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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Create powerful data visualizations and unlock intelligent business insights with Tableau
Key Features
- Explore all the latest Tableau 2019 features and redefine business analytics for your organization
- Create impressive data visualizations and interactive dashboards to get insights from your data
- Learn effective data storytelling to transform how your business leverages data and makes decisions
Book Description
Tableau is the gold standard of business intelligence and visual analytics tools in every industry. It enables rapid data visualization and interpretation with charts, graphs, dashboards, and much more. Updated with the latest features of Tableau, this book takes you from the foundations of the Tableau 2019 paradigm through to advanced topics.
This third edition of the bestselling guide by Tableau Zen Master, Joshua Milligan, will help you come to grips with updated features, such as set actions and transparent views. Beginning with installation, you'll create your first visualizations with Tableau and then explore practical examples and advanced techniques. You'll create bar charts, tree maps, scatterplots, time series, and a variety of other visualizations. Next, you'll discover techniques to overcome challenges presented by data structure and quality and engage in effective data storytelling and decision making with business critical information. Finally, you'll be introduced to Tableau Prep, and learn how to use it to integrate and shape data for analysis.
By the end of this book, you will be equipped to leverage the powerful features of Tableau 2019 for decision making.
What you will learn
- Develop stunning visualizations that explain complexity with clarity
- Explore the exciting new features of Tableau Desktop and Tableau Prep
- Connect to various data sources to bring all your data together
- Uncover techniques to prep and structure your data for easy analysis
- Create and use calculations to solve problems and enrich analytics
- Master advanced topics such as sets, LOD calcs, and much more
- Enable smart decisions with clustering, distribution, and forecasting
- Share your data stories to build a culture of trust and action
Who this book is for
This Tableau book is for anyone who wants to understand data. If you're new to Tableau, don't worry. This book builds on the foundations to help you understand how Tableau really works and then builds on that knowledge with practical examples before moving on to advanced techniques. Working experience with databases will be useful but is not necessary to get the most out of this book.
Das E-Book können Sie in Legimi-Apps oder einer beliebigen App lesen, die das folgende Format unterstützen:
Seitenzahl: 459
Veröffentlichungsjahr: 2019
Ähnliche
Joshua N. Milligan
Learning Tableau 2019 Third Edition
Copyright © 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor:Vedika NaikAcquisition Editor: Joshua Nadar Content Development Editor: Chris D'cruz Technical Editor: Sagar SawantCopy Editor: Safis EditingProject Coordinator: Hardik Bhinde Proofreader: Safis EditingIndexer: Rekha Nair Graphics: Tom Scaria Production Coordinator: Deepika Naik
First published: April 2015 Second edition: September 2016Third edition: March 2019
Production reference: 1220319
Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK.
ISBN 978-1-78883-952-5
www.packtpub.com
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Contributors
About the author
Joshua N. Milligan is a five-time Tableau Zen Master, the highest recognition of excellence from Tableau Software for masters and teachers of Tableau and collaborators within the community. He was one of three Iron Viz Global finalists in 2017. He is passionate in serving others through data visualization and analytics. As a consultant with Teknion Data Solutions since 2004, he has extensive real-world experience across many industries. In addition to authoring every edition of Learning Tableau, he was a technical reviewer for Tableau Data Visualization Cookbook and Creating Data Stories with Tableau Public. He shares Tableau and Tableau Prep tips on VizPainter and his Twitter handle is @VizPainter. He lives with his family in Tulsa.
About the reviewers
Dave Dwyer has a BSc in information systems from RIT (Rochester Institute of Technology), an MBA from Drexel University, and is a certified Six Sigma Black Belt and PMP. In his 20+ years as an IT professional, he has worked in a wide range of technical and leadership roles, in companies ranging from start-ups to Fortune 100 enterprises. A chance introduction to reporting and analytics 10 years ago got him hooked and he never left. Dave believes that the data science landscape of analytics, visualization, big data, and machine learning will drive more genuine changes in business over the next 10 years than any other area.
Dmitry Anoshin is an expert in the field of analytics with 10 years of experience. He started using Tableau as a primary BI tool in 2011 in his role as a BI consultant for Teradata. He is certified with both Tableau Desktop and Server. He leads probably the biggest Tableau user community with more than 2,000 active users. This community has 2-3 Tableau talks every month, headed by the top Tableau experts, Tableau Zen Masters, and Viz Champions. In addition, Dmitry has previously written three books with Packt and reviewed a further seven. Finally, he is an active speaker at data conferences and helps to adopt cloud analytics.
Packt is searching for authors like you
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Table of Contents
Title Page
Copyright and Credits
Learning Tableau 2019 Third Edition
About Packt
Why subscribe?
Packt.com
Contributors
About the author
About the reviewers
Packt is searching for authors like you
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
Reviews
Section 1: Tableau Foundations
Taking Off with Tableau
The cycle of analytics
Connecting to data
Foundations for building visualizations
Measures and dimensions
Discrete and continuous fields
Discrete fields
Continuous fields
Visualizing data
Bar charts
Iterations of bar charts for deeper analysis
Line charts
Iterations of line charts for deeper analysis
Geographic visualizations
Filled maps
Symbol maps
Density maps
Using Show Me
Putting everything together in a dashboard
The Dashboard interface
Building your dashboard
Summary
Working with Data in Tableau
The Tableau paradigm
A simple example
Connecting to data
Connecting to data in a file
Connecting to data on a server
Connecting to data in the cloud
Shortcuts for connecting to data
Managing data source metadata
Working with extracts instead of live connections
Creating extracts
Using extracts
Performance
Portability and security
When to use an extract
Tableau file types
Joins and blends
Joining tables
Cross database joins
Blending data sources
A blending example
Filtering data
Filtering discrete (blue) fields
Filtering continuous (green) fields
Filtering dates
Other filtering options
Summary
Venturing on to Advanced Visualizations
Comparing values
Bar charts
Bar chart variations
Bullet chart – comparing to a goal, target, or threshold
Bar-in-bar chart
Highlighting categories of interest
Visualizing dates and times
Date parts, date values, and exact dates
Variations of date and time visualizations
Gantt Charts
Relating parts of the data to the whole
Stacked bars
Treemaps
Area charts
Pie charts
Visualizing distributions
Circle charts
Jittering
Box and whisker plots
Histograms
Visualizing multiple axes to compare different measures
Scatterplot
Dual axis and combination charts
Summary
Section 2: Leveraging the Full Power of Tableau
Starting an Adventure with Calculations
Introduction to calculations
Creating and editing calculations
Additional functions and operators
Four main types of calculations
Example data
Row-level calculations
Aggregate-level calculations
Why the row-level/aggregate-level difference matters
Level of detail calculations
Level of detail syntax
Level of detail types
FIXED
INCLUDE
EXCLUDE
Level of detail example
Parameters
Creating parameters
Practical examples of calculations and parameters
Fixing data issues
Extending the data
Enhancing user experience, analysis, and visualizations
Ad hoc calculations
Performance considerations
Summary
Diving Deep with Table Calculations
An overview of Table Calculations
Creating and editing Table Calculations
Quick Table Calculations
Relative versus fixed
Scope and direction
Working with scope and direction
Addressing and partitioning
Advanced addressing and partitioning
Custom Table Calculations
Meta table functions
Lookup and previous value
Running functions
Window functions
Rank functions
Script functions
The Total function
Practical examples
Year over Year Growth
Dynamic titles with totals
Late filtering
Data densification
When and where data densification occurs
An example of leveraging data densification
Summary
Making Visualizations That Look Great and Work Well
Visualization considerations
Leveraging formatting in Tableau
Workbook-level formatting
Worksheet-level formatting
Field-level formatting
Custom number formatting
Custom date formatting
Null formatting
Additional formatting options
Adding value to visualizations
Tooltips
Viz in Tooltip
Summary
Telling a Data Story with Dashboards
Key concepts for dashboards
Dashboard definition
Dashboard objectives
Dashboard approaches
Designing dashboards in Tableau
Objects
Tiled versus floating
Manipulating objects on the dashboard
Dashboard example – is least profitable always unprofitable?
Building the views
Creating the dashboard framework
Implementing actions to guide the story
Interlude – context filtering
Designing for different displays and devices
How actions work
Filter actions
Highlight actions
URL actions
Set actions
Sets
A set action example
Dashboard example – regional scorecard
Stories
Summary
Digging Deeper - Trends, Clustering, Distributions, and Forecasting
Trends
Customizing Trend Lines
Trend models
Linear
Logarithmic
Exponential
Power
Polynomial
Analyzing trend models
Exporting statistical model details
Advanced statistics (and more!) with R and Python
Clustering
Distributions
Forecasting
Summary
Section 3: Data Prep and Structuring
Cleaning and Structuring Messy Data
Structuring data for Tableau
Good structure – tall and narrow instead of short and wide
Wide data
Tall data
Wide and tall in Tableau
Good structure – star schemas (Data Mart/Data Warehouse)
Dealing with data structure issues
Restructuring data in Tableau connections
Union files together
Cross database joins
A practical example – filling out missing/sparse dates
Working with different levels of detail
Overview of advanced fixes for data problems
Summary
Introducing Tableau Prep
Getting prepped to explore Tableau Prep
Understanding the Tableau Prep Builder Interface
Flowing with the fundamental paradigm
Connecting to data
Cleaning the data
Union, merging mismatched fields, and removing unnecessary fields
Grouping and cleaning
Calculations and aggregations in Tableau Prep
Filtering in Tableau Prep
Transforming the data for analysis
Options for automating flows
Summary
Section 4: Advanced Techniques and Sharing with Others
Advanced Visualizations, Techniques, Tips, and Tricks
Advanced visualizations
Slope Charts
Lollipop Charts
Waterfall Charts
Step Lines and Jump Lines
Spark Lines
Dumbbell Charts
Unit chart/symbol charts
Marimekko Charts
Sheet swapping and dynamic dashboards
Dynamically showing and hiding other controls
Mapping techniques
Supplementing the standard in geographic data
Manually assigning geographic locations
Creating custom territories
Ad hoc custom territories
Field-defined custom territories
Leveraging spatial objects
Some final map tips
Using background images
Animation
Transparency
Summary
Sharing Your Data Story
Presenting, printing, and exporting
Presenting
Printing
Exporting
Sharing with users of Tableau Desktop or Tableau Reader
Sharing with Tableau Desktop users
Sharing with Tableau Reader users
Sharing with users of Tableau Server, Tableau Online, and Tableau Public
Publishing to Tableau Public
Publishing to Tableau Server and Tableau Online
Interacting with Tableau Server
Additional distribution options using Tableau Server
Summary
Other Books You May Enjoy
Leave a review - let other readers know what you think
Preface
What is it about Tableau that inspires an ever growing community to hold up signs that read I ♥ Tableau and excitedly share data visualizations on social media? Why do so many organizations turn to Tableau as the gold standard for visual analytics? How can an analytics platform be so fun, engaging, and useful all at once?
Tableau disrupted the paradigm for visually interacting with data. It made it easy and intuitive (and fun!) to be hands-on with the data, to receive instant visual feedback with every action, and to ask questions and uncover insights in a natural flow of thought and interaction. And Tableau continues to expand and evolve in ways that make seeing and understanding data easier and more powerful. New features such as Set Actions, geospatial support, and new statistical models expand the analysis that's possible. Transparency, density maps, new color palettes, and formatting options greatly enhance the visual story you can tell. The introduction of Tableau Prep brings the same intuitive instant feedback to data prep and cleansing that Tableau Desktop brought to data visualization. We'll cover these new features (and more) in the chapters of this book!
We'll look at Tableau through the lens of understanding the underlying paradigm of how and why Tableau works in the context of practical examples. And then we'll build on this solid foundation of understanding so that you will have the requisite tools and skills to tackle even the toughest data challenges!
Who this book is for
This book is for anyone who needs to see and understand their data! From the casual business user to the hardcore data analyst, everyone needs to have the ability to ask and answer questions of data. Having a bit of background with data will definitely help, but you don't need to know scripting, SQL, or database structures. Whether you're new to Tableau or have been using it for months or even years, you'll gain a solid foundation for understanding Tableau and possess the tools and skills to build toward advanced mastery of the tool.
What this book covers
Chapter 1, Taking Off with Tableau, introduces the foundational principles of Tableau. We'll go through a series of examples that will introduce the basics of connecting to data, exploring and analyzing the data visually, and finally putting it all together in a fully interactive dashboard.
Chapter 2, Working with Data in Tableau, focuses on essential concepts of how Tableau works with data. You will look at multiple examples of different connections to different data sources, consider the benefits and potential drawbacks of using data extracts, consider how to manage metadata, dive into details on joins and blends, and finally, take a look at options for filtering data.
Chapter 3, Venturing on to Advanced Visualizations, explores how to create the various types of views and how to extend basic visualizations using a variety of advanced techniques such as simple calculations, jittering, multiple mark types, and dual axis. Along the way, we will also cover some details on how dates work in Tableau.
Chapter 4, Starting an Adventure with Calculations, focuses on laying a foundation and also gives a number of practical examples, by means of which you will understand the key concepts behind how calculations work in Tableau.
Chapter 5, Diving Deep with Table Calculations, explores the final main type of calculations: table calculations. These are some of the most powerful calculations in terms of their ability to solve problems and open up incredible possibilities for in-depth analysis. In practice, they range from very easy to exceptionally complex.
Chapter 6, Making Visualizations that Look Great and Work Well, explains how formatting works in Tableau, giving you the ability to refine the visualizations you created in discovery and analysis into incredibly effective communication of your data story.
Chapter 7, Telling a Data Story with Dashboards, demonstrates how Tableau allows you to bring together related data visualizations in a single dashboard. This dashboard could be a static view of various aspects of the data, or a fully interactive environment, allowing users to dynamically filter, drill down, and interact with the data visualizations. In this chapter, you will take a look at most of these concepts within the context of several in-depth examples, where you will walk through the dashboard design process step by step.
Chapter 8, Digging Deeper – Trends, Clustering, Distributions, and Forecasting, explains how Tableau enables you to quickly enhance your data visualizations with statistical analysis. Built-in features, such as trend models, clustering, distributions, and forecasting, allow you to quickly add value to your visual analysis. You will take a look at these concepts in the context of a few practical examples using some sample datasets.
Chapter 9, Cleaning and Structuring Messy Data, focuses on a number of principles for structuring data to work well with Tableau, as well as some specific examples of how to address common data issues.
Chapter 10, Introducing Tableau Prep, works through a practical example as we explore the paradigm of Tableau Prep, enabling the reader to understand the fundamental transformations and see many of the features and functions of Tableau Prep.
Chapter 11, Advanced Visualizations, Techniques, Tips, and Tricks, explains a number of advanced techniques in a practical context. You'll learn things such as creating advanced visualizations, dynamically swapping views on a dashboard, using custom images, and advanced geographic visualizations.
Chapter 12, Sharing Your Data Story, explains how Tableau enables you to share your work using a variety of methods. In this chapter, we'll take a look at the various ways to share visualizations and dashboards, along with what to consider when deciding how you will share them.
To get the most out of this book
This book does not assume any specific database knowledge, but it definitely will help to have some basic familiarity with data itself. We'll cover the foundational principles first, and while it may be tempting to skip the first chapter, please don't! We'll lay a foundation of terminology and the paradigm that will be used throughout the remainder of the book.
You'll be able to follow along with many of the examples in the book using Tableau Desktop and Tableau Prep Builder (in Chapter 10, Introducing Tableau Prep). You may download and install the most recent versions from Tableau using the following links:
Tableau Desktop
:
https://www.tableau.com/products/desktop/download
Tableau Prep Builder
:
https://www.tableau.com/products/prep/download
Please speak to a Tableau representative for licensing information. In most cases, you may install a 14-day trial of each product if you do not currently have a license.
Download the example code files
For most chapters, you'll find applicable data files (Excel and text files) and a set of Tableau Workbook files, (.twbx) , or Tableau Flow files, (.tfl) , which you may open in Tableau Desktop or Tableau Prep Builder, respectively. These will follow the convention ChapterNN_Starter and ChapterNN_Complete (where NN is the chapter number). The starter workbooks and flows are intended to allow you to work through the examples in the book on your own, though at times, they will include completed examples for reference. The complete workbooks are entirely finished and are intended to allow you to check your work or see the finished example.
You may download the example files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
Log in or register at
www.packt.com
.
Select the
SUPPORT
tab.
Click on
Code Downloads & Errata
.
Enter the name of the book in the
Search
box and follow the onscreen instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
WinRAR/7-Zip for Windows
Zipeg/iZip/UnRarX for Mac
7-Zip/PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Learning-Tableau-2019. In case there's an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://www.packtpub.com/sites/default/files/downloads/9781788839525_ColorImages.pdf.
Get in touch
Feedback from our readers is always welcome.
General feedback: If you have questions about any aspect of this book, mention the book title in the subject of your message and email us at [email protected].
Errata: Although we have taken every care to ensure the accuracy of our content, mistakes do happen. If you have found a mistake in this book, we would be grateful if you would report this to us. Please visit www.packt.com/submit-errata, selecting your book, clicking on the Errata Submission Form link, and entering the details.
Piracy: If you come across any illegal copies of our works in any form on the internet, we would be grateful if you would provide us with the location address or website name. Please contact us at [email protected] with a link to the material.
If you are interested in becoming an author: If there is a topic that you have expertise in, and you are interested in either writing or contributing to a book, please visit authors.packtpub.com.
Reviews
Please leave a review. Once you have read and used this book, why not leave a review on the site that you purchased it from? Potential readers can then see and use your unbiased opinion to make purchase decisions, we at Packt can understand what you think about our products, and our authors can see your feedback on their book. Thank you!
For more information about Packt, please visit packt.com.
Section 1: Tableau Foundations
This section lays the foundations for data visualization in Tableau. It provides an overview of the interface and terminology, explains data connections, and covers a wide variety of visualization types.
This section consists of the following chapters:
Chapter 1
,
Taking Off with Tableau
Chapter 2
,
Working with Data in Tableau
Chapter 3
,
Venturing on to Advanced Visualizations
Taking Off with Tableau
When you first encounter a dataset, often the first thing you see is the raw data—numbers, dates, text, field names, and data types. Almost certainly, there are insights and stories that need to be uncovered and told, decisions to make, and actions to take. But how do you find the significance? How do you uncover the meaning and tell the stories that are hidden in the data?
Tableau is an amazing platform for seeing, understanding, and making key decisions based on your data! With it, you will be able to achieve incredible data discovery, data analysis, and data storytelling. You'll accomplish these tasks and goals visually using an interface that is designed for a natural and seamless flow of thought and work.
To leverage the power of Tableau, you don't need to write complex scripts or queries. Instead, you will be interacting with your data in a visual environment where everything that you drag and drop will be translated into the necessary queries for you and then displayed visually. You'll be working in real time, so you will see results immediately, get answers as quickly as you can ask questions, and be able to iterate through potentially dozens of ways to visualize the data to find a key insight or tell a piece of the story.
This chapter introduces the foundational principles of Tableau. We'll go through a series of examples that will introduce the basics of connecting to data, exploring and analyzing the data visually, and finally putting it all together in a fully interactive dashboard. These concepts will be developed far more extensively in subsequent chapters. But don't skip this chapter, as it introduces key terminology and key concepts, including the following:
The cycle of analytics
Connecting to data
Foundations for building visualizations
Creating bar charts
Creating line charts
Creating geographic visualizations
Using Show Me
Bringing everything together via a dashboard
The cycle of analytics
As someone who works with and seeks to understand data, you will find yourself working within the cycle of analytics. This cycle might be illustrated as follows:
Tableau allows you to jump to any step of the cycle, move freely between steps, and iterate through the cycle very rapidly. With Tableau, you have the ability to do the following:
Data
discovery
: You can very easily explore a dataset using Tableau and begin to understand what data you have visually.
Data preparation
: Tableau allows you to connect to data from many different sources and, if necessary, create a structure that works best for your analysis. Most of the time, this is as easy as pointing Tableau to a database or opening a file, but Tableau gives you the tools to bring together even complex and messy data from multiple sources.
Data analysis
: Tableau makes it easy to visualize the data, so you can see and understand trends, outliers, and relationships. In addition to this, Tableau has an ever-growing set of analytical functions that allow you dive deep into understanding complex relationships, patterns, and correlations in the data.
Data storytelling
: Tableau allows you to build fully interactive dashboards and stories with your visualizations and insights so that you can share the data story with others.
All of this is done visually. Data visualization is the heart of Tableau. You can iterate through countless ways of visualizing the data to ask and answer questions, raise new questions, and gain new insights. And you'll accomplish this as a flow of thought.
Connecting to data
Tableau connects to data stored in a wide variety of files and databases. This includes flat files, such as Excel documents, spatial files, and text files; relational databases, such as SQL Server and Oracle; cloud-based data sources, such as Google Analytics and Amazon Redshift; and OLAP data sources, such as Microsoft Analysis Services. With very few exceptions, the process of analysis and creating visualizations will be the same, no matter what data source you use.
We'll cover details of connecting to different types of data sources in Chapter 2, Working with Data in Tableau. And we'll cover data spanning a wide variety of industries in other chapters. For now, we'll connect to a text file, specifically, a comma-separated values file (.csv). The data is a variation of the sample that ships with Tableau: Superstore, a fictional retail chain that sells various products to customers across the United States. Please use the supplied data file instead of the Tableau sample data, as the variations will lead to differences in visualizations.
The Chapter 1 workbooks, included with the code files bundle, already have connections to the file, but for this example, we'll walk through the steps of creating a connection in a new workbook:
Open Tableau. You should see the home screen with a list of connection options on the left and, if applicable, thumbnail previews of recently edited workbooks in the center, along with sample workbooks at the bottom.
Under
Connect
and
To a File
, click
Text File
.
In the
Open
dialogue box, navigate to the
\Learning Tableau\Chapter 01
directory and select the
Superstore.csv
file.
You will now see the data connection screen, which allows you to visually create connections to data sources. We'll examine the features of this screen in detail in the
Connecting to data
section of
Chapter 2
,
Working with Data in Tableau
. For now, Tableau has already added and given a preview of the file for the connection:
For this connection, no other configuration is required, so simply click on the Sheet 1 tab at the bottom to start visualizing the data! You should now see the main work area within Tableau, which looks like this:
We'll refer to elements of the interface throughout the book using specific terminology, so take a moment to familiarize yourself with the terms used for various components numbered in the preceding screenshot:
The
Menu
contains various menu items for performing a wide range of functions.
The
Toolbar
allows for common functions such as undo, redo, save, add a data source, and so on.
The
Side Bar
contains tabs for
Data
and
Analytics
. When the
Data
tab is active, we'll refer to the side bar as the data pane. When the
Analytics
tab is active, we'll refer to the side bar as the analytics pane. We'll go into detail later in this chapter, but for now, note that the data pane shows the data source at the top and contains a list of fields from the data source below, divided into
Dimensions
and
Measures
.
Various shelves such as
Columns
,
Rows
,
Pages
, and
Filters
serve as areas to drag and drop fields from the data pane. The
Marks
card contains additional shelves such as
Color
,
Size
,
Text
,
Detail
, and
Tooltip
. Tableau will visualize data based on the fields you drop on to the shelves.
The
c
anvas
or
view
is where Tableau will draw the data visualization. You may also drop fields directly on to the view. You'll find the seamless title at the top of the canvas. By default, it will display the name of the sheet, but it can be edited or even hidden.
Show Me
is a feature that allows you to quickly iterate through various types of visualizations based on data fields of interest. We'll look at
Show Me
toward the end of the chapter.
The tabs at the bottom of the window give you options for editing the data source, as well as navigating between and adding any number of sheets, dashboards, or stories. Many times, any tab (whether it is a sheet, a dashboard, or a story) is referred to generically as a
sheet
.
As you work, the status bar will display important information and details about the view, selections, and the user.
Various controls allow you to navigate between sheets, dashboards, and stories, as well as view the tabs with
Show Filmstrip
or switch to a sheet sorter showing an interactive thumbnail of all sheets in the workbook. Now that you have connected to the data in the text file, we'll explore some examples that lay the foundation for data visualization and then move on to building some foundational visualization types. To prepare for this, please do the following:
From the menu, select
File |
Exit
.
When prompted to save changes, select
No
.
From the
\learning Tableau\Chapter 01
directory, open the file
Chapter 01 Starter.twbx
. This file contains a connection to the
Superstore
data file and is designed to help you walk through the examples in this chapter.
With a connection to the data, you are ready to start visualizing and analyzing the data. As you begin to do so, you will take on the role of an analyst at the retail chain. You'll ask questions of the data, build visualizations to answer those questions, and ultimately design a dashboard to share the results. Let's start by laying some foundations for understanding how Tableau visualizes data.
Foundations for building visualizations
When you first connect to a data source such as the Superstore file, Tableau will display the data connection and the fields in the data pane on the left Side Bar. Fields can be dragged from the data pane onto the canvas area or onto various shelves such as Rows, Columns, Color, or Size. As we'll see, the placement of the fields will result in different encodings of the data based on the type of field.
Measures and dimensions
The fields from the data source are visible in the data pane and are divided into Measures and Dimensions. The difference between measures and dimensions is a fundamental concept to understand when using Tableau:
Measures
are values that are aggregated. For example, they are summed, averaged, counted, or have a minimum or a maximum.
Dimensions
are values that determine the level of detail at which measures are aggregated. You can think of them as slicing the measures or creating groups into which the measures fit. The combination of dimensions used in the view define the view's basic level of detail.
As an example (which you can view in the Chapter 01 Starter workbook on the Measures and Dimensions sheet), consider a view created using the Regionfields and Sales from the Superstore connection:
The Sales field is used as a measure in this view. Specifically, it is being aggregated as a sum. When you use a field as a measure in the view, the type aggregation (for example, SUM, MIN, MAX, and AVG) will be shown on the active field. Note that in the preceding example, the active field on rows clearly indicates the sum aggregation of Sales: SUM(Sales).
The Region field is a dimension with one of four values for each record of data: Central, East, South, or West. When the field is used as a dimension in the view, it slices the measure. So, instead of an overall sum of sales, the preceding view shows the sum of sales for each region.
Discrete and continuous fields
Another important distinction to make with fields is whether a field is being used as discrete or continuous. Whether a field is discrete or continuous, determines how Tableau visualizes it based on where it is used in the view. Tableau will give a visual indication of the default for a field (the color of the icon in the data pane) and how it is being used in the view (the color of the active field on a shelf). Discrete fields, such as Region in the previous example, are blue. Continuous fields, such as Sales, are green.
Discrete fields
Discrete (blue) fields have values that are shown as distinct and separate from one another. Discrete values can be reordered and still make sense. For example, you could easily rearrange the values of Region to be East, South, West, and Central, instead of the default order in the preceding screenshot.
When a discrete field is used on the Rows or Columns shelves, the field defines headers. Here, the discrete field Region defines column headers:
Here, it defines row headers:
When used for color, a Discrete field defines a discrete color palette in which each color aligns with a distinct value of the field:
Continuous fields
Continuous (green) fields have values that flow from first to last as a continuum. Numeric and date fields are often (though not always) used as continuous fields in the view. The values of these fields have an order that it would make little sense to change.
When used on Rows or Columns, a continuous field defines an axis:
When used for color, a continuous field defines a gradient:
It is very important to note that continuous and discrete are different concepts from Measure and Dimension. While most dimensions are discrete by default, and most measures are continuous by default, it is possible to use any measure as a discrete field and some dimensions as continuous fields in the view.
In general, you can think of the differences between the types of fields as follows:
Choosing between dimension and measure tells Tableau
how to slice
or
aggregate
the data
Choosing between d
iscrete and continuous tells Tableau
how to display
the data with a header or an axis and defines individual colors or a gradient.
As you work through the examples in this book, pay attention to the fields you are using to create the visualizations, whether they are dimensions or measures, and whether they are discrete or continuous. Experiment with changing fields in the view from continuous to discrete, and vice versa, to gain an understanding of the differences in the visualization.
Visualizing data
A new connection to a data source is an invitation to explore and discover! At times, you may come to the data with very well-defined questions and a strong sense of what you expect to find. Other times, you will come to the data with general questions and very little idea of what you will find. The visual analytics capabilities of Tableau empower you to rapidly and iteratively explore the data, ask new questions, and make new discoveries.
The following visualization examples cover a few of the most foundational visualization types. As you work through the examples, keep in mind that the goal is not simply to learn how to create a specific chart. Rather, the examples are designed to help you think through the process of asking questions of the data and getting answers through iterations of visualization. Tableau is designed to make that process intuitive, rapid, and transparent.
Bar charts
Bar charts visually represent data in a way that makes the comparing of values across different categories easy. The length of the bar is the primary means by which you will visually understand the data. You may also incorporate color, size, stacking, and order to communicate additional attributes and values.
Creating bar charts in Tableau is very easy. Simply drag and drop the measure you want to see onto either the Rows or Columns shelf and the dimension that defines the categories onto the opposing Rows or Columns shelf.
As an analyst for Superstore, you are ready to begin a discovery process focused on sales (especially the dollar value of sales). As you follow the examples, work your way through the sheets in the Chapter 01 Starter workbook. The Chapter 01 Complete workbook contain, the complete examples so you can compare your results at any time:
Click on the the
Sales by Department
tab to view that sheet.
Drag and drop the
Sales
field from
Measures
in the data pane onto the
Columns
shelf. You now have a bar chart with a single bar representing the sum of sales for all the data in the data source.
Drag and drop the
Department
field from
Dimensions
in the data pane to the
Rows
shelf. This slices the data to give you three bars, each having a length that corresponds to the sum of sales for each department:
You now have a horizontal bar chart. This makes comparing the sales between the departments easy. The mark type drop-down menu on the Marks card is set to Automatic and shows an indication that Tableau has determined that bars are the best visualization given the fields you have placed in the view. As a dimension, the Department slices the data. Being discrete, it defines row headers for each department in the data. As a measure, the Sales field is aggregated. Being continuous, it defines an axis. The mark type of bar causes individual bars for each department to be drawn from 0 to the value of the sum of sales for that department.
Tableau draws different marks in different ways; for example, bars are drawn from 0 (or the end of the previous bar, if stacked) along the axis. Circles and other shapes are drawn at locations defined by the value(s) of the field defining the axis. Take a moment to experiment with selecting different mark types from the drop-down menu on the Marks card. Having an understanding of how Tableau draws different mark types will help you master the tool.
Iterations of bar charts for deeper analysis
Using the preceding bar chart, you can easily see that the technology department has more total sales than either the furniture or office supplies departments. What if you want to further understand sales amounts for departments across various regions? Follow these two steps:
Navigate to the
Bar Chart (two levels)
sheet, where you will find an initial view identical to the one you created earlier
Drag the
Region
field from
Dimensions
in the data pane to the
Rows
shelf and drop it to the left of the
Department
field already in view
You should now have a view that looks like this:
You still have a horizontal bar chart, but now you've introduced Region as another dimension that changes the level of detail in the view and further slices the aggregate of the sum of sales. By placing Region before Department, you are able to easily compare the sales of each department within a given region.
Now you are starting to make some discoveries. For example: the Technology department has the most sales in every region, except in the East, where Furniture had higher sales. Office Supplies never has the highest sales in any region.
Let's take a look at a different view, using the same fields arranged differently:
Navigate to the
Bar Chart (stacked)
sheet, where you will find a view identical to the original bar chart.
Drag the
Region
field from the
Rows
shelf and drop it on to the
Color
shelf:
Instead of a side-by-side bar chart, you now have a stacked bar chart. Each segment of the bar is color-coded by the Region field. Additionally, a color legend has been added to the workspace. You haven't changed the level of detail in the view, so sales are still summed for every combination of region and department:
If
Department
is the only field used as a dimension, you will have a view at the department level of detail, and all measures in the view will be aggregated per department.
If
Region
is the only field used as a dimension, you will have a view at the region level of detail, and all measures in the view will be aggregated per region.
If you use both
Department
and
Region
as dimensions in the view, you will have a view at the level of department and region. All measures will be aggregated per unique combination of department and region, and there will be one mark for each combination of department and region.
Stacked bars can be useful when you want to understand part-to-whole relationships. It is now fairly easy to see what portion of the total sales of each department is made in each region. However, it is very difficult to compare sales for most of the regions across departments. For example, can you easily tell which department had the highest sales in the East region? It is difficult because, with the exception of West, every segment of the bar has a different starting place.
Now take some time to experiment with the bar chart to see what variations you can create:
Navigate to the
Bar Chart (experimentation)
sheet.
Try dragging the
Region
field from
Color
to the other various shelves on the
Marks
card, such as
Size
,
Label
, and
Detail
. Observe that in each case the bars remain stacked but are redrawn based on the visual encoding defined by the
Region
field.
Use the
Swap
button on the
Toolbar
to swap fields on
Rows
and
Columns
. This allows you to very easily change from a horizontal bar chart to a vertical bar chart (and vice versa):
Drag and drop
Sales
from the
Measures
section of the data pane on top of the
Region
field on the
Marks
card to replace it. Drag the
Sales
field to
Color
if necessary, and notice how the color legend is a gradient for the continuous field.
Experiment further by dragging and dropping other fields onto various shelves. Note the behavior of Tableau for each action you take.
From the
File
menu, select
Save
.
Line charts
Line charts connect related marks in a visualization to show movement or relationship between those connected marks. The position of the marks and the lines that connect them are the primary means of communicating the data. Additionally, you can use size and color to communicate additional information.
The most common kind of line chart is a Time Series. A time series shows the movement of values over time. Creating one in Tableau requires only a date and a measure.
Continue your analysis of Superstore sales using the Chapter 01 Starter workbook you just saved:
Navigate to the
Sales
over time
sheet.
Drag the
Sales
field from
Measures
to
Rows
. This gives you a single, vertical bar representing the sum of all sales in the data source.
To turn this into a time series, you must introduce a date. Drag the
Order Date
field from
Dimensions
in the data pane on the left and drop it into
Columns
. Tableau has a built-in date hierarchy, and the default level of
Year
has given you a line chart connecting four years. Notice that you can clearly see an increase in sales year after year:
Use the drop-down menu on the
YEAR(Order Date)
field on
Columns
(or right-click the field) and switch the date field to use
Quarter
. You may notice that
Quarter
is listed twice in the drop-down menu. We'll explore the various options for date parts, values, and hierarchies in the
Visualizing Dates and Times
section of
Chapter 3
,
Venturing on to Advanced Visualizations
. For now, select the second option:
Notice that the cyclical pattern is quite evident when looking at sales by quarter:
Iterations of line charts for deeper analysis
Right now, you are looking at the overall sales over time. Let's do some analysis at a slightly deeper level:
Navigate to the
Sales
over time (overlapping lines)
sheet, where you will find a view identical to the one you just created.
Drag the
Region
field from
Dimensions
to
Color
. Now you have a line per region, with each line a different color, and a legend indicating which color is used for which region. As with the bars, adding a dimension to color splits the marks. However, unlike the bars, where the segments were stacked, the lines are not stacked. Instead, the lines are drawn at the exact value for the sum of sales for each region and quarter. This allows for easy and accurate comparison. It is interesting to note that the cyclical pattern can be observed for each region:
With only four regions, it's fairly easy to keep the lines separate. But what about dimensions that have even more distinct values? The steps are as follows:
Navigate to the
Sales
over time (multiple rows)
sheet, where you will find a view identical to the one you just created.
Drag the
Category
field from
Dimensions
and drop it directly on top of the
Region
field currently on the
Marks
card. This replaces the
Region
field with
Category
. You now have 17 overlapping lines. Often, you'll want to avoid more than two or three overlapping lines. But you might also consider using color or size to showcase an important line in the context of the others. Also note that clicking an item in the color legend will highlight the associated line in the view. Highlighting is an effective way to pick out a single item and compare it to all the others.
Drag the
Category
field from
Color
on the
Marks
card and drop it into
Rows
. You now have a line chart for each category. Now you have a way of comparing each product over time without an overwhelming overlap, and you can still compare trends and patterns over time. This is the start of a spark-lines visualization that will be developed more fully in the
Advanced Visualizations
section of
Chapter 11
,
Advanced Visualizations, Techniques, Tips, and Tricks
.
Geographic visualizations
In Tableau, the built-in geographic database recognizes geographic roles for fields, such as Country, State, City, Airport, Congressional District, or Zip Code. Even if your data does not contain latitude and longitude values, you can simply use geographic fields to plot locations on a map. If your data does contain latitude and longitude fields, you may use those instead of the generated values.
Tableau can also read shape files and geometries from some databases. These and other geographic capabilities will be covered in more detail in the Mapping Techniques section of Chapter 11, Advanced Visualizations, Techniques, Tips, and Tricks. In the following examples, we'll consider some of the key concepts of geographic visualizing.
Geographic visualization is incredibly valuable when you need to understand where things happen and whether there are any spatial relationships within the data. Tableau offers three main types of geographic visualization:
Filled maps (simply referred to as
maps
in the Tableau interface)
Symbol maps
Density maps
