Working with Tableau filters.
Tableau is amazing tool. It provides users with different tools that can help the user to analyze and examine data. Such features include sets, groups and filters. We can use the filter.
To minimize the available data
Target specific situations or regions
Remove immaterial fields.
To set up ranges to examine and analyze periods or categories.
Filters are great tools to process information and derive meaningful insights from data.
Types of Filters used in Tableau.
Tableau has several filters for different purposes as per the requirement of user’s operations. They are as follows:
· To limit data
· Focus specific fields.
· Streamline the data visualization process.
Multiple filters are available in Tableau like extraction filters, data source filters, context filters, dimension filters. Different types of filters have the following functionalities:
Extraction filters: It allows users to connect to the data source directly and extract data from the source which creates a copy in the Tableau repository. Extraction filter is of use for the latter.
Data source filters: This is to restrict the data coming into Tableau, thereby improving its performance.
Context filter: It is an independent filter, and it computes the selecting a separate dataset.
Dimension filters: It includes dimensions, groups, sets, categories, and more.
Measure filters: It includes mathematical terms like sum, average, median, standard deviation, etc.
Context Filter and its significance
Tableau filters can read all fields in the source data to deliver users with the necessary results; hence, they act individually of one another. But in some instances, user can be required to analyze only specific data. In these cases, context filters analyze data already filtered by a first filter. Context filters are independent filter.
The most crucial feature of context filters is that they process before any other filter. Other than extract and data source filters. Context filters are to decrease the data size, improve the performance, and include only such data of interest. Any set of categories available in the data set can be selected and made into context filter as per the need of a user.
Tableau’s Order of Operations
In Tableau, by applying several filters to the data, we can control and filter data. But the most exciting part here is that Tableau has a precise and pre-set order of using these filters. Tableau follows a specific order called Tableau’s Order of Operations.
Understanding the order of operations helps in understanding why undesired or unexpected results come when using filters. The order of operations is as follows:
The Extract filters.
The Data source filters.
The Context Filters.
Multiple Sets, conditional and top N filters, fixed level of detail expressions (calculated).
The Dimension Filters.
The Data Blending.
To Include and Exclude level of detail expressions (determined).
The Totals (estimated).
To Forecasts and table calculations.
The Trend lines, reference lines (calculated).
The relevance of the order of operations to context filters
As per the Tableau’s order of operations, extract, and data source filters are the first ones that are applied to the data source. Fundamentally these are useful to filter data even before it enters Tableau. Then comes context filters. Which mean, if there is more than one type of filter applied and one of them is a context filter.
The rest of the filters are only there to the data that makes it through the context filter. So, the first filter to be used will be the context filter. And all the other filters are dependent on the context filter selected by the user. In Tableau, you can make dimensional filters into context filters. For this, you need to set the dimensional filter as a context filter. To achieve this, right-click on the filter and select “Apply to context” option. When context filter is activated, it is seen in grey color as against others that are in blue. The use of context filters is to minimize and filter the categories to reach results for specific data, regions, timelines, etc. As always to understand how it works in tableau users need to experiment with filters.
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