Today, we will be mainly looking at the six types of filters that are available in Tableau. Additionally, we will also explore other types that can be very useful.
Data filtering can help eliminate observations in an analysis that may be undesirable or contain errors. But, let’s understand how Tableau can help work with data in the first place.
Introduction to Tableau
Tableau is a reporting and analytics tool that allows one to integrate and combine data, extracted from various sources, for the purpose of visualization. The visualization is primarily to make sense of the data in a more insightful form. Tableau can work conveniently with a lot of other tools or platforms, such as R, Hadoop, MongoDB, and so on.
Here is a video by Intellipaat that explains sorting and filtering in Tableau:
All the integrated data, visualizations, and reports can be accessed through Tableau Desktop, which has a dashboard that can be shared via Tableau Server to other members of your team or to the management.
What are filters in Tableau?
Coming back to the topic of filters, let’s first understand what filters are and what their purpose is. Filtering is nothing but removing some scope of data from a dataset. Filters can help minimize the dataset size for efficient use, eliminate irrelevant dimension elements, clean up underlying data, set date ranges and measures as required, simplify and organize the data, etc.
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Types of Filters in Tableau Desktop
There are mainly six types of filters in Tableau. This blog further explains them one by one.
Extract filters in Tableau are used to extract a small subset of data from the original data source. Tableau then creates a local copy of that dataset to be stored in the repository. You can save a screenshot of how it looks in your workbook. These methods reduce Tableau queries. The data size can be further reduced by applying the measure/dimension filter to the extract as required.
Data Source Filter
Data source filters in Tableau are mainly used to restrict sensitive data from the viewer and reduce data feeds. Viewers can, however, have certain access rights to view the underlying data. The data source filters allow direct application to the source data. One important thing to mention is that the extract filter and the data source filter are not linked, and if you happen to go back to a live connection, the data source filter will still remain intact.
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The context filter in Tableau can help create datasets by applying relevant presets for compilation. In Tableau, the context filter is always processed and applicable first, despite if other filters are applied. The multiple preset categories in the worksheet can be divided into many more parts that end up working like a context filter in itself. The datasets are created based on the original datasheet, and the data can be minimized efficiently to allow for viewing all data rows despite the constraints. The sheets can be chosen as and when needed.
The context filter adds an actionable context to the whole data analysis, but if the data is not reduced enough, the cost of computing can be very high.
Go through our blog on Parameters in Tableau to know in detail.
Dimension filters in Tableau are non-aggregated filters. The dimensions that are used are mostly blue pills. Blue pills correspond to discrete data. The dimension filter can be applied by dragging them from the Filters pane. The same can also be achieved by right-clicking on a particular dimension and selecting Show Filter. This way, you can exclude or include the values that you want to analyze.
If there are many dimensions, you can search for them. The filter provides four options, viz. General, Wildcard, Condition, and Top/Bottom. You can pick up any to select the right data or remove the unwanted ones. You can create your own formula as well and then use it in the Condition filter and the Top/Bottom filter for data selection. They provide a channel from dimension to measure to get the data that is required.
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Using a Measure filter in Tableau allows for various operations and aggregate functions, such as sum, median, avg, standard deviation, etc. Aggregated filters are always applied after non-aggregated filters, no matter what the order is on the Filters pane. The filters are applied to Measure fields, consisting of quantitative data.
In the next stage in a subsequent dialog box, you will get four types of filters:
- Range: Select the range of values to include in the result
- At least: Select the minimum value of a measure
- At most: Select the maximum value of a measure
- Special: Select null or non-null values
Table Calculation Filter
The Table Calculation filter is the last filter that is applied after the view has been created. If you want to add a filter to the view, the Table Calculation filter will do the job for you without filtering the underlying data.
Apart from the six main types of filters in Tableau, you will also come across other types of filters that are very convenient. Some of them are given below:
- Global filter: The Global filter can be applied across multiple worksheets using the same source data within a workbook. The filter can be applied to all worksheets using the same data as well.
- Quick filter: The various filter types in Tableau are quickly accessible using the right-click option. These filters are known as Quick filters, and they have sufficient functionality for all common filtering needs. Also, Quick filters in Tableau can be implemented on dimensions or measures.
- Cascading filter: Cascading filters in Tableau allow for the selections in the first filter to change the options in the second filter. This helps limit the values to ones that are only relevant to the first filter and prevents the user from selecting irrelevant data, which creates a better user experience.
- User filter: The User filter in Tableau, a.k.a. the row-level security, is a feature that restricts and manages the data a user can view or access based on the authority given.
Filtering plays an essential role in data analysis. This article explores the many ways you can use the filters in Tableau to have a better view of your data as and when required.
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