14 March 2022 Unpivot data in Qlik with the CrossTable prefix 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 how to unpivot data in Qlik: The answers to this question were unanimous, and you were right: The correct answer is answer B: CrossTable(Quarter, Budget, 2) Since the pivot table is a great way to analyze data in excel, it is just a matter of time before you will come across one. Contrary to a straight table, it consists out of grouped values in which you can expand the rows as you please to show more detailed aggregations. However, the format of a pivot table makes it less than ideal for integrating directly into your data model. You will have to ‘unpivot’ the values. Unpivot data with the CrossTable prefix This is done by using the CrossTable prefix. But how does this work? Let’s have a look at the table loaded into the example: The values are aggregated by Channel, Region and Quarter, where the quarters are shown in individual columns. If we load this table directly into Qlik without any transformations, we get a separate field for each quarter named 2022-Q1, 2022-Q2, etc, making this not an ideal way for an end user to analyze. So how do we solve this? As mentioned before the CrossTable Prefix. If we look at the official Qlik documentation the prefix is used as following: crosstable (attribute field name, data field name [ , n ] ) ( loadstatement | selectstatement ) There is an attribute field name, consisting of the attribute values, A data field name, consisting of the data values, And n, consisting of the number of qualifying fields preceding the table to be transformed. So how does it work? A lot of theory, but how does this work in practice? Let’s make things a bit more clear by taking the example table from the question, labeling the relevant columns and cells and unpivoting the data. The schematic below shows how this works: The labels for the different quarters, 2022-Q1, 2022-Q2, etc. become the contents for the attribute field Quarter. The values in the columns 2022-Q1, 2022-Q2, etc. become the content for the data field Budget. The names of the attribute and data fields can be arbitrarily chosen. We used Quarter and Budget because these best describe the contents of the fields. The first 2 columns, Channel and Region do not need to be transformed. We specify the value 2 for the number of qualifier fields. This means that only the columns after the first 2 columns will be ‘unpivoted’. This leads to the following prefix: CrossTable(Quarter, Budget, 2) That’s it. We look forward to seeing your comments and hope to see you again 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. Data Model Friday Qlik Test Prep Script Solution How can we help? Barry has over 20 years experience as a Data & Analytics architect, developer, trainer and author. He will gladly help you with any questions you may have. Call us Mail us 4 December 2024 New critical security patches for Qlik Sense Enterprise for Windows A new security vulnerability in Qlik Sense Enterprise for Windows has been disclosed, affecting versions from February 2023 to November 2024. Ensure your systems are updated with the latest patches to protect against this issue. New Release Qlik Vulnerability 27 November 2024 Structured Data vs Unstructured Data The difference between structured and unstructured data is fundamental to data management and analytics. Here’s an overview of the two types. 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
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