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Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks
Now more than ever, content must be visual if it is to travel far. Readers everywhere are overwhelmed with a flow of data, news, and text. Visuals can cut through the noise and make it easier for readers to recognize and recall information. Yet many researchers were never taught how to present their work visually.
This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message.
This book details essential strategies to create more effective data visualizations. Jonathan Schwabish walks readers through the steps of creating better graphs and how to move beyond simple line, bar, and pie charts. Through more than five hundred examples, he demonstrates the do’s and don’ts of data visualization, the principles of visual perception, and how to make subjective style decisions around a chart’s design. Schwabish surveys more than eighty visualization types, from histograms to horizon charts, ridgeline plots to choropleth maps, and explains how each has its place in the visual toolkit. It might seem intimidating, but everyone can learn how to create compelling, effective data visualizations. This book will guide you as you define your audience and goals, choose the graph that best fits for your data, and clearly communicate your message.
464 pages, Paperback
Published February 9, 2021
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Displaying 1 - 14 of 14 reviews
April 29, 2021
If you think a stacked bar chart with 20 different colors per bar is a good idea, then this book is for you.
September 15, 2021
More of a sampling of the various charts and plot types. Useful for becoming more aware of the different options available. Won’t help with actually creating plots using software tools.
March 14, 2023
Solid book. It is sort of like a "dictionary" of data visualizations, with useful advise for each. A good addition to the library of data viz practitioners.
April 29, 2021
How a wider repertoire of charts gives Better Data Visualizations
The latest book to join the pantheon of my recommended Data Viz texts is “Better Data Visualizations” from Jon Schwabish. This one was keenly awaited. I’d heard Jon’s updates on progress for months beforehand. He was even a guest on our podcast while he was still working on it.
So, I’m delighted to say that it has proven well worth the wait. It also nicely fills a gap, such that it complements those I have recommended previously. It is also clearly laid out and has a pleasing symmetry. I recommend it highly. Especially for analysts seeking to expand the repertoire of chart types that they use to visualize data.
Jon is the founder of the data visualization and presentation skills firm, PolicyViz. He’s also a Senior Fellow at the Urban Institute, a nonprofit research institution in Washington DC. Jon’s a generous blogger, podcast host, and producer of data viz education tools. So, I was not surprised to find this book packed with practical examples and advice analysts can apply at work.
Structuring a Data Visualisation education
As a trainer of Data Visualisation myself, it is always interesting to see how others approach the subject. Each person has their own unique style. Cole Nussbaumer Knaflic focuses on storytelling in a business context and decluttering your graphs to focus the eyes on the key messages. Andy Kirk focuses on both a thorough grounding in theory and application of best practice through each step of a consistent workflow (as well as real depth as a reference work).
Perhaps not surprisingly given Jon’s role, he structures his book to provide a simple overview of key principles needed and then spends most of his time expanding our repertoire of charts. He uses the 3 parts of this book to clearly structure his material:
In Part One he summarises visual processing, guidelines for chart design, and how understanding your audience should guide your choices.
Part Two reviews a wealth of different chart types under 11 groupings, including most of those highlighted in his famous Graphic Continuum visual summary.
Finally, Part Three covers Data Visualisation Style Guides and example makeovers (case studies in improving charts).
There are also two bumper appendices that provide advice on data viz tools and plenty of CPD resources for your further study.
So, let’s review in a bit more detail how each of those parts can help you improve your Data Visualisation craft.
Part One: Principles as groundwork for the rest of this book
After a nicely personal introduction (so you can relate to Jon’s own learning journey) he manages to condense a surprising amount of important theory into only 53 pages. These include research on perceptual rankings (most effective visual channels), Gestalt visual perception principles, Anscombe’s quartet, and preattentive processing examples.
Building on this, Jon artfuly simplifies a range of best practice design advice into 5 overarching principles:
Show the Data (prioritisation, clarity on insight/message and drawing focus to the key message)
Reduce the Clutter (worked examples of how to do that, with many more later in the book)
Integrate the graphics and text (from the need for active headings to appropriate use of annotation)
Avoid the Spaghetti Chart (from use of Small Multiples to recognising how changing chart type can help)
Start with Grey (selective use of colour to draw the eye to the most important data in meaningful context)
The final chapter of this part is entitled Form and Function. As well as covering that design principle, this chapter considers the multiple need for visualizing data. It is a helpful reminder that although most of this book focuses on static explanatory charts, data viz has a clear role to play for both exploratory work and increasingly through interactive visualizations. This latter point is also considered during the book from time to time.
Part Two: More Chart Types than you can shake a stick at
Ok, if you are reading this blog you probably have at least a passing interest in data visualisation. You may think you already have a wide knowledge of chart types. Well, I guarantee you will find at least one example you’ve didn’t know before in this section. It is the longest collection of different chart/graph types that I have so far seen in print. A great opportunity to expand your graphicacy and to be prompted to test new types on your audience.
This part is broken down into 10 different types of data, showing potential chart types that could be appropriate. Plus, Jon has added a chapter on better table design, for when that level of detail is appropriate to visualise.
Here is what I mean by the wealth of examples that Jon shows, including a supporting explanation and design advice for each chart type. For each of these different types of data types he includes this many examples:
Categorical data/comparing categories (17 different types)
Time series data (14 different types)
Distributions of data (9 different types)
Geospatial/Geographical data (5 different map types, with multiple variants of each)
Relationship data (8 different types)
Part-to-Whole data (5 different types)
Qualitative data (9 different visualisations, a topic often missed in other texts)
I mentioned the final chapter in this section covers tables. This is very helpful with examples showing how you can transform an impenetrable table into an effective visualization. This includes two walkthroughs of data table redesigns and Jon’s 10 guidelines for better tables. Given how often analysts (and other technical teams) still need to include tables of data this is an important aspect to not overlook.
Part three: Finishing in style
Like an expert author, Jon closes this book by weaving together themes he has introduced and been playing with throughout. The first of those is a topic that Jon has been championing on his blog and podcast for some time. The need for Data Visualisation style guides in organizations. But, rather than have this as a block of theory at the end of the book, he has been showcasing style guides throughout. At this stage in reading, you recall that each of those different chapters on chart types introduced a consistent style guide for each chapter. So, you have already seen how this can provide a consistent visual impact and aid comparison.
In this chapter, Jon deconstructs the anatomy of a chart and then walks through how a style guide can ensure consistency and professional presentation for each element. He covers colour palettes (building on the colour theory embedded earlier in the book). Jon also covers style guidance for Fonts, Chart Types, Exporting Images, Accessibility and Diversity. So, this is so much more than just a template PowerPoint deck. I heartily recommend both reading this chapter and putting in the work to develop your own in-house Data Viz Style Guide.
In line with the practical style of this whole book, Jon’s closing chapter is on the topic of redesigns or makeovers. He includes eight examples of improving on data visualisations or tables through applying the principles and chart options from earlier in the book. In this way, this closing chapter is also a pleasing weaving together of earlier threads. We are reintroduced to the power of some earlier praised chart types, like dot plots, scatter charts, line charts, and heatmaps It really helps ground in practical examples the thinking needed to improve.
How will you develop Better Data Visualisations?
Well first, I recommend that you buy this book. Plus, if you are interested in a particular chart type that you have not used before, it is well worth checking out the related episode in Jon’s #OneChartAtATime YouTube series.
But, data visualization is a craft rather than a subject learned by rote. So the real learning happens in your own practice. I hope this book encourages you to experiment more and to give time to revisit and hone your charts.
One of the other things to praise in this book is Jon’s honesty and humility in making clear where there is no one right answer. In many examples, he brings to life how much it is the audience, insight, and your own aesthetic that will guide which chart type you use. His principles can help you avoid pitfalls, but you also need to get your eye in. I hope this beautiful and enjoyable book encourages you to do just that as you expand the repertoire of chart types in your palette.
The latest book to join the pantheon of my recommended Data Viz texts is “Better Data Visualizations” from Jon Schwabish. This one was keenly awaited. I’d heard Jon’s updates on progress for months beforehand. He was even a guest on our podcast while he was still working on it.
So, I’m delighted to say that it has proven well worth the wait. It also nicely fills a gap, such that it complements those I have recommended previously. It is also clearly laid out and has a pleasing symmetry. I recommend it highly. Especially for analysts seeking to expand the repertoire of chart types that they use to visualize data.
Jon is the founder of the data visualization and presentation skills firm, PolicyViz. He’s also a Senior Fellow at the Urban Institute, a nonprofit research institution in Washington DC. Jon’s a generous blogger, podcast host, and producer of data viz education tools. So, I was not surprised to find this book packed with practical examples and advice analysts can apply at work.
Structuring a Data Visualisation education
As a trainer of Data Visualisation myself, it is always interesting to see how others approach the subject. Each person has their own unique style. Cole Nussbaumer Knaflic focuses on storytelling in a business context and decluttering your graphs to focus the eyes on the key messages. Andy Kirk focuses on both a thorough grounding in theory and application of best practice through each step of a consistent workflow (as well as real depth as a reference work).
Perhaps not surprisingly given Jon’s role, he structures his book to provide a simple overview of key principles needed and then spends most of his time expanding our repertoire of charts. He uses the 3 parts of this book to clearly structure his material:
In Part One he summarises visual processing, guidelines for chart design, and how understanding your audience should guide your choices.
Part Two reviews a wealth of different chart types under 11 groupings, including most of those highlighted in his famous Graphic Continuum visual summary.
Finally, Part Three covers Data Visualisation Style Guides and example makeovers (case studies in improving charts).
There are also two bumper appendices that provide advice on data viz tools and plenty of CPD resources for your further study.
So, let’s review in a bit more detail how each of those parts can help you improve your Data Visualisation craft.
Part One: Principles as groundwork for the rest of this book
After a nicely personal introduction (so you can relate to Jon’s own learning journey) he manages to condense a surprising amount of important theory into only 53 pages. These include research on perceptual rankings (most effective visual channels), Gestalt visual perception principles, Anscombe’s quartet, and preattentive processing examples.
Building on this, Jon artfuly simplifies a range of best practice design advice into 5 overarching principles:
Show the Data (prioritisation, clarity on insight/message and drawing focus to the key message)
Reduce the Clutter (worked examples of how to do that, with many more later in the book)
Integrate the graphics and text (from the need for active headings to appropriate use of annotation)
Avoid the Spaghetti Chart (from use of Small Multiples to recognising how changing chart type can help)
Start with Grey (selective use of colour to draw the eye to the most important data in meaningful context)
The final chapter of this part is entitled Form and Function. As well as covering that design principle, this chapter considers the multiple need for visualizing data. It is a helpful reminder that although most of this book focuses on static explanatory charts, data viz has a clear role to play for both exploratory work and increasingly through interactive visualizations. This latter point is also considered during the book from time to time.
Part Two: More Chart Types than you can shake a stick at
Ok, if you are reading this blog you probably have at least a passing interest in data visualisation. You may think you already have a wide knowledge of chart types. Well, I guarantee you will find at least one example you’ve didn’t know before in this section. It is the longest collection of different chart/graph types that I have so far seen in print. A great opportunity to expand your graphicacy and to be prompted to test new types on your audience.
This part is broken down into 10 different types of data, showing potential chart types that could be appropriate. Plus, Jon has added a chapter on better table design, for when that level of detail is appropriate to visualise.
Here is what I mean by the wealth of examples that Jon shows, including a supporting explanation and design advice for each chart type. For each of these different types of data types he includes this many examples:
Categorical data/comparing categories (17 different types)
Time series data (14 different types)
Distributions of data (9 different types)
Geospatial/Geographical data (5 different map types, with multiple variants of each)
Relationship data (8 different types)
Part-to-Whole data (5 different types)
Qualitative data (9 different visualisations, a topic often missed in other texts)
I mentioned the final chapter in this section covers tables. This is very helpful with examples showing how you can transform an impenetrable table into an effective visualization. This includes two walkthroughs of data table redesigns and Jon’s 10 guidelines for better tables. Given how often analysts (and other technical teams) still need to include tables of data this is an important aspect to not overlook.
Part three: Finishing in style
Like an expert author, Jon closes this book by weaving together themes he has introduced and been playing with throughout. The first of those is a topic that Jon has been championing on his blog and podcast for some time. The need for Data Visualisation style guides in organizations. But, rather than have this as a block of theory at the end of the book, he has been showcasing style guides throughout. At this stage in reading, you recall that each of those different chapters on chart types introduced a consistent style guide for each chapter. So, you have already seen how this can provide a consistent visual impact and aid comparison.
In this chapter, Jon deconstructs the anatomy of a chart and then walks through how a style guide can ensure consistency and professional presentation for each element. He covers colour palettes (building on the colour theory embedded earlier in the book). Jon also covers style guidance for Fonts, Chart Types, Exporting Images, Accessibility and Diversity. So, this is so much more than just a template PowerPoint deck. I heartily recommend both reading this chapter and putting in the work to develop your own in-house Data Viz Style Guide.
In line with the practical style of this whole book, Jon’s closing chapter is on the topic of redesigns or makeovers. He includes eight examples of improving on data visualisations or tables through applying the principles and chart options from earlier in the book. In this way, this closing chapter is also a pleasing weaving together of earlier threads. We are reintroduced to the power of some earlier praised chart types, like dot plots, scatter charts, line charts, and heatmaps It really helps ground in practical examples the thinking needed to improve.
How will you develop Better Data Visualisations?
Well first, I recommend that you buy this book. Plus, if you are interested in a particular chart type that you have not used before, it is well worth checking out the related episode in Jon’s #OneChartAtATime YouTube series.
But, data visualization is a craft rather than a subject learned by rote. So the real learning happens in your own practice. I hope this book encourages you to experiment more and to give time to revisit and hone your charts.
One of the other things to praise in this book is Jon’s honesty and humility in making clear where there is no one right answer. In many examples, he brings to life how much it is the audience, insight, and your own aesthetic that will guide which chart type you use. His principles can help you avoid pitfalls, but you also need to get your eye in. I hope this beautiful and enjoyable book encourages you to do just that as you expand the repertoire of chart types in your palette.
March 6, 2022
I'm afraid I found myself getting frustrated with this book due to what I fear was poor editing and proof reading. There were a number of inaccuracies that I picked up as I read that I found really distracting.
For example, on page 71 the text says that the author is creating a bar chart for the 10 most populous countries in the world. The chart that follows doesn't reflect this (where's Indonesia and Pakistan?) In that case the theory isn't affected but later it gets more confusing. Page 127 refers to the "unspecified" category on the chart - the problem is there isn't one. Another example is on page 45, where inexplicably, in a quartet of charts, the lines labelled as Germany and USA are switched for one of them.
One other minor frustration was the tendency of the text to refer to charts without explicitly making it clear which it was referring to. Often the chart would be on the next or previous page. This makes it harder to quickly understand the points being made.
These are perhaps minor points but personally I found them frustrating, particularly in a book on the subject of communicating data well.
What the book does well is cover a really good breadth of chart types and some of the key considerations in whether you should use them or not. The ambitious scope means that this isn't the book to read if you want to understand any particular chart type in detail but as an overview it does make the main points effectively.
For example, on page 71 the text says that the author is creating a bar chart for the 10 most populous countries in the world. The chart that follows doesn't reflect this (where's Indonesia and Pakistan?) In that case the theory isn't affected but later it gets more confusing. Page 127 refers to the "unspecified" category on the chart - the problem is there isn't one. Another example is on page 45, where inexplicably, in a quartet of charts, the lines labelled as Germany and USA are switched for one of them.
One other minor frustration was the tendency of the text to refer to charts without explicitly making it clear which it was referring to. Often the chart would be on the next or previous page. This makes it harder to quickly understand the points being made.
These are perhaps minor points but personally I found them frustrating, particularly in a book on the subject of communicating data well.
What the book does well is cover a really good breadth of chart types and some of the key considerations in whether you should use them or not. The ambitious scope means that this isn't the book to read if you want to understand any particular chart type in detail but as an overview it does make the main points effectively.
January 4, 2022
When I got our work-based, data visualization book club going, I suggested a few reads like Tufte and Few. Tufte I described as the Old Testament of data visualization and Few as the New Testament. Tufte is foundational, very dry, older, very "thou shalt and shalt not", and based in a world that is technologically so different from today. Few is still foundational, less dry (sorry - still pretty dry), newer, softer, and updated (most recent version) to the technology that we engage with today. But Schwabish is my number one pick. Everyone found this more engaging, easier to read and follow, more directly applicable to their work. The graphics and layout were very visually appealing, which made the whole experience better.
In particular, Schwabish does a great job throughout of not just saying "it depends" but also "it depends on xyz". When I first started in this space, I was so inflexible on what visual to use when and why. And now that I've done so much more work in this space, I have a far more nuanced approach. Schwabish helped me think in this book of several more layers of nuance and things to consider to make my work better.
Before we read it for book club, I had already ordered it because it has a super handy chapter on creating visualization standards and guidelines. I would recommend this book just for that section alone ("Developing a Data Visualization Style Guide"). It was super thorough and helped me get our corporate standards off the ground considering all kinds of things I wouldn't have thought of on my own.
When I recommend a primer to get you started in the data visualization space, I'm using this one moving forward.
In particular, Schwabish does a great job throughout of not just saying "it depends" but also "it depends on xyz". When I first started in this space, I was so inflexible on what visual to use when and why. And now that I've done so much more work in this space, I have a far more nuanced approach. Schwabish helped me think in this book of several more layers of nuance and things to consider to make my work better.
Before we read it for book club, I had already ordered it because it has a super handy chapter on creating visualization standards and guidelines. I would recommend this book just for that section alone ("Developing a Data Visualization Style Guide"). It was super thorough and helped me get our corporate standards off the ground considering all kinds of things I wouldn't have thought of on my own.
When I recommend a primer to get you started in the data visualization space, I'm using this one moving forward.
May 11, 2021
I think this is one of the books that was missing in my data viz library, one that is solely focused on showing the pros and cons of different chart types, when they might be useful and what are their weak spots.
This book is easy to read, it has many interesting examples for each chart type. I think it might be the most useful book for beginners in the field, since it is not focused on the issues or pitfalls of visualization but a through exploration of what already exists.
I only wish the section that is focused on creating style guides was a little bit longer.
If you are interested in data viz, get this book.
This book is easy to read, it has many interesting examples for each chart type. I think it might be the most useful book for beginners in the field, since it is not focused on the issues or pitfalls of visualization but a through exploration of what already exists.
I only wish the section that is focused on creating style guides was a little bit longer.
If you are interested in data viz, get this book.
September 10, 2021
I’ve read and skimmed quite a few books on dataviz. This is one of the better ones I’ve come across. It’s not overly technical but it’s not too simplistic either. The balance between theory and practice seems just about right. The meat of the book is the author’s comprehensive overview of just about every type of data visualization in use today. He gives helpful advice on when and how to use each type and how to improve upon many visualizations. If you use any type of data visualization regularly in your professional life this resource is worth checking out.
August 16, 2023
I was hoping for a more in-depth discussions of visualizations. A lot of the advice fell under common sense. (Like the fact that if you put too many slices in a pie chart it becomes hard to read. Or never have the sections of a pie chart total anything other than 100%). There were some useful pieces of information, but overall I thought the majority wasn't as helpful as I wanted it to be. Time will tell if it makes a useful reference book.
Currently reading
March 9, 2023provides a look at a full-range of tools from drag & drop to programming
Examples from Low to High 'barrier to entry'
datawrapper, vizzlo, infofr.am
rawgraphs, fourish, chartculator
mapbox, qlik, tableau
R, python, plotly
Examples from Low to High 'barrier to entry'
datawrapper, vizzlo, infofr.am
rawgraphs, fourish, chartculator
mapbox, qlik, tableau
R, python, plotly
October 3, 2021
A chart dictionary for a newbie like me. Well-balanced between statistic theories and practical examples in multiple domains.
September 9, 2022
Such an informative book.
March 24, 2023
Very good book for showcasing a variety of data visualization techniques, and does a good job explaining statistics in order to understand them
October 15, 2021
Pretty good.
There were a handful of things I hadn't come across in other data viz books.
The author tends to waffle on though.
I think it's the best looking data viz book I've read though.
I was having trouble embedding images in Goodreads, so my notes are hosted here.
There were a handful of things I hadn't come across in other data viz books.
The author tends to waffle on though.
I think it's the best looking data viz book I've read though.
I was having trouble embedding images in Goodreads, so my notes are hosted here.
Displaying 1 - 14 of 14 reviews