- Blog Categories
- Software Development Projects and Ideas
- 12 Computer Science Project Ideas
- 28 Beginner Software Projects
- Top 10 Engineering Project Ideas
- Top 10 Easy Final Year Projects
- Top 10 Mini Projects for Engineers
- 25 Best Django Project Ideas
- Top 20 MERN Stack Project Ideas
- Top 12 Real Time Projects
- Top 6 Major CSE Projects
- 12 Robotics Projects for All Levels
- Java Programming Concepts
- Abstract Class in Java and Methods
- Constructor Overloading in Java
- StringBuffer vs StringBuilder
- Java Identifiers: Syntax & Examples
- Types of Variables in Java Explained
- Composition in Java: Examples
- Append in Java: Implementation
- Loose Coupling vs Tight Coupling
- Integrity Constraints in DBMS
- Different Types of Operators Explained
- Career and Interview Preparation in IT
- Top 14 IT Courses for Jobs
- Top 20 Highest Paying Languages
- 23 Top CS Interview Q&A
- Best IT Jobs without Coding
- Software Engineer Salary in India
- 44 Agile Methodology Interview Q&A
- 10 Software Engineering Challenges
- Top 15 Tech's Daily Life Impact
- 10 Best Backends for React
- Cloud Computing Reference Models
- Web Development and Security
- Find Installed NPM Version
- Install Specific NPM Package Version
- Make API Calls in Angular
- Install Bootstrap in Angular
- Use Axios in React: Guide
- StrictMode in React: Usage
- 75 Cyber Security Research Topics
- Top 7 Languages for Ethical Hacking
- Top 20 Docker Commands
- Advantages of OOP
- Data Science Projects and Applications
- 42 Python Project Ideas for Beginners
- 13 Data Science Project Ideas
- 13 Data Structure Project Ideas
- 12 Real-World Python Applications
- Python Banking Project
- Data Science Course Eligibility
- Association Rule Mining Overview
- Cluster Analysis in Data Mining
- Classification in Data Mining
- KDD Process in Data Mining
- Data Structures and Algorithms
- Binary Tree Types Explained
- Binary Search Algorithm
- Sorting in Data Structure
- Binary Tree in Data Structure
- Binary Tree vs Binary Search Tree
- Recursion in Data Structure
- Data Structure Search Methods: Explained
- Binary Tree Interview Q&A
- Linear vs Binary Search
- Priority Queue Overview
- Python Programming and Tools
- Top 30 Python Pattern Programs
- List vs Tuple
- Python Free Online Course
- Method Overriding in Python
- Top 21 Python Developer Skills
- Reverse a Number in Python
- Switch Case Functions in Python
- Info Retrieval System Overview
- Reverse a Number in Python
- Real-World Python Applications
- Data Science Careers and Comparisons
- Data Analyst Salary in India
- Data Scientist Salary in India
- Free Excel Certification Course
- Actuary Salary in India
- Data Analyst Interview Guide
- Pandas Interview Guide
- Tableau Filters Explained
- Data Mining Techniques Overview
- Data Analytics Lifecycle Phases
- Data Science Vs Analytics Comparison
- Artificial Intelligence and Machine Learning Projects
- Exciting IoT Project Ideas
- 16 Exciting AI Project Ideas
- 45+ Interesting ML Project Ideas
- Exciting Deep Learning Projects
- 12 Intriguing Linear Regression Projects
- 13 Neural Network Projects
- 5 Exciting Image Processing Projects
- Top 8 Thrilling AWS Projects
- 12 Engaging AI Projects in Python
- NLP Projects for Beginners
- Concepts and Algorithms in AIML
- Basic CNN Architecture Explained
- 6 Types of Regression Models
- Data Preprocessing Steps
- Bagging vs Boosting in ML
- Multinomial Naive Bayes Overview
- Gini Index for Decision Trees
- Bayesian Network Example
- Bayes Theorem Guide
- Top 10 Dimensionality Reduction Techniques
- Neural Network Step-by-Step Guide
- Technical Guides and Comparisons
- Make a Chatbot in Python
- Compute Square Roots in Python
- Permutation vs Combination
- Image Segmentation Techniques
- Generative AI vs Traditional AI
- AI vs Human Intelligence
- Random Forest vs Decision Tree
- Neural Network Overview
- Perceptron Learning Algorithm
- Selection Sort Algorithm
- Career and Practical Applications in AIML
- AI Salary in India Overview
- Biological Neural Network Basics
- Top 10 AI Challenges
- Production System in AI
- Top 8 Raspberry Pi Alternatives
- Top 8 Open Source Projects
- 14 Raspberry Pi Project Ideas
- 15 MATLAB Project Ideas
- Top 10 Python NLP Libraries
- Naive Bayes Explained
- Digital Marketing Projects and Strategies
- 10 Best Digital Marketing Projects
- 17 Fun Social Media Projects
- Top 6 SEO Project Ideas
- Digital Marketing Case Studies
- Coca-Cola Marketing Strategy
- Nestle Marketing Strategy Analysis
- Zomato Marketing Strategy
- Monetize Instagram Guide
- Become a Successful Instagram Influencer
- 8 Best Lead Generation Techniques
- Digital Marketing Careers and Salaries
- Digital Marketing Salary in India
- Top 10 Highest Paying Marketing Jobs
- Highest Paying Digital Marketing Jobs
- SEO Salary in India
- Brand Manager Salary in India
- Content Writer Salary Guide
- Digital Marketing Executive Roles
- Career in Digital Marketing Guide
- Future of Digital Marketing
- MBA in Digital Marketing Overview
- Digital Marketing Techniques and Channels
- 9 Types of Digital Marketing Channels
- Top 10 Benefits of Marketing Branding
- 100 Best YouTube Channel Ideas
- YouTube Earnings in India
- 7 Reasons to Study Digital Marketing
- Top 10 Digital Marketing Objectives
- 10 Best Digital Marketing Blogs
- Top 5 Industries Using Digital Marketing
- Growth of Digital Marketing in India
- Top Career Options in Marketing
- Interview Preparation and Skills
- 73 Google Analytics Interview Q&A
- 56 Social Media Marketing Q&A
- 78 Google AdWords Interview Q&A
- Top 133 SEO Interview Q&A
- 27+ Digital Marketing Q&A
- Digital Marketing Free Course
- Top 9 Skills for PPC Analysts
- Movies with Successful Social Media Campaigns
- Marketing Communication Steps
- Top 10 Reasons to Be an Affiliate Marketer
- Career Options and Paths
- Top 25 Highest Paying Jobs India
- Top 25 Highest Paying Jobs World
- Top 10 Highest Paid Commerce Job
- Career Options After 12th Arts
- Top 7 Commerce Courses Without Maths
- Top 7 Career Options After PCB
- Best Career Options for Commerce
- Career Options After 12th CS
- Top 10 Career Options After 10th
- 8 Best Career Options After BA
- Projects and Academic Pursuits
- 17 Exciting Final Year Projects
- Top 12 Commerce Project Topics
- Top 13 BCA Project Ideas
- Career Options After 12th Science
- Top 15 CS Jobs in India
- 12 Best Career Options After M.Com
- 9 Best Career Options After B.Sc
- 7 Best Career Options After BCA
- 22 Best Career Options After MCA
- 16 Top Career Options After CE
- Courses and Certifications
- 10 Best Job-Oriented Courses
- Best Online Computer Courses
- Top 15 Trending Online Courses
- Top 19 High Salary Certificate Courses
- 21 Best Programming Courses for Jobs
- What is SGPA? Convert to CGPA
- GPA to Percentage Calculator
- Highest Salary Engineering Stream
- 15 Top Career Options After Engineering
- 6 Top Career Options After BBA
- Job Market and Interview Preparation
- Why Should You Be Hired: 5 Answers
- Top 10 Future Career Options
- Top 15 Highest Paid IT Jobs India
- 5 Common Guesstimate Interview Q&A
- Average CEO Salary: Top Paid CEOs
- Career Options in Political Science
- Top 15 Highest Paying Non-IT Jobs
- Cover Letter Examples for Jobs
- Top 5 Highest Paying Freelance Jobs
- Top 10 Highest Paying Companies India
- Career Options and Paths After MBA
- 20 Best Careers After B.Com
- Career Options After MBA Marketing
- Top 14 Careers After MBA In HR
- Top 10 Highest Paying HR Jobs India
- How to Become an Investment Banker
- Career Options After MBA - High Paying
- Scope of MBA in Operations Management
- Best MBA for Working Professionals India
- MBA After BA - Is It Right For You?
- Best Online MBA Courses India
- MBA Project Ideas and Topics
- 11 Exciting MBA HR Project Ideas
- Top 15 MBA Project Ideas
- 18 Exciting MBA Marketing Projects
- MBA Project Ideas: Consumer Behavior
- What is Brand Management?
- What is Holistic Marketing?
- What is Green Marketing?
- Intro to Organizational Behavior Model
- Tech Skills Every MBA Should Learn
- Most Demanding Short Term Courses MBA
- MBA Salary, Resume, and Skills
- MBA Salary in India
- HR Salary in India
- Investment Banker Salary India
- MBA Resume Samples
- Sample SOP for MBA
- Sample SOP for Internship
- 7 Ways MBA Helps Your Career
- Must-have Skills in Sales Career
- 8 Skills MBA Helps You Improve
- Top 20+ SAP FICO Interview Q&A
- MBA Specializations and Comparative Guides
- Why MBA After B.Tech? 5 Reasons
- How to Answer 'Why MBA After Engineering?'
- Why MBA in Finance
- MBA After BSc: 10 Reasons
- Which MBA Specialization to choose?
- Top 10 MBA Specializations
- MBA vs Masters: Which to Choose?
- Benefits of MBA After CA
- 5 Steps to Management Consultant
- 37 Must-Read HR Interview Q&A
- Fundamentals and Theories of Management
- What is Management? Objectives & Functions
- Nature and Scope of Management
- Decision Making in Management
- Management Process: Definition & Functions
- Importance of Management
- What are Motivation Theories?
- Tools of Financial Statement Analysis
- Negotiation Skills: Definition & Benefits
- Career Development in HRM
- Top 20 Must-Have HRM Policies
- Project and Supply Chain Management
- Top 20 Project Management Case Studies
- 10 Innovative Supply Chain Projects
- Latest Management Project Topics
- 10 Project Management Project Ideas
- 6 Types of Supply Chain Models
- Top 10 Advantages of SCM
- Top 10 Supply Chain Books
- What is Project Description?
- Top 10 Project Management Companies
- Best Project Management Courses Online
- Salaries and Career Paths in Management
- Project Manager Salary in India
- Average Product Manager Salary India
- Supply Chain Management Salary India
- Salary After BBA in India
- PGDM Salary in India
- Top 7 Career Options in Management
- CSPO Certification Cost
- Why Choose Product Management?
- Product Management in Pharma
- Product Design in Operations Management
- Industry-Specific Management and Case Studies
- Amazon Business Case Study
- Service Delivery Manager Job
- Product Management Examples
- Product Management in Automobiles
- Product Management in Banking
- Sample SOP for Business Management
- Video Game Design Components
- Top 5 Business Courses India
- Free Management Online Course
- SCM Interview Q&A
- Fundamentals and Types of Law
- Acceptance in Contract Law
- Offer in Contract Law
- 9 Types of Evidence
- Types of Law in India
- Introduction to Contract Law
- Negotiable Instrument Act
- Corporate Tax Basics
- Intellectual Property Law
- Workmen Compensation Explained
- Lawyer vs Advocate Difference
- Law Education and Courses
- LLM Subjects & Syllabus
- Corporate Law Subjects
- LLM Course Duration
- Top 10 Online LLM Courses
- Online LLM Degree
- Step-by-Step Guide to Studying Law
- Top 5 Law Books to Read
- Why Legal Studies?
- Pursuing a Career in Law
- How to Become Lawyer in India
- Career Options and Salaries in Law
- Career Options in Law India
- Corporate Lawyer Salary India
- How To Become a Corporate Lawyer
- Career in Law: Starting, Salary
- Career Opportunities: Corporate Law
- Business Lawyer: Role & Salary Info
- Average Lawyer Salary India
- Top Career Options for Lawyers
- Types of Lawyers in India
- Steps to Become SC Lawyer in India
- Tutorials
- C Tutorials
- Recursion in C: Fibonacci Series
- Checking String Palindromes in C
- Prime Number Program in C
- Implementing Square Root in C
- Matrix Multiplication in C
- Understanding Double Data Type
- Factorial of a Number in C
- Structure of a C Program
- Building a Calculator Program in C
- Compiling C Programs on Linux
- Java Tutorials
- Handling String Input in Java
- Determining Even and Odd Numbers
- Prime Number Checker
- Sorting a String
- User-Defined Exceptions
- Understanding the Thread Life Cycle
- Swapping Two Numbers
- Using Final Classes
- Area of a Triangle
- Skills
- Software Engineering
- JavaScript
- Data Structure
- React.js
- Core Java
- Node.js
- Blockchain
- SQL
- Full stack development
- Devops
- NFT
- BigData
- Cyber Security
- Cloud Computing
- Database Design with MySQL
- Cryptocurrency
- Python
- Digital Marketings
- Advertising
- Influencer Marketing
- Search Engine Optimization
- Performance Marketing
- Search Engine Marketing
- Email Marketing
- Content Marketing
- Social Media Marketing
- Display Advertising
- Marketing Analytics
- Web Analytics
- Affiliate Marketing
- MBA
- MBA in Finance
- MBA in HR
- MBA in Marketing
- MBA in Business Analytics
- MBA in Operations Management
- MBA in International Business
- MBA in Information Technology
- MBA in Healthcare Management
- MBA In General Management
- MBA in Agriculture
- MBA in Supply Chain Management
- MBA in Entrepreneurship
- MBA in Project Management
- Management Program
- Consumer Behaviour
- Supply Chain Management
- Financial Analytics
- Introduction to Fintech
- Introduction to HR Analytics
- Fundamentals of Communication
- Art of Effective Communication
- Introduction to Research Methodology
- Mastering Sales Technique
- Business Communication
- Fundamentals of Journalism
- Economics Masterclass
- Free Courses
6 Types of Filters in Tableau: How You Should Use Them
Updated on 10 July, 2024
69.37K+ views
• 13 min read
Table of Contents
Tableau is one of the most popular tools in data visualization and analysis that facilitates brands across all domains to leverage the reckoning potential of acquiring Business Intelligence. For its seamless capability to yield readable insights and simplified dashboards, tableau has been instrumental for even non-technical subscribers to have access to personalized datasheets.
Tableau Filters benefit organizations because they help them present insightful data to clients and business stakeholders. This data is presented in the form of a worksheet or a dashboard. This facilitates better decision-making in business. The tableau filters can filter out sensitive data and share it only with those with access authority.
upGrad’s Exclusive Data Science Webinar for you –
There are different types of filters in a tableau that can be used to organize data based on predefined conditions and use them for data visualization. Such ability to filter large data sets in the Business Intelligence tool helps prepare for analysis, including removing irrelevant data records, reducing data sizes for faster processing, and more. The filters are required to highlight any underlying insights that can be derived from the data upon visualizing in a readable, actionable format. Check out our data science courses to learn more about data visualization.
You can go through the different types of Tableau filters discussed below if you are confused about which of the following are appropriate uses for filters in tableau? select all that apply.
What are Filters in Tableau?
Filters in Tableau are essential tools that allow users to refine and control the data displayed in visualizations. Serving as a means of data subset selection, different types of filters in Tableau are primarily used to focus on specific portions of the dataset based on predefined conditions or criteria. These conditions can be applied at different levels, such as the data source, worksheet, or dashboard.
Filter types in Tableau play a pivotal role in enhancing the analytical process by enabling users to isolate relevant information, exclude unnecessary data, and tailor visualizations to specific subsets. They contribute to creating more meaningful and targeted insights by facilitating dynamic adjustments to the displayed data.
Whether it’s restricting the timeframe through relative date filters, emphasizing top performers, or refining data based on custom conditions, Tableau filters empower users to interactively explore and analyze datasets, fostering a more efficient and tailored data visualization experience.
Why Do We Perform Filtering in Tableau?
Filtering in Tableau serves several important purposes, contributing to the overall effectiveness of data analysis and visualization. Here are some key reasons why filtering is performed in Tableau-
- Focus on relevant data: Filtering allows users to concentrate on specific subsets of data that are relevant to their analysis. By excluding unnecessary information, users can focus on the key aspects of their dataset, making it easier to identify patterns and insights.
- Enhance data exploration: Filters provide an interactive way to explore and interact with data. Users can dynamically adjust the displayed information, drill down into details, and gain a deeper understanding of the dataset by isolating specific dimensions or measures.
- Improve performance: Applying filters can significantly enhance performance by reducing the amount of data loaded and processed. This is particularly important when dealing with large datasets, as filters help optimize the speed of visualizations and dashboards.
- Tailor visualizations: Filters allow users to customize visualizations based on specific criteria. Whether it’s highlighting top-performing items, focusing on a particular time period, or isolating specific categories, filters help create more targeted and meaningful visualizations.
- Support comparative analysis: Filtering enables users to conduct comparative analysis by selectively including or excluding data points. This is useful for scenarios such as comparing performance across regions, products, or time periods, providing valuable insights into trends and variations.
- Simplify dashboards: Filters play a key role in simplifying dashboards by offering users the ability to control what they see. This helps in creating clean and concise visualizations that communicate insights effectively without overwhelming the audience with unnecessary details.
- Dynamic time analysis: Filters like relative date filters in Tableau allow for dynamic time analysis. Users can easily switch between different time periods, compare trends, and analyze changes over time without manually adjusting date ranges.
- Interactivity for users: Interactive dashboards with filters empower end-users to explore data on their terms. Quick filters and other interactive elements provide a user-friendly experience, allowing individuals to tailor the visualization to their specific needs and questions.
How Many Types of Filters Are There in Tableau?
There are six types of filters in Tableau that users can apply to their data to refine and customize their visualizations. These filter types in Tableau collectively provide users with a powerful set of tools to refine and analyze data in Tableau, offering flexibility and interactivity in exploring insights from their datasets. They are:
- Extract Filters
- Context Filters
- Data Source Filters
- Measure Filters
- Dimension Filters
- Table Filters
Before going into detail about each filter, here’s a brief overview of what each of the 6 filters listed above offer to users.
Extract Filters enable users to limit the data extracted from the original source, optimizing performance. Context Filters in Tableau help prioritize and limit data by creating subsets that subsequent filters will consider. Tableau’s Data Source Filters operate at the source level, affecting the entire workbook by restricting the data available for analysis.
Measure Filter in Tableau, on the other hand, enable the filtration of data based on specific measures, offering flexibility in analyzing numerical aspects. The Dimension Filter in Tableau allow users to filter data based on specific dimensions, refining the focus of visualizations.
Lastly, Table Filters provide an interactive way to filter data directly within a table, allowing for a more detailed examination of specific elements.
Different Types of Filters in Tableau
Filters are a smart way to collate and segregate data based on its dimensions and sets to reduce the overall data frequency for faster processing. There are six different types of filters in tableau desktop based on their various objectives and are mentioned below as per their execution steps.
1. Extract Filters
As understood by its name, the extract filters are used to extract data from the various sources, by saving a screengrab of the way it gets added on your file. Such methods can help in lowering the tableau queries to the data source. As soon as you are done extracting data into your dashboard, you can create the extract and execute Hide All Unused Files to clear the columns unused in the datasheet of your panel.
Also read: Free data structures and algorithm course!
The extract filter in Tableau extracts a tiny subset of data from the primary data source. Subsequently, Tableau creates the data set’s local copy, which will be saved in the repository.
This filter allows you to save a screenshot of how it appears in your workbook. The corresponding methods decrease Tableau queries. The data size can be decreased even more by implementing the dimension filter or measuring the extract as needed.
The steps to create an Extract filter in Tableau:
- Connect Tableau with the text file.
- Click on the “Extract button”. It will create a local copy in the Tableau source.
iii. Select the “Edit” option from the drop-down menu close to the Extract button in the upper right corner. It will open the Extract Data window. Now select the “Add” option in the Window.
- In this step, you need to select a Tableau filter condition from the “Add Filter” window. You can add any of the displayed fields as an Extract filter. Now select the category from the list and then click “OK”.
- A filtered window will be shown. It depicts data that was extracted through the Extract Tableau filter. You can customize the list or use all values within the list.
The extract filter is one of the versatile tableau filters. This is because it presents various options in addition to the general category to extract data. For example, the Wildcard option helps you to filter fields through a Wildcard match. It allows users to type the character, and the field will be filtered as per the match. The various types of matches are:
- Contains: Select the members if the member name comprises typed characters.
- Starts with: Select the members if the member name begins with typed characters.
iii. Ends with: Select the members if the member name terminates with typed characters.
Exactly matches: Select the members if the member name precisely matches with typed characters.
These matches help you to customize your data and finally provide you with filtered data.
You can filter data using various Byfield conditions after implementing the below steps:
- Select the “Condition” tab in the Filter window.
- Click on the “Byfield” button.
iii. Select the name of the field you want to filter.
- Now select the aggregation type like average, sum, and median from the drop-down list.
- Select an operator from the drop-down list.
- Enter the value to filter the selected field.
vii. Click “OK”.
2. Data Source Filter
Used mainly to restrict sensitive data from the data viewers, the data source filters are similar to the extract filters in minimizing the data feeds for faster processing.
The data source filter in tableau helps in the direct application of the filter environment to the source data and quickly uploads data that qualifies the scenario into the tableau workbook. To execute such processes, you need to go to the Data Source tab and select the Add option in the upper right corner.
Our learners also read: Python online course free!
Clicking on the Add option in the menu would open into a dialog box, where you can select the field and choose through the values you want to record. Once you press confirmation, you shall be presented with a summary of the presets selected from the data source filters.
3. Context Filter
A context filter is a discrete filter on its own, creating datasets based on the original datasheet and the presets chosen for compiling the data. Since all the types of filters in tableau get applied to all rows in the datasheet, irrespective of any other filters, the context filter would ensure that it is first to get processed.
Despite being constrained to view all data rows, it can be implemented to choose sheets as and when required to optimize its performance by minimizing the data efficiently.
The context filter helps in applying a relevant, actionable context to the entire data analysis in tableau. If there are multiple filter preset categories used in the worksheet, dividing it into many parts can overall turn into a context filter in itself that guides all the other filters present in the datasheet.
It helps you to generate data sets by employing appropriate presets for compilation. It is always processed first, although other tableau filters are used. The multiple preset categories existing in the worksheet can be categorized into several parts that would work like a context filter. The data sets are generated according to the original datasheet. Moreover, the data can be efficiently minimized to allow viewing of all data rows, notwithstanding the constraints. You can choose the sheets when required.
You need to open the Context menu of a prevailing categorical filter and choose “Add to Context”. The context is calculated after the view is created. Subsequently, the context is used to count all other filters.
Go to the context menu of a prevailing categorical filter and select Add to Context to make a Context filter in tableau. Once the view is created, the context is calculated. The context is then used to calculate all other filters. You can use Context Filter to find the topmost 10 subcategories of items in the Furniture category.
Read: Banking Data Analysis Under the Scanner
4. Dimension filter
Now that you’ve chosen the data, you can access the values highlighted or remove them from the selected dimension, represented as strikethrough values. You can click All or None to select or deselect based on your operation in case of multiple dimensions.
Check out: Tableau Developer Resume Samples
5. Measure Filters
In this filter, you can apply the various operations like Sum, Avg, Median, Standard Deviation, and other aggregate functions. In the next stage, you would be presented with four choices: Range, At least, At most, and Special for your values. Every time you drag the data you want to filter, you can do that in a specific setting.
Must read: Learn excel online free!
Aggregated filters are always employed after non-aggregated filters, irrespective of the order present on the Filters pane. Measure filters are used to measure fields comprising quantitative data.
Measure filter in tableau shows four types of filters, as mentioned below.
- Range: Selects the range of values to incorporate into the result. This could include specifying a minimum and maximum value to include in the result set. For instance, if you’re analyzing sales data, you might use the Range filter to focus on a specific sales range, such as values between $100,000 and $500,000.
- At least: Selects a measure’s minimum value of. This filter is particularly useful when you want to emphasize a lower limit for a measure. For example, you might use “At Least” to analyze products with sales exceeding a certain threshold.
- At most: Selects a measure’s maximum value. This filter is beneficial when you want to emphasize an upper limit for a measure. For instance, you might use “At Most” to focus on customers with a purchase history below a certain total.
- Special: Selects null or non-null values. This can be useful when dealing with missing data or when you specifically want to analyze data points with null values. For instance, you might use the Special filter to isolate and investigate cases where certain measures are not available.
upGrad’s Exclusive Data Science Webinar for you –
Top Data Science Skills to Learn
6. Table Filters
The last filter to process is the table calculation that gets executed once the data view has been rendered. With this filter, you can quickly look into the data without any filtering of the hidden data.
Unlike other types of filters in Tableau applied during the data processing phase, the table calculation filter operates post-rendering, allowing users to interact with the data without directly affecting the underlying dataset.
When you apply a table calculation filter, you essentially perform calculations on the displayed results rather than the raw data itself. This allows for dynamic and interactive exploration of the data without applying permanent changes to the dataset. Users can quickly analyze trends, patterns, or outliers without altering the original data view or applying restrictions to hidden data.
This type of filter is particularly valuable for on-the-fly analysis and exploration. It provides a real-time, responsive way to manipulate visualizations without committing to permanent changes in the data. For example, you might apply a table calculation filter to dynamically compute moving averages, percent changes, or other aggregations based on the current display, offering a more interactive and exploratory data analysis experience.
Also Read: Tableau Developer Salary in India
In addition to these six major types of filters in Tableau, you may have to use other types of filters. Some of them are discussed below.
Quick filter:
Quick Filters provide a swift and accessible means to implement various filter types in Tableau. By simply right-clicking on a field, users can efficiently access different filtering options. It helps you to quickly access different filter types in Tableau through the right-click option. It owns features required to fulfill all typical filtering needs. You can implement Quick filters in Tableau on measures or dimensions.
This feature is particularly handy for users who want a fast and uncomplicated way to interactively explore and analyze their data.
Global filter:
It can be used over multiple worksheets by using the same source data in a workbook. Moreover, it can be used on all worksheets by utilizing the same data. This ensures consistency in data representation throughout various sheets. Changes made to the filter criteria in one place automatically propagate to affect all related sheets, promoting coherence and efficiency in data analysis.
Global Filters are valuable for maintaining uniformity and avoiding discrepancies in data visualization across different parts of a workbook.
Cascading filter:
It allows for the selections in the first filter to modify the options in the second filter. So, it restricts the values to those that are only significant to the first filter. Moreover, it avoids users from choosing irrelevant data. Hence, it offers an improved user experience. In other words, the Cascading Filter helps users focus on specific subsets of data by tailoring available options based on their initial selections.
This functionality is particularly beneficial when dealing with large datasets or complex data structures, as it streamlines the process of narrowing down choices and avoids overwhelming users with irrelevant information.
User filter:
Its alternate name is row-level security. This filter in tableau restricts and administers the data that users can view or access depending on the authority specified. Essentially, the User Filter allows administrators to define rules and permissions that determine which data rows individual users or groups are permitted to access. By associating specific users or groups with particular data filters, Tableau ensures that users only see the subset of data they are authorized to view.
This functionality is particularly crucial when dealing with confidential or sensitive information, as it enables organizations to implement strict access controls, aligning with privacy and compliance standards.
Conclusion
This is how different types of filters in tableau work in various processes.
Hence, the various types of filters in Tableau are like handy tools that play a crucial role in different parts of working with data. They help us dig out important insights, make our visualizations clearer, and focus on specific details. Whether we’re using context filters for a closer look or top N filters to narrow down our focus, Tableau gives us the freedom to adapt our approach.
By working with these different types of filters in Tableau, your data exploration can be smooth and interactive experience, making your analyses more efficient and impactful. These filters become our reliable companions as we navigate through the world of data, making our Tableau journey more insightful and enjoyable.
Read our popular Data Science Articles
If you are curious to learn about tableau, data science, check out IIIT-B & upGrad’s Executive PG Programme in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
Frequently Asked Questions (FAQs)
1. What are some of the basic filters in Tableau?
Filtering is the process of removing certain information from the available data by putting different filters. Tableau is the most widely used data visualization tool with plenty of features to simplify the process. Tableau provides both basic filters to deal with simple scenarios and advanced context-based filters for performing complex calculations. The three types of basic filters available in Tableau are: Filter Dates – This filter is applied on the date fields to remove specific date entries that are not required. Filter Measures – This filter is applied to the measure fields to remove specific measures based on the requirements. Filter Dimensions – This filter is applied on the dimension fields for removing certain measures that are not required for the calculation.
2. What is the difference between a normal filter and a quick filter in Tableau?
In Tableau, filters are useful for restricting the data from the database. There are different types of filters available in Tableau for performing different functions. Every filter has its own purpose and use, which makes it a worthy one in the list of available basic and advanced filters in tableau. A normal filter is useful for restricting the database's data based on the selected measure or dimension. This traditional filter can be created by simply dragging a field onto the shelf of filters. Quick filter helps us view all the filtering options and filter every worksheet on the dashboard by changing the values dynamically. This could be done even during the run time within the range that has been defined.
3. What are the different types of filters based on the purpose available in Tableau?
Tableau filters can be utilized for restricting the number of records that are present in a worksheet. Different types of filters are applied to a dataset based on the purpose and requirements. The filters are executed in a particular order for performing all the actions. The following is the list of the filters that are sorted in the order of their execution in Tableau: Extract filters Data Source filters Context filters Dimension filters Measure filters Every filter has its own purpose and is used for organizing and simplifying the available dataset in different ways through its application.
Did you find this article helpful?
Get Free Consultation
By clicking "Submit" you Agree toupGrad's Terms & Conditions
FREE COURSES
Start Learning For Free