helpt je graag met al je vragen. Call us Mail us 8 October 2024 Artificial Intelligence, Machine Learning, and Deep Learning Explained: How They Impact Your Business In today’s rapidly evolving technological landscape, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming industries and redefining how businesses operate. In this blog post, we will break down these three definitions and elaborate on them. AI 25 September 2024 Building Ethical AI: Practical Frameworks for Responsible Innovation AI is transforming industries with innovation and efficiency. But with great power comes great responsibility. The real question is: How do you turn ethical principles into actionable guidelines for AI development? And what steps should your team take to make it happen? AI 17 September 2024 What is AI Ready Data Data quality is all about how accurate, consistent, complete, and up-to-date your data is. If your data is good, you’ll get reliable insights and be able to make smarter decisions. It’s a key part of making sure your AI and machine learning projects are successful. AI Qlik
8 October 2024 Artificial Intelligence, Machine Learning, and Deep Learning Explained: How They Impact Your Business In today’s rapidly evolving technological landscape, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are transforming industries and redefining how businesses operate. In this blog post, we will break down these three definitions and elaborate on them. AI
25 September 2024 Building Ethical AI: Practical Frameworks for Responsible Innovation AI is transforming industries with innovation and efficiency. But with great power comes great responsibility. The real question is: How do you turn ethical principles into actionable guidelines for AI development? And what steps should your team take to make it happen? AI
17 September 2024 What is AI Ready Data Data quality is all about how accurate, consistent, complete, and up-to-date your data is. If your data is good, you’ll get reliable insights and be able to make smarter decisions. It’s a key part of making sure your AI and machine learning projects are successful. AI Qlik