What is Tableau ? Tableau Tutorial | Tableau Tutorial For Beginners

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What is Tableau ? Tableau Tutorial | Tableau Tutorial For Beginners

Tableau Tutorial provides basic and advanced concepts of Tableau. Our Tableau Tutorial is designed for beginners and professionals both.

Tableau is a data visualization tool or business intelligence tool which analyzes and shows data in a chart or report fastly. It is very easy to use, because it does not require any programming skill.

Our Tableau Tutorial includes all topics of Tableau such as What is Tableau, introduction, history, applications, advantages and disadvantages, tools, working, architecture, versions, desktop workspace, navigation, data sorting, sort data, replacing data source, data connection with database, alternatives, visualizations, filter data in tableau etc.

Contents

Prerequisite

To learn Tableau, you must have the basic knowledge of HTML and CSS.

Audience

Our Tableau Tutorial is designed to help beginners and professionals.

Problem

We assure that you will not find any problem in this Tableau tutorial. But if there is any mistake, please post the problem in contact form.

What is Tableau?

What is Tableau

Tableau is the fastly growing and powerful data visualization tool. Tableau is a business intelligence tool which helps us to analyze the raw data in the form of the visual manner; it may be a graph, report, etc.

Example: – If you have any data like Big Data, Hadoop, SQL, or any cloud data and if you want to analyze that given data in the form of pictorial representation of data, you can use Tableau.

Data analysis is very fast with Tableau, and the visualizations created are in the form of worksheets and dashboards. Any professional can understand the data created using Tableau.

Tableau software doesn’t require any technical or any programming skills to operate. Tableau is easy and fast for creating visual dashboards.

Why use Tableau?

Here are some reasons to use Tableau:

  • Ultimate skill for Data Science
  • User-Friendly
  • Apply to any Business
  • Fast and Easy
  • You don’t need to do any Coding
  • Community is Huge
  • Hold the power of data
  • It makes it easier to understand and explain the Data Reports

Features of Tableau

  • Data Blending: Data blending is the most important feature in Tableau. It is used when we combine related data from multiple data sources, which you want to analyze together in a single view, and represent in the form of a graph.

Example: Assume, we have Sales data in relational database and Sales Target data in an Excel sheet. Now, we have to compare actual sales with target sales, and blend the data based on common dimensions to get access. The two sources which are involved in data blending referred to as primary data and secondary data sources. A left join will be created between the primary data source and the secondary data source with all the data rows from primary and matching data rows from secondary data source to blend the data.

  • Real-time analysis: Real-Time Analysis makes users able to quickly understand and analyze dynamic data, when the Velocity is high, and real-time analysis of data is complicated. Tableau can help extract valuable information from fast moving data with interactive analytics.
  • The Collaboration of data: Data analysis is not isolating task. That’s why Tableau is built for collaboration. Team members can share data, make follow up queries, and forward easy-to-digest visualizations to others who could gain value from the data. Making sure everyone understands the data and can make informed decisions is critical to success.

What is Data Visualization?

Data visualization is a graphical representation of quantitative information and data by using visual elements like graphs, charts, and maps.

Data visualization convert large and small data sets into visuals, which is easy to understand and process for humans.

Data visualization tools provide accessible ways to understand outliers, patterns, and trends in the data.

In the world of Big Data, the data visualization tools and technologies are required to analyze vast amounts of information.

Data visualizations are common in your everyday life, but they always appear in the form of graphs and charts. The combination of multiple visualizations and bits of information are still referred to as Infographics.

Data visualizations are used to discover unknown facts and trends. You can see visualizations in the form of line charts to display change over time. Bar and column charts are useful for observing relationships and making comparisons. A pie chart is a great way to show parts-of-a-whole. And maps are the best way to share geographical data visually.

Today’s data visualization tools go beyond the charts and graphs used in the Microsoft Excel spreadsheet, which displays the data in more sophisticated ways such as dials and gauges, geographic maps, heat maps, pie chart, and fever chart.

What makes Data Visualization Effective?

Effective data visualization are created by communication, data science, and design collide. Data visualizations did right key insights into complicated data sets into meaningful and natural.

American statistician and Yale professor Edward Tufte believe useful data visualizations consist of ?complex ideas communicated with clarity, precision, and efficiency.

Data Visualization

To craft an effective data visualization, you need to start with clean data that is well-sourced and complete. After the data is ready to visualize, you need to pick the right chart.

After you have decided the chart type, you need to design and customize your visualization to your liking. Simplicity is essential – you don’t want to add any elements that distract from the data.

History of Data Visualization

The concept of using picture was launched in the 17th century to understand the data from the maps and graphs, and then in the early 1800s, it was reinvented to the pie chart.

Several decades later, one of the most advanced examples of statistical graphics occurred when Charles Minard mapped Napoleon’s invasion of Russia. The map represents the size of the army and the path of Napoleon’s retreat from Moscow – and that information tied to temperature and time scales for a more in-depth understanding of the event.

Computers made it possible to process a large amount of data at lightning-fast speeds. Nowadays, data visualization becomes a fast-evolving blend of art and science that certain to change the corporate landscape over the next few years.

Data Visualization

Importance of Data Visualization

Data visualization is important because of the processing of information in human brains. Using graphs and charts to visualize a large amount of the complex data sets is more comfortable in comparison to studying the spreadsheet and reports.

Data visualization is an easy and quick way to convey concepts universally. You can experiment with a different outline by making a slight adjustment.

Data visualization have some more specialties such as:

  • Data visualization can identify areas that need improvement or modifications.
  • Data visualization can clarify which factor influence customer behavior.
  • Data visualization helps you to understand which products to place where.
  • Data visualization can predict sales volumes.

Data visualization tools have been necessary for democratizing data, analytics, and making data-driven perception available to workers throughout an organization. They are easy to operate in comparison to earlier versions of BI software or traditional statistical analysis software. This guide to a rise in lines of business implementing data visualization tools on their own, without support from IT.

Why Use Data Visualization?

  1. To make easier in understand and remember.
  2. To discover unknown facts, outliers, and trends.
  3. To visualize relationships and patterns quickly.
  4. To ask a better question and make better decisions.
  5. To competitive analyze.
  6. To improve insights.

Top 10 Data Visualization Tools

There are tools which help you to visualize all your data in a few minutes. They are already there; only you need to do is to pick the right data visualization tool as per your requirements.

Data visualization allows you to interact with data. GoogleAppleFacebook, and Twitter all ask better a better question of their data and make a better business decision by using data visualization.

Here are the top 10 data visualization tools that help you to visualize the data:

1. Tableau

Tableau is a data visualization tool. You can create graphs, charts, maps, and many other graphics.

Top 10 Data Visualization Tools

A tableau desktop app is available for visual analytics. If you don’t want to install tableau software on your desktop, then a server solution allows you to visualize your reports online and on mobile.

A cloud-hosted service also is an option for those who want the server solution but don’t want to set up manually. The customers of Tableau include Barclays, Pandora, and Citrix.

2. Infogram

Infogram is also a data visualization tool. It has some simple steps to process that:

  1. First, you choose among many templates, personalize them with additional visualizations like maps, charts, videos, and images.
  2. Then you are ready to share your visualization.
  3. Infogram supports team accounts for journalists and media publishers, branded designs of classroom accounts for educational projects, companies, and enterprises.
Top 10 Data Visualization Tools

An infogram is a representation of information in a graphic format designed to make the data easily understandable in a view. Infogram is used to quickly communicate a message, to simplify the presentation of large amounts of the dataset, to see data patterns and relationships, and to monitor changes in variables over time.

Infogram abounds in almost any public environment such as traffic signs, subway maps, tag clouds, musical scores, and weather charts, among a huge number of possibilities.

3. Chartblocks

Chartblocks is an easy way to use online tool which required no coding and builds visualization from databases, spreadsheets, and live feeds.

Top 10 Data Visualization Tools

Your chart is created under the hood in html5 by using the powerful JavaScript library D3.js. Your visualizations is responsive and compatible with any screen size and device. Also, you will be able to embed your charts on any web page, and you can share it on Facebook and Twitter.

4. Datawrapper

Datawrapper is an aimed squarely at publisher and journalist. The Washington Post, VOX, The Guardian, BuzzFeed, The Wall Street Journal and Twitter adopts it.

Top 10 Data Visualization Tools

Datawrapper is easy visualization tool, and it requires zero codings. You can upload your data and easily create and publish a map or a chart. The custom layouts to integrate your visualizations perfectly on your site and access to local area maps are also available.

5. Plotly

Plotly will help you to create a slick and sharp chart in just a few minutes or in a very short time. It also starts from a simple spreadsheet.

Top 10 Data Visualization Tools

The guys use Plotly at Google and also by the US Air Force, Goji and The New York University.

Plotly is very user-friendly visualization tool which is quickly started within a few minutes. If you are a part of a team of developers that wants to have a crack, an API is available for JavaScript and Python languages.

6. RAW

RAW creates the missing link between spreadsheets and vector graphics on its home page.

Top 10 Data Visualization Tools

Your Data can come from Google Docs, Microsoft Excel, Apple Numbers, or a simple comma-separated list.

Here the kicker is that you can export your visualization easily and have a designer to make it look sharp. RAW is compatible with Inkscape, Adobe Illustrator, and Sketch. RAW is very easy to use and get quick results.

Top 10 Data Visualization Tools

7. Visual.ly

Visual.ly is a visual content service. It has a dedicated data visualization service and their impressive portfolio that includes work for Nike, VISA, Twitter, Ford, The Huffington post, and the national geographic.

Top 10 Data Visualization Tools

By a streamlined online process, you can find entire outsource your visualizations to a third-party where you describe your project and connected with a creative team that will stay with you for the entire duration of the project.

Visual.ly sends you an email notification for all the event you are hitting, and also it will give you constant feedback to your creative team. Visual.ly offer their distribution network for showcasing your project after it’s completed.

8. D3.js

D3.js is a best data visualization library for manipulating documents. D3.js runs on JavaScript, and it uses CSShtml, and SVG. D3.js is an open-source and applies a data-driven transformation to a webpage. It’s only applied when data is in JSON and XML file.

Top 10 Data Visualization Tools

D3.js emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a single framework and combining powerful visualization components.

D3.js is as powerful as it is a cutting-edge library, so it comes with no pre-built charts and only IE9+ supports this library.

9. Ember Charts

Ember charts are based on the ember.js and D3.js framework, and it uses the D3.js under the hood. It also applied when the data is in JSON and XML file.

Top 10 Data Visualization Tools

It includes a bar, time series, pie, and scatter charts which are easy to extend and modify. These chart components represent our thoughts on best practices in chart presentation and interactivity.

The team behind Ember Charts is also the same that created Ember.js. It puts a lot of focus on best practices and interactivity. Error handling is very graceful, and your app will not crash after finding irrelevant data or corrupt data.

10. NVD3

NVD3 is a project that attempts to build reusable charts and components. This project is to keeps all your charts neat and customizable.

Top 10 Data Visualization Tools

NDV3 is a simpler interface on the top of the D3.js and keeps all of its powerful features under the hood.

The front end engineers develop NDV3, and they use their insight into charting technology. This charting technology is used to provide powerful analytics to clients in the financial industry.

Advantages of Tableau

Advantage of tableau
  • Data Visualization:- Tableau is a data visualization tool, and provides complex computation, data blending, and dashboarding for creating beautiful data visualizations.
  • Quickly Create Interactive Visualization:- Users can create a very interactive visual by using drag n drop functionalities of Tableau.
  • Comfortable in Implementation:- Many types of visualization options are available in Tableau, which enhances the user experience. Tableau is very easy to learn in comparison to Python. Who don’t have any idea about coding, they also can quickly learn Tableau.
  • Tableau can Handle Large Amounts of Data:- Tableau can easily handle millions of rows of data. A large amount of data can create different types of visualization without disturbing the performance of the dashboards. As well as, there is an option in Tableau where the user can make ‘live‘ to connect different data sources like SQL, etc.
  • Use of other Scripting Language in Tableau:- To avoid the performance issues and to do complex table calculations in Tableau, users can include Python or R. Using Python Script, user can remove the load of the software by performing data cleansing tasks with packages. However, Python is not a native scripting language accepted by Tableau. So you can import some of the packages or visuals.
  • Mobile Support and Responsive Dashboard:- Tableau Dashboard has an excellent reporting feature that allows you to customize dashboard specifically for devices like a mobile or laptops. Tableau automatically understands which device is viewing the report by the user and make adjustments to ensure that accurate report is delivered to the right device.

Disadvantages of Tableau

Disadvantages of tableau
  • Scheduling of Reports:- Tableau does not provide the automatic schedule of reports. That’s why there is always some manual effort required when the user needs to update the data in the back end.
  • No Custom Visual Imports:- Other tools like Power BI, a developer can create custom visual that can be easily imported in Tableau, so any new visuals can recreate before imported, but Tableau is not a complete open tool.
  • Custom Formatting in Tableau:- Tableau conditional formatting, and limited 16 column table that is very inconvenient for users. Also, to implement the same format in multiple fields, there is no way for the user that they can do it for all fields directly. Users have to do that manually for each, so it is a very time-consuming.
  • Static and Single Value Parameter:- Tableau parameters are static, and it always select a single value as a parameter. Whenever the data gets changed, these parameters also have to be updated manually every time. There is no other option for users that can automate the updating of parameters.
  • Screen Resolution on Tableau Dashboards:- The layout of the dashboards is distributed if the Tableau developer screen resolution is different from users screen resolution.
    Example:- If the dashboard is created on the screen resolution of 1920 X 1080 and it viewed on 2560 X 1440, then the layout of the dashboard will be destroyed a little bit, their dashboard is not responsive. So, you will need to create a dashboard for desktop and mobile differently.

Tools of Tableau

A list of Tableau tools:

  • Tableau Desktop
  • Tableau Public
  • Tableau Online
  • Tableau Server
  • Tableau Reader
Tableau Tools

Data analytics in Tableau is classified into two parts:-

  1. Developer Tools:- The Tableau tools which are used for development such as the creation of charts, dashboards, report generation and visualization are known as developer’s tools. Tableau Desktop and the Tableau Public, are the example of this type.
  2. Sharing Tools:- The role of these tools are sharing the reports, visualizations, and dashboards that were created using the developer tools. The Tableau tools that fall into this category are Tableau Server, Tableau Online, and Tableau Reader.

Let’s see all the Tools one by one:

Tableau Desktop

Tableau Desktop has a rich feature set and allows us to code and customize reports. Right from creating the reports, charts to blending them all to form a dashboard, all the necessary work is created in Tableau Desktop.

For live data analysis, Tableau Desktop establish connectivity between the Data Warehouse and other various types of files. The dashboards and the workbooks created here can be either shared locally or publicly.

Based on the connectivity to the publishing option and data sources, Tableau Desktop is also classified into two parts-

  • Tableau Desktop Personal:- The personal version of the Tableau desktop keeps the workbook private, and the access is limited. The workbooks can’t be published online. So, it should be distributed either offline or in Tableau public.
  • Tableau Desktop Professional:- It is similar to Tableau desktop. The main difference is that the workbooks created in the Tableau desktop can be published online or in Tableau server. In the professional version, there is full access to all sorts datatypes. It is best for those who want to publish their workbook in Tableau server.

Tableau Public

This Tableau version is specially built for cost-effective users. The word ‘Public‘ means that the created workbooks cannot be saved locally. They should be kept on the Tableau’s public cloud, which can be accessed and viewed by anyone.

There is no privacy of the files saved on the cloud, so anyone can access and download the same data. This version is the best for them who want to share their data with the general public and for the individuals who want to learn Tableau.

Tableau Online

Its functionality is similar to the tableau server, but data is stored on the servers that hosted on the cloud, which is maintained by the Tableau group.

There is no storage limit on the data which is published in the Tableau Online. Tableau Online creates a direct link over 40 data sources who are hosted in the cloud such as the Hive, MySQL, Spark SQL, Amazon Aurora, and many more.

To be published, both Tableau Server and Tableau online require the workbooks that are created by Tableau Desktop. Data that flow from the web applications say Tableau Server and Tableau Online also support Google Analytics and Salesforce.com.

Tableau Server

The software is correctly used to share the workbooks, visualizations, which is created in the Tableau Desktop application over the organization. To share dashboards in the Tableau Server, you should first publish your workbook in the Tableau Desktop. Once the workbook has been uploaded to the server, it will be accessible only to the authorized users.

It’s not necessary that the authorized users have the Tableau Server installed on their machine. They only require the login credentials by which they can check reports by the web browser. The security is very high in Tableau server, and it is beneficial for quick and effective sharing of data.

The admin of the organization has full control over the server. The organization maintains the hardware and the software.

Tableau Reader

Tableau Reader is a free tool which allows us to view the visualizations and workbooks, which is created using Tableau Desktop or Tableau Public. The data can be filtered, but modifications and editing are restricted. There is no security in Tableau Reader as anyone can view workbook using Tableau Reader.

If you want to share the dashboards which are created by you, the receiver should have Tableau Reader to view the document.

Tableau Architecture

Tableau Server is designed to connect many data tiers. It can connect clients from Mobile, Web, and Desktop. Tableau Desktop is a powerful data visualization tool. It is very secure and highly available.

It can run on both the physical machines and virtual machines. It is a multi-processmulti-user, and multi-threaded system.

Providing such powerful features requires unique architecture.

The different layers used in Tableau server are given in the following architecture diagram:-

Tableau Architecture

Let’s study about the different component of the Tableau architecture:

1. Data server:- The primary component of Tableau Architecture is the Data sources which can connect to it.

Tableau can connect with multiple data sources. It can blend the data from various data sources. It can connect to an excel file, database, and a web application at the same time. It can also make the relationship between different types of data sources.

2. Data connector:- The Data Connectors provide an interface to connect external data sources with the Tableau Data Server.

Tableau has in-built SQL/ODBC connector. This ODBC Connector can be connected with any databases without using their native connector. Tableau desktop has an option to select both extract and live data. On the uses basis, one can be easily switched between live and extracted data.

  • Real-time data or live connection: Tableau can be connected with real data by linking to the external database directly. It uses the infrastructure existing database by sending dynamic multidimensional expressions (MDX) and SQL statements. This feature can be used as a linking between the live data and Tableau rather than importing the data. It makes optimized and a fast database system. Mostly in other enterprises, the size of the database is large, and it is updated periodically. In these cases, Tableau works as a front-end visualization tool by connecting with the live data.
  • Extracted or in-memory data: Tableau is an option to extract the data from external data sources. We make a local copy in the form of Tableau extract file. It can remove millions of records in the Tableau data engine with a single click. Tableau’s data engine uses storage such as ROM, RAM, and cache memory to process and store data. Using filters, Tableau can extract a few records from a large dataset. This improves performance, especially when we are working on massive datasets. Extracted data allows the users to visualize the data offline, without connecting to the data source.

3. Components of Tableau server: Different types of component of the Tableau server are:

  • Application server
  • VizQL server
  • Data server

A. Application server: The application server is used to provide the authorizations and authentications. It handles the permission and administration for mobile and web interfaces. It gives a guarantee of security by recording each session id on Tableau Server. The administrator is configuring the default timeout of the session in the server.

B. VizQL server: VizQL server is used to convert the queries from the data source into visualizations. Once the client request is forwarded to the VizQL process, it sends the query directly to the data source retrieves information in the form of images. This visualization or image is presented for the users. Tableau server creates a cache of visualization to reduce the load time. The cache can be shared between many users who have permission to view the visualization.

C. Data server: Data server is used to store and manage the data from external data sources. It is a central data management system. It provides data security, metadata management, data connection, driver requirements, and data storage. It stores the related details of data set like calculated fields, metadata, groups, sets, and parameters. The data source can extract the data as well as make live connections with external data sources.

4. Gateway: The gateway directed the requests from users to Tableau components. When the client sends a request, it is forwarded to the external load balancer for processing. The gateway works as a distributor of processes to different components. In case of absence of external load balancer, the gateway also works as a load balancer. For single server configuration, one gateway or primary server manages all the processes. For multiple server configurations, one physical system works as a primary server, and others are used as worker servers. Only one machine is used as a primary server in Tableau Server environment.

5. Clients: The visualizations and dashboards in Tableau server can be edited and viewed using different clients. Clients are a web browser, mobile applications, and Tableau Desktop.

  • Web Browser: Web browsers like Google Chrome, Safari, and Firefox support the Tableau server. The visualization and contents in the dashboard can be edited by using these web browser.
  • Mobile Application: The dashboard from the server can be interactively visualized using mobile application and browser. It is used to edit and view the contents in the workbook.
  • Tableau Desktop: Tableau desktop is a business analytics tool. It is used to view, create, and publish the dashboard in Tableau server. Users can access the various data source and build visualization in Tableau desktop.

Download and Installation of Tableau

Tableau is available in two ways:-

  • Tableau Public (Free)
  • Tableau Desktop (Commercial)

Here is a comparison between the Tableau Public and Tableau Desktop

Tableau Public

  • Tableau Public is a free and open-source.
  • Tableau public data source can connect to Excel and Text files.
  • Tableau public can be installed on Window and Mac operating system.
  • Data and Visualizations are not secured in the Tableau public because it is available in public.
  • In Tableau public, data cannot be obtained from different data sources as it is limited to connect only Excel and Text files.
  • Tableau public uses the details at Personal level.

Tableau Desktop

  • Tableau Desktop is a paid source, personal edition- $35 per month and professional edition- $70 per month.
  • Tableau desktop data source can connect to any data source file, including databases, web applications, and more.
  • Tableau desktop can also install on Window and Mac operating system.
  • Data and Visualization are secured in Tableau desktop.
  • In Tableau desktop, data can extract from various data sources and stored as Tableau extract file.
  • Tableau desktop uses the details at Professional and Enterprise level.

Lets install the Tableau Desktop on Window machine and go through step by step:-

Step1:- Go to https://www.tableau.com/products/desktop on your Web browser.

Step2:- Click on the ‘Try Now’ button.

Download and Installation

Step3:- Now, enter your Email id and click on the ‘Download Free Trial’ button.

Download and Installation1

Step4:- This will start downloading the .exe File for window machine by default.

Step5:- Open the download file, and click on the ‘Run’ button.

Download and Installation2

Step6:- Accept the terms and condition and click on ‘Install’ button.

Download and Installation3

Step7:- A pop message will be shown on the screen to get the approval of the administrator to install the Tableau software. Click on ‘yes’ to approve it than installation will be started.

Download and Installation4

Step8:- Once the installation is completed, then open the Tableau desktop software.

Step9:- In the registration window

  1. Click on Activate Tableau and fill your complete details.
  2. Click on start trial now.

Step10:- Wait for complete registration.

Download and Installation5

Step11:- Start screen of the Tableau Desktop.

Download and Installation6

Now, you are all set to use your Tableau desktop on your window machine.

Using the Workspace Control Effectively

If you are addicted to working with spreadsheets or other analysis tools, learning Tableau’s desktop environment will be helpful. If you have no familiarity with spreadsheets or database terminology, you can still be effectively using Tableau within a few days.

The Data Connection Page and Start Page

Open Tableau, and you see the start page of Tableau Desktop.

Using the Workspace Control Effectively

On the left side, the data window gives connection options. If you click on that to connect to the Data, you are taken to the data connection workspace. You can also access this page by clicking on the hard disk tab which is next to the Start button. If you want to connect to one of the data sources listed On a Server section, you must to go to Tableau?s website and download a connector for the required database. Here is no limit on the number of data connection drivers you can install, but some dealer requires that you validate a valid license to their software before downloading their connector.

On the right side of the Connect to the Data page, you will see saved data connections. Tableau provides four as sample data for learning. Any other links you have collected (.tds files) are displayed there as well. Return to the Home button and look at the Workbooks area in the start page. The Workbooks area saves the last nine workbooks you’ve opened. If you want to keep a workbook there that you frequently use, go over the workbook image and click on the push pin. That will prevent the workbook from being cycled out of view.

Using the Workspace Control Effectively1

To remove saved workbooks from the start page click on the red X that appears when you float over the workbook’s image. At the bottom of this start page, the Getting Started area provides links to training videos and promotional materials. The sample workbook area provides links to sample workbooks containing excellent example material. Clicking on More Samples takes you to Tableau’s visual gallery on the web with even more example workbooks.

Tableau Desktop Workspace

Click on the Tableau icon displayed in the left-hand side of the Tableau worksheet page and expose the contents of the worksheet tab selected at the bottom of the screen. When you connect with a new data source, this is the default workspace view.

Tableau Desktop Workspace

Go to the home page and select the global superstore sales-Excel sheet.

Tableau Desktop Workspace1

Open a connection to a saved data source, you also should have an open blank worksheet.

Tableau Desktop Workspace2

In many ways, you can open a workspace page; for example, go to the display Tableau’s icon on your desktop and you have a data source shown on your desktop. Dragging any data source icon and dropping it on the Tableau icon opens Tableau’s worksheet page for the selected data source. Also, you can open as many connections as you need in Tableau by going to the data connection page or start page and select a new connection.

Now, the worksheet is connected to the global Superstore Sales-Excel dataset.

Tableau Desktop Workspace Menu

The Tableau desktop workspace consists of various elements as given below:

Tableau Desktop Workspace3

Menu Bar: It consists of menu options like File, Data, Worksheet, Dashboard, Story, Analysis, Map, Format, Server, Window, and Help. The options in the menu bar, including features like data source connection, file saving, design, table calculation options, and file export features for creating a dashboard, worksheet, and storyboard.

  • File Menu: For any Windows program the file menu contains New, Open, Close, Save, Save As, and Print, functions. The most frequently used feature found in this menu is the Print to pdf option. This allows us to export our dashboard or worksheet in pdf form. If you don’t remember where Tableau places files, or you want to change the default file-save location, use the repository location option for review the file and change it. We can create a packaged workbook from the export packaged workbook option in a fast manner.
  • Data Menu: You can use a data menu if you find some interesting tabular data on a website that you want to analyze with Tableau. Highlight and copy the data from the site, then use the Paste Data option to input it into Tableau. Once pasted, then Tableau will copy the data from the Windows clipboard and add a data source in the data window. The Edit Relationships menu option is used in data blending. This menu option is needed if the field names are not identical in two different data sources. It allows you to define the related fields correctly.
  • Worksheet Menu: The Export option allows you to export the worksheet as an Excel crosstab, an image, or in Access database file format. The Duplicate as Crosstab option creates a crosstab version of the worksheet and places it in a new worksheet.
  • Dashboard Menu: The Action Menu is a useful feature that is reachable from both the Worksheet Menu and the Dashboard Menu.
  • Analysis Menu: In this menu, you can access the stack marks and aggregate measures options. These switches allow you to adjust default Tableau behaviors that are useful if you required to build non-standard chart types. The Create Edit Calculated Field and Calculated Field options are used to make measures and new dimensions that don’t exist in your data source.
  • Map Menu: The Map Menu bar is used to alter the base map color schemes. The other menu bar are related in the way of replacing Tableau’s standard maps with other map sources. You can also import the geocoding for the custom locations using the geocoding menu.
  • Format Menu: This menu is not used very commonly because pointing at anything, and right-clicking gets you to a context-specific formatting menu more quickly. You may need to alter the cell size in a worksheet rarely. If you don’t like the default workbook theme, use the Workbook Theme menu to select one of the other two options.

Toolbar Icon: Toolbar icon below the menu bar can be used to edit the workbook using different features like redo, undo, new data source, save, slideshow, and so on.

Dimension Shelf: The dimension presents in the data source for example- customer (customer name, segment), order (order date, order id, ship date, and ship mode), and location (country, state, and city) these all type of data source can be viewed in the dimension shelf.

Measure Shelf: The measures present in the data source, for example- Discount, Profit, Profit ratio, Quantity, and Sales- These all types of data source can be viewed in the measure shelf.

Sets and Parameters Shelf: The user-defined sets and parameters can view in the sets and parameters. It is also used to edit the existing sets and parameters.

Page Shelf: Page shelf is used to view the visualization in video format by keeping the related filter on the page shelf.

Filter Shelf: Filter Shelf is used to filter the graphical view by the help of the measures and dimensions.

Masks Cards: Marks card is used to design the visualization. The data components of the visualization like size, color, path, shape, label, and tooltip are used in the visualizations. It can be modified in the marks card.

Worksheet: The worksheet is the space where the actual visualization, design, and functionalities are viewed in the workbook.

Tableau Repository: Tableau repository is used to store all the files related to the Tableau desktop. It includes various folders like Connectors, Bookmarks, Data sources, Logs, Extensions, Map sources, Shapes, Services, Tab Online Sync Client, and Workbooks. My Tableau repository is located in the file path C:\Users\User\Documents\My Tableau Repository.

Tableau Navigation

Tableau Navigations of the workbook can be explained using the below diagram:

Tableau Navigation

Data Source: We can modify existing data source, and create or add the new data source using the ‘Data source’ tab, which is present at the bottom of the Tableau desktop window.

Current Sheet: Current Sheet is a sheet of workbook in which we are currently working. All the dashboards, worksheets, and storyboard present in the workbook, are available in this tab.

New Sheet: The new sheet icon presents in the tab is used to create a new worksheet in the Tableau workbook.

New Dashboard: The new dashboard icon presents in the tab is used to create a new dashboard in the Tableau workbook.

New Storyboard: The new storyboard icon presents in the tab is used to create a new storyboard in the Tableau Workbook.

Tableau Navigation

First Sheet: This first sheet icon presents in the tab at the bottom of the right-hand side of Tableau desktop window is used for visiting the first sheet directly.

Previous Sheet: The previous sheet icon is used to return back to the last worksheet from the new sheet.

Next Sheet: The next sheet icon is used to jump to the next worksheet of Tableau desktop.

Last Sheet: The last sheet icon is used to visit the final sheet of tableau workbook.

Show Sheet Sorter: You can view all the created worksheet in tableau desktop by clicking on the show sheet sorter icon.

Tableau Navigation

Show Filmstrip: All the tabs are shown here with their icons by clicking on the show filmstrip.

Tableau Navigation

Show Tabs: This tab concludes all tabs such as worksheets, data sources, dashboards, and storyboard.

Tableau Navigation

Tableau Data Terminology

Tableau is a powerful data visualization tool; that’s why Tableau has many unique terminologies and definitions. You should know their meaning before you start using these features in Tableau.

Tableau Data Terminology

The most commonly used Tableau terminologies are listed below:

  1. Alias: Alias is an alternative that you can assign to a dimension member, to a measurement part or a field.
  2. Bin: Bin is a user-defined group of measures in the data source.
  3. Bookmark: A .tbm document in the bookmarks folder in the Tableau repository that contains a single worksheet. It helps in improving data analysis. Unlike, web browser bookmarks, .tbm files are a compatible way to display various studies quickly.
  4. Calculated field: Calculated field is a new field that the user creates derived files by using a formula to modify the existing fields in your data source. It is used to make your work simple and easy.
  5. Crosstab: Crosstab is used for a text table view. It uses various text tables to display the numbers associated with dimension members.
  6. Dashboard: The dashboard is a combination of several views that are arranged on a single page. In Tableau, dashboards are used to observe and compare a variety of data together, and also it allows interacting with other worksheets.
  7. Data Pane: The data pane is on the left side of the workbook displays the fields of the data sources to which Tableau is connected. The fields are further divided into measures and dimensions. The data pane also reflects custom fields such as groups, binned fields, calculations, and many more. You can build views of your data by dragging fields from the data pane onto the various shelves, which is a part of every worksheet.
  8. Data Source Page: Data Source is a page where you can set up your data source. This data source page generally consists of four main areas ? join area, left pane, a preview area, and metadata area.
  9. Dimension: Dimension is commonly known as a field of categorical data. Dimensions hold discrete data such as members and hierarchies that cannot be aggregated. It also contains characteristic values such as dates, names, and geographical data. The dimensions used to reveal details of your information.
  10. Extract: An extract is a saved subset of a data source which is used to improve performance and study offline. The users can create an extract by defining limits and filters that contain the data which you want in the extract.
  11. Filters Shelf: Filter shelf is located on the left side of the workbook. Filters shelf is used to exclude the data from a view by filtering it using both dimensions and measures.
  12. Format Pane: The Format pane is on the left side of the workbook, and it contains various formatting settings. It controls the entire view of the worksheet, as well as the individual fields in the view.
  13. Level of Detail expression (LOD): The level of detail Expression is a syntax that supports the combination of various dimensions other than the view level. With the help of detail expressions, one can attach multiple dimensions with an aggregate expression.
  14. Marks: Marks is a part of the view that visually represents one or more rows in a data source. It can be a line, square, or bar. You can control and alter the size, type, and color of marks.
  15. Marks Card: Marks card is on the left side of the worksheet. The user can drag fields to the control mark properties such as color, type, shape, size, label, detail, and tooltip.
  16. Pages Shelf: Page shelf is on the left side of the view. With the help of the page shelf, you can split a view into a sequence of pages based on the values and members in a continuous or discrete field. Adding a field with the pages shelf is similar to adding a field in rows shelf. For each new row, a new page is created.
  17. Rows shelf: Row shelf is on the top of the workbook. It is used to create the rows of a data table. The Row shelf provides any numbers of measures and dimensions. When you placed a dimension on the Rows shelf, then Tableau creates headers for the members of that dimension. And when you place a measure on the Rows shelf, Tableau creates quantitative axes for that particular measure.
  18. Shelves: The shelves are named areas that are located on the top and left of the view. You can build views by placing fields onto the shelves. Some shelves are only available when you select a particular mark type. For example, The Shape shelf is only open when you choose the specific Shape mark type.
  19. Workbook: A workbook is a file with .twb extension that holds one or more worksheets as well as dashboards and stories.
  20. Worksheet: The worksheet is a collection of sheets. It’s a place where you build views of your data by dragging various fields onto the shelves.

The Data Window, Data Types in Tableau

Data Window in Tableau

Data window is a way to show the connection between Tableau and data source. You can connect to as multiple different data sources in a single workbook. The small icons associated with data connections provide additional details about the nature of the connection.

Here, a workbook that shows the three different data connection given below:

Data Window and Data Types in Tableau

The green line next to the global superstore data connection indicates that it is the active connection in the worksheet. So, the bar chart in the spreadsheet was created using ‘dimensions and measures‘ from that data source. Thus the bar chart is created using the dimensions and measures from the data source.

The Olympic Athletes data connection is a direct connection that is also indicated by the grey highlights. Those data source fields are currently displayed on the measures and dimensions shelves. The clipboard data source at the top of the data window was dragged and dropped into Tableau.

When you create data connections, Tableau will automatically evaluate the fields and place them on the measures and dimensions shelves.

Data Window and Data Types in Tableau

Usually, Tableau placed most of the fields correctly. If something is incorrectly placed, drag the field to the correct location. Errors sometimes occur when numbers are used to illustrate dimensions.

For example, if you want to connect a spreadsheet that contains Olympic Athletes details and you want to know how many gold medals were won by different countries in last years, that field is placed into the measures shelf. Dragging gold medal field from the measures shelf and dropped into the worksheet would result in the field being summed. Properly placed on the dimension shelf, the athletes country would behave like a dimension and be expressed in a column or row. In the same way, the gold medal and country are represented in the above Figure.

Data Types in Tableau

Tableau expresses fields and assigns data types automatically. If the data source appoints the data type, Tableau will use that data type. If the data source doesn’t individually assign a data type, Tableau will assign one. Tableau consist of the following data types:

  • Date values
  • Text values
  • Numerical values
  • Date and time values
  • Boolean values (True or False conditions )
  • Geographic values (longitude and latitude used for maps)
Data Window and Data Types in Tableau

In the above figure, focus on the icons next to the fields in the measures and dimension shelves. These icons denote specific data types. A calendar with a clock is a date or time field. Numeric values have pound signs, and “abc” icons indicate text fields. Boolean fields have “True or False” values.

Data Aggregation in Tableau

It is useful to look at numeric values using different aggregations function. Tableau supports many different aggregation types, such as:

  • Sum
  • Average
  • Count
  • Count Distinct
  • Median
  • Minimum
  • Maximum
  • Variance
  • Variance of Population
  • Standard Deviation
  • Standard Deviation of Population
  • Attribute
  • Dimension

In Tableau, you can create aggregation dimensions and measures. Whenever you add measures to your view, an aggregation is applied to those measures by default. The type of Aggregation used depends on the context of the view.

If you are not familiar with the database, then refer to Tableau manual for detailed definition of these aggregate types. You are adding fields into the visualization by default then it will be displayed.

Tableau allows you to change or alter the aggregation level for a specific view. To change the default aggregation, do right click on that field inside the data shelf and change its default by selecting the menu options (default properties or Aggregation).

You can also change the Aggregation of a field for specific use in a worksheet.

For example: By right-clicking on the SUM (Sales) pill and selecting the Measure (SUM) menu option, you can choose any of the aggregations highlighted.

Data Aggregation in Tableau

The data source used in the above figure is a data extract of an Excel spreadsheet. It is important to understand that if you depend on a direct connection to Excel, the median and count (distinct) aggregations would not be available. Access, Excel, and text files do not support these aggregate types. Tableau’s extract engine do this task.

Aggregating Measures

When you add a measure to the view, Tableau automatically aggregates its value. Average, sum and median are the common aggregation functions. The current Aggregation looks like part of the measure’s name in the view.

For example: Sales becomes SUM (Sales), and every measure has a default aggregation, which is set by Tableau when you connect to a data source. You can change or view the default aggregation for measures.

  • You can aggregate a measure using Tableau only for relational data sources.
  • Multidimensional data sources contain data sources which are already aggregated.
  • In Tableau, the multidimensional data source is supported only in windows.

Set the default Aggregation for Measures

You can set the default aggregations for any measures. It is not a calculated field that itself contains an aggregate, such as AVG ([Discount]). A default aggregation is the preferred calculation for summarizing a discrete or continuous field. The default aggregation is used when you drag a measure to a view automatically.

To change the default Aggregation

Right-click on a measure menu option in the Data field and select Default Properties then select Aggregation, and then select one of the aggregation options.

  • You cannot set default aggregation for the published data source. The default aggregation is set only when the data source is initially published.
Data Aggregation in Tableau

How to Disaggregate the Data

When you add a measure to your view, then Aggregation is applied to that measure automatically. This default is controlled by the Aggregate Measures setting in the Analysis menu.

If you want to see all of the marks in the view at the most detailed level of the model, you can disaggregate the view. Disaggregating your data means that the Tableau will display a separate mark for every data value in every row of your data source.

Disaggregation in all Measures in the view

Click on the analysis then go to aggregation measures option. When Aggregate Measures is selected, then automatically Tableau will attempt to aggregate measures in the view. Means that it collects individual row values from your data source into a single value that is adjusted to the level of detail in your view.

The different aggregations available for measures determine how the individual values are collected: they can be averaged (AVG), added (SUM), or set to the minimum (MIN) or maximum (MAX) value from the individual row values.

Data Aggregation in Tableau

If it is already selected, click aggregation measures once for deselecting it. Then, you can see the changes.

Data Aggregation in Tableau

Disaggregating data can be useful for analyzing measures which you want to use both dependently and independently in the view.

Note: If your data source is very large, then, as a result, disaggregating the data can degrade in significant performance.

Aggregating Dimensions

You can aggregates dimension in the view as MaximumMinimumCount, and Count Distinct. When you aggregate a dimension, you have to create a new temporary measure column, so the dimension takes on the characteristics of a measure.

Data Aggregation in Tableau

Note: The Count Distinct aggregation does not support the Text File and Microsoft Excel data sources using the inheritance connection. If you are connected to one of these types of data sources, then the Count Distinct aggregation is unavailable, and it shows the remark “Requires extract.” If you save the data sources as an extract, you will be able to use the Count Distinct aggregation.

Another way to view a dimension as an attribute. You can change it by choosing the Attribute from the context menu for the dimension.

The attribute aggregation has several uses:

  • It ensures a consistent level of detail when blending multiple data sources.
  • It provides a way to aggregate the dimension when computing table calculations, which require an aggregate expression.
  • It improves query performance due to locally computed.

Tableau calculates the Attribute using the below given formula:

  1. If MIN (dimension) = MAX (dimension) then MIN (dimension) else “*” end   
  • This given formula is calculated in Tableau after the data is retrieved from the initial query.
  • The asterisk (*) is a visual indicator of a special type of Null value it occurs when there are multiple values.
Data Aggregation in Tableau

Above is an example of using Attribute in a table calculation. This table shows the market, market size, state, and sales by the market that is SUM (sales). Suppose, you want to compute the percent of the total sales according to each state contribution for the market. When you add some Percent of Total in table calculation that calculates along State, the calculation computes within the black area shown above figure just because the Market Size of dimension is partitioning the data.

When you aggregate the Market Size as an Attribute, the calculation is computed within the Market (East), and the Market Size information is used as a label in the display.

Data Aggregation in Tableau

Tableau File Types

Tableau’s output after data analysis can be saved into different formats, which further can be distributed into different platforms.

There are various forms of different file categories, and the multiple different extensions identify them. Their extension depends on how it produces and for what purposes they are used in which format.

These all are generally stored as xml file format, and it can be easily open and edited.

You can save your work using several different Tableau specific file types such as bookmarks, workbooks, data extracts, packaged data files, and data connection files. Each of these files is described below in detail:

TypeFile ExtensionPurpose
Tableau workbook(.twb)Tableau workbook can hold one or more worksheets, and also hold zero or more stories and dashboards.
Tableau Bookmarks(.tbm)Tableau bookmarks can hold a single worksheet that can be easily shared, and pasted into other workbooks.
Tableau Packaged workbook(.twbx)Tableau packaged workbook is a single zip file which contains a workbook along with any supporting local file data and background images. This is the best way to package your work for sharing with others who don’t have access to the original data.
Tableau data Extract(.hyper or .tde)Tableau data extract is a local copy of the entire data set. It is used to share the data with others when you worked offline, and want to improve the performance.
Tableau data Source(.tds)Tableau data source file is a shortcut for quickly connecting to the original data that you use regularly. Data source file does not contain the actual data, and they only contain the necessary information to connect with the actual data. You can modify the top of the actual data such as creating calculated fields, changing default properties, adding groups, and so on.
Tableau Packaged Data Source(.tdsx)Tableau packaged data source is very similar to the tableau data source, but it has an addition of data along with the connection details.
Tableau Preferences(.tps)This file stores the color preferences, which is used among all the datasheets. It is also used to generate a customized look for the users.

These files are saved in the associated folders in the My Tableau Repository directory, which is created in your My Documents folder by default when you install Tableau. Also, Your work files can be saved in other locations, such as a network directory or your desktop.

How to Change the Tableau Repository Location

You can be specified a new location for the Tableau repository if you are not using the default location in your Document folders.

For example: If you want to have your data on a network server instead of your local machine, then you can see the remote repository.

  1. Select File then go to Repository Location.
  2. Select a new folder that will be the new repository location in the select a repository dialog box.
  3. Restart Tableau then it uses the new repository.

Changing the repository location does not include the original repository. Alternatively, Tableau creates a new repository where you can store your files.

Data Connection with Data Sources

Tableau can connect with all the accessible data sources which are broadly used. It can link to Excel files, PDF files, text files, etc. It can also connect to various databases using its ODBC connector. Tableau can connect to web connectors and servers.

Tableaus native connectors can connect to the following types of data sources:

  • File Systems: Such as Microsoft Excel, CSV, etc.
  • Cloud Systems: Such as Google bigQuery, Windows Azure, etc.
  • Relational System: Such as Microsoft SQL Server, Oracle, DB2, etc.
  • Other Sources: It uses ODBC.

The given below picture shows all of the data sources available through Tableau’s native data connectors.

Data Connection with Data Sources

Connect Live

The Connect Live feature is used in real-time data analysis. In connect live case, Tableau connects with the real-time data source, and it keeps read the data. Thus, the result of the data analysis is up to the second, and the latest changes are reflected in this result. However, on the drawback, it’s the source system as it has to keep send the data to Tableau.

In-Memory

Tableau can also process the data in-memory by caching them in memory, and it not being connected to the source anymore while analyzing the data. Of course, there will be a limit on the amount of data cached depending on the availability of the memory.

Combine Data Sources

Tableau can connect with different data sources at the same time.

For example: In a single workbook, you can connect to a relational source and a flat file by defined the multiple connections. This is also used in data blending, which is a unique feature in Tableau.

Data Connection with Text File

Tableau can connect to the text file data and set up the data sources. Tableau connects to following text files (*.csv, *.txt, *.tsv, *.tab).

How to Make the Connection and Set up the Data Sources

Step1: Open Tableau.

Step2: Below Connect, click on Text File.

Tableau Data Connection with Text File

Step3: Go to the next screen,

  • Select the file you want to connect such as SalesJan2009.CSV
  • Click on Open option.
Tableau Data Connection with Text File
  • On the left-hand side of the data source, you will see the CSV file.
Tableau Data Connection with Text File

Data Connection with Text File Example

Here is an example which shows the data connection with the text file.

Tableau Data Connection with Text File

And the worksheet looks like

Tableau Data Connection with Text File

Data Connection with Microsoft Excel

Step1: Click on the Microsoft Excel option given in the data tab.

Tableau Data Connection with Microsoft Excel

Step2: In the next screen,

  1. Select the Microsoft Excel file you want to connect such as sample-superstore.xls.
  2. Click on open option.
Tableau Data Connection with Microsoft Excel

Step3: It connects the Microsoft Excel file to Tableau. The sheets present in the Microsoft Excel file are shown on the left-hand side of the window.

Tableau Data Connection with Microsoft Excel

Step4: You can drag one or more sheets from the sheets data tab such as Orders.

Tableau Data Connection with Microsoft Excel

Then the data source looks like the below image:

Tableau Data Connection with Microsoft Excel

And the worksheet looks like:

Tableau Data Connection with Microsoft Excel

Tableau Extracting Data

In Tableau, Data extraction creates a subset of data from the data source. Data extraction is useful for increasing the performance by applying filters. It also helps in using some features of Tableau. Probably, which is not available in the data source like finding the distinct values in the data. However, the data extract feature is the most commonly used to create a local drive for offline access by Tableau.

Creating an Extract

Extraction of the data is done by following the menu:

Data → Extract Data.

It creates multiple options such as applying limits to how many rows to extract and whether to aggregate data for dimensions.

The below figure shows the Extract Data option to you.

Tableau Extracting Data

Applying Extract Filters

For extract a subset of data from the data source, you can create ,filters which only return the relevant rows.

For example: The Sample Superstore data set.

  • Click on an extract data,
Tableau Extracting Data
  • Click on the Add button.
Tableau Extracting Data
  • Add any filter or select a field among all options such as sub-category and click OK button.
Tableau Extracting Data
  • Choose from the list and tick mark the checkbox value for which you need to pull the data from the source and click on the OK button.
Tableau Extracting Data

Adding New Data to Extract

Add more data for an already created extract, and you have to choose the option Data → Extract → Append Data from File.

In this case, browse the file containing the data and click on the OK button to finish. Of course, the number and data type of columns in the file should be in sync with the existing data.

Tableau Extracting Data

Extract History

You can also verify the history of data extraction to know about how many times the data extraction has happened and at what times.

For this, you have to use the menu – Data → Extract History.

Tableau Extracting Data

And then it shows you all the data extraction history.

Tableau Extracting Data

Tableau Editing Metadata

After connecting with the data source, Tableau captures the metadata details of the source, such as the columns and their data types. This is used to create the measures, dimensions, and calculated fields used in views. You can browse the metadata and change their properties for some specific requirements.

Checking the Metadata

After connecting with a data source all possible tables and columns will be displayed in the data source.

Example: The source ‘Sample Coffee Chain’ for checking the metadata.

  • Click the Data menu and select to connect with a data source. Browse for the MS access file named as ‘Sample Coffee chain.’
  • Drag the table which is named Product, to the data canvas.
  • After choosing the file, you will get the below-given screen that shows the column names, and their data types. In Tableau, the string data types are shown as “Abc,” and Numeric data types are shown as “#.”
Tableau Editing Metadata

Changing the Data Type

You can change the data type for some of the fields (if required). Depending on the nature of the source data, sometimes Tableau may fail in recognizing the data type from the data source. In this structure, you can manually edit the data type. The below screenshot shows the options.

Tableau Editing Metadata

Renaming and Hiding

You can change the column names by using the renaming option. You can also hide a column, after that it will not appear in the data view. All these options are available after clicking on the data type icon in the metadata grid, you can see in the below screenshot.

Tableau Editing Metadata

Column Alias

Each column of the data source is assigned as aliases, which helps in better understanding the nature of the column.

Tableau Editing Metadata

Choose the aliases option from the above figure, and a screen comes up, which is used to Edit or Create the aliases.

Tableau Editing Metadata

Click on the OK button, and after that, you can see the changes in the column of the data sources.

Tableau Editing Metadata

Tableau Data Joining

Data joining is a common requirement in any data analysis. You may need to join data from different tables in a single source or join data from multiple sources.

Tableau provides the feature to join the tables by using the data pane that is available in the Data menu.

A join means combining columns from one or more tables in a relational database. It also creates a set that can be saved as a table, or it can be used as it is.

Joins are specifies into five types:

  1. Cross Join.
  2. Inner Join.
  3. Natural Join.
  4. Outer Join.
    1. Left Outer Join.
    2. Right Outer Join.
    3. Full Outer Join.
  5. Self-Join.

Overview of Types of Joins

A join section is used to combine rows from two or more tables, based on a related column between them.

1. Cross Join: Cross join produces rows which combine each row from the first table with each row from the second table.

Tableau Data Joining

2. Inner Join: An inner join returns the matching rows from the tables that are being joined.

Tableau Data Joining

3. Natural Join: Natural join is not used any comparison operator. It does not concatenate the way.

Only we can perform a Natural Join if there is at least one common attribute that exists between two relations. Also, the attributes must have the same name and domain.

Natural join works on those matching attributes where the values of attributes in both the relation are same.

4. Outer Join: An outer join is an extended form of the inner join.

It returns both matching and non-matching rows for the tables that are being joined.

Types of outer joins are as follows:

i) Left Outer Join: The left outer join returns matching rows from the tables being joined, and also non-matching rows from the left table in the result and places NULL values in the attributes that come from the right table.

Tableau Data Joining

ii. Right Outer Join: The right outer join operation returns matching rows from the tables being joined, and also non-matching rows from the right table in the result and places NULL values in the attributes that come from the left table.

Tableau Data Joining

iii. Full Outer Join: The full outer join is used to combine tables. As a result, it contains all values from both tables.

When a value from a table doesn’t have a match with the other table, then it returns a NULL value in the data grid.

Tableau Data Joining

5. Self-Join: The self-join is used to join a table with itself. It means that each row of the table is combined with itself as well as with every other row of the table.

Creating a Join in Tableau

Let’s assume a data source Sample-superstore to create a join between two tables such as Orders and Returns.

  • Go to the Data menu and choose Microsoft Excel option below connect.
  • Then select sample-superstore as a data source and click the Open button.
  • Drag Orders and Returns tables from sheets of the data source to the data pane. After that Tableau will automatically create a join between Orders and Returns tables which can be changed later as per required joins.
Tableau Data Joining
  • Below screenshot shows the building inner join between Orders and Returns tables by using the Order id field.
Tableau Data Joining

Edit a Join Type in Tableau

Tableau automatically creates a type of join between two tables, but it can be changed as per need.

  • Click on the middle of two circles that showing the auto-created join.
  • After clicking, a popup window appears which shows all the four types of the joins.
  • In below screenshot, you can see all the joins such as inner joinleft outer joinright outer join, and full outer join.
Tableau Data Joining

How to Edit Join Fields in Tableau

  • Also, you can change the fields by clicking the Data Sources option to add a new join clause that is available in the join popup window.
  • While selecting the field, you can search for the field using a search text box.
Tableau Data Joining

Data Blending in Tableau

Data Blending is a powerful feature of Tableau. It is used to analyze the data in a single view from a related data in multiple data source.

For example: Suppose a Sales data is present in a relational database and Sales Target data in an Excel sheet.

Now, for comparing the actual sales with the target sales, you have blended the data based on common dimensions to get access into the Sales Target measure.

The two data sources are involved in data blending are referred as the primary data source and the secondary data source.

A left join is built between the primary and the secondary data source with all the data rows from primary and only matching data rows from the secondary data source.

How to do data blending

Tableau has two inbuilt data sources that are Sample coffee chain.mdb and Sample-superstore, which can be used to illustrate data blending.

  • First, load the sample coffee chain into Tableau and visualize its metadata.
Data Blending in Tableau
  • Go to the data source below connect → click on MS Access database file and browse for the sample coffee chain file.

The below screenshot shows the different tables and joins available in the file:

Data Blending in Tableau

How to Add Secondary Data Source

Add the secondary data source which name is Sample-superstore.xls with the following steps:

  • Click on Add button of the data source.
  • Add a new connection to use cross-database joins to a file and choose the data source such as Microsoft Excel.
  • Now, both the data sources appear on the Data window, as shown in the below screenshot.
Data Blending in Tableau

Blending the Data

You can integrate the data from sample-superstore and sample coffee chain sources based on a common dimension.

  • A small chain image appears in the dimension field that is State. It indicates the common dimension between the sample coffee chain and sample-superstore data sources.
  • Drag the field State from the primary data source into the rows shelf and also drag the field Profit from the secondary data source into the Columns shelf.
  • Then, select the horizontal bar option from Show Me to get the graphical visualization.
  • The chart shows how the profit varies for each State in both the sample coffee chain and sample-superstore data sources. Shown in the below screenshot:
Data Blending in Tableau

Tableau Data Sorting

In the data source, data can be stored based on the user requirement. It can be sorted using data source order such as A to Z ascendingZ to A descendingA to Z ascending per table and Z to A descending per table.

Once the data is connected with Tableau, data sorting is done using the Sort Fields option. The Sort Fields option is present in the Data Source tab.

There are two ways to sort the data in Tableau:

  1. Manual sorting: Manual sorting is a sort that rearranges the order of dimension fields by dragging them next to each other in ad hoc fashion.
  2. Computed sorting: The computed sorting is a sort which is directly applied on the axis using the sort dialog button.
Tableau Data Sorting

When viewing a visualization, data can be stored using the single-click option from a header, an axis or field label.

Quick Sort from an Axis, Header and Field Label

There are many ways to sort a visualization with single click sort buttons:

  • In all cases, one-click means sorts the data in ascending order, and two-click means it sorts the data in descending order, and three-click means clear the sorts.
  • If the underlying data changes, the sort will update correctly.
Tableau Data Sorting

Sort from an Axis

  • Float over a numerical axis to get the sort icon.
  • Click that icon to sort.
Tableau Data Sorting

In the above example, the sort is applied on Color rows based on the values of Metric A. If there are hierarchical dimensions shown in above example, that type of sort is used on the inner dimension. Here, it means that Color rows will sort inside Hue. Dark magenta cannot be sorted at the top of the viz because it should stay inside the Purple Hue.

Sort from a Header

  • Float over a header to get the sort icon.
  • Click that icon to sort.
Tableau Data Sorting

In the above example, the sort is applied to a Material column such as Paint, Paper and Fabric based on the values of Green since the header is used for the sort.

Sort from a Field Label

1. Float over a field label to get the sort icon.

  • For a field label, the sort icon is slightly different from an axis or a header. Alphabetical sorting is the default option, but there is also a menu for choosing to sort by a field in the view.
Tableau Data Sorting

2. Click on the A-Z icon to sort alphabetically, or open the menu to see a list of fields which is possible to sort according to the field. Then, click on sort after the icon switches to the bar icon.

Tableau Data Sorting

In the above example, the sort is applied to the outermost dimension such as Hue is based on Metric B. (Metric B is aggregated for all the colors inside each Hue, and Hue is sorted as first is Purple, then Green, then Blue.)

Missing Sort Icons in Tableau

  • If the sort icons do not appear, then this functionality may be turned off, or it cannot be possible to sort the view.
  • For example, Scatter plots cannot be sorted by a numerical axis because the data entirely determine the position of the marks. No sort icon will appear on the axis in scatter plots.

Sort Options While Authoring in Tableau

In an authoring environment, there are some additional sorting options, such as:

Sort from the Toolbar

1. Select the dimension which you want to sort.

  • The default behavior has to sort the deepest dimension If you do not select a field before sorting,

2. Choose the appropriate sort button such as ascending or descending order in the toolbar.

Tableau Data Sorting

In the above example, the sort is applied on Hue unless the Material field is selected before sorting. In the case of Metric B, the toolbar sort applies to the leftmost measure.

And to sort by Metric A, it would be necessary to use another method of sorting or reverse their order on the Columns shelf. (To see the effect of sorting by Material, Hue is removed from the view. this makes it easy to see how the sort is computed.)

Sort by Drag and Drop

To sort manually, select a header in Viz or on a legend and drag it to the current location shown below:

Tableau Data Sorting

Tableau Replacing Data Source

Tableau can connect multiple data sources within a single workbook. The different data sources can be used to create various dashboards and sheets in Tableau. In some cases, the data source is needed to replace with the updated file.

Tableau has the data source replacing feature which can replace the data source.This feature does not affect the already built visualizations using the old data source. It is important to keep or replace all the used dimensions and measures while replacing the data source.

The data source connected in Tableau can be replaced with another data source. The procedure for replacing data source is shown in the below screenshot:

Tableau Replacing Data Source

Step1: Go to the connected data source or multi connection in Tableau.

Step2: Then,

  • Select the data source which you want to replace.
  • Right click on the data source.
  • Select the “Replace Data Source” option.
Tableau Replacing Data Source

Step3: It opens the “Data Source Replacement” window.

  • Fill the Current option.
  • Then fill the Data Source Replacement option.
  • Click on OK button to replace the data source.
Tableau Replacing Data Source

Tableau Calculation

There are four necessary components to the calculation in Tableau:

  1. Function: Function statements are used to transform the values or members in a field.
    For Example: The format of all functions in Tableau such as SUM (expression).
  2. Fields: Field is dimensions and measures from your data source.
    For Example: A field in a calculation is often surrounded by brackets [ ] such as [Sales].
  3. Operators: Operator is a symbol that denotes an operation between the operands.
    For Example: The types of operators you can use in Tableau calculations, as well as the order they are performed in a formula such as +, -, *, /, %, ==, =, >, <, >=, <=, ! =, <>, ^, AND, OR, NOT, ( )
  4. Literal Expression: Literals expression are represent the constant values “as is” such as “profitable” and “unprofitable”.
    For Example: See the below calculation
  1. IF [Profit per Day] > 5000THEN”Highly Profitable”  
  2. ELSEIF [Profit per Day] <= 0THEN”Unprofitable”  
  3. ELSE “Profitable”  
  4. END  

The component of the above calculation can be further divided into the following:

  1. Functions: IF, THEN, ELSEIF, ELSE, and END.
  2. Field: Profit per Day.
  3. Operators: > and <=.
  4. Literal Expression
    • String Literals: “Highly Profitable”, “Unprofitable”, and “Profitable”.
    • Numeric Literals: 5000, and 0.

Note: Not all calculation needs to contain all the four components.

Here is some important point for literal expression syntax:

  • Numeric literals are written as numbers.
    Example: 27 or 1.3567
  • String literals are written with quotation marks.
    Example: “profitable.”
  • Date literals are written with the # symbol.
    Example: # June 8, 2018 #
  • Boolean literals are written as either true or false.
    Example: “True” or “False”
  • Null literals are written as null.
    Example: “NULL”

Two more calculations contain by Tableau

  1. Parameters: Parameter is a placeholder variable that can be inserted into calculations to replace the constant values.
    A parameter in a calculation is surrounded by brackets [ ].
    For Example: [Profit Bin Size]
  2. Comments: Comment is defined as the notes about a calculation or its parts, but comments not included in the computation of the calculation.
    To enter a comment in a calculation, use two forward slashes //.
    For Example
  1. SUM ([Sales]) / SUM ([Profit]) // Nick’s calculation  
  2.  // to be used for profit ratio  
  3.  // Do not edit   

Tableau Operators

An operator is a symbol for performing specific mathematical and logical operations through the compiler.

Tableau has several numbers of operators which are used to create calculated fields and formulas.

Here are the types of operators with their order of precedence of operation:

Types of operators

  1. General operators
  2. Arithmetic operators
  3. Relational operators
  4. Logical operators

1. General Operators

Here are some general operators supported by Tableau. These operators act on the character, numeric, and date data type.

  • Addition (+): By the help of the addition operator, we can add the two numbers, concatenate two strings and also add days to dates.
    Example: 10+15=25
                     Sales+ profit
                     ‘XYZ’+ ‘PQR’= XYZPQR
                     # June 8, 2018 # + 7= # June 15, 2018 #
  • Subtraction (-): By the help of the subtraction operators, we can subtract two numbers and subtract days from dates.
    Example: – (10+15) = -25
                      # June 8, 2018 # – 7= # June 1, 2018 #

2. Arithmetic Operators

Here are some arithmetic operators supported by Tableau. All these operators act only on the numeric data type.

  • Multiplication (*): we can multiply two numbers by the help of multiplication operator.
    Example: 5 * 2 = 10
  • Division (/): we can divide two numbers by the help of the division operator.
    Example: 15 / 5 = 3
  • Modulo (%): modulo operator gives you the remainder of the numeric division.
    Example: 17 % 2 = 1
  • Power (^): raised to the power.
    Example: 2 ^ 2 = 4

3. Relational Operators

Here are the relational operators supported by Tableau. These operators are used in the expressions. Each relational operator compares two numbers, strings, or dates and returns a Boolean value (True or False).

However, Boolean operators themselves cannot be compared using these operators.

  • Equal to (= or = =): It compares two numbers, strings or two dates to be similar and returns the Boolean values, true if they are equal else returns False.
    Example: ‘hello’ = ‘hello’, returns True
                      ‘2’ = ’10/5′, returns True
                      ‘Hello’ = ‘hey’, returns False
  • Not equal to (! = or <>): It compares two numbers, two strings, or dates to be unequal. And returns the Boolean values, true if they are equal else returns False.
    Example: ‘cold’ <> ‘hot’
                      ’13’ != ’24/2′
  • Greater than (>): It compares two numbers, two strings or two dates where the first argument is greater than second, it Returns the Boolean value True else returns False.
    Example: [Profit] > 10000
                      [Category] > ‘Q’
                      [Ship date] > #April 1, 2018#
  • Less than (<): It compares two numbers, two strings or two dates, where the first argument is smaller than the second. It returns the Boolean value True, else returns false.
    Example: [Profit] < 10000
                      [Category] < ‘Q’
                      [Ship date] < #April 1, 2018#

4. Logical operators

Here are the logical operators supported by Tableau. These operators are used in an expression whose result is a Boolean value (True or False).

  • AND: If the Boolean values present on both sides of AND operator is evaluated to be TRUE, then the result is TRUE. Else the result is FALSE.
    Example: [Ship Date] > #April 1, 2018# AND [Profit] > 20000
  • OR: If anyone or both of the Boolean values present on both sides of the OR operator analyses to be TRUE, then the result is TRUE. Else the result is FALSE.
    Example: [Ship Date] > #April 1, 2018# OR [Profit] > 20000
  • NOT: This operator reverses the Boolean value of the expression.
    Example: NOT [Ship Date] > #April 1, 2018#

Precedence of Operator

The below table is describing the order of precedence of the operator. The top row of below table has the highest precedence. Some operators in the same row have the same precedence.

If two operators have the same precedence, they are analyzed from left to the right in the formula. Parentheses can also be used in the same order, and the inner parentheses are evaluated before the outer parentheses.

Order of PrecedenceOperators
1-(negate)
2^(power)
3*, /, %
4+, –
5==, >, <, >=, <=, !=
6NOT
7AND
8OR

Tableau Functions

Data analysis involves a lot of calculations. In Tableau, the calculation editor has applied calculations to the fields being analyzed. Tableau has multiple inbuilt functions which help in creating expressions for complex calculations.

There is a list of Tableau functions that are categorized into five parts:

  1. Number functions
  2. String functions
  3. Date functions
  4. Logical functions
  5. Aggregate functions

1. Number Functions

Number function is a function that uses for the numeric calculations. They take only numbers as inputs.

Let’s see some essential examples of number functions:

  • Ceiling (Number): It rounds a number to the nearest integer of equal or greater values.
    Example: CEILING (4.155) = 5
  • Power (Number, Power): It raises the number to the specified power.
    Example: POWER (2^3) = 8
  • Round (Number, Decimals): It rounds the number to a specified number of digits.
    Example: ROUND (5.14522) = 5.14

2. String Functions

String functions are used for the manipulation of the string.

Let’s see some essential examples of string functions:

  • LEN (String): LEN string returns the length of the string.
    Example: LEN (“Tableau”) = 7
  • LTrim (String): It returns a string that contains a copy of the specified string with no leading (LTrim) or trailing (RTrim) spaces.
    Example: LTrim (” Tableau “) = “Tableau”
  • REPLACE (String, Substring Replacement): It searches the string for substring and replaces it. If the substring is not found, that string is not changed.
    Example: REPLACE (“Green yellow Green”, “yellow”, “Red”) = “Green Red Green”
  • UPPER (String): It returns the string with all uppercase characters.
    Example: UPPER (“Tableau”) = “TABLEAU”

3. Date Functions

Tableau has many date functions, and all the date functions use the date_part, this is the string indicating part of the date such as day, month, or year.

Let’s see some essential examples of date functions:

  • DATEADD (date_part, increment, date): It’s added an increment to the date. The type of increment is specified in the date_part.
    Example: DATEADD (‘month’, 5, #2018-06-15#) = 2018-11-15 01:00:00 AM
  • DATENAME (date_part, date, start_of_week): It returns date_part of date as a string. And the start_of_week parameter is optional.
    Example: DATENAME (‘month’, #2018-03-15#) = “March”
  • DAY (date): It returns the day of the given date in integer form.
    Example: DAY (#2018-04-12#) = 12
  • NOW (): It returns the current time and date.
    Example: NOW ( ) = 2018-04-15 1:08:21 PM

4. Logical Functions

These functions evaluate some single values and produce a Boolean output.

See some essential examples of logical function:

  • IFNULL (expression1, expression2): If the result is not null, then IFNULL function returns the first expression, and if it is null, then it returns the second expression.
    Example: IFNULL ([Sales], 0) = [Sales]
  • ISDATE (string): If the string argument can be converted to a date, the ISDATE function returns TRUE, and if it cannot, it returns FALSE.
    Example: ISDATE (“12/06/99”) = “TRUE”
                      ISDATE (“14/06/99”) = “FALSE”
  • MIN (expression): The MIN function returns the minimum result for each record.

5. Aggregate Functions

Let’s see some essential examples of aggregate functions:

  • AVG (expression): It returns the average of all the values in the expression. AVG is used only with numeric fields. And the Null values are ignored.
  • COUNT (expression): It returns the number of items in a group. And the Null values are not counted.
  • MEDIAN (expression): It returns the median of an expression over all records. Median can only be used with numeric fields, and Null values are ignored.
  • STDEV (expression): It returns the statistical standard deviation of all values in the given expression based on a sample of the population.

Tableau Numeric Calculations

In Tableau, numeric calculations are done using a wide range of inbuilt functions available in the formula editor.

Let’s see how to apply calculations to the fields. The calculations are simple as subtracting the values of two fields or using an aggregate function to a single field.

Here are the steps to create a calculation field and use numeric functions in it.

How to Create a Calculated Field

  • After connecting to a data source such as Sample-Superstore.
  • Go to Analysis menu.
  • And click on Create Calculated Field as shown in the below image.
Tableau Numeric Calculations

Calculation Editor in Tableau

The above process opens a calculation editor which lists all the functions available in Tableau.

Tableau Numeric Calculations

You can change the dropdown value and only see the related functions to numbers.

Tableau Numeric Calculations

Create a Formula

o visualize the difference between Profit and Discount for different shipping mode of the products, create a formula that subtracts the Discount from the Profit, as shown in the below image, and the name of this field is profit_n_discount.

Tableau Numeric Calculations

Using the Calculated Field

The above-calculated field can be used in the view by dragging it to the Rows shelf as shown in the below screenshot.

It produces a bar chart that shows the difference between profit and discount for different shipping modes.

Tableau Numeric Calculations

Applying the Aggregate Calculations

You also can create a calculated field using an aggregate function.

  • First, create AVG (sales) values for different ship mode.
  • Then, Write the formula in the calculation editor as shown in the below screenshot.
Tableau Numeric Calculations
  • Click OK and dragging the Avg_Sales field to the Rows shelf, then you get the following view.
Tableau Numeric Calculations

Tableau String Calculations

Tableau has many inbuilt string functions used for string manipulation such as concatenating, comparing, and replacing few characters from a string, etc.

Here are some steps to create a calculation field and use string function in it:

How to Create Calculated Field

  • After connecting to a data source such as Sample superstore.
  • Then, go to the Analysis menu.
  • And click ‘Create Calculated Field‘ as shown in the below image.
Tableau String Calculations

Calculation Editor in Tableau

The above process opens a calculation editor that contain all the functions available in Tableau.

Tableau String Calculations

You can change the dropdown value and only see the related functions to strings.

Tableau String Calculations

Create a Formula

  • If you want to find out the Sales in the Cities, that contain the letter “A“, create the formula as shown in the below image.
Tableau String Calculations

How to Use the Calculated Field

To see the created field into a graphical representation, you can drag City field into the Rows shelf and drag the Sales field into the Columns shelf.

The below image shows the Sales values for Cities:

Tableau String Calculations

Tableau Date Calculations

Tableau can provide a large number of inbuilt functions such as dates. Dates are one of the critical fields which are extensively used in most of the data analysis.

You can manipulate the simple date such as adding or subtracting days from a date. Also, you can create complex expressions that include dates.

Here are the steps to create a calculation field and use date functions in it.

How to create a calculated field

  • After connecting to a data source such as sample superstore.
  • Then go to the Analysis menu.
  • And click on the ‘Create Calculated Field’ as shown in the below image.
Tableau Date Calculations

Calculation Editor in Tableau

The above process opens a calculation editor that lists all the functions available in Tableau.

Tableau Date Calculations

You can change the dropdown value and only see the related functions to date, shown in the below image:

Tableau Date Calculations

Create a Formula

If you want to find out the Sales volume along with the difference in the date of sales in months from 15/06/2015 to 15/02/2015, create the formula as shown in the below image.

Tableau Date Calculations

Using the Calculated Field

To see the created field in graphical representation, you can drag Month and date_diff field into the Rows shelf and drag the Sales field to the Columns shelf. Also, drag the ship Date with months.

The below screenshot shows the Sales volume along with the difference in the date of sales:

Tableau Date Calculations

Tableau Table Calculations

A table calculation is a transformation that applies to the values in a visualization. Table calculation is a special type of calculated field that computes on the local data in Tableau.

They are calculated based on current visualization and do not consider any dimensions or measures that are filtered out of the visualization.

These calculations are applied to the values of the entire table, not on the some selected rows or columns.

Table calculations are used for a variety of purposes, such as:

  • Transforming values to rankings.
  • Transforming values to show running totals.
  • Transforming values to show the percent of the total.

For any Tableau visualization, there is a virtual table which is determined by the dimensions in the view. This table is not the same as the tables in your data source. Mainly, the virtual table is determined by the dimensions within the “level of detail” means the dimensions on any of the following shelves in a Tableau worksheet:

Tableau Table Calculations

For example, for calculating an average, we need to apply a single method of calculations on an entire column. These calculations cannot be performed on some selected rows.

The table has a feature known as “Quick Table Calculations“, which is used to create such calculations.

Following are the steps applied in quick table calculations as:

Step1: Select the Measure on which the table calculation has to be used and drag it to the column shelf.

Step2: Right-click on the Measure and choose the option Quick Table Calculation.

Step3: Choose one option among the following options to be applied to the Measure.

  • Running Total
  • Difference
  • Percent Difference
  • Percent of Total
  • Rank
  • Percentile
  • Moving Average
  • Year to Date (YTD) Total
  • Compound Growth Rate
  • Year over Year Growth
  • Year to Date (YTD) Growth

1. Table (Across): It computes across the length of the table and restarts after every partition.

For example, in the below screenshot, the calculation is computed across columns such as “Year (Order Date)” for every row such as “Month (Order Date)”.

Tableau Table Calculations

2. Table (Down): It computes down the length of the table and restarts after every partition.

For example, in the below screenshot, the calculation is computed down rows such as “Month (Order Date)” for every column such as “Year (Order Date)”.

Tableau Table Calculations

3. Table (Across then Down): It computes across the length of the table, and then down the length of the table.

For example, in the below screenshot, the calculation is computed across columns such as “Year (Order Date)”, down a row such as “Month (Order Date)”, and then across columns again for the entire table.

Tableau Table Calculations

4. Table (Down then Across): It computes down the length of the table, and then across the length of the table.

For example, in the below screenshot, the calculation is computed down rows such as “Month (Order Date)”, across a column such as “Year (Order Date)”, and then down rows again.

Tableau Table Calculations

5. Pane (Down): It computes down an entire pane.

For example, in the below screenshot, the calculation is computed down rows such as “Month (Order Date)” for a single pane.

Tableau Table Calculations

6. Pane (Across then Down): It computes across an entire pane and then down the pane.

For example: In the below screenshot, the calculation is computed across columns such as “Year (Order Date)” for the length of the pane, down a row such as “Month (Order Date)”, and then across columns for the length of the pane again.

Tableau Table Calculations

7. Pane (Down then Across): It computes down an entire pane and then across the pane.

For example, in the below screenshot, the calculation is computed down rows such as “Month (Order Date)” for the length of the pane, across a column such as “Year (Order Date)”, and then down the length of the pane again.

Tableau Table Calculations

Tableau LOD Expressions

LOD (level of Details) expression is used to run complex queries involving many dimensions at the data sources instead of bringing all the data to the Tableau interface.

Types of LOD expression

There are three types of LOD expressions in the Tableau:

  • FIXED LOD: This LOD expression computes the values using the specified dimensions without reference to any other dimensions in the view.
  • INCLUDE LOD: This LOD expression computes the values using the specified dimensions with any other dimensions in the view.
  • EXCLUDE LOD: These LOD expressions subtract dimensions from the view level of detail.

FIXED Level of Detail Expressions

For example, if you want to calculate the number of Sales for each state in each region. Then,

First, create the formula field named regional_sales using the formula as shown in the below screenshot.

Tableau LOD Expressions
  • Then, drag the Region and State field to the Rows shelf and the calculated field (regional_sales) to the Text shelf under the Marks card.
  • Also, drag the Region field to the Color shelf.
  • This creates the below view, that shows a fixed value for different states because we fixed the dimension as a region for the calculation of Sales value.
Tableau LOD Expressions

INCLUDE Level of Detail Expressions

INCLUDE level of detail expressions compute values using the specified dimensions whatever dimensions are in the view.

For example, if you want to calculate the sum of sales per state for each sub-category of products. Then,

  • Drag the Sub-Category field to the Rows shelf.
  • And, write the expression ” {INCLUDE [State] : SUM(Sales)} ” in the Columns shelf.
  • It creates the view that includes both the dimensions in the calculations as shown in the below screenshot.
Tableau LOD Expressions

EXCLUDE Level of Detail Expressions

EXCLUDE level of detail expressions describe the dimensions to exclude from the view level of detail.

For example, Exclude Region from the Sales figure calculated for every month. First,

  • Create the formula ” {EXCLUDE [Region] : SUM([Sales])} ” as shown in the below screenshot.
Tableau LOD Expressions
  • On dragging the relevant fields to the respective shelves, you get the final view for the EXCLUDE level of detail expressions as shown in the below screenshot.
Tableau LOD Expressions

Tableau Basic Filters

Filtering is the process of removing specific values from a result set. Tableau filtering feature allows both simple scenarios using field values and advanced calculation or context-based filters.

In Tableau, there are three types of basic filters as follows:

  • Filters Dimensions: Filter dimensions are the filters applied to the dimension fields.
  • Filters Measures: Filter measures are the filters applied to the measure fields.
  • Filter Dates: Filter dates are the filters applied on the date fields.

Filters Dimensions

These filters are applied to the dimension fields only. Below examples include filtering based on categories of numeric values or text values with logical expressions less than, or greater than conditions. In dimension filters, you can use only values to filter.

For example, consider a data source such as Sample – Superstore, to apply dimension filters on the sub-category of products.

We have to create a view for showing profit for each sub-category of products according to their shipping mode.

  • Drag the dimension field Sub-Category to the Rows shelf
  • And, the measure field profit to the Columns shelf.
Tableau Basic Filters
  • Next, drag the Sub-Category dimension to the Filters shelf to open the Filter dialog box.
  • And, click on the None button at the bottom of the list to deselect all segments.
  • Then, select the Exclude option in the lower right corner of the dialog box.
  • Last, select Labels and Storage and click on OK button. The below screenshot shows the result with the excluded above two categories.
Tableau Basic Filters

Filters Measures

These filters are applied only on the measure fields. In measures filter, you can use calculations based on fields.

For example, consider a data source such as Sample – Superstore, to apply dimension filters on the average value of the profits.

  • Create a view with ship mode and sub-category as dimensions and Average of Profit as shown in the below screenshot:
Tableau Basic Filters
  • Then, drag the profit value to the filter pane. Choose Average as the filter mode and click the Next button.
Tableau Basic Filters
  • After that, choose At least and give value to filter the rows, which meet this criteria and click on OK button.
Tableau Basic Filters
  • After all of the above steps, you get the final view that showing only the sub-categories whose average Profit is greater than 25 in the below screenshot:
Tableau Basic Filters

Filter Dates

Tableau distributes the date field in three different ways while applying the date field. It can apply filter by taking a relative date as compared to today, a perfect date, or Range of dates. Each of these options presented when a date field is dragged out of the filter pane.

For example, consider a data source such as sample – Superstore and,

  • Create a view with Order Date in the column shelf and Profit in the rows shelf as shown in the below screenshot.
Tableau Basic Filters
  • Then, drag the Order Date field to the filter shelf and choose Range of dates in the filter dialog box and click on the Next button.
Tableau Basic Filters
  • After that, choose the dates and click on the OK button as shown in the below screenshot.
Tableau Basic Filters
  • After all of the above steps, you get the final view that showing chosen Range of dates shown in the below screenshot.
Tableau Basic Filters

Tableau Filter Operations

Any data analysis and visualization work involve the use of extensive filtering of data. Tableau has a variety of filtrations to address these needs.

Tableau has many inbuilt functions for applying filters on the data using both measures and dimensions.

For the measures, the filter option offers numeric calculations. The filter option for dimension offers using a custom list of values or choosing string values from a menu.

Creating Filters

  • Filters are designed by dragging the required field to the Filters shelf.
  • Then, create a horizontal bar chart by dragging the dimension (Sub-Category) to the Rows shelf and the measure (sales) to the Columns shelf.
  • Again, drag the Sales into the Filters shelf, select sum option among all options, and click on the Next button.
  • Once this filter is created, right-click and choose the Edit Filter option from the pop-up menu.
Tableau Filter Operations
  • Select one option among these options and click on OK button to apply the filter as shown in below screenshot.
Tableau Filter Operations
  • The final view after applying filter looks like below screenshot:
Tableau Filter Operations

Create Filters for Measures

Measures are numeric fields. So, the filter options for such fields involve choosing values. There are following types of filters for measures in Tableau:

  • Range of values: It specifies the minimum and maximum values of the range to include in the view.
  • At Least: It includes all values that are greater than or equal to a specified minimum value.
  • At Most: It includes all values that are less than or equal to a specified maximum value.
  • Special: It helps you filter on Null values. It includes Null values, Non-null values, or All Values.

Below screenshot shows all these filters for Measures:

Tableau Filter Operations

Create Filters for Dimensions

Dimensions are descriptive fields having string values. There are following types of filters for dimensions in Tableau:

  • General Filter: It allows to select specific values from a list.
  • Wildcard Filter: It allows to mention wildcards like cha* to filter all string values starting with cha.
  • Condition Filter: It applies conditions such as sum of sales.
  • Top Filter: It chooses the records representing a range of high values.

Below screenshot shows all these filters for Dimensions:

Tableau Filter Operations

How to Clear Filters

Filters can be easily removed after selecting the filter Remove options as shown in the below screenshot.

Tableau Filter Operations

Tableau Extract Filters

Extract filter is used to filter the extracted data from the data source. This filter is utilized if the user extracts the data from the data source.

After connecting the text file to Tableau, you can see the two options, Live and Extract in the top right corner of the data source tab.

A live connection is directly connected to a data source. And extract connection extracts the data from the data source and creates a local copy in Tableau repository. The procedure for creating an extracting filter is given below step by step as follows.

Step1: Connect a text file with Tableau.

  • Click on the “Extract” radio button as shown in below screenshot.
Tableau Extract Filters
  • It creates a local copy in Tableau repository.

Step2: Then,

  • Click on the “Edit” option that placed on the top right corner near to Extract button.
  • It opens the “Extract data” window. Click on the “Add” option present in the Window.
Tableau Extract Filters

Step3: “Add Filter” Window is opened to select a filter condition.

You can choose any of the fields and add as Extract filter. In this example, we have selected “Category” as extract filter.

  1. Select the Category field from the list.
  2. Click on OK button.
Tableau Extract Filters

After clicking on the OK button, it opens a filter window shown in the below screenshot.

Tableau Extract Filters

Tableau Quick Filters

In Tableau, many filter types are quickly available using the right-click option on the measure and dimension. These filters have enough functionality to solve most of the everyday filtering needs. These filters are known as Quick filters.

The below screenshot shows how the quick filters are accessed:

Tableau Quick Filters

The given below table lists the various quick filters and their uses in Tableau.

Filter NamePurpose
Single Value (List)It selects only one value at a time in the list.
Single Value (Dropdown)It selects a single value in a drop-down list.
Multiple Values (List)It can select one or more values in a list.
Multiple Values (Dropdown)It selects one or more values in the drop-down list.
Multiple Values (Custom List)It selects and searches for one or more values.
Single Value (Slider)It drags a horizontal slider for selecting a single value.
Wildcard MatchIt selects values containing the specified characters.

For example, consider a data source such as Sample-Superstore, to apply some quick filters. First,

  • Choose the sub-category field as the row shelf and sales as the column shelf that produces a horizontal bar chart.
  • Drag the sub-category field to the filters pane. Apply wildcard filtering using the expression p* to select all subcategory names starting with “p“.

The below screenshot shows the result after applying this filter where only the sub-categories starting with “p” are displayed:

Tableau Quick Filters

How to Clear the Filter

After the analysis is completed by applying the filter, you can remove it by using the clear filter option.

  • First, go to the Filter Pane.
  • And, right-click on the field and choose Clear Filter option as shown in the below screenshot.
Tableau Quick Filters

After clearing filter from the filter pane, the worksheet looks like the below screenshot:

Tableau Quick Filters

Tableau Context Filters

All filters that you arrange in Tableau are computed independently. And, each filter accesses all rows in your data source without view to other filters.

You can arrange one or more categorical filters as context filters for the view. And context filter can work as an independent filter. Any other filters that you arrange are defined as dependent filters because they process only the data that passes through the context filter.

Context filter is created because of the following reasons:

  • Improve Performance: If you want to set a lot of filters or have a significant data source, then queries start running slowly. In such case, you can set one or more context filters to improve performance.
  • Create a Dependent Numerical or Top N Filter: You can set a context filter to include the data of interest only, and arrange a numerical or a top N filter.

Create a Context Filter

To create a context filter, first select Add to Context from the context menu of an existing categorical filter. The context is computed once the view is generated. All other filters are then calculated relative to the context. Context filters are:

  • Appeared on the top of the filters pane.
  • Identified by the gray color on the filters pane.
  • Not rearranged on the filters pane.

For example, consider the data source such as Sample-superstore, find the top 10 Subcategory of products for the category called Furniture. There are the following steps.

Step1: Drag the Sub-Category field to the Rows shelf and Sales field to the Columns Shelf.

Step2: And, choose the horizontal bar chart from the “Show Me” tab.

Step3: Again, drag the Sub-Category to the Filters shelf. You get the chart shown in the below screenshot.

Tableau Context Filters

Step4: Right-click on the Sub-Category field in the filter shelf and click on “Edit Filter ” option then go the ” Top ” tab in the pop-up window.

Step5: And, choose the “By field ” option. From the next drop-down, choose the option Top 10 by Sales Sum as shown in the below screenshot.

Tableau Context Filters

Step6: Drag the Category field to the filter shelf. Right-click on the Category field to edit and choose Furniture from the list. It shows three subcategories of products as a result shown in below screenshot.

Tableau Context Filters

Step7: Now, adding the context filter, Right-click on the Category: Furniture filter and select the “Add to Context” option.

Tableau Context Filters

Step8: Above all steps produce the final result that shows the subcategory of products from the category Furniture.

Tableau Context Filters

Tableau Condition Filters

In Tableau, condition filter is used to apply some conditions to already existing filters. These conditions are very simple, for example, finding only those sales which are higher than a certain amount. Also, these conditions can be applied to create a range filter.

Create a Condition Filter

For example, consider the data source such as Sample-superstore, let’s find the sub-category of products across all Segments whose sales exceed two million. There are some steps for creating condition filter in Tableau.

Step1: Drag the Segment field and the Sales field to the Column shelf.

Step2: Next, drag the Sub-Category field to the Rows shelf. Choose the horizontal bar chart option. And you get view shown in below screenshot.

Tableau Condition Filters

Step3: Again, drag the Sub-Category field to the Filters Shelf.

Step4: Right-click on Sub-Category field to edit and then go to the “Condition” tab. And, choose the radio “By field ” option. From the drop-down, select Sales, Sum, and greater than equal to symbol specifying the value 200000.

Tableau Condition Filters

After completing the above steps, you get a view which shows only those subcategory of products that have the required amount of sale. Also, this shows all the available Segments where the condition is True that shown in below screenshot.

Tableau Condition Filters

Tableau Data Source Filters

The data source filter is used to filter the data in data source proportion. It restricts the files present in the data set. This filter is similar to the extract filter in securing the data. But data source filter and extract filter both are different, and they are not linked to each other. A data source filter works on both Live connection and Extract connection. The procedure to select a data source filter is given as step by step below.

Step1: Click on the “Add” button placed at the top right corner of the data source tab, shown in the following screenshot.

Tableau Data Source Filters

Step2: It opens the “Edit Data Source Filters” Window. Then, click on “Add” Option of the window that shown in below screenshot.

Tableau Data Source Filters

Step3: “Add Filter” Window is opened to select the filter conditions.

Select any of the fields and add as extract filter. For example, you want to select the Category field as an extract filter. Then,

  1. Select Category from the list.
  2. Click on OK button, shows in below screenshot.
Tableau Data Source Filters

After clicking on OK button, it opens a filter window shown in below screenshot.

Tableau Data Source Filters

Tableau Top Filters

In Tableau, Top filter is used to set the limit of result from a screen. For example, if you want to get only the top 10 values from a large set of records. Then, you can apply this filter using the inbuilt options for limiting the files in many ways or by creating a formula.

Create a Top Filter

For example, consider the data source such as Sample-superstore, and you want to find the sub-category of products that represents the top 10 sales amount. There are the following steps, such as:

Step1: Drag the Sub-Category field to the Rows shelf and the Sales field to the Columns shelf. Choose the horizontal bar from the “Show Me” tab. Tableau shows the following view:

Tableau Top Filters

Step2: Right-click on the Sub-Category field and go to the “Top” tab. And, choose the second radio “By field” option. From the drop-down, select the Top 10 options by Sum of Sales.

Tableau Top Filters

After completing the above all steps, you will get the following view, which shows the top 10 Sub-Category of products by sales shown in the below screenshot.

Tableau Top Filters

Tableau Sort Data

Data present in the worksheet can be sorted based on the requirement. It can sort the data based on the data source such as ascending, descending order, or depend on any measured value.

The procedure for sorting the data is given below, step by step:

For example, consider a data source such as sample-superstore, and you want to sort the dimensions and the measures fields as follows.

Step1: Add the sample-superstore data source with Tableau and drag the Order table to the pane shown in the below screenshot.

Tableau Sort Data

Step2: Go to the worksheet and drag the dimension Category to the row shelf and the measure Sales to the column shelf.

Tableau Sort Data

It creates a horizontal bar chart. Category field present in the visual order, and it is sorted based on data source by default. We can change the order of sorting by following the below procedure.

Step3: Right-click on the Category field and select Sort option.

Tableau Sort Data

After that, it opens the Sort window. All options present inside the sort window is shown below as follows:

Tableau Sort Data
Tableau Sort Data

Sort Order

  • Ascending: It sorts the order of selected dimensions and measures in ascending order.
  • Descending: It sorts the order of selected dimensions and measures in descending order.

Sort By

The field can be sorted in different types of methods that are explained below as follows.

  • Data source order: It sorts the field based on data source order.
  • Alphabetic: It sorts the dimensions and measures in alphabetical order.
  • Field: It sorts the field based on the other measure or dimension values.
  • Manual: It can manually sort the data.

For example, suppose the Category field is sorted based on another field such as ‘Sales‘.

Step1: Click on ‘Field‘ radio button.

Step2: Select the field on which the Category is to be filtered.

Step3: Select the aggregation type.

Step4: Click on Clear button.

Tableau Sort Data

In the above example, it filters the Category field based on the sum of sales in ascending order. And it sorts the data which is shown in below screenshot.

Tableau Sort Data

Tableau Build Groups

It creates a group to combine related members in the field. If you are working with a view and you want to group specific fields to create significant categories.

For example, consider the data source such as sample-superstore, then drag the Sales field in column shelf and Category field in row shelf and then sort them in ascending order (discussed in Tableau sort data).

The aggregated values of Furniture and Office Supplies can be obtained by using the group.

Once the group is built, the aggregated value of Furniture and Office Supplies can be shown in the visuals. The procedure to create a group is given below step by steps as follows.

Step1: Right click on the Category field.

Step2: Click on the “Create” option.

Step3: Then, select “Group” option shown in below screenshot.

Tableau Build Groups

Step4: It opens the “Create Group” window. Then,

  1. Write the name of the group.
  2. Select the members which you want to be grouped.
  3. Click on the “Group” button.
Tableau Build Groups

Step5: In “Edit Group” window,

  1. It creates a group of Furniture and Office Supplies.
  2. Then, click on the OK button to create the group.
Tableau Build Groups

It created a group whose field name is Category (Group) and added in the dimension list. This is used for visualizing the group of members present in a field.

The below screenshot explains the functionality. The sum of sales is visualized for both Furniture and Office Supplies.

Tableau Build Groups

Tableau Build Hierarchy

In Tableau, Hierarchies can be built to visualize the data. It can be created in the Tableau by following the below steps:

For example, consider the data source such as Sample-Superstore and its dimensions and measures.

Step1: First go to the worksheet. Then,

  1. Select a dimension and right-click on that dimension to create a hierarchy.
  2. Go to “Hierarchy” option.
  3. And, click on the “Create Hierarchy” option shown in the below screenshot.
Tableau Build Hierarchy

Step2: It opens the “Create Hierarchy?” window. Then,

  1. Enter a name of hierarchy.
  2. And click on the OK button.
Tableau Build Hierarchy

It creates a hierarchy shown in below screenshot.

Tableau Build Hierarchy

Also, you can add another field in the hierarchy. For example, the State is inserted into the Country hierarchy. Then,

1. Drag a field and drop it directly on top of another field in the hierarchy.

It insert the State field into the Country hierarchy shown in the below screenshot.

Tableau Build Hierarchy

To Remove a Hierarchy

From the data pane, you can remove the inbuilt hierarchy as well. Here are the following steps to remove the hierarchy.

Step1: Select the hierarchy which you want to remove.

Step2: Right-click on that hierarchy.

Step3: And select the “Remove Hierarchy” option shown in below screenshot.

Tableau Build Hierarchy

The fields in the hierarchy are also removed from the hierarchy, and the hierarchy disappears from the Data pane.

Tableau Build Sets

Sets are custom fields and it defines a subset of data based on some conditions. Sets create a set of members out of the field present in a data set.

It acts as a separate field or dimension. The procedure to build sets is given step by step as follows.

For examples, consider the data source such as Sample-Superstore and use its dimensions and measures to build the Sets.

Step1: Go to the worksheet. And,

  1. Right-click on a dimension Sub-Category.
  2. Select “Create” option.
  3. Then click “Set” option shown in the following image.
Tableau Build Sets

Step2: It opens the Create Set window.

  1. Enter the set name to be created.
  2. Select the members which you want to add in the set.
  3. Click on the OK button.
Tableau Build Sets

It creates a set of the selected members shown in the below screenshot.

Tableau Build Sets

Show Members in Set

You can also see the selected members after a created or inbuilt set from the following steps:

Step1: Right-click on the Set.

Step2: Click on the “Show Members” option.

Tableau Build Sets

After clicking the “Show Members” option, it will show all the members present in the set shown in below screenshot.

Tableau Build Sets

Edit the set

You also edit the set after created or inbuilt from the following steps:

Step1: Right-click on the set.

Step2: Click on the “Edit Set” option.

Tableau Build Sets

After clicking on the “Edit Set” option, Edit Set window will be opened with the set name. Now, you can edit the set shown in below screenshot

Tableau Build Sets

Tableau Bar Chart

In Tableau, there are various types of bar chart that can be created by using the dimensions and measures.

A bar chart represents the data in rectangular bars. Tableau automatically produces a bar chart when you drag a dimension to the Row shelf and measure to the Column shelf.

The bar chart option present in the “Show Me” button. If the data is not appropriate for the bar chart, then this option will be automatically blocked out.

A bar chart can compare the data in different categories. The height of the bar represents the measured value of the category. It can be described as vertical and horizontal type bar charts. The procedure to create a bar chart is given below through an example.

For example, consider a data source such as Sample-Superstore and its dimensions and measures.

Step1: First, go to the worksheet and,

Step2: Drag the Category field into the column shelf.

Step3: Drag Profit field into the row shelf.

Step4: By default, it creates the bar chart shown in the below screenshot.

Tableau Bar Chart

Bar Chart with Color Range

You can apply colors to the bars based on their ranges. The longer bars get darker shades, and the smaller bars get the lighter shades. Let’s see step by steps,

Step1: Drag the Category field into the column shelf.

Step2: Drag Profit field into the row shelf.

Step3: Also, drag the Profit field to the Color pane under the Marks Pane and, it produces a different color for negative bars.

Tableau Bar Chart

Stacked Bar Chart

You can also add one more dimension to the above bar chart to produce a stacked bar chart that shows different colors in each bar.

Step1: Drag the Segment field.

Step2: And drop the Segment field into Color pane.

The below-stacked chart appears that shows the distribution of each segment in each bar.

Tableau Bar Chart

Tableau Line Chart

A line Chart can compare the data over different periods. A series of dots create a line chart. These dots represent the measured values in each period.

measure and a dimension are taken two axes of the chart area in the line chart. The pair of values for each observation becomes a point. After joining all these points would become a line that shows the variation between the dimensions and measures.

The procedure to create a line graph is shown step by step below.

For example, consider a data source such as Sample-Superstore and its dimensions and measures.

Step1: Select one dimension and one measure to create a simple line chart.

1) Drag the dimension Order Date into Columns Shelf.

2) And Sales into the Rows shelf.

3) It creates the line chart by default or Chooses the Line chart from the “Show Me” button.

You will view the following line chart that shows the variation of Sales for different Order Date showing in the below screenshot.

Tableau Line Chart

Multiple Measure Line Chart

You can use one dimension with two or more measures in a single line chart. It produces various line charts in one pane. Each pane represents the variation between a dimension and the measures.

Step1: Drag the dimension Order Date into Columns Shelf.

Step2: Drag measures Sales and Discount into the Rows shelf.

Tableau Line Chart

Line Chart with Label

Each of the points that creates the line chart are labeled to make the values of the measure visible.

Step1: Drop another measure Profit ratio into the “Labels” pane in the “Marks” card.

Step2: Choose average as the aggregation, and you will view the below chart showing the labels.

Tableau Line Chart

Tableau Pie Chart

The pie chart shows the segment-wise data. It can show the contribution of measures over different members in a dimension. The angle of pie determines the measured value. Different colors can be assigned to pie to represent the members in a dimension.

A pie chart represents the data in the form of the circle slice with different size and colors. These slices are labeled, and the numbers corresponding to each slice is also represented in the chart.

You select the pie chart option from the “Show Me” pane to create a pie chart.

For example, consider a data source such as sample-superstore and Choose one dimension and one measure to create a simple pie chart.

Step1: Go to the worksheet.

Step2: Drag the dimension Segment and drop into the Color and Label pane.

Step3: Drag the measured Profit and drop into the Size pane.

Step4: Choose the chart type from “Show Me” pane.

The following chart will appear that shows the three segments in different colors with labels.

Tableau Pie Chart

Drill Down Pie Chart

You can choose a dimension with the hierarchy or go deeper into the hierarchy. The chart changes reflect the level of the selected dimension.

For example, consider a data source such as sample-superstore, then take the dimension Product, which has four more levels such as Category, Sub-Category, Manufacturer, and Product Name.

Drag the measured Profit and drop it to the Labels pane. The following pie chart appears that shows the values for each slice.

Tableau Pie Chart

Here is one more level into this hierarchy, we get the manufacturer as the label shown in the below screenshot.

Tableau Pie Chart

Tableau Bubble Chart

A bubble chart is visualizing the measures and dimensions in the form of bubbles.

A bubble chart is a group of circles. Each value of the dimension field represents the circles, and the value of measure represents the size of those circles.

The color of bubbles is set to differentiate the members present in a dimension. Here is the procedure to create a bubble chart as follows.

For example, consider a data source such as sample-superstore, and if you want to find the Profits for different Ship Mode. Then,

Step1: Drag the measures Profit and drop into the “Size” pane.

Step2: Drag the dimensions Ship Mode and drop into the “Labels” pane.

Step3: Also drag the dimension Ship Mode into the “Color pane” under the “Marks” card.

Tableau Bubble Chart

Bubble Chart with Measure Values

Also, you can show the value of the measures field that decides the size of the circles.

First, drag the measure Sales into the “Labels” pane. Show the following screenshot.

Tableau Bubble Chart

Bubble Chart with Measure Color

You can also use the same color with different shades for all the different size circles.

For this, drag the measure Sales into the “Color” pane. The darkest color shows the largest size of the circle and the lighter color shows the smallest size of the circle shown in the below screenshot.

Tableau Bubble Chart

Tableau Bump Chart

The bump chart is used to compare two dimensions using one of the measure value. It explores the changes in Rank of value over a time dimension or place dimension or any other relevant dimension.

The bump chart can take two dimensions with zero or more measures.

For example, consider a data source such as sample-superstore, if you want to find variations between the Ship Mode of the product and the Sub-Category.

Step1: Drag the dimension Sub-Category to the column shelf.

Step2: Drag the dimension Ship Mode to the color shelf.

By default, it creates the following view of the chart.

Tableau Bump Chart

Step3: Create the calculated field.

1) Go to the Analysis and create a calculated field.

2) Give a field name to the calculated field such as Rank.

3) Write the expression “Index ()” in the calculated field calculation area. It creates an index for the current row in the partition.

4) Click on the OK button.

Tableau Bump Chart

5) The new calculated field Rank will be visible in the Measure section.

6) Drag the Rank field to the row shelf.

7) Right-click on the Rank field and convert into “Discrete”.

Tableau Bump Chart

After that, the following view appears that shows the dimension Sub-Category with each Ship Mode.

Tableau Bump Chart

Step5: Apply some more calculations to the calculated field Rank using the measure Profit.

1) Right-click on the Measure Rank. And

2) Choose “Edit Table Calculation” option.

Tableau Bump Chart

3) It opens the Table Calculation window.

4) Then, choose the “Specific Dimensions” option.

Tableau Bump Chart

5) Select the Sub-Category field and Ship Mode field.

6) And, choose the sorting by the Profit field using partition by Sub-Category and addressed by Ship Mode.

The following view will appear shown in the below screenshot.

Tableau Bump Chart

After completion of the all above steps, you will get the bump chart as shown in the below screenshot. It shows the variation of the Profit for each Ship Mode across different subcategories.

Tableau Bump Chart

Tableau Gantt Chart

A Gantt chart is used for the comparison of data between categories. Also, it can be used to identify the time taken for each process.

It shows the progress of the value of a task over some time. It is broadly used in project management and other types of variation study over some time.

Gantt chart takes at least a dimension and a measure in addition to the time dimension.

For example, using the data source such as Sample-Superstore, time is taken for shipping by each type of Ship Mode is given. For create, a Gantt chart follows the procedure is given as follows.

Step1: go to the worksheet.

  1. Click on the drop-down button in the “Marks” pane.
  2. Select the “Gantt bar” option from the given list.
Tableau Gantt Chart

Step2: Drag Order Date into the column shelf.

Step3: Right click on the Order Date field and select the “Day” as shown in the below screenshot.

Tableau Gantt Chart

Step4: Click on the Analysis option in the menu bar.

Step5: And select the Create Calculated Field option from the list.

Tableau Gantt Chart

Step6: Enter the name of the calculated field such as “shipping“.

Step7: Write the expression “DATEDIFF (‘day’, [Order Date], [Ship Date])” to create the difference between the Order Date and Ship Date.

Step8: Click on the OK button.

Tableau Gantt Chart

Step9: Drag Ship Mode into the rows shelf.

Step10: Drag calculated field Shipping into the Size pane under Marks shelf.

Tableau Gantt Chart

After completing all the above steps, it creates the Gantt chart that shows the time taken for each shipment across different Ship Mode, shown in the below screenshot.

Tableau Gantt Chart

Tableau Crosstab Chart

A crosstab chart is also called a Text table that shows the data in textual form.

The crosstab chart takes one or more dimensions and one or more measures. This chart can also show different calculations on the values of the measures field such as percentage total, running total, etc.

For example, consider a data source such as Sample-Superstore, if you want to find the number of Sale for each Segment in each Region. To display the data for each year using the available Order Dates below are some steps to create a crosstab chart.

Step1: Drag the dimension Order Date into the columns shelf.

Step2: Also, drag the dimensions Region and Segment into the row shelf.

Step3: Drag the measure Sales into the “Labels” shelf under the Marks pane.

The below screenshot shows the crosstab chart.

Tableau Crosstab Chart

In the crosstab chart, you can get the values color encoded by dropping the Sales field into the Color shelf.

The color-coding shows the strength of the color depending on the value of the measure. The highest values have a darker shade of color, and the smaller values have a lighter shade of color, as shown in the below screenshot.

Tableau Crosstab Chart

Also, in addition to the color encoding, you can get calculations applied to the values from the measure.

For example, you can apply the calculation to find the percentage total of sales in each row instead of only the Sales field.

1. Right-click on the Sales field in the Marks shelf.

2. And choose the Add Table Calculation option.

Tableau Crosstab Chart

3. It opens the Table Calculation window.

4. Then, choose the “Percent of Total” option as Calculation Type and “Table (Across)” option as Compute Using.

Tableau Crosstab Chart

After completing the above steps, you get the crosstab chart created with percentage values, as shown in the below screenshot.

Tableau Crosstab Chart

Tableau Motion Chart

Motion chart is used to show the data using X-axes and Y-axes that display the change over time by showing the movement of the data points as well as variations in the color of the lines.

The motion chart has the advantage to view the trail of how the data has changed over time.

Motion chart needs only one Time Dimension and one Measure in tableau.

For example: consider the data source such as Sample-Superstore, and if you want to find the variation of Profits over the Months. For this, there are the following steps given below, such as.

Step1: Drag the Dimension Order Date into the Columns Shelf.

Step2: Again, drag the dimension Order Date into the Pages Shelf.

Step3: Right-click on the Order Date field in the Pages shelf, and choose Month option.

Step4: Then, drag the measure Profit to the Rows Shelf. And appear chart Shown in the below screenshot.

Tableau Motion Chart

Step5: Put a checkmark in the box next to “Show History” button and then click on the dropdown arrow next to it.

Tableau Motion Chart

Step6: For “Marks to Show History For” option, select All. And under “Show” option, you can select BothMarks option shows only the points and also selects Trails option shows only the line. Click the Pslay button and below chart appears.

Tableau Motion Chart

After allowing the chart to run from January to December, it creates a chart that shows how profits varied over each month in the whole year.

According to the appear chart, the data changes in the recent month get a dark shade of the color, and the historical data gets a light shade of the color. You can see in the below screenshot.

Tableau Motion Chart

Tableau Waterfall Chart

Waterfall Chart is visualizing the cumulative effect of measures over dimensions. It also can show the contribution of growth and decline by each member in dimensions.

Also, Waterfall charts display the cumulative effect of sequential positive and negative values. It shows where an amount starts, ends and how it gets there incrementally. So, we can see both the size of changes and difference in values between consecutive data points.

Tableau needs only one Dimension and one Measure to create a Waterfall chart.

For example, the data source such as Sample-Superstore now sees the contribution of Sales by each Sub-Category using a waterfall chart.

In Tableau, the waterfall chart will be designed by following the given steps.

Step1: Go to the worksheet.

  1. Drag the dimension Sub-Category into the column shelf.
  2. Drag the measure Sales into the rows shelf.
Tableau Waterfall Chart

Step2: Right click on the Sales field present in the measures shelf.

Step3: Choose to “Create” option from the list.

Step4: And then select the “Calculated Field” option.

Tableau Waterfall Chart

Step5: It open Calculates Field window. Then,

  1. Enter the name of calculates field such as -Sales.
  2. Write the expression “-[Sales]” in the calculation area shown in the below screenshot.
  3. Click on the OK button.
Tableau Waterfall Chart

Step6: Drag the newly created calculation field “-Sales” into the Size shelf under the Marks pane.

Tableau Waterfall Chart

Step7: Right click on the SUM(Sales) present in the rows shelf.

Step8: Select the Quick Table Calculation from the list.

Step9: Then click on the Running Total option.

Tableau Waterfall Chart

Step10: Click on the drop-down option in the marks pane.

Step11: Select the Gantt chart option from the list.

Tableau Waterfall Chart

After complete above all the steps, it creates the waterfall chart shown in the below screenshot.

Tableau Waterfall Chart

Tableau Bullet Chart

A bullet chart is used as a gauge or indicator to show the performance of measures. It can compare the two measures to each other using the bullet graph.

A bullet chart is also a variation of Bar chart. In the bullet chart, we compare the value of one measure with another measure in the context of finding the difference between the first measure and the second measure.

It’s like two bars drawn upon one another to indicate their values at the same position in the graph. It can be used as combining two graphs as one to view a comparative result easily.

For example, consider the data source such as Sample-Superstore and you want to compare the Estimated Profit with Actual Profit. Then you can easily compare both of them using the bullet chart.

The procedure to create a bullet chart is given as follows.

Step 1: Drag the dimension Sub-Category into the column shelf.

Step 2: Drag the measures Profit and Sales into the rows shelf.

The below graph shows that the two measures as two separate categories of bar charts and each representing the values of sub-categories.

Tableau Bullet Chart

Step 3: Again, drag the measure Sales into the Detail marks pane from the rows shelf.

Step 4: Go to the Show Me option located on the top right side in the worksheet shown below:

Tableau Bullet Chart

Step 5: Then, choose the bullet graph option from the Show Me graphs options and bullet graph appears shown in the below screenshot.

Tableau Bullet Chart

Tableau Area Chart

The area chart represents any quantitative or measures data over different time.

In Tableau, it is a line graph where the area between line and axis is generally filled with color.

For example, consider a data source such as Sample-Superstore, take its dimensions and measures.

The procedure to create the area chart is given below step by step, such as.

Step 1: Go to the worksheet.

1) Hold the Ctrl key in the keyboard.

2) And select the dimension Order Date and measure Quantity, as shown in the following screenshot.

Tableau Area Chart

Step 2: Click on the “Show Me” option located at the top right corner of the worksheet.

Step 3: Select the area chart option as shown in the below screenshot.

Tableau Area Chart

Step 4: Drag dimension Region and drop into the Color shelf under the Marks pane.

Tableau Area Chart

It creates an area chart that shows the Quantity according to the Order Date in a year, as shown in the following screenshot.

Tableau Area Chart

Tableau Pareto Chart

A Pareto chart consists of two graphs, such as bar graph and line graph. The same measure is used to create the graph, but the measure values are handled differently.

In Tableau, the purpose of using the Pareto Chart is to identify the contribution of members present in a field.

For example, the data source such as Sample-Superstore, the measure Profit contributed by different dimension, i.e., Sub-Category of products which can be analyzed using the Pareto Chart.

It shows the top members and their contribution. Here is the procedure to create a Pareto Chart as given below.

Step 1: Go to the worksheet.

1) Drag the dimension Sub-Category into the columns shelf.

2) Drag the measure Profit into the rows shelf.

Tableau Pareto Chart

Step 2: Right-click on the Sub-Category field.

Step 3: And select the “Sort” option from the list.

Tableau Pareto Chart

Step 4: It opens the sort window.

1) Click on the Descending option in sort order.

2) Select the Field Name option under sort section.

3) And select the Field Name as Profit and choose Sum as the aggregation function.

Tableau Pareto Chart

Step 5: Again, drag measure Profit into the rows shelf. Then,

1) Right-click on the newly added Profit field.

2) And select the Dual Axis option from the list.

Tableau Pareto Chart

It merges the X-axes of both measures and converts the visualization shown in the below screenshot.

Tableau Pareto Chart

Step 6: Go to the Marks pane. And

1) Select the SUM(Profit) form the marks pane.

2) Click on the drop-down button.

3) And select the Bar chart from the list, as shown in the following screenshot.

Tableau Pareto Chart

Step 7: Again,

1) Select the Sum(profit)(2) from the marks pane list.

2) Click on the drop-down button.

3) And select the line chart option from the list shown in the below screenshot.

Tableau Pareto Chart

Step 8: Select the SUM(profit) on the right side of the rows shelf.

1) Right-click on the SUM(profit) field.

2) And select the Add Table Calculation from the list as shown in the below screenshot. Tableau Pareto Chart

Step 9: It creates the “Table Calculation” window.

1) Select the Running Total option as “Calculation Type“.

2) Choose Sum as the aggregation function.

3) Select Table(across) option as the “Compute Using“.

4) Then click on the Add Secondary Calculation checkbox.

5) It expands further window as Secondary Calculation Type.

6) Select the Percent of Total option from the list.

7) Again select the Table(across) option as the “compute using“.

8) Click on the Closing icon.

Tableau Pareto Chart

Step 10: Go to the Marks pane. And

1) Go to the SUM(profit)(2).

2) Click on the color icon in the marks pane.

3) Choose a color from the color options.

Tableau Pareto Chart

After choosing a color, you can see this color line in the graph, and this is the procedure to create a Pareto chart shown in the below screenshot.

Tableau Pareto Chart

Tableau Dual Axis Chart

The dual axis chart is used to visualize two different measures in two different chart types. A date column and two measures are necessary to build a dual axis chart.

The different scales are used in the graph that helps the user to understand both measures. The procedure to create a dual axis chart is given step by step below.

For example, consider a data source such as Sample-Superstore and its measures and dimensions.

Step 1: Go to the worksheet.

Step 2: Hold the Ctrl key in the keyboard.

Step 3: And select the dimension OrderDate, measures Sales and Quantity as shown in the below screenshot.

Tableau Dual Axis Chart

Step 4: Click on “Show Me” option located on the top right corner of the worksheet.

Step 5: Select the “dual combination” icon, as shown in the below screenshot.

Tableau Dual Axis Chart

After completion of above all steps, it creates the dual axis chart as shown in the below screenshot.

Tableau Dual Axis Chart

Tableau Box Plot

The box plot is also called the box-and-whisker plots. They show the distribution of value along an axis.

All box indicates the middle 50 percent of the data where the middle two quartiles of the data’s distribution. On both sides, the remaining 50 percent of data represents by lines called whiskers.

To display all points within 1.5 times of interquartile range, which is all aspects within 1.5 times of the width of the adjoining box, or all points at the maximum area of the data.

The Box Plot takes one or more measures with zero or more dimensions.

For example, consider the data source such as Sample-Superstore and find the size of Profits field for the dimension Category for each ShipMode field values. Below are the steps to create a box plot.

Step 1: Drag the dimension Category and drop into the Columns shelf.

Step 2: Drag the measure Profit and drop into the Rows shelf.

Step 3: Also drag the dimension ShipMode and drop into the right of the Category field in the Columns shelf.

Tableau Box Plot

Step 4: Choose the Box-and-Whisker plot from “Show Me“.

Tableau Box Plot

The below chart appears that shows the box-and-whisker plot.Automatically Tableau reassigns the ShipMode to the Marks pane.

Tableau Box Plot

Tableau Heat Map

The heat map is used to visualize the data in the form of size and color on different measures.

Two different measures are visualized simultaneously using a heat map. One measure is assigned to size, whereas another measure is attached to the color of the heat map.

For example, consider the data source such as the Sample-Superstore and its dimensions and measures.

The procedure to create a heat map is given step by step as follows:

Step 1: First, go to the worksheet.

Step 2: Hold the Ctrl key in the keyboard.

Step 3: Select the dimension Sub-Category and measure Sales as shown in the following screenshot.

Tableau Heat Map

Step 4: Click on the “show me” button of the worksheet.

Step 5: And select the Heatmap icon, as shown in the following screenshot.

Tableau Heat Map

Step 6: Drag measure Profit and drop into the Color shelf under the Marks pane.

Tableau Heat Map

Step 7: Drag the dimension Region and drop into the column shelf.

Tableau Heat Map

After completing all the above steps, it creates the Heatmap, which is used to visualize the Sales field and Profit field across different the dimension.

Tableau Heat Map

Tableau Tree Maps

The treemap displays the data in nested rectangles. The dimensions define the structure of the treemap and measures determine the color or size of the individual square.

The squares are easy to visualize as the size and shade of the color of the square reflects the value of the measure.

A Treemap is created using one or more dimension with one or two measures.

For example, consider the data source such as Sample-Superstore, if you want to find the size of Profits for each ShipMode values. Below are the following steps to create a treemap.

Step 1: Drag the measures Profit and drop into the color shelf under Marks pane.

Step 2: Again, drag the measures Profit and drop into the Size shelf.

Tableau Heat Map

Step 3: Drag the dimension Ship Mode and drop into the Label shelf.

Tableau Heat Map

Step 4: Choose the treemap option from the “show me“.

Tableau Heat Map

After completing all the above steps, it creates the treemap shown in the below screenshot.

Tableau Heat Map

Tableau Scatter Plot

The scatter plot is used to visualize the relationship between the two measures. It is designed by adding measures in both x-axis and y-axis. This can show the trend or relationship between the measures selected.

To create a scatter plot, you should have at least one measure in the rows shelf and one measure in the columns shelf. However, you can add the dimensions field to the scatter plot that plays a role of different color making for already existing points in the scatter graph.

For example, consider the data source such as Sample-Superstore, if you want to find the variation of Sales field and Profit field as the two axes of the Cartesian plane is distributed according to their Sub-Category field.

To create a scatter plot, there are the following steps, such as:

Step 1: Drag the measure Sales and drop into the columns shelf.

Step 2: Drag the measure Profit and drop into the rows shelf.

Tableau Scatter Plot

Step 3: Drag the dimension Sub-Category and drop into the Label shelf under the Marks pane.

Tableau Scatter Plot

After that, it creates the scatter plot that shows how the Profit field and Sales field is distributed across the dimension Sub-Category of products.

Tableau Scatter Plot

Step 4: You can also get the values color encoded after dragging the Sub-Category field into the Color Shelf.

Below chart appears that show the scatter points with a different color for each point.

Tableau Scatter Plot

The same scatter plot can show different values when you choose a dimension with hierarchy.

For example, expand the dimension Sub-Category to show the scatter plot values for the Manufacturers field.

Tableau Scatter Plot

Tableau Histogram

A histogram chart is a chart that displays the shape of the distribution.

A histogram looks like a bar chart but group values for a continuous measure into range. In the histogram, each bar represents the height of the number of values present in that range.

To create a histogram, we need only one measure. It creates the additional bin field for the measure.

For example, consider the data source such as Sample-Superstore, and if you to find the Quantities of sales for different Segment. For this, follow the below procedure step by step, such as:

Step 1: Go to the worksheet.

Step 2: Drag the measure Quantity into the columns shelf.

Histogram

Step 3: Click on the “show me” toolbar and select the histogram chart icon, shown in the below screenshot.

Histogram

NOTE: The histogram chart is available in “show me” when the view contains only one measure and no dimensions.

Step 4: After selecting the histogram chart as the chart type. Then,

  • The view changes and shows vertical bars, with a continuous X-axis and Y-axis.
  • The measure Quantity with SUM aggregate in columns shelf is replaced by continuous Quantity(bin) dimension.
  • The Quantity field moves to the rows shelf and aggregation changes from SUM to CNT or (Count).
Histogram

Step 5: Drag the dimension Segment and drop into the Color shelf under the Marks pane.

Histogram

After adding the Segment field to Color shelf, you can see a relationship between the Segment field and the Quantity of item as per order is shown in the below screenshot.

Histogram

Step 6: Hold the Ctrl key in the keyboard and drag CNT(Quantity) field from the rows shelf Label shelf under the Marks pane.

Histogram

Step 7: Right-click on the CNT(Quantity) field in Marks pane. And

  • Click on the Quick Table Calculation option from the list.
  • Select the Percent of Total option.
Histogram

Now each colored section of each bar shows its percentage of total quantity shown in the following screenshot.

Histogram

Tableau Vs. Power BI

Tableau and Power BI both are excellent visualization tools in recent time. Tableau has established itself as the market leader for data analytics and BI tools.

Power BI is the closest competitor for the Tableau. Both the visualization tools have their strengths and specialties, and each will used in business as per requirements.

Tableau offers a visualization tool to make data easily understandable for all the users at any level in companies.

Power BI serves to small businesses and offers an easy way to use interface with the ability to create powerful dashboards.

Tableau and Power BI both tools offer advanced reporting and visualization capabilities.

Tableau

Tableau offers a robust BI tool to enhance data visualization and discovery for all types of business and organizations users. Tableau has a simple drag and drop features, and users can analyze critical data quickly. Users can share critical insights across the enterprise and create innovative visualization and reports in Tableau.

Tableau allows embedding dashboards into existing business application such as SharePoint for quick analytics.

Power BI

Power BI is a cloud-based analytics and business intelligence platform. Power BI offers a full overview of critical organization data.

It simplifies the data sharing and evaluation for users by connecting to all of their data sources. And it offers scalable dashboards that make it easy for users to choose various visualization as blueprints, then drag and drop the data from the navigation into the visualization.

We compared the Tableau and Power BI to see the main differences and factors which help you to decide the best for your needs.

ParametersTableauPower BI
MeaningTableau is the data analytics and business intelligence tool for generating reports and data visualization tool with high flexibility.Power BI is the business analytics tool to analyze the business and derive insight from it.
YearTableau was established in 2003.Power BI was established in 2013.
CostTableau is more expensive when it comes to large enterprise, and it paid more when connected to third party application.Power BI is less expensive when compared to the Tableau.
Data visualizationTableau is a more preferred tool when it comes to data visualization.Power BI focused on predictive modeling and reporting.
Data sourceTableau has access to many database sources and servers.
Ex: Text file, Excel, JSON file, Access, PDF file, Statistical file, Spatial file, etc.
Power BI has limited access to other database and servers.
Ex: Access database, SQL server database, SQL server analysis services database, IBM DB2 database, Oracle database, etc.
DeploymentTableau have more flexible deployment. It available on-premises and cloud both model options.Power BI is available as SaaS model options only.
User interfaceTableau has a slick user interface that enables the user to create a customized dashboard.Power BI has a more understandable interface and much simpler to learn. Due to its simplicity and easy to use, that’s why business users prefer power BI.
Data capacityTableau works on the columnar based structure that stores unique values for each column, making it possible to fetch millions of rows.Power BI can Handel up to 10 GB of data. For more than 10 GB, data should be in the cloud (Azure). If it is in the local database, then Power BI selects the data from the database but does not import.
Machine learningPython machine learning capacities is in build with a Tableau that makes it efficient for performing machine learning operation over the datasets.Power BI is integrated with Microsoft Azure that helps in analyzing the data and understanding the pattern of the business.
PerformanceTableau can handle huge data with better performance.Power BI can handle limited data only.
UsersTableau required analysts users for their analytics purpose.Power BI required both technical and non-technical users.
InfrastructureTableau provides flexible infrastructure.Power BI provides software as a service infrastructure.
Overall functionalityTableau has excellent functionality.Power BI has good functionality.
Support levelTableau has a high support level in comparison to power BI.Power BI has a low support level.
Programming tools supportTableau integrates much better with R language as compared to power BI.Power BI is also connected to the R language using Microsoft revaluation analytics. But it is only available for enterprises level users.

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