Discover the Power of Set Analysis in Qlik Sense: A Step-by-Step Guide with Real-Life Code Examples

Table of content

  1. Introduction to Set Analysis
  2. Understanding Set Analysis Expressions
  3. Working with Variables in Set Analysis
  4. Creating Advanced Set Analysis Expressions
  5. Leveraging Set Analysis in Real-Life Scenarios
  6. Best Practices for Set Analysis in Qlik Sense
  7. Troubleshooting Set Analysis Errors
  8. Conclusion and Next Steps

Introduction to Set Analysis

Set analysis is a powerful feature in Qlik Sense that allows you to define a set of data to be analyzed based on specific conditions or criteria. It enables you to create complex calculations and provide precise information to support decision-making processes. In this section, we'll provide an overview of set analysis and its key components.

  • What is a set? A set is a group of data or values that fulfill specific criteria defined by the user. In Qlik Sense, a set can be created by selecting one or more fields and specifying the relevant conditions. You can use sets to define complex data relationships and extract insights from your data.

  • What is set analysis? Set analysis is a syntax used in Qlik Sense to define complex sets of data and conditions. It allows you to extract a subset of data from your applications based on predefined criteria. Set analysis is particularly useful for creating more advanced calculations and visualizations.

  • How is set analysis used in Qlik Sense? Set analysis is used when you want to analyze a subset of data or perform specialized calculations that cannot be achieved using standard aggregation functions. You can use set analysis to extract a specific period of time, a specific region, or a specific product category, for example.

  • What are some common set analysis operators? Set analysis supports various operators, including:

    • = and <> used to specify exact matches and exclude specific values from a set.
    • > and < used to specify values that are above or below a certain threshold.
    • >= and <= used to specify values that are above or equal to a certain threshold.
    • AND and OR used to combine multiple conditions.

In summary, set analysis is a valuable tool for data analysis in Qlik Sense, enabling you to define complex sets of data and perform advanced calculations. Stay tuned for more in-depth explanations and code examples in the upcoming sections!

Understanding Set Analysis Expressions

When working with large datasets in Qlik Sense, it can be challenging to narrow down data to a specific subset that is relevant for a particular analysis. This is where set analysis expressions come in handy. Set analysis enables users to create complex expressions that allow them to filter data based on one or more conditions.

Here are some key concepts to understand when working with set analysis expressions:

  • Set modifiers: Set modifiers are symbols that define a set of values in Qlik Sense. Common set modifiers include:

    • {}: used to define a set of field values.
    • *: represents all field values.
    • $: represents the current selection.
    • >, >=, <, <=: used to define relative value ranges.
  • Set operators: Set operators allow users to combine or intersect different sets of field values. Common set operators include:

    • +: adds one set to another.
    • -: removes one set from another.
    • *: creates an intersection of sets.
  • Aggregation functions: Aggregation functions allow users to perform calculations on selected data values. Common aggregation functions include:

    • sum(): calculates the sum of a field or expression.
    • avg(): calculates the average of a field or expression.
    • count(): counts the number of values in a field or expression.

By using set modifiers, operators and aggregation functions, users can create powerful expressions that filter data and perform calculations in ways that are not otherwise possible with standard Qlik Sense expressions.

Here is an example of a set analysis expression that filters data based on a specific condition:

sum({<Region={'East'}, Year={2018}>} Sales)

This expression uses set modifiers to select data where the Region field value is 'East' and the Year field value is 2018. The sum() aggregation function calculates the total sales for all selected data that meets this condition.

By leveraging the power of set analysis expressions, Qlik Sense users can perform complex analyses and gain insights that would be difficult or impossible to obtain with standard expressions.

Working with Variables in Set Analysis

Set analysis in Qlik Sense allows users to apply filters to isolate specific subsets of data in their visualizations. One powerful feature of set analysis is the use of variables, which can be used to dynamically adjust the filters being applied. Here's a step-by-step guide on how to work with variables in set analysis:

Creating a Variable

  1. Open the "Edit Script" window in Qlik Sense.
  2. Click on "Variables" in the left-hand menu.
  3. Click on "Add" to create a new variable.
  4. Enter a name for the variable and define its value using the appropriate syntax (e.g. vMyVariable = 123;).

Using a Variable in Set Analysis

  1. In the Qlik Sense interface, create a new sheet and add a visualization.
  2. Open the "Add/Edit Expression" window for the visualization.
  3. Write your set analysis expression using the curly braces notation (e.g. {<MyField={$(vMyVariable)}>}). Note that "$" is used to reference the variable.
  4. Save and apply the expression to see the results.

Advantages of Using Variables in Set Analysis

Using variables in set analysis has several advantages, including:

  • Greater flexibility in adjusting the filters being applied to a visualization.
  • The ability to share variables across multiple visualizations, making it easier to maintain consistency in your analysis.
  • The potential for significant time savings when working with large data sets, as variables can be used to automate repetitive tasks.

By following these steps and using variables in set analysis, users can gain new insights into their data and streamline their analysis processes. So why not give it a try in your next Qlik Sense project?

Creating Advanced Set Analysis Expressions

In Qlik Sense, Set Analysis is a powerful tool for analyzing data based on selected values or fields. It allows you to perform advanced calculations and make comparisons between multiple sets of data. But Set Analysis can be complex, and creating advanced expressions requires a deeper understanding of the syntax and functions involved.

Here are some tips for in Qlik Sense:

Use Logical Operators to Combine Sets

Set Analysis allows you to combine sets of data using logical operators such as AND, OR, and NOT. This can be especially useful when dealing with complex data sets where you need to filter out specific data points or make comparisons between multiple sets of data.

For example, you can use the following expression to create a set of data that includes all sales made by Apple or Samsung products in the year 2021:

{< Product = {"Apple","Samsung"}, Year = {2021} >}

Utilize Aggregation and Accumulation Functions

Set Analysis also allows you to use aggregation and accumulation functions to perform calculations on sets of data. These functions include Sum(), Avg(), Count(), and more.

For example, you can use the following expression to calculate the average sales price of all products sold in the year 2021:

=Avg({< Year = {2021} >} SalesPrice)

Define Complex Sets with Multiple Conditions

You can also define complex sets in Set Analysis using multiple conditions, including ranges and wildcards. This can be useful when you need to filter data based on specific criteria, such as sales amounts that fall within a certain range or products that contain specific keywords.

For example, you can use the following expression to create a set of data that includes all sales made by products that have the word "phone" in their name:

{< Product = {"*phone*"} >}

In summary, in Qlik Sense requires a clear understanding of logical operators, aggregation and accumulation functions, and the ability to define complex sets with multiple conditions. By mastering these techniques, you can gain deeper insights into your data and make more meaningful comparisons between different sets of information.

Leveraging Set Analysis in Real-Life Scenarios

Set Analysis is a powerful tool in Qlik Sense that enables developers to create complex data analyses with ease. It allows Qlik users to define subsets of data in order to perform specific calculations and comparisons within a given dataset.

Here are a few examples of how Set Analysis can be leveraged in real-life scenarios:

  • Sales Analytics: Set Analysis can be extremely helpful for performing sales analyses. For instance, one could use Set Analysis to pinpoint specific products that have a high number of sales but low profitability. One could also use Set Analysis to compare sales figures over time, looking for trends and patterns that might inform future sales strategies.

  • Marketing Analytics: Set Analysis is essential for marketing analytics as it helps businesses segment and target their audiences more effectively. For example, a business could use Set Analysis to analyze customer purchasing behavior by segmenting them into different groups based on age, demographics, and location.

  • Financial Analytics: In financial analytics, Set Analysis is used to create comparisons and visualizations between financial data. For instance, it could help a business compare revenue and expenses across different years or identify specific areas of the business that are lagging behind.

  • Human Resources Analytics: Set Analysis can be used in HR analytics to analyze employee performance data. For example, an organization could use Set Analysis to analyze each employee's performance across a given period and identify top-performing employees who can be incentivized further.

In conclusion, Set Analysis is a powerful tool that can be used creatively and extensively for business decision-making. By segmenting data and visualizing it in meaningful ways, organizations can discover trends, patterns, and insights that would have otherwise remained hidden.

Best Practices for Set Analysis in Qlik Sense

When working with Qlik Sense and Set Analysis, there are several best practices to keep in mind to ensure that your applications run smoothly and effectively. Here are some tips to help you get the most out of Set Analysis:

Use Simple Expressions

One of the main advantages of Set Analysis is its ability to work with complex expressions. However, it's important to use simple expressions whenever possible, as they are generally easier to understand and maintain. Keep in mind that complex expressions can also be more resource-intensive and take longer to calculate.

Use Aggregation Functions Wisely

Aggregation functions like Sum(), Avg(), and Count() are powerful tools for working with data in Qlik Sense. However, they can also have an impact on performance if used improperly. In general, it's a good idea to avoid using these functions within Set Analysis expressions whenever possible.

Define Your Sets Carefully

The Sets feature in Qlik Sense allows you to define specific groups of values, which you can then use within Set Analysis expressions. When defining sets, be sure to consider the following:

  • Use meaningful names that accurately describe the set
  • Keep sets as small as possible to ensure optimal performance
  • Avoid overlapping sets, as this can result in unexpected results

Use Set Analysis in the Right Context

While Set Analysis can be a very powerful tool, it's important to use it in the right context. Specifically, Set Analysis works best when used to analyze a subset of data within a larger data set. If you try to use Set Analysis to analyze the entire data set, it may be more efficient to use other Qlik Sense features instead.

By following these best practices, you can make sure that your use of Set Analysis in Qlik Sense is as effective and efficient as possible. Whether you're working with small data sets or large, complex data sets, Set Analysis can help you gain valuable insights into your data and make better-informed decisions.

Troubleshooting Set Analysis Errors

Set analysis can be a powerful tool in Qlik Sense, but it can also be easy to make mistakes that lead to errors. Here are some common issues and tips for :

Missing Parentheses

One common mistake is forgetting to include parentheses in your set analysis expressions. Make sure you have open and close parentheses where needed, and that they are in the correct order.

Missing Set Modifiers

Another common mistake is forgetting to include set modifiers in your set analysis expressions. Make sure you include the correct set modifier, such as excluding or including certain values or sets.

Incorrect Syntax

Check your syntax carefully to make sure it is correct. One misplaced comma or quotation mark can cause an error.

Incorrect Field Names

Make sure you are using the correct field names in your set analysis expressions. If you are referencing a table or field that does not exist or is misspelled, your expression will not work.

Data Formatting Issues

Set analysis expressions can be sensitive to formatting issues, such as different date formats or numerical precision. Make sure your data is formatted correctly and consistently to avoid errors.

Nested Set Analysis

If you are using nested set analysis expressions, it can be easy to make mistakes with your syntax or set modifiers. Double-check your expression and make sure you have the correct nesting order.

By being aware of these common mistakes and applying some troubleshooting tips, you can avoid or quickly remedy set analysis errors in Qlik Sense.

Conclusion and Next Steps


In conclusion, Set Analysis is a powerful feature in Qlik Sense that allows you to create complex expressions for your data analysis. By using it, you can create subsets of data that meet specific criteria and calculate metrics based on them.

Hopefully, this guide has provided you with a solid understanding of the basics of Set Analysis and the syntax involved in creating expressions. You should now be able to apply this knowledge to your own data analysis in Qlik Sense.

Next Steps

Here are some next steps you can take to continue your learning:

  • Practice creating Set Analysis expressions for your own data sets in Qlik Sense
  • Explore more advanced Set Analysis features, such as nested sets and inter-record set analysis
  • Read the Qlik Sense documentation on Set Analysis to learn more about the syntax and advanced features
  • Join Qlik Community forums to connect with other users and ask for help or advice on Set Analysis and other topics in Qlik Sense.
Cloud Computing and DevOps Engineering have always been my driving passions, energizing me with enthusiasm and a desire to stay at the forefront of technological innovation. I take great pleasure in innovating and devising workarounds for complex problems. Drawing on over 8 years of professional experience in the IT industry, with a focus on Cloud Computing and DevOps Engineering, I have a track record of success in designing and implementing complex infrastructure projects from diverse perspectives, and devising strategies that have significantly increased revenue. I am currently seeking a challenging position where I can leverage my competencies in a professional manner that maximizes productivity and exceeds expectations.
Posts created 1778

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Posts

Begin typing your search term above and press enter to search. Press ESC to cancel.

Back To Top