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Visual Data Storytelling with Tableau, 1st edition
Published by Addison-Wesley Professional (May 10, 2018) © 2018
- Lindy Ryan
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The modules in this book will go beyond the dashboard to communicate business-relevant implications of data analyses using the analytic, visualization, and storytelling capabilities of Tableau, the most popular visualization software in use by businesses world today. Each chapter will split focus between discussing key components of design practice and data visualization and introducing a format for representing information with step-by-step guides for using Tableau. By the end of this book, readers will not only understand how data stories differ from traditional storytelling and how to purposefully craft a compelling data story, but also how to employ the horsepower of Tableau to structure data analysis projects so that they can effectively analyze, visualize, and communicate insights in a way that is meaningful for stakeholders across a variety of communication mediums.
The Tableau software does not come with the book, however Tableau for Teaching provides free one year licenses for students and for faculty (which can be renewed). Instructors can also register their classes for class licensing and be given access to sandbox working environments for their class to collaborate together. Details are explained in the book. The links to Tableau for Teaching are:
Foreword    xii
Preface    xiii
Acknowledgments    xxiv
About the Author    xxvi
Chapter 1 Storytelling in a Digital Era    1
A Visual Revolution    2
From Visualization to Visual Data Storytelling: An Evolution    6
From Visual to Story: Bridging the Gap    8
Summary    13
Chapter 2 The Power of Visual Data Stories    15
The Science of Storytelling    16
   The Brain on Stories    16
   The Human on Stories    18
The Power of Stories    20
   The Classic Visualization Example    20
   Using Small Personal Data for Big Stories    23
   The Two-or-Four Season Debate    27
   Napoleon’s March    29
   Stories Outside of the Box    31
Summary    32
Chapter 3 Getting Started with Tableau    33
Using Tableau    34
Why Tableau?    34
The Tableau Product Portfolio    36
   Tableau Server    37
   Tableau Desktop    37
   Tableau Online    37
   Tableau Public    37
Getting Started    38
Connecting to Data    38
   Connecting to Tables    39
   Live Versus Extract    41
   Connecting to Multiple Tables with Joins    42
Basic Data Prep with Data Interpreter    44
Navigating the Tableau Interface    45
   Menus and Toolbar    46
   Data Window    47
   Shelves and Cards    47
   Legends    47
Understanding Dimensions and Measures    48
   Dimensions    48
   Measures    48
   Continuous and Discrete    48
Summary    49
Chapter 4 Importance of Context in Storytelling    51
Context in Action    53
   Harry Potter: Hero or Menace?    53
   Ensuring Relevant Context    55
Exploratory versus Explanatory Analysis    56
Structuring Stories    58
   Story Plot    59
   Story Genre    60
Audience Analysis for Storytelling    61
   Who    61
   What    62
   Why    62
   How    63
Summary    64
Chapter 5 Choosing the Right Visual    65
The Bar Chart    66
   Tableau How-To: Bar Chart    68
The Line Chart    70
   Tableau How-To: Line Chart    72
The Pie and Donut Charts    73
   Tableau How-To: Pie and Donut Charts    74
The Scatter Plot    78
   Tableau How-To: Scatter Plots    79
The Packed Bubble Chart    83
   Tableau How-To: Packed Bubble Charts    83
The Treemap    85
   Tableau How-To: Treemaps    86
The Heat Map    88
   Tableau How-To: Heat Maps    89
Maps    91
   Connecting to Geographic Data    92
   Assigning Geographic Roles    93
   Creating Geographic Hierarchies    95
   Proportional Symbol Maps    97
   Choropleth Map    100
Summary    106
Chapter 6 Curating Visuals for Your Audience    107
Visual Design Building Blocks    110
Color    110
   Stepped Color    114
   Reversed Color    115
Color Effects    118
   Opacity    118
   Mark Borders    119
   Mark Halos    120
The Truth about Red and Green    121
Lines    124
   Formatting Grid Lines, Zero Lines, and Drop Lines    128
   Formatting Borders    131
   Formatting, Shading, and Banding    134
Shapes    139
   Shape Marks Card    139
   Custom Shapes    140
Summary    142
Chapter 7 Preparing Data for Storytelling    143
Basic Data Prep in Tableau: Data Interpreter    144
   Data Interpreter in Action    145
   Handling Nulls in Tableau    147
Cleaning Messy Survey Data in Excel    148
   Step 1: Surface Cleaning    150
   Step 2: Creating a Numeric Copy    151
   Step 3: Creating the Meta Helper File    153
Pivoting Data from Wide to Tall    155
Reshaping Survey Data with Tableau 10Â Â Â Â 156
   Step 1: Creating Extracts    156
   Step 2: Joining Data Sources    160
Summary    165
Chapter 8 Storyboarding Frame by Frame    167
Understanding Stories in Tableau    168
   Individual Visualizations (Sheets)    169
   Dashboards    169
   Story Points    172
The Storyboarding Process    176
   Planning Your Story’s Purpose    176
   Storyboarding Your Data Story    177
Building a Story    178
   Making Meta Meaningful    179
   Visualizing Survey Demographics    179
   Act One: Demographic Dashboard and Key Question    185
   Act Two: Questioning Character Aggression    187
   Act Three: The Reveal    188
Summary    190
Chapter 9 Advanced Storytelling Charts    191
Timelines    192
Bar-in-Bar Charts    199
Likert Visualizations    202
   100% Stacked Bar Chart    203
   Divergent Stacked Bar Chart    205
Lollipop Charts    215
   Labeled Lollipops    219
Word Clouds    221
Summary    224
Chapter 10 Closing Thoughts    225
Five Steps to Visual Data Storytelling    226
   Step 1 Find Data That Supports Your Story    226
   Step 2 Layer Information for Understanding    227
   Step 3 Design to Reveal    227
   Step 4 Beware the False Reveal    227
   Step 5 Tell It Fast    228
The Important Role of Feedback    228
Ongoing Learning    229
   Teach Yourself: External Resources    229
   Companion Materials to This Text    231
Index    233
Preface    xiii
Acknowledgments    xxiv
About the Author    xxvi
Chapter 1 Storytelling in a Digital Era    1
A Visual Revolution    2
From Visualization to Visual Data Storytelling: An Evolution    6
From Visual to Story: Bridging the Gap    8
Summary    13
Chapter 2 The Power of Visual Data Stories    15
The Science of Storytelling    16
   The Brain on Stories    16
   The Human on Stories    18
The Power of Stories    20
   The Classic Visualization Example    20
   Using Small Personal Data for Big Stories    23
   The Two-or-Four Season Debate    27
   Napoleon’s March    29
   Stories Outside of the Box    31
Summary    32
Chapter 3 Getting Started with Tableau    33
Using Tableau    34
Why Tableau?    34
The Tableau Product Portfolio    36
   Tableau Server    37
   Tableau Desktop    37
   Tableau Online    37
   Tableau Public    37
Getting Started    38
Connecting to Data    38
   Connecting to Tables    39
   Live Versus Extract    41
   Connecting to Multiple Tables with Joins    42
Basic Data Prep with Data Interpreter    44
Navigating the Tableau Interface    45
   Menus and Toolbar    46
   Data Window    47
   Shelves and Cards    47
   Legends    47
Understanding Dimensions and Measures    48
   Dimensions    48
   Measures    48
   Continuous and Discrete    48
Summary    49
Chapter 4 Importance of Context in Storytelling    51
Context in Action    53
   Harry Potter: Hero or Menace?    53
   Ensuring Relevant Context    55
Exploratory versus Explanatory Analysis    56
Structuring Stories    58
   Story Plot    59
   Story Genre    60
Audience Analysis for Storytelling    61
   Who    61
   What    62
   Why    62
   How    63
Summary    64
Chapter 5 Choosing the Right Visual    65
The Bar Chart    66
   Tableau How-To: Bar Chart    68
The Line Chart    70
   Tableau How-To: Line Chart    72
The Pie and Donut Charts    73
   Tableau How-To: Pie and Donut Charts    74
The Scatter Plot    78
   Tableau How-To: Scatter Plots    79
The Packed Bubble Chart    83
   Tableau How-To: Packed Bubble Charts    83
The Treemap    85
   Tableau How-To: Treemaps    86
The Heat Map    88
   Tableau How-To: Heat Maps    89
Maps    91
   Connecting to Geographic Data    92
   Assigning Geographic Roles    93
   Creating Geographic Hierarchies    95
   Proportional Symbol Maps    97
   Choropleth Map    100
Summary    106
Chapter 6 Curating Visuals for Your Audience    107
Visual Design Building Blocks    110
Color    110
   Stepped Color    114
   Reversed Color    115
Color Effects    118
   Opacity    118
   Mark Borders    119
   Mark Halos    120
The Truth about Red and Green    121
Lines    124
   Formatting Grid Lines, Zero Lines, and Drop Lines    128
   Formatting Borders    131
   Formatting, Shading, and Banding    134
Shapes    139
   Shape Marks Card    139
   Custom Shapes    140
Summary    142
Chapter 7 Preparing Data for Storytelling    143
Basic Data Prep in Tableau: Data Interpreter    144
   Data Interpreter in Action    145
   Handling Nulls in Tableau    147
Cleaning Messy Survey Data in Excel    148
   Step 1: Surface Cleaning    150
   Step 2: Creating a Numeric Copy    151
   Step 3: Creating the Meta Helper File    153
Pivoting Data from Wide to Tall    155
Reshaping Survey Data with Tableau 10Â Â Â Â 156
   Step 1: Creating Extracts    156
   Step 2: Joining Data Sources    160
Summary    165
Chapter 8 Storyboarding Frame by Frame    167
Understanding Stories in Tableau    168
   Individual Visualizations (Sheets)    169
   Dashboards    169
   Story Points    172
The Storyboarding Process    176
   Planning Your Story’s Purpose    176
   Storyboarding Your Data Story    177
Building a Story    178
   Making Meta Meaningful    179
   Visualizing Survey Demographics    179
   Act One: Demographic Dashboard and Key Question    185
   Act Two: Questioning Character Aggression    187
   Act Three: The Reveal    188
Summary    190
Chapter 9 Advanced Storytelling Charts    191
Timelines    192
Bar-in-Bar Charts    199
Likert Visualizations    202
   100% Stacked Bar Chart    203
   Divergent Stacked Bar Chart    205
Lollipop Charts    215
   Labeled Lollipops    219
Word Clouds    221
Summary    224
Chapter 10 Closing Thoughts    225
Five Steps to Visual Data Storytelling    226
   Step 1 Find Data That Supports Your Story    226
   Step 2 Layer Information for Understanding    227
   Step 3 Design to Reveal    227
   Step 4 Beware the False Reveal    227
   Step 5 Tell It Fast    228
The Important Role of Feedback    228
Ongoing Learning    229
   Teach Yourself: External Resources    229
   Companion Materials to This Text    231
Index    233
Lindy Ryan is passionate about telling stories with data. She specializes in translating raw data into insightful stories through carefully curated visuals and engaging narrative frameworks.
Before joining academia, Lindy was the Research Director for research and advisory firm Radiant Advisors from 2011 through 2016. In this role Lindy led Radiant’s analyst activities in the confluence of data discovery, visualization, and visual analytics. She also developed the methodology for the Data Visualization Competency Center (DVCC), a framework for helping data-driven organizations to effectively implement data visualization for enterprise-wide visual data analysis and communication. Her tool-agnostic approach has been successfully implemented at a variety of organizations across several industries and with multiple visualization technologies, including Tableau, Qlik, and GoodData. She remains a respected analyst in the data visualization community and is a regular contributor to several industry publications as well as a speaker at conferences worldwide.
Lindy began her academic career as an associate faculty member at City University of Seattle’s School of Applied Leadership where she taught graduate courses in business leadership from 2012 to 2016. In early 2016 she joined the ambitious team at the Rutgers Discovery Informatics Institute and began contributing to multidisciplinary research focused on designing solutions for the next generation of supercomputers tasked with enabling cutting-edge extreme-scale science. Currently, Lindy leads RDI2’s research on understanding and preventing cyberbullying behaviors in emerging technology users through advanced computing approaches.
Today, Lindy teaches courses in visual analytics and data visualization in Rutgers University’s Professional Science Masters program and in Montclair State University’s Business Analytics program. She is a recipient of the MSU Professing Excellence Award, which recognizes professors’ teaching excellence, particularly those who inspire and motivate students. This honor is especially meaningful to Lindy because in addition to her passion for teaching, her research includes a commitment to STEM advocacy, and she spends time on research related to increasing gender equity in CS&E and finding new and novel ways to nurture visual data literacy skills in early STEM learners.
Lindy is an active committee member of the New Jersey Big Data Alliance, a partnership of New Jersey-based academic institutions that serves as the State’s legislated consortium on research, education and outreach in advanced computation and big data. She is the author of The Visual Imperative: Creating a Culture of Visual Discovery released by Morgan Kaufmann in 2016, and the owner of Black Spot Books, a traditional, analytics-driven small-press publishing house.
Learn more about Lindy at www.visualdatastorytelling.com. You can also follow her on Twitter @lindy_ryan or view samples of her work on her Tableau Public page at https:// public.tableau.com/profile/lindyryan#!/.
Before joining academia, Lindy was the Research Director for research and advisory firm Radiant Advisors from 2011 through 2016. In this role Lindy led Radiant’s analyst activities in the confluence of data discovery, visualization, and visual analytics. She also developed the methodology for the Data Visualization Competency Center (DVCC), a framework for helping data-driven organizations to effectively implement data visualization for enterprise-wide visual data analysis and communication. Her tool-agnostic approach has been successfully implemented at a variety of organizations across several industries and with multiple visualization technologies, including Tableau, Qlik, and GoodData. She remains a respected analyst in the data visualization community and is a regular contributor to several industry publications as well as a speaker at conferences worldwide.
Lindy began her academic career as an associate faculty member at City University of Seattle’s School of Applied Leadership where she taught graduate courses in business leadership from 2012 to 2016. In early 2016 she joined the ambitious team at the Rutgers Discovery Informatics Institute and began contributing to multidisciplinary research focused on designing solutions for the next generation of supercomputers tasked with enabling cutting-edge extreme-scale science. Currently, Lindy leads RDI2’s research on understanding and preventing cyberbullying behaviors in emerging technology users through advanced computing approaches.
Today, Lindy teaches courses in visual analytics and data visualization in Rutgers University’s Professional Science Masters program and in Montclair State University’s Business Analytics program. She is a recipient of the MSU Professing Excellence Award, which recognizes professors’ teaching excellence, particularly those who inspire and motivate students. This honor is especially meaningful to Lindy because in addition to her passion for teaching, her research includes a commitment to STEM advocacy, and she spends time on research related to increasing gender equity in CS&E and finding new and novel ways to nurture visual data literacy skills in early STEM learners.
Lindy is an active committee member of the New Jersey Big Data Alliance, a partnership of New Jersey-based academic institutions that serves as the State’s legislated consortium on research, education and outreach in advanced computation and big data. She is the author of The Visual Imperative: Creating a Culture of Visual Discovery released by Morgan Kaufmann in 2016, and the owner of Black Spot Books, a traditional, analytics-driven small-press publishing house.
Learn more about Lindy at www.visualdatastorytelling.com. You can also follow her on Twitter @lindy_ryan or view samples of her work on her Tableau Public page at https:// public.tableau.com/profile/lindyryan#!/.
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