I’m getting most of the basic principles and best practices of information visualization and design from books. I read fictions on my iPad, but when I want to study I still need an actual book in my hands in which to annotate and quickly flip through. Below you can find my reviews and thoughts on the content and usefulness of books that I’ve read (so this list will continue to grow as I read more books). These are literally all the books that I own relating to data visualization. I hope these reviews will assist you to figure out which books you want to read (to learn more) about data visualization. They’re all quite high in stars because I’ve basically only included/kept good (enough) books.
Learning Data Visualization
- Data Visualization Handbook | Juuso Koponen and Jonatan Hildén ★★★★★
- The Functional Art | Alberto Cairo ★★★★★
- The Visual Display of Quantitative Information | Edward R. Tufte ★★★★★
- Information Visualization | Colin Ware ★★★★★
- Interactive Data Visualization for the Web | Scott Murray ★★★★
- Envisioning Information | Edward R. Tufte ★★★★
- Beautiful Evidence | Edward R. Tufte ★★★★
- Visual Explanations | Edward R. Tufte ★★★★
- Show Me the Numbers | Stephen Few ★★★★
- The Truthful Art | Alberto Cairo ★★★
- Visualize This | Nathan Yau ★★★
- Design for Information | Isabel Meirelles ★★★
- Visualization Analysis and Design | Tamara Munzner ★★★
- Now You See It | Stephen Few ★★★
- Visual Language for Designers | Connie Malamed ★★
Data Visualization Collections
These books are more collections of data visualizations made by various artists and curated into a book. Either very general or about a specific topic.
- Where the Animals Go | James Cheshire and Oliver Uberti ★★★★★
- History of Information Graphics | Sandra Rendgen ★★★★★
- The Book of Circles | Manuel Lima ★★★★★
- Understanding the World | Sandra Rendgen ★★★★
- Am I Overthinking This? | Michelle Rial ★★★★
- Visual Complexity | Manuel Lima ★★★★
- The Book of Trees | Manuel Lima ★★★★
- Infographic Designers’ Sketchbooks | Steven Heller and Rick Landers ★★★
- Playful Data ★★★
- Visual Journalism ★★★
- Data Visualization for Success ★★★
- Visual Storytelling ★★
Other Useful Books
A few books that are not specifically about data visualization, but that contain some very useful information that can be applied with data visualization nonetheless.
Data Visualization Handbook
This is probably my favorite book in terms of teaching you the best practices of data visualization. Not just on the “chart”, but also everything that is related to it, such as typography, colors, layout, and more. And, especially for a text book, it looks quite amazing, with a great use of typography, gutter notes, call-out colors and its packed with visual examples that highlight each of the advice/rules that are given. It’s also completely tool agnostic. No matter what you use to visualize your data with, this book is for you.
The book has six main chapters, each of which is divided into smaller sections (and sub sections). Starting with a short introduction that explains the general terms of information design, and why we should visualize in the first place (and when not), and other basic information. The second chapter on visual perception is a topic that I’ve always been intrigued about. This is basically a summary of the most important parts, related to visualizing data, from Colin Ware’s excellent book. How do we process our vision, tunable features (also often known as pre-attentive), the Gestalt laws, how good humans can “read” certain visual variables (e.g. length, color, shape, etc.), and of course there’s also an in-depth part on colors. Each of these sections are accompanied by many small visual examples/proof.
The third chapter is about the general principles of visualization design. This is more about the overall layout of the visual, making sure that it guides the eyes of the reader in the right order for example. To make it aesthetically pleasing, and what about chart junk? But also has some very specific sections, such as being consistent across charts, and a really good part about the impact of different ways to bin your data.
The fourth chapter, information design genres, is this book’s version of a dataviz catalogue, explaining the types of visuals. I find that it goes much broader than most online catalogues that I’ve seen even; it touches upon annotated photographs and technical drawings for example. But also looks into scientific visualizations, networks and even pictograms. I also loved how, this being a book about dataviz in general, it includes a large section about maps, without going too deep into the cartographic jargon. Explaining and showing different ways of placing data on a map, showing some beautiful/famous old maps, and even giving advice about designing the base map (in terms of colors, scale, etc.).
I was pleasantly surprised when I turned my page to find the title of the fifth chapter, text and typography. This is such an important, but often forgotten part of making good data visualization. Like I said, this book makes sure that you’re exposed to so much more than “picking the right chart”. This chapter teaches you the basic typography terms, the different typeface classifications, how your font choice can affect legibility, laying out the text. There’s even a full page that goes into kerning! And then it ends with a lovely section on the good use of text in maps, which is again chock full of small examples.
The final chapter takes a step back from the details and looks into the information design workflow. What are the different phases that one goes through when creating a visual and how you might work in teams. But it also muses a little on the role of a dataviz designer as a journalist (or not). The last section dives into the ethics of information design, ending with a very useful list of ethical “guidelines for visual journalists”.
Scattered throughout the book are also deep dives into certain topics, such as one on depth perception, map projections, but also a several short history lessons, such as one on Otto Neurath & Isotype, which I very much enjoyed.
Something else that was just rather refreshing was the fact that this book’s examples and visuals are all not based on US data, but from a Finland / Scandinavia / Europe instead.
I’d highly recommend this book to any skill level. It will probably be most useful to the beginner. Nevertheless, having a book that brings together so many insights in creating good data visualization will make it extremely valuable to an expert dataviz designer as well.
The Functional Art
This book is divided into four parts: Foundations, Cognition, Practice and Profiles. The foundation starts, like many other books on this subject, with explaining why you should visualize in the first place, what are the benefits. The second chapter starts with the statement that “Function constrains the form” to convey the idea that, unlike art, information design is meant to help us in the visual analysis of data. A fact that is often forgotten in all those awful marketing infographics & infoposters that you see these days.
I found Alberto’s starting example in which he redesigns a graphic about the armed forces in South America extremely useful. It shows how difficult it is to get insights from the original design and offsets this to how easy it is to get them from the redesign. Perhaps the most important message from the book (to me) is in this section. Try to put yourself in the viewer’s place and think about what insights/answers you want to find from the visualization. Do you want to make comparisons? See correlations? Use these questions to make sure that your design follows a form that makes answering these questions easy for the viewer.
The third chapter is about a topic that I haven’t seen in any other book so far, but that plays a big role when designing data visualizations more meant for promotion or for online use (instead of dashboards): The beauty paradox. You cannot really create a chart that both contains a lot of (different types of) information and that can be understood within seconds. Alberto introduces us to The Visualization Wheel (image bottom right), which covers the main features you need to balance during a design. A more familiar design means less originality. How to find the best middle ground in the visualization wheel, somewhere between radical minimalism and very playful is the subject of chapter 4 “The Complexity Challenge”. My favorite quote of this chapter is the following: Graphics should not simplify the messages, they should clarify.
The second section, Cognition, about the psychology of seeing is Colin Ware’s book Information Visualization in about 50 pages, which is more than any other book besides Colin’s, so bonus points for that. We get a short introduction into the workings of the eye, preattentive features and Gestalt laws with nice examples made clear through (simple and print) charts.
The third section is about Practice, in which the first chapter takes us through the design choices of ±4 infographics that Alberto has made. I always enjoy reading about the process behind a beautiful end result. It lets you find out the struggles the designer faced, the lessons learned and the effort that somebody has put in. The final chapter is about the rise of interactive graphics. The pictures in this chapter feel a bit outdated, but the general ideas about feedback, styles of interaction (instruction versus exploration) and ways in which a reader can navigate the visual (overview first, zoom and filter, details on demand by Ben Schneiderman) are still relevant.
The final section, Profiles, are a collection of 10 interviews with the creme-de-la-creme of the data visualization world: John Grimwade, Hans Rosling, Stefanie Posavec, Jan Willem Tulp and Moritz Stefaner, to name a few. And they work with and come from very different backgrounds. Stefanie is more hands-on, John Grimwade works in the illustrated infographics world and Jan Willem Tulp & Moritz Stefaner use coding to create their online visualizations. I found it really refreshing to have such a section at the end of this book. Again, learning how all these people work and how they tackle visualizations is very useful to figure out what way works best for yourself.
To wrap it up, I found this to be a really good book! Especially how it teaches you to think about the design; what questions you should ask yourself to improve the design. A good portion on visualization principles and interviews with several of the experts in the field. Even more so, it’s also well written, I couldn’t put it down and read it all in two days while on holiday. I think this is a book that I would recommend be one of the first to read if you haven’t read any other book about data visualization, because with just the teachings from this book I think you can already create some effective and beautiful data visualizations.
The Visual Display of Quantitative Information
This can perhaps be seen as the "bible" of data visualization
There is a lot of information packed in this ±200 page book. I found it to be a very educative but also fun read. It’s packed with gorgeous data visualizations, most of which I had never seen before, but that were made before the computer age really took flight.
The book is divided into two sections. The first “Graphical Practice”, has three chapters. The first chapter is a bit of a history lesson and showcase where we see many different types of charts that belong in Tufte’s Hall of Fame as it were. He is particularly fond of graphics that manage to pack an enormous amount of data into a 2-dimensional display and he has often calculated how many numbers there are visible per square inch.
The second chapter is about the other side, the “Hall of Shame”. Where terms such as the “Lie factor” are introduced. It’s most important part, I would say, is the six principles of Graphical integrity on the last page of this chapter.
The second section (about 100 pages) Theory of Data Graphics consists of 6 chapters. It aims to provide a set of rules by which you can assess the effectiveness of a chart. His famous “data-ink” ratio is explained here and he gives several examples of how to gradually remove non-data ink from different kinds of charts to improve readability. Although I do see an amazing improvement in how the newer charts convey information I do sometimes feel that Tufte takes it one step too far in his minimization. But then again, it’s good to see how far you can go just to be aware of it.
Chartjunk gets a chapter in which things such as Moire effects are condemned. Although perhaps in our age of colorful print and computer screens, the need for different types of cross-hatching to create categories is less relevant (my guess is that if Tufte had written this book after 2000 he would have replaced the Moire effect with all the horrible unnecessary 3D graphics out there).
In the chapter on data maximization I loved his idea of a “range-frame” where the axes on a scatterplot run from min to max only, thereby giving the axes a data conveying element as well. Strange that I haven’t seen this idea around in practice yet.
His chapter on High-resolution Data Graphics showcases all kinds of charts that manage to display vast amounts of data. But it is the “simple” sparkline that really shines here. He spends several pages on examples and explaining their use and by the end you really want to employ them everywhere. The final chapter is a bit of a random collection of final rules to improve the aesthetics of data graphical design. Topics such as combining words and text and the proportion of height versus width come to pass.
I loved reading this book. Its ratio of text and graphics is very pleasant. I had first expected that most other data visualization books that I’ve read before this one would summarize practically all of the good points from this books. But that wasn’t entirely the case. Even though it was first published in the 80’s there were still a lot of new insights to be found here for me. Ideas on how to make even a common chart such as the scatterplot even more effective. In hindsight, I should’ve read this book as one of my first dataviz related books. I highly recommend it to anyone interested in data visualization, even if you’re new of a more experienced practitioner.
I find the psychology of seeing or perception an extremely interesting topic. I still remember that it first started to intrigue me when I heard about the book “The man who mistook his wife for a hat” and read a short excerpt from it. The fact that this man could not really make any sense of what he was seeing, but being completely unaware of this, was so strange, that I wanted to know more about how this could happen. What really happens in between the light falling onto the back of our eyes and our understanding of what we are seeing.
The first two chapters of Information Visualization dive straight into this topic and the biological workings of our eyes. A model of perceptual processing is explained that runs between the eyes and the brain, super acuity (where our brains make very smart use of all the visual input to see details on a very fine scale) and the distribution of nerves attached to our light receptors.
The addition of Design Guidelines boxes in the current edition that formulate the impact of some insight into a clear design motivations and rules help to make them practical. It also makes this book a gem for practitioners of data visualization, not just researchers.
Next is a chapter on Lightness followed by a chapter on Color. In these chapters you really learn how often your brain is fooling you in what you think you’re seeing and what is physically true. Two patches of grey that have exactly the same RGB values, but that do not seem the same because one patch is embedded in a light area and the other in a dark area. The book also tries to explain why we are seeing something different than is actually there. Sometimes getting a bit technical with actual formulas.
In the chapter on Color we first get a short introduction into color specification schemes. But most of the chapter contains fascinating insights into how we perceive color. How to spiral through different hues and different luminosities if you want to see both patterns of highs and lows and read of rather exact values from a legend key (the right most square in the photo on the bottom left shows such a color scheme) If there is one thing to remember from these chapters, it’s that Context is Everything.
The next chapter dives into the topic of Finding Information. It starts of a bit technical again about the workings in the brain, but in essence this is a chapter on Preattentive processing. In preattentive processing our eyes and brain are doing some serious hard lifting and parallel processing, without us really noticing. That a red dot immediately stands out in a group of grey dots. There are several features that are preattentively processed, such as color, orientation, size and many more. This chapter explains all features currently know with examples. The last part goes into the combination of features, which are separable (color and shape) and which are inseparable (width and height, we can only easily see the complete shape) and other more advanced topics relating to how you make it easy for the viewer to find information.
I've definitely become a fan of the streamlet due to the examples in this book
In Static and Moving Patterns we are introduced to the Gestalt Laws of grouping, or how our brain intuitively sees groups and how we perceive patterns in general. But also on visualizing flows. Again a chapter packed with extremely useful information for data visualization in which it is often the goal to show patterns. It ends with a few sections on seeing patterns through motion
And all that is just in the first half of the book. However, I found the second half to be less intriguing and it took me a lot longer to read it than the first half. The chapter on Visual Objects and Data Objects was an exception and still very useful However, even though some of remaining chapters have interesting titles, such as Visual Thinking Processes, overall, the content felt less applicable to myself. Nonetheless, there is just so much information, useful guidelines and good examples in this book, that it really is a must-read for data visualization designers.
Interactive Data Visualization for the Web
I still remember when and where I read this book. I was alone on a two week project in Paris during the summer and it was amazing. Sitting in one of the metal benches in Les Tuileries enjoying the last rays of the Sun for the day.
After discovering d3 in 2015, this was the book that I bought to get myself started. I had already done a few chapters that were then available online. Even though I didn’t quite understand the whole
data().enter().append() back then, I was able to actually create a bar chart and a scatterplot in d3 by following the examples. Therefore, happy with the success I bought the book itself.
The chapter on Data is where you first make something appear on the screen, and also the chapter that introduces you to the d3 chaining method. Next in Drawing with Data we get to draw a bar chart. In just 20 pages, we go from nothing to a beautiful bar chart with labels and colored according to height. Even the smallest step is thoroughly explained and always accompanied with an image of the result to make sure that you really understand what each step of the code adds to the end result.
The chapters on Scales and Axes are essential too. We can’t really make any interesting charts if we had to do the mapping of the range of the data to the locations of the pixels on the screens all the time. But it’s the next two chapters that really hooked me on d3; Updates, Transitions and Motion and Interactivity. Continuing with the bar chart from the Drawing with Data chapter, the book now explains how to make updates to the data and see your bar chart move to adjust, add new bars or remove bars. I think I didn’t fully understand the enter, update, exit routine at the time, but I understood it enough to get it to work in my own example.
In the Interactivity chapter it’s crazy when you see how easy it is to add simple interactive elements, such as a color change of a bar when you hover over it. Even though it can be done in just a few lines of code, after creating your own hover effect for the very first time, it feels like you’ve made your bar chart 300% more interesting than before (ﾉ◕ヮ◕)ﾉ*:・ﾟ✧
It took me many, many more hours of coding in d3 and searching on StackOverflow after reading this book before I could start to really make custom made visualizations, but I think I couldn’t have gotten a better start into the wonderful world of d3 than with this book.
Another amazing book by Tufte. In terms of style of print, this book looks the same as his The Visual Display of Quantitative Information. With a large portion of the page’s width devoted to notes (or note-taking) and many images in between the text. It’s a bit shorter with 120 pages than the other but I would say this is definitely one of the must-have books for those venturing into data visualization. Also just like most of Tufte’s books, this one is also filled with many wonderful data visualizations that I had never seen before. Some already several hundreds of years old. It seems that Tufte also has a love for how the Japanese visualize data, since it features quite a few images from Japan.
The book has six chapters, each with its subject on how to display information in a specific way. The first, Escaping Flatland feels a bit as an introduction to the rest. It showcases many dataviz examples that were able to convey a lot of information in a small amount of space. Sometimes going into the history of a specific chart, such as how sunspots have been recorded since Galileo.
The second chapter, Micro/Macro Readings, is about charts which can be read on multiple levels of hierarchy. The overall view, but also a detailed view (think of maps for example). Such as the stem-and-leaf plots where you can see the distribution by the shape but also read off the exact numbers of each data point.
Chapter three, Layering and Separation, is about the 1+1=3 effect (in this case not a good thing) of how visual clutter can create noise where you see more than there really is. It starts with examples that are very good at layering their information in such a way to complement each other and the chapter ends with several examples in which the non-data is too prevalent (of which Tufte redesigns a few).
The subject of chapter four, Small Multiples, seems to have risen in popularity in the past year or so. It’s a relatively short chapter in terms of text, but it is nice to see examples of small multiples that are not necessarily a grid of boxes with a similar image (but of course each slightly different) in each.
Chapter five, Color and Information, dives a bit into how color can be used effectively. It’s a brief intro, and I would recommend reading Colin Ware’s book for those looking an in-depth piece on the effects of color in (data) visualization. But the pages about Oliver Byrne’s explanations of mathematical proof’s using colors and shapes are truly amazing.
The final chapter, Narratives of Space and Time, is about the display of time (and a bit about location) in a chart. And no, you won’t find a conventional line chart in this chapter. As with the rest of the book, this chapter has many wonderful examples of data visualizations that have used a (slightly) different way to convey the passages of time, modelled on their dataset, from dancing to flight schedules.
This book is all about the 6 subjects of the chapters. There are no data visualization rules in here like The Visual Display of Quantitiative Information. Instead each chapter is comprised of examples that apply the subject of the chapter in an effective way and Tufte explains to the reader what it is exactly that works so well. Like his previous book I also very much enjoyed reading this book and learning from the many examples and would definitely recommend it.
After reading The Visual Display of Quantitative Information and Envisioning Information by Tufte, I was very happy when Edward Tufte was so generous to send me his other two books of which this is one. And I can say that these are of the same high standards as the first two books and they were an absolute joy to read.
This book revolves around the idea of presenting evidence. Evidence can come in words, numbers, images, diagrams, still or moving. What are the best ways to combine these aspects into one coherent story? There are 7 main chapters in this book with 2 bonus chapters relating to Tufte’s work in art. We start with Mapped Pictures: Images as Evidence and Explanation. This chapter makes it very clear that many data visualizations these days are missing their scales: Mapped pictures combine representational images with scales, diagrams, overlays, numbers, words, images. Of course, we get to see beautiful examples; of art, a stork, dancing and more.
The second chapter Sparklines: Intense, Simple, Word-Sized Graphics is an expanded version from the Sparklines section in his previous book The Visual Display of Quantitative Information and it brings enough new examples and information to be deserving of its own chapter now. We should really be employing sparklines more.
In Links and Causal Arrows: Ambiguity in Action we get into the realm of networks and graphs, which encompasses much more than what we would typically call a network visualization these days. It’s about linking things objects or words with arrows. But arrows can be rather ambiguous, what do they really mean? He gives tips on how to create effective diagrams.
In Words, Numbers, Images—Together Tufte explains very well the issue that you see so often when the evidence is not presented together. Images at the far end of a book, difficult number mappings in an image to explanations somewhere in the text. We are shown the excellent example of Hyperotomachia and Galileo’s Sidereus Nuncius in which words and images, evidence in the latter case, are presented together. And how Isaac Newton’s Opticks with its images bound together, away from the text, is a pain to read and understand. The chapter ends with a few nice redesigns of how to integrate words within a chart without it being crowded and obscuring the data.
The fifth chapter The Fundamental Principles of Analytical Design is all about Minard’s famous map of Napoleon’s campaign to Moscow and back. It starts with a wonderful fold-out large example of the (translated) map. By studying Minard and the map Tufte teaches us 6 very useful lessons on presenting evidence. From the importance of showing comparisons to credibility to content counting most of all. A great chapter!
Chapter six deals with the opposite of Minard’s map Corruption in Evidence Presentations: Effects without Causes, Cherry-Picking, Overreaching, Chartjunk, and the Rage to Conclude, quite a mouthful. It’s about the different ways that somebody might be trying to corrupt reasoning through presentations. The title of this chapter sums up the subsections of this chapter and Tufte explains and shows (elaborate) examples on all.
I love that he ends with The Cognitive Style of PowerPoint: Pitching out Corrupts Within. We get a first-row seat on how awful a technical report (from NASA on the Columbia disaster) becomes when it has gone through PowerPoint. It’s very clear after reading this chapter that when you want to present (beautiful) evidence, don’t use PowerPoint.
I am always amazed how Tufte manages to find so many wonderful examples from all over history to accompany his teachings. Although he sometimes re-uses an example that has come to pass in another title of his, it is always either in short passing or a much more elaborate telling (such as the sparklines and Minard’s map in this book). Definitely a book that will teach you much more about data visualization if you care about data and how to convey insights to your intended audience!
After reading The Visual Display of Quantitative Information and Envisioning Information by Tufte, I was very happy when Edward Tufte was so generous to send me his other two books of which this is one. And I can say that these are of the same high standards as the first two books and they were an absolute joy to read.
As said in his introduction, this book “describes design strategies—the proper arrangement in space and time of images, words, and numbers—for presenting information about motion, process, mechanism, cause, and effect”.
The first chapter Images and Quantities is a short one about the scales in images. Appropriate scales to help the reader understand the context are often forgotten or incomplete, especially in scientific images.
The second chapter is definitely my favorite one, Visual and Statistical Thinking: Displays of Evidence for Making Decisions. This chapter is devoted to the analytical and decision process behind two major events. The first is the cholera epidemic in London during September 1845 from which some of you might already know Jon Snow’s map (see the image below left). In this case, the analysis and visual evidence was done in an exemplary form. The other case concerns the launch of the space shuttle Challenger on January 28, 1986. Sadly, this is the opposite, where the visual evidence was conveyed very poorly which resulted in the shuttle taking off and exploding, killing all 7 astronauts inside. I appreciated how elaborate Tufte goes through both events and highlights the critical issues that made a difference.
Chapter three Explaining Magic: Pictorial Instructions and Disinformation Design has a nice twist. By looking at how magic tries to obscure, to deceive the audience, Tufte teaches us how to not do this for good information design. It starts with some fun examples of magic tricks and how these are visualized in learning books and the chapter ends with 6 learnings on how to give a good information presentation. For example, instead of not telling the audience anything, as is normal in magic, turn that around and start your presentation by explaining the problem, why it is important and what the solution is.
Chapter four The Smallest Effective Difference is another short chapter that revolves around the idea to make all visual distinctions as subtle as possible, but still clear and effective. This chapter has some nice and convincing redesigns that you can learn from.
In chapter 5 Parallelism: Repetition and Change, Comparison and Surprise we see the first ideas of (small) multiples. Although in this chapter that can also mean just two different versions side by side instead of many. This chapter again has a wonderful long explanation about the cyclogram from the Salyut 6 space flight (image below right) and many other examples ranging from areas such as landscape design to typography.
And then in chapter 6 Multiples in Space and Time we get to the >2 versions that are slightly different. Tufte explains the benefits of small multiples. And we again see a very diverse collection of examples such as an image of 13 different interpretations of Saturn from the 1600’s.
The final chapter Visual Confections: Juxtapositions from the Ocean of the Streams of Story started out a bit confusing for me. Although I liked the examples from centuries ago I couldn’t quite get the idea of confections, until I realized that it was another term of/relates to Infographics in the data visualization realm of today. Some of the examples in this chapter were maybe a bit too much into the realm of art for my taste.
As always, the collection of examples that span centuries and are not constrained to what we nowadays typically think of as data visualizations is what I love the most about all of Tufte’s books and this is no exception. However, I will be honest and say that if you are still new to the world of data visualization I would recommend other books first to get a good basis. I felt that Visual Explanations has less direct learnings for data visualization and felt more as a wonderful collection (and explanation) of examples. However, once you’ve read the basics and best practices of data visualization, I would definitely recommend this book to expand your growing understanding of visualization and your collection of data visualization books.
Show Me the Numbers
Stephen Few isn’t the guru on dashboard design for nothing. His books are packed with useful and practical information and examples on the many ways to display data. His books are not meant for fancy charts, no streamgraphs in here. Instead they are for the basic charts and tables that have been at our disposal for many years. Nonetheless, I think you need to first understand how to make a simple chart effective before you can take on the “fancy charts”.
This book starts by explaining the different types of data; quantitative versus categorical. Afterwards, the basic statistical measures are explained; mean, median, standard deviation, distributions, ratios and measures of correlation. The third chapter is a short one, explaining when to use tables and when to use charts.
The next two chapters are about the fundamental variations of either tables or graphs. What kind of relationships can you display in a table; quantitative to categorical or quantitative to quantitative. The fundamentals of graphs is a much longer chapter. That this book really starts at the basics comes across here. The start of the chapter tells the reader how points and bars can be used to encode data. Next, the visual attributes such as position, color and shape are explained to create categorical subdivisions in a graph.
Afterwards, things get a bit more interesting when the section on Relationships in Graphs begin. Few points out that there are 7 types of relationships that business graphs usually display, such as ranking, deviation and correlation. For each of the 7 options Few shows what chart types work particularly well. Here we see one or two non-typical charts, such as box plots (which I like a lot) and two bars on top of each other where one set of bars is thinner than the other (not yet sure what I think of these).
Chapter 6 is in essence Colin Ware’s book Information Design in 25 pages. It quickly tells you about the working of the eye, preattentive design, colors, context and the Gestalt Laws. A bit short for my taste. I feel that those subjects which have such a big impact on the effectiveness of a chart, could use more pages. The famous data-ink ratio, by Edward Tufte, is quickly explained in chapter 7; reduce the non-data ink and enhance the data-ink.
Chapter 8 is all about the design of tables. Even though I hardly ever design tables, this chapter points out some very useful tips to make elegant tables with as few row filling colors and cell borders as possible.
Although chapter 9 is called General Graph Design, it is mostly about what not to do. Try to use zero based scaled and 3D is bad (the pie chart was already addressed in one of the first chapters). Then we finally get to, I think, the most useful chapter of the book Component-Level Graph Design. Here are the tips that are least obvious, but can still make a big impact of chart effectiveness ; how much white space should there be between bars, the best x/y axis width ratio, using trend or reference lines to make the pattern more apparent, when and how to eliminate legends, tick marks or grid lines. Most of the chapters before this one are about how to display the data, what chart types. This one really shows the extra mile that a chart can achieve by optimizing the visual side of a chart once you’ve chosen a specific chart type. The book ends with a short chapter on displaying multiple variables, by using small multiples for example.
I have to admit that I read through the first ~6 chapters really quickly because I already knew most of what was being explained. Few writes in a very entertaining fashion making sure that it never gets boring to read about tables or data. This book is one big list of useful tips tied together with extensive explanations and examples. Many are obvious, but having them all enumerated with nice tables and charts to prove the point is what makes this book such a valuable read.
The Truthful Art
The Truthful Art is the second book in Alberto’s trilogy (I think) of which The Functional Art, one of my favorite books, is the first. In short, this book is a statistics book for journalists I’d say. And that’s also the reason that I find this book difficult to review. With my background in Astronomy, I’ve had 5 years of mathematics and physics. Therefore, I am already aware of the formulas and ideas presented in this book. Although it was certainly nice to see data visualization examples with each subject. I can’t say for sure, but I think that if you are new to statistics then this can be a good and gentle introduction to the main topics. Some things might still be explained a bit confusing. But most is explained in small steps with easy maths and examples.
The book has 12 chapters divided int four parts. The first part Foundations talks about the differences between infographics, data visualizations, charts, and maps. But it also points out and explains the qualities of great visualizations: truthful, functional, beautiful, insightful and enlightening.
Part II is called Truthful talks about visualization being a model of the truth. We can never be absolutely true, a visual just isn’t real life, but there are better models. Alberto also talks about the common mistakes due to dubious models and error prone human reasoning.
I find it interesting that Part III is called Functional, which is by far the biggest section in the book. I guess he wasn’t ready with it after his 1st book. It’s here that we dive into the statistics and see some formulas; (weighted) means, standard deviations, histograms, the normal distribution, boxplots, percentiles, visualizing trends and seasonality, ratios, log scales, correlation (coefficient), z-scores, parallel coordinates, linear regressions are terms that you will now know of after reading these ±200 pages. And then there’s suddenly a chapter on mapping data on maps before it ends with a chapter on Uncertainty and Significance which I find a fascinating subject in visualization. Many of the terms above are preceded with a fun example of a simple dataset that Alberto takes apart and visualizes in several slightly different ways.
The last part is about Practice and is a collection of the wonderful work that other data visualization practitioners have been doing. This is definitely a useful book to have when you’re new to statistics and data manipulation.
This was the first book that I read about data visualization, even before I had figured out that I wanted to specialize in the area. It’s been quite a while since then so it’s a bit difficult to remember, but I do remember that I really enjoyed reading this book and learned a lot from it.
The first 3 chapters of the book are about the preparation; how to get the data in the right form and choosing your tools to visualize. A whole bunch of links to (mostly) open data sources are shared and next we learn how to use Python to scrape weather data. There are many cases throughout the book where Yau shares the complete code (and explains what each step/line of code means) to create a visualization. He does this in many different tools, all of them free. This is one of the reasons why I learned so much from this book, by following the examples I had a good first step into a new tool, made something cool which gave me enough motivation to want to learn more about the tool (although I ditched Python for R).
The next five chapters are similar to the idea of Stephen Few’s book Now you see it and are about one particular visual analysis task; visualizing patterns over time, proportions, relationships, differences and spatial relationships. Each chapter explains several chart types from the ground up (so even the bar chart is explained like we’ve never seen it before) and then shows how to create one and make it look elegant and effective. Especially creating a simple chart in R and the refining the look in Illustrator (or InkScape) opened my eyes to how much you can improve a simple chart be not sticking to program defaults.
One thing that I found interesting is that, for the most part, one type of chart only appears in one of the chapters. So it’s not all line charts and bar charts. More complex charts such as Treemaps & Parallel Coordinates are also explained.
This is the most practical book that I’ve read that combines visual best practices with learning exactly how to (re)create a chart. It contains tons of examples, is written in an engaging manner and is not too long. I would definitely recommend this book to those who already know how to program and want to dive into the world of data visualization.
Design for Information
This is the most colorful book in my possession that also teaches you about information visualization. There are six chapters in the book and each chapter focuses on a different type of data: hierarchical structures (trees), relational structures (networks), temporal structures (timelines and flows), spatio-temporal structures and textual structures.
Each chapter consists of a history of the type of chart. These are the most extensive histories that I’ve read and they are accompanied by many beautiful vintage visualizations that were made hundred(s) of years ago. Next is an explanation on how to create/interpret the chart and what kind of variations there are. A tree can be visualized as a treemap or a sunburst for example. And finally there are several case studies. A short introduction about the visualization is given, why was there a need or desire to create it and an explanation of what is visualized. All of them are presented with many beautiful screenshots.
Dispersed throughout the book are also small breakouts that explain several Gestalt Laws, such as similarity and continuity and Preattentive principles.
If I were to describe this book in one sentence, it would be a book that visualizes and (quickly) explains a lot of the more famous visualizations found online. If you really want to learn about best practices and visualization principles I would suggest to go for a few other books on this list, such as Colin Ware’s book Information Visualization or Stephen Few’s Now You See It. But if you want to be inspired by beautiful examples, get a bit of background about them, get introduced to a few visualization best practices and are interested in a piece of history on several chart types, this is the book for you.
Visualization Analysis and Design
After hearing the Data Stories episode with Tamara Munzner I knew I wanted to read her book. She’s done, seen, and thought about many aspects of Information Visualization.
Each chapter starts with a small schematic representation of the topics that will be discussed. In the bottom left image you can see such a presentation for chapter 3 on the different types of abstract data tasks
Chapter 2 introduces us to data, what data types are there, but this goes much further than the typical ordinal and nominal. It sets apart networks from geometry and sequential versus cyclic. In the next chapter Munzner explains what the main abstract reasons are for using a visualization tool in the first place. I felt the schematic at the start of this page (image bottom left) is one of the most useful in the book and very useful when you are at the start of a project to figure out the point of the visual. And that there is actually an “enjoy” task as well.
Once you know the data and the task you can start on the design and chapter 4 explains the Four levels of Validation or the four levels that exist in visualization design; domain situation, data/task abstraction, visual encoding/interaction choice and creating and algorithm to handle the visual encoding. On each of these levels you need to perform validation to make sure that your visualization program is effective.
Chapter 5 on Marks and Channels, together with chapter 10, is this book’s version of Colin Ware’s book. I do really like Figure 5.8 (image bottom right) that ranks how good people are at judging exact values from several different studies, including crowd sourcing.
One of my favorite chapters was number 6 on The Rules of Thumb with 10 basic rules such as Eyes Beat Memory and No Unjustified 3D. At the start of the chapter Munzner explains each of them. The rules have a catchy title in hopes that you’ll remember it as a slogan.
The next 3 chapters are all about how to visually arrange data; data that comes from tables, spatial data and networks. Using radial layouts or parallel layouts for “normal” data for example. Munzner uses many case studies where many are research related (about biology for example).
Chapter 10 Map Color and Other Channels was my other favorite chapter of this book. Even though most can also be found in Colin Ware’s book I did learn a few more things about color and there are a few very nice examples here that I hadn’t seen before.
The final 4 chapters are about the major strategies that are available to manage complexity in visualizations; changing a view over time, faceting data into multiple views, reducing items and attributes and embedding focus and contextual information in one view. Each chapter explains what it means, why to do it and then several options of how you can do it.
After reading it all, I do have to admit that even though there are so many interesting topics covered in this book, I found it difficult to pick it up again in the evenings and read. The text felt a bit dry, in that it wasn’t pulling me in. It felt more like an enumeration and explanation of facts.
I also feel that this books will be more useful to researchers than practitioners. It focuses more on theory, creating complete visualization tools and uses many examples from tools created as part of research for a very specific task or user group. Depending on where your own preference in data visualization lies, I would suggest more graphical practitioners to go with the other books on this list (first).
Now You See It
This book is about what charts to use for the most common tasks in data analysis. There are two big sections in the book. One about building the core skills for visual analysis which involves general concepts and principles. The second section is about honing skills for diverse types of visual analysis to identify patterns for example.
The first 6 chapters about building the core skills start of with a really brief history of data visualization, what makes a good analyst (curiosity) and what is meaningful data. Chapter 3 Thinking with our eyes is a summary of Colin Ware’s book, just like chapter 6 in Few’s previous book Show me the Numbers. Color, luminosity and preattentive are really quickly explained.
Chapter 4 is about 13 different analytical interactions that you can have with the data (visualization) to get an understanding of it; comparing, highlighting, zooming and annotating for example. Each of the 13 options are explained with many visual examples. Chapter 5 then looks at several techniques that can improve the effectiveness of visual analysis. Using brushing in a dashboard of charts or adding reference lines. Finally chapter 6 ends the first section by quickly introducing six different analytical patterns which will get their own full chapter in the second section.
The six chapters in the second section are devoted to time-series analysis, part-to-whole and ranking analysis, deviation analysis, distribution analysis, correlation analysis and multivariate analysis. Each of these types is introduced more thoroughly than in chapter 6 after which Few shows the reader what chart types can be used to perform the analysis with. For some analyses, such as deviation analysis, there are quite a few different options available, whereas time-series analysis really only has two (bar and line chart). Each chapter ends with a section on best practices for the chart types. How to select the best interval for binning data to do distribution analysis for example.
This book is rather big, but it actually doesn’t contain a lot of text and I was able to finish it within a week. It uses big gutters for notes and there might be more space filled by charts than text (which I don’t mind). In the end, I think this book is more meant for the data analyst who needs to get a grip on the data or create dashboards or reports for the management about the operation of the business. What types of charts can you use that will transform the data in such a manner that the viewer can visually see a pattern is the main point, not necessarily visualization best practices.
For a data visualization designer who has no experience as a data analyst this book is a good read to understand how to get meaning from data. But for those who are learning data visualization from a data scientist like background this books offers little new knowledge and you are better off picking Few’s first book Show me the Numbers.
Visual Language for Designers
This book consists of two sections. The first explains how we humans process visual information and the second section talks about principles to improve your design. The book is filled with colorful and large examples. From data/information visualization designers such as Nigel Holmes and Nicholas Felton to marketing pieces to art. Each example is accompanied by a small explanation about how it connects with the lessons you’ve just learned from the main body of text. In fact, most of the book is taken up by the visual content so in terms of reading, it doesn’t take that long to read through the entire book.
The first section contains elements that I’ve seen in other data visualization books as well: That we can only hold a few chunks of information in our working memory. Especially a data visualization designer needs to keep that in mind. There is also a nice section on schemas; our mental representations that embody our understanding of the world.
The next three quarters of the book is devoted to section two the Principles, which contains 6 subsections. Each subsection first explains what the general idea is and then has a section on applying the principle to designs in several ways.
Organize for Perception is a bit about out preattentive processing, but using very different examples and wording than all the other books in this list. It’s more about how to design a piece where the viewer can get a sense about the (most important) information quickly. Direct the Eyes talks about compositional and signaling techniques that are effective at guiding the eyes to a specific location, such as emphasis and position. Reduce realism explains that it can help to make it easier for a viewer to understand the graphic more quickly and let it stick in long-term memory of you reduce the realism. Make the Abstract Concrete is about diagrams and flows, how visuals can help us to think so we do not have to hold it all in memory. In Clarify Complexity we learn different techniques on how complex concepts, such as science or medical topics can be visually represented. Many of these examples remind me of National Geographic style visualizations. The final subsection Charge it Up is about creating an emotional response when somebody views your design (such as storytelling, metaphors, humor).
This book is aimed at graphic/information designers in general and not strictly about visualizing data (or information). Therefore, not all examples are as relevant to a data visualization designer. Nonetheless, I feel this book shares many useful lessons about making a visualization that stands out in a crowd, that is easy to understand for the audience and that makes a lasting impact.
Where the Animals Go
Gosh, such a lovely book this is! It’s filled with beautiful maps, each designed quite differently. But what makes this book truly special is the data that is plotted on top of those maps; animal movement. Whales roaming across all the oceans, wolfs in national parks, elk, baboons, zebra, even ants! And of course many species of birds. With each map you also get to read about the species, about the data, and often about the researches that painstakingly collected the data. Some of these are quite elaborate stories that are very fun to read. I also liked the variety of ways in which the animal movement is shown. Many feature squiggly lines of course, but some are completely different. This book was a joy to read. Definitely a good gift for a friend (or yourself)!
History of Information Graphics
I own two books by Sandra Rendgen and they have two things in common; you might kill your cat if you dropped the enormous book on them accidentally (I mean it, these books only fit on top of my book case) and they are both really wonderful! The History of Information Graphics takes the reader through the evolution of data visualization from the Middle Ages until today. For me personally the Middle Ages wasn’t quite my thing, too much about (religious) genealogy, but it’s fascinating to see what they could even make back then, and how elaborate and creative it already was. And that creativity really shines in the large section on the 19th century when statistical charts start to rise. It’s a great curated and large collection in a lovely designed book. Furthermore, I also really enjoyed the four themed contributions, such as the amazing maps chosen by David Rumsey, or a look at early dataviz in newspaper by Scott Klein. I’d definitely recommend to either get this one, or gift it to a dataviz enthusiast!
Understanding the World
This wonderful, colorful, giant book is a great collection of data visualizations and infographics. The fact that the book is just so big makes the page filling visuals really nice to inspect to the minutiae and be able to read every small annotation in there. Sandra has grouped the visuals into five categories, such as Nature & Environment and Science & Technology (my personal two favorites). The quality of the projects included is generally very high. This book is a great resource for anyone wanting to be inspired or pick up some new ways to visualize their own data.
Am I Overthinking This?
This book isn’t quite like all the others on this page. It’s its own unique thing, and it’s wonderful. It’s a joy to read, makes you think about life questions, makes you laugh, makes you look at charts from a different angle. You can even share it with your non-dataviz friends or family and they’ll laugh too (and hopefully get better at reading “normal” dataviz even!). It’s also really affordable, and I’ve found that I can read it all for another time after a few months and be surprised again. Just get it (or gift it!).
I’m a big fan of Manuel Lima’s three books, this book being the first one. This one is a great collection of network visualizations. Manuel has categorized the networks into categories, each getting its own chapter, where the two first chapters are a really interesting history lesson about the evolution of tree-like and network visuals. Because it’s so specifically about one type of visual, networks, I actually grab this book from my bookcase quite often when I start on a new network related project. It gives a good dose of inspiration for what I could use for my own visuals.
The Book of Trees
This lovely second book by Manuel Lima dives specifically into tree-like visuals. From those that feature entities connected by lines, but also at treemaps, voronois, sunbursts and circle packing. The book is overflowing with examples, some that you might’ve seen before, many that are probably new to you. The design of the book is very nice and I also love that there’s an (even bigger than his first book) history lesson about tree visuals as the first chapter. A great resource to have if you’re looking for ways to visualize hierarchical data.
The Book of Circles
The third book by Manuel Lima has the same concept as the other two, but this one looks at any visual that is circular. This is definitely the broadest of the three in terms of types of data, since almost anything can be visualized in a circular layout. Still, Manuel has found a good way to categorize the visuals into chapters. The first chapter being a in-depth history lesson about circular visuals, after which it dives into a great curated collection of radial visuals. Not just recent ones, but radial visuals from centuries ago as well. Such a burst of inspiration in this book! I can open it to any page and I’ll like what I see (which is quite rare for me for these “collections” type books). Although all three books by Manuel are really good, this is probably my favorite because, well, I love radial visuals.
A book that is filled with lush, colorful, page filling infographics by various artists. Geared towards the more graphical type of infographic, but there are some really lovely designs in here. I also like how the visuals are from all over the world.
A big, beautiful book with page filling images of some great work done by newsrooms from across the world. It also has and several short essays by renowned names in the field (e.g. Alberto Cairo, Jennifer Christiansen), and short stories about several masters and talent. Definitely a book that I would recommend, but personally I would’ve preferred it if there were stories about the design process of certain projects, instead of the biographies about the masters/talent.
Data Visualization for Success
Literally as the subtitle says, this book features interviews with 40 data visualization creators. Each interview is interspersed with screenshots of their work. I found that they’ve curated the 40 people really well, with famous names but also some people that I hadn’t heard of yet but that have done great work. The interviews also supply some extra value over just a book with big beautiful images.
Infographic Designers’ Sketchbooks
Also called “Raw Data” in some countries, this big and beautifully designed book is a collection of works by many different artists, however with the inclusion of one or two work-in-progress shots besides the final result. It covers quite a wide range of information design areas. Some are about data visualizations, many are infographic-like, while others are more process diagrams, or barely dataviz even?, and some are art (also some that seem quite far from dataviz). This book was a hit-or-miss for me, where some artists shared really nice / insightful rough sketches, and others only had an almost-finished state to share besides the final result. I wish that the authors had curated the quality of which works to include in this book a little better. Fewer projects, but each with more background information, so I don’t only see the “sketches”, but also learn more about the why and the how.
A book full with diverse information design projects. Really a hit-or-miss for me. There are some amazing (more data art) projects in here that I hadn’t seen before. But there are also some awful infographics that are nothing more than some icons with a number next to it. Definitely more good than bad stuff thankfully, so I’d still recommend it if you can get it for a nice price.
I actually bought the Dutch version of this book on a whim while at Infographic Conference in the Netherlands and I’m very glad that I did! This is a wonderfully complete book about Typography. Each spread of two pages is almost its own sub-sub chapter and explains one facet of typography. This can range from history lessons to more technical subjects like kerning to a specific category of fonts. Each page has a whole array of wonderful full-color examples to accompany the lesson of that spread, which makes for a very entertaining read.
Even though each 2-pager can be read on its own, they are grouped into 6 actual chapters. The first is a very general chapter about reading and seeing; ways to read, how typography is used in marketing. Next a short chapter on organizing and planning a page by using grids. The third chapter is the biggest of the book and concerns type. How to recognize the different categories, how to choose a type and how to combine them. We get an entire history lesson from before Gutenberg to the typographic noise of the first commercial spreads to the (digital) trends of today. I loved reading it all.
But there is also more than enough about the technical side, especially in the fourth chapter on typographic detail. We learn about the do’s and don’ts. Each gets its own spread which explains the why’s and shows good and bad examples and the chapter contains much more. How to get effective texts for reading, but also on creating big captions. The final two chapters are a bit smaller again. Design strategies swiftly introduces us to typographic logos, corporate identities and the current vibe of going back to the printing press for authenticity. The final chapter contains a few spreads on the history of the actual printing of text. What kind of work was needed and what types of machines have been used in the past.
This is by far the best book on typography I’ve read so far (although I have to admit that I haven’t read that many, but still). It’s a very complete introduction into typography that doesn’t shy away from becoming technical or detailed from time to time. I love the fact that by using history it teaches the reader the why on how some conventions, types or words (like uppercase & lowercase) came into being. It will not satisfy those looking for an entire chapter on specific things like kerning. For that you’ll need a much more specialized book. But for all who want to learn about the world of typography, I would definitely suggest you start with this book!
Note: If you’re doubting about this book or Thinking with Type by Ellen Lupton, then I would definitely recommend Shaping Text. I won’t write a full review of Thinking with Type on this because one book about typography in a data visualization section is enough. But I found Shaping Text to be a much better book to learn from. It has a more elaborate history, more examples about the different technical things and most importantly, it doesn’t fill its pages with fluffy words and anecdotes with metaphors about text/typography. Shaping Text covers all that Thinking with Type covers, but more complete and more clearly. But this is just my opinion of course.
How to Lie with Statistics
Lovely little book (only 124 A5-ish pages) written more than 50 years ago, but still very much relevant. Perhaps even more so, in this data overloaded online world now. It is packed full with wonderful real-life examples of cases where scientists and businessmen have made false conclusions form and with numbers. And, sadly enough, those false conclusions are not always by accident. There are several chapters about how graphs can be misleading, such as the bar chart that doesn’t start at zero.
If you’re working with numbers, this really is a must read. This humorous book contains many valuable lessons. Both on on catching a wonky statistic you read about and teaching you how not to make the mistakes yourself.