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aise your hand if your approach to creating a graph goes something like this: You analyze some data. Write up the results. Make a graph and drop it into the report, surrounded by text. Label it something benign like “Figure 1. Average Earnings, 1990–2020.” Save it as a PDF. Post it to the world. It might have taken you months or even years to compile and analyze the data and write the report. For many, it takes far less time to design the graphs that showcase that data. You might open a program like Microsoft Excel, paste in the data, click through the drop-down menu, select one you’ve used dozens or hundreds of times, accept the default formatting, and paste it into the report. But at any point in this sequence did you pause to consider what’s most important about communicating the work? It’s the audience. People will read your report. People will listen to you discuss your work. And yet many of us spend far too little time thinking about how we can best present our findings. Instead we use whatever default approach is quickest and easiest. Why is this? Maybe you don’t believe you have the technical skills or design know-how to create complex, attractive graphs. Or you worry it’s not worth the effort, because your managers or tenure committee or whoever else won’t see it as time well spent. Many people simply think that their reader will just “get it,” as if everyone has seen the content a hundred times before. But many readers, especially those who can make change or implement policy, may have never seen this content before. In these cases—which are probably most of them— thinking carefully about how data is presented is just as important as the data itself. This book is about how to create better, more effective visualizations of your data. It aims to expand your graphic literacy and put more graphs in your toolbox. The next time you open
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Excel, Tableau, R, or whatever your software tool of choice, you won’t be bound by the graphs in the dropdown menus or the tutorial manual. This book will guide you to choose the graph that is the best fit for your data and will most effectively communicate your message. People often tell me they can’t create some of these different, nonstandard graphs because their colleague or manager or audience won’t understand them. We are not born knowing instinctively how to read a bar chart or line chart or pie chart. As Scott Klein, deputy managing editor at ProPublica once wrote, “There is no such thing as an innately intuitive graphic. None of us are born literate in reading visualizations.” As data visualization creators, we must understand our audience and know when a different graph can engage readers—and help them expand their own graphic literacy.
This book has three main parts. Part 1 covers general guidelines to creating effective visualizations. We’ll learn the importance of our audience and how to consider what category of graph will best meet their needs. No data visualization book will contain every lesson to create effective graphs, but there are some best practices that can guide your work. As you go
Each of these six charts visualizes the same data: The share of people earning minimum wage or less in each state.
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forward creating more visuals and seeing their effect on your audience, you’ll develop your own aesthetic and learn when to bend or break these guidelines. Part 2 is the meat of the book. We will define and discuss more than eighty graphs, categorized into eight broad categories: Comparisons, Time, Distribution, Geospatial, Relationship, Part-to-Whole, Qualitative, and Tables. We will see how each graph works and the advantages and disadvantages of each. Graphs overlap between these categories—a bar chart, for example, can be used to show changes over time or comparisons between groups. The categorizations here are based on a graph’s primary purpose. But even that’s not an objective truth, and your perspective and situation may differ. I do not discuss every single possible graph—there are many specialized graphs in fields like architecture, biology, and engineering that are excluded here. Instead, these chapters cover the most common and flexible graphs that can showcase the sorts of data most people will need to display. I tie these chapters together in part 3 with a chapter on building a data visualization style guide and a chapter on how to pull the different lessons together in a series of graph redesigns. If you’ve ever written a research paper, or even a book report, you are probably aware of the array of writing style guides, from the Chicago Manual of Style to the Modern Language Association. These guides break down writing into component parts and prescribe their proper use. A data visualization style guide does the same for graphs—defines their parts and how to style and use them. In the final chapter, we apply the lessons to redesign a series of graphs to improve how they communicate data. This book will guide you as you explore your data and how it might be visualized. Now more than ever, content must be visual if it is to travel far. Your clients and colleagues, and your audiences of policymakers, decisionmakers, and interested readers are inundated with a flow of information. Visuals cut through that. Anyone can improve the way they visualize and communicate their data—and you don’t need a graduate degree in marketing or design or advertising. Take it from me, I started my career as an economist in the federal government.
HOW I LEARNED TO VISUALIZE MY DATA Once I settled on declaring my economics major at the University of Wisconsin at Madison (there was an ill-fated attempt to also be a math major, but I hit a wall at Markov chains), I knew I wanted to end up in Washington, DC. I wanted to be near the center of public policy and politics. I wanted to explore the real problems of the day and help craft solutions.
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I moved to DC in 2005 to join the Congressional Budget Office (CBO). My job was to help work on the long-term microsimulation model that is used to examine the Social Security system and forecast the long-term finances of the federal budget. The spring of 2005 was an exciting time to work on Social Security—President George W. Bush had made Social Security a central component of his second term. In his 2005 State of the Union address, he said, “We must pass reforms that solve the financial problems of Social Security once and for all.” Reform would stall later that year, but in the course of my first few months on the job, my group at CBO estimated and analyzed the effects of dozens of policy proposals. Five years later, I had expanded my work to include issues around policies that affected disabled workers, immigration, and food stamps (now called the Supplemental Nutrition Assistance Program or SNAP). In 2010, three of my colleagues were drafting a special report on policy options for Social Security. In it, they would show the impact of thirty different options for reform. One of the central figures in the report would show changes in taxes received by the system, benefits paid out from the system, the balance between the two, and other measures of fiscal solvency for these thirty options. It looked something like this:
Author’s rendering of early draft of exhibit from the Congressional Budget Office.
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You don’t need to be a government economist to know that members of Congress are unlikely to read something that looks like a spreadsheet. There are too many rows, too many columns, too many numbers—too much information. It was right then that I first started thinking about better ways to present this information. This was the result. We replaced some numbers with small area charts, which give the reader an immediate visual impression of each option—which ones increased the solvency of the program and which ones did not.
Final version of that main exhibit in the Congressional Budget Office report on Social Security. Notice that there is less data and more graphs. Source: Congressional Budget Office.
The report worked. We received good feedback from colleagues at CBO and other agencies, as well as readers on Capitol Hill and elsewhere, noting how easy it was to read and digest the graphs. It was maybe the first time I (and perhaps the agency) thought carefully
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and strategically about our data visuals. From there, I started reading books on data visualization, design, color theory, and typography. Working with our editorial department and designers, we began to improve the graphs in our basic reports and started creating new report and graph types. We made infographics—what was then a buzzword referring (sometimes derisively) to longer graphics that combine data, text, images, and more into a single visual. In 2012, we created this infographic to accompany and summarize The Long-Term Budget Outlook, a 109-page report.
One-page infographic about the 2012 Long-Term Budget Outlook from the Congressional Budget Office. Source: Congressional Budget Office.
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That June, CBO’s director sat in front the U.S. House Budget Committee to relay the results of our analysis. As the hearing played on a TV out in the hallway, I suddenly heard yells of, “Jon! Jon! Come out! Your infographic is on TV!” And, sure enough, Congressman Chris Van Hollen was holding up the infographic on C-SPAN, covered with scribbles and notes. The visualization had captured and engaged the attention of one of the busiest people in America, and someone who could do something about the pressures facing the federal budget. That was the moment I knew that how we presented our data could matter as much as the data itself. In 2014, I moved to the Urban Institute, a nonprofit research institution in Washington, DC, to spend half of my time conducting research and half of my time in the Communications department, helping colleagues present and visualize their data. Since that time, I have conducted hundreds of workshops, delivered lectures around the globe, and published two books on data communication. The world, it seemed, had seen what I saw—better visual content and better presentations were the currency of research and
Maryland Congressman Chris Van Hollen holding up that Long-Term Budget Outlook infographic in a House Budget Committee hearing. Source: C-SPAN2.
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policy adoption. The advance of computing power, social media platforms, and the expanding media landscape made visual content more important, perhaps even necessary. Today, I work with people in nonprofits, government agencies, private sector companies, and everything in between to improve how they create their graphs and communicate their content. I’ve worked with junior economists and analysts dealing with enormous data sets; health care workers trying to communicate results to patients, families, and hospital administrators; human resource representatives working with databases of job-seekers; advertisers and marketing executives selling products to clients; and many more. I’ve seen hundreds of different kinds of data visualization challenges. The skills to meet them, unfortunately, are not yet regularly taught in schools or professional development programs. But these skills can be learned. We can learn how to read chart types we’ve never seen before, even if they are complex. And we can learn how to communicate our work in better and more effective ways. Eventually, I discovered that one of the most important things I can show people is the incredibly wide array of graphs available to them. And that is precisely the content of this book, a survey of more than eighty types of data visualizations, from the familiar to the nonstandard. But before we get to the library of graph types, we’ll consider some of the science behind how we process visual information and some best practices and approaches to visualizing data.
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is an economist and writer, teacher, and creator of policy-relevant data visualizations. He helps nonprofits, research institutions, and governments at all levels improve how they communicate their work and findings to their colleagues, partners, clients, and constituents. He is the author of Better Presentations: A Guide for Scholars, Researchers, and Wonks (Columbia, 2016).
COLUMBIA UNIVERSITY PRESS / NEW YORK cup.columbia.edu Printed in the U.S.A.