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Visualize This: The FlowingData Guide to Design, Visualization, and Statistics

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Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.


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Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks Practical data design tips from a data visualization expert of the modern age Data doesn't decrease; it is ever-increasing and can be overwhelming to organize in a way that makes sense to its intended audience. Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships. Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

30 review for Visualize This: The FlowingData Guide to Design, Visualization, and Statistics

  1. 4 out of 5

    Chelsea Lawson

    Nathan Yau is the BEST. I have been trying to learn R on my own for some time now and it has been quite frustrating. The documentation on rdocumentation.org is written in such an esoteric way. I wish it were a wiki so that people could provide more examples or make it sound more like plain English. Anyway, Visualize This covered everything I was looking for to learn basic data visualization in R, from timelines to proportions/relationships and maps, as well as some instruction for Illustrator/In Nathan Yau is the BEST. I have been trying to learn R on my own for some time now and it has been quite frustrating. The documentation on rdocumentation.org is written in such an esoteric way. I wish it were a wiki so that people could provide more examples or make it sound more like plain English. Anyway, Visualize This covered everything I was looking for to learn basic data visualization in R, from timelines to proportions/relationships and maps, as well as some instruction for Illustrator/Inkscape. I would recommend first reading Data Points as it is more introductory - what ARE the different kinds charts and graphs vs how to make them.

  2. 4 out of 5

    Uroš

    This is a BEGINNERS guide to (mostly) static data graphics design for websites and newspapers. Don't expect a lot about both data science (statistics and data analysis) and data art (artistic visualization, animation, interactive video, etc). Pros: - Very practical, with a lot of code - Good combination of statistics and design practice - Online examples - Clear writing style - Useful design tips Cons: - Lacks subtlety and depth in terms of both science and art - Lacks materials about animation and inter This is a BEGINNERS guide to (mostly) static data graphics design for websites and newspapers. Don't expect a lot about both data science (statistics and data analysis) and data art (artistic visualization, animation, interactive video, etc). Pros: - Very practical, with a lot of code - Good combination of statistics and design practice - Online examples - Clear writing style - Useful design tips Cons: - Lacks subtlety and depth in terms of both science and art - Lacks materials about animation and interactive visualization - Too "American" in terms of examples and approach - Examples are too simple, yet explained tediously - The author has an awful taste in films

  3. 5 out of 5

    Yerzhan Karatay

    Nathan Yau's Visualize This basically lives up to its full name, it's a broad guide to design, data visualization tools, and methods, I must say that the broadness compensates the depth as I took several notes of the tools that I could use in personal projects. Nathan Yau constantly reminds the reader about the importance of programming as it provides one with the best opportunities, and goes through data handling (including how to web-scrape), visualizing time series, proportions, relationships Nathan Yau's Visualize This basically lives up to its full name, it's a broad guide to design, data visualization tools, and methods, I must say that the broadness compensates the depth as I took several notes of the tools that I could use in personal projects. Nathan Yau constantly reminds the reader about the importance of programming as it provides one with the best opportunities, and goes through data handling (including how to web-scrape), visualizing time series, proportions, relationships, spotting differences, and spatial relationships. I wish I found it earlier as I had these doubts about adding a final touch to my R plots with Adobe Illustrator around 2 years ago but it's in professional practice as I learnt back then. One other important takeaway would be that one shouldn't neglect other tools even if he or she believes to have gained enough programming skills. This is not to be overlooked as there is no perfect data visualization instrument (the author also mentions their disadvantages which is important) and that's why such book was in need of being written.

  4. 5 out of 5

    Alyson Hurt

    Targeted toward beginners who don't fear code, this is a useful introduction to the world of data visualization, from data collection/research to display. Coming at the book from a graphic/web design background, I found the code examples most useful – particularly those relating to the statistical software R, which I've always found a bit intimidating. Note: Some of the software/frameworks cited are out of date: Protovis is now D3.js, and Flex Builder is now Flash Builder (and most folks are movi Targeted toward beginners who don't fear code, this is a useful introduction to the world of data visualization, from data collection/research to display. Coming at the book from a graphic/web design background, I found the code examples most useful – particularly those relating to the statistical software R, which I've always found a bit intimidating. Note: Some of the software/frameworks cited are out of date: Protovis is now D3.js, and Flex Builder is now Flash Builder (and most folks are moving / have moved away from Flash-based presentations anyway...).

  5. 4 out of 5

    Michael Scott

    TODO full review: + Reads like a primer on information-visualization techniques, plus a primer on interesting software to use the information-visualization techniques in practice. Also comes with various links to meaningful datasets. + The visuals are rich, interesting, and correctly executed. There is little of the egregious visualization that have annoyed Stephen Few and William S. Cleveland or, for a stricter take, Edward R. Tufte. +++ The data sources focus on data provided by (1) universities TODO full review: + Reads like a primer on information-visualization techniques, plus a primer on interesting software to use the information-visualization techniques in practice. Also comes with various links to meaningful datasets. + The visuals are rich, interesting, and correctly executed. There is little of the egregious visualization that have annoyed Stephen Few and William S. Cleveland or, for a stricter take, Edward R. Tufte. +++ The data sources focus on data provided by (1) universities (Carnegie Mellon, UCLA, and UC Berkeley), (2) specialized but global data providers (the World Health Organization, the United Nations Data division, the World Bank, the OECD Statistics division, etc.), (3) specialized data provided by national and local data providers (lots of governments and census bureaus here), (4) general data providers (Amazon, Wikipedia, etc.), etc. +++/- The software includes (1) various data scrapers, to collect data from the Internet, (2) various data converters, to make data provided from a source be useful to processing tools expecting another format, (3) out-of-the-box tools for visualization (Microsoft Excel, Google Spreadsheets, Tableau Software, Many Eyes), (4) programming tools for visualization (Python + NumPy/SciPy), Processing, R). There is nothing specific about data ingestion and cleaning. Some of the products already seem obsolete (PHP, Flash/ActionScript), and some of the important current players are omitted (plot.ly, the advent of JavaScript, e.g., D3.js). + Some of the more exotic software: (1) Mapping: Modest Maps, Polymaps (JavaScript), (2) General: the Flare visualization toolkit from UC Berkeley's Visualization Lab (Flash/ActionScript), (3) Coloring: Cynthia Brewer's ColorBrewer. --- The examples in the book seem beautiful and graphically polished, but the programmatic examples that supposedly lead to them are always complemented by much work in Adobe Illustrator (or Inkscape). The level of work required to achieve the same in, say, R, is left for further discussion. Even one example of complete work in R would have been interesting.

  6. 5 out of 5

    Joe

    Good, but not the first book you should read on data visualization (probably start with Cole Knaflic's Storytelling with Data first). The book has lots of good examples; everyone will probably learn something from it. For me, the most valuable parts were: (1) an overview of data visualization tools from someone who has tried many of them (including Adobe Illustrator, which should have been on my radar long before I read this), (2) a good walkthrough on scraping data from the web, which is someth Good, but not the first book you should read on data visualization (probably start with Cole Knaflic's Storytelling with Data first). The book has lots of good examples; everyone will probably learn something from it. For me, the most valuable parts were: (1) an overview of data visualization tools from someone who has tried many of them (including Adobe Illustrator, which should have been on my radar long before I read this), (2) a good walkthrough on scraping data from the web, which is something I've only done once or twice, and (3) a collection of resources for making maps (usually the hardest visualization). You won't want to read this if you're just going to do visualizations in Excel or PowerPoint. Instead, the author tries to get you closer to getting your visualizations professional enough for publication in a newspaper. He shows an example of how a visualization goes from basics in R to how it's ready for The New York Times. It's rare that I'll need to make my visualizations look that good, but it's also nice to know I've got a reference that will get me most of the way there.

  7. 4 out of 5

    Cyrus Molavi

    This was a very complete book. It reads very much like a textbook or workbook. A concept is introduced, a challenge is laid out, and then the author walks through the steps of getting there while the reader works through the problem actively. Data and code is made available for download to use while following along. It dives into pragmatic uses of Python, R, CSS, Illustrator, XML, and a variety of visualizations. This is the depth of coding and design that I'm interested in as an analyst, and th This was a very complete book. It reads very much like a textbook or workbook. A concept is introduced, a challenge is laid out, and then the author walks through the steps of getting there while the reader works through the problem actively. Data and code is made available for download to use while following along. It dives into pragmatic uses of Python, R, CSS, Illustrator, XML, and a variety of visualizations. This is the depth of coding and design that I'm interested in as an analyst, and this book was perfectly tailored for this function. I'll be keeping it as a reference for when I want to pull out the skills I now have some practice with but might need a refresher on. One thing I might add is that it's starting to age; there are sections that introduce outdated tools, some code no longer works with updates to languages, and there is the odd broken link.

  8. 5 out of 5

    Lukas Rubikas

    I feel some of the tools mentioned here (Pyhton 2, Flash, Protovis) are outdated but the practices mentioned here are applicable regardless. This book is much more focused on hands-on approach than Storytelling with Data: A Data Visualization Guide for Business Professionals but I liked it, especially the hands-on bits on putting finishing touch on your graphs with Adobe Illiustrator as I'm yet to learn how to adapt this widely popular tool for my needs. It's good for beginners interested in data I feel some of the tools mentioned here (Pyhton 2, Flash, Protovis) are outdated but the practices mentioned here are applicable regardless. This book is much more focused on hands-on approach than Storytelling with Data: A Data Visualization Guide for Business Professionals but I liked it, especially the hands-on bits on putting finishing touch on your graphs with Adobe Illiustrator as I'm yet to learn how to adapt this widely popular tool for my needs. It's good for beginners interested in data viz, but less so for experienced so.

  9. 4 out of 5

    Merilin

    I'm a huge fan of Nathan Yau's blog which is also why I picked up this book. Unfortunately, the target audience for this one seem to be everyone on the other end of the spectrum from myself - i.e. anyone but data science/analytics people working with immense and non-static datasets. *If this was my introduction to dataviz: 5 stars* Great for: - tips for transferring your ad hoc reporting from excel to any other environment (Tableau, js etc) - learning dataviz in R - understanding the where when why I'm a huge fan of Nathan Yau's blog which is also why I picked up this book. Unfortunately, the target audience for this one seem to be everyone on the other end of the spectrum from myself - i.e. anyone but data science/analytics people working with immense and non-static datasets. *If this was my introduction to dataviz: 5 stars* Great for: - tips for transferring your ad hoc reporting from excel to any other environment (Tableau, js etc) - learning dataviz in R - understanding the where when why and how of using different visualisation types - learning the basics of human perception, colour theory etc for beginners Redundant for: - anyone with any working knowledge and experience with these topics

  10. 4 out of 5

    Eric

    I didn’t learn a ton from this book, but there were a few interesting examples. Also it feels a bit dated with some of the examples and technologies employed (though that’s the nature of the discipline).

  11. 5 out of 5

    Alok Pepakayala

    Great read to finish in one go, most of the code examples and tech stack is outdated for today but still holds its value and the author keeps the prose skinny to the bone. Its a cross between a textbook and coffee table flip through.

  12. 4 out of 5

    Sweemeng Ng

    Some of the tools is out dated. But it is still good insight on what is going on behind making data viz.

  13. 4 out of 5

    Kevin Goldsmith

    Good introductory book, but the code examples and discussion have not aged well.

  14. 5 out of 5

    This Is Not The Michael You're Looking For

    Visualize This is a book about designing visualizations for data ("graphs" more or less, although there are visualizations which are not, strictly speaking, graphs). The focus of the book was not what I expected; given that the author is a graduate student in statistics, I expected the book to have more of a scientific focus. Instead, it is mostly focused on designing visualizations for websites and/or newspapers and magazines. While there can be a lot of overlap between these tasks and more dir Visualize This is a book about designing visualizations for data ("graphs" more or less, although there are visualizations which are not, strictly speaking, graphs). The focus of the book was not what I expected; given that the author is a graduate student in statistics, I expected the book to have more of a scientific focus. Instead, it is mostly focused on designing visualizations for websites and/or newspapers and magazines. While there can be a lot of overlap between these tasks and more directly scientific output, the focus made the book less useful and interesting to me than I had hoped it would be. In some sense, the book is schizophrenic. It talks a lot about visual design, but is more holistic than anything one might consider to be visualization theory. At many times the book is more focused on how you create a graphic (i.e., specific software, tools, code, etc.) rather than what the graph should or should not contain. The tool use is mostly focused on a combination of R and Illustrator (or Inkscape), but occasionally wanders (for little apparent reason at times) into Python, Javascript, Flash, ActionScript, and other similarly random packages. Since it's not a textbook on their use, I question how useful these examples are for the greater purpose of learning. Is the book about what graphs should (and should not) look like to maximally display the information/tell the story you wish to convey or is about the mechanics of getting a computer to draw a specific style? It tries to be both and, therefore, somewhat fails at both. It is also a bit overly redundant at times; since many of the chapters were explicitly designed to allow a reader to jump in and read independent of the rest, the same basic tasks and ideas are often introduced in multiple chapters, which is frustrating to someone who sits down to read it cover-to-cover. This is not to imply the book does not contain useful information, it certainly does. It introduces a decent variety of graph styles, many of which are likely unfamiliar to readers. It does discuss valuable resources of which the reader may be unaware, such as Inkscape (a free, open-source alternative to Adobe Illustrator) and 0to255.com (a website for helping choose color schemes). When it does slip into visualization theory the advice is solid and to the point (e.g., how to scale bubble plots so the area of the bubbles properly reflects relatives sizes). Although I was clearly disappointed in the text and have many criticisms, in the end it has enough advantages to be a worthwhile read, particularly for those who are complete newcomers to visualization or who have a particular interest in data visualization for the web.

  15. 4 out of 5

    Jonathan Jeckell

    This is a nice supplement to the Tufte series, focusing exclusively on data, numeric, and statistical graphics, including animations. Edward Tufte even referred to this book during his One Day Seminar. Unlike Tufte, this contains a lot of detailed, step-by-step directions to obtain data and how to build the graphics he shows in the book. While I love the practical directions, rather than just showing us the graphic and letting us ponder how to make something like it, I wish there was a greater s This is a nice supplement to the Tufte series, focusing exclusively on data, numeric, and statistical graphics, including animations. Edward Tufte even referred to this book during his One Day Seminar. Unlike Tufte, this contains a lot of detailed, step-by-step directions to obtain data and how to build the graphics he shows in the book. While I love the practical directions, rather than just showing us the graphic and letting us ponder how to make something like it, I wish there was a greater seperation in the text between this and how the chart works, when to use it, etc. Because the book is deeply imbued with lines of code and screenshots from Flash editors, you can't just read this book in a linear fashion to see different types of ways of portraying data and their relative merits. It's more useful as a handbook or reference unless you are good at skimming over parts you aren't ready to experiment with on your own. I'm also a little concerned about how some of the specific instructions will age, and how soon some of this code will hit an expiration date. The code was generic enough that someone with a little programming knowledge can apply it to another they are familiar with, and command line languages can be fairly durable; it's the GUI shots, especially for proprietary software that mostly worries me. Concerns about the longevity of the usefulness of the practical application tips and the tight integration with the rest of the text aside, this is a very useful book if you work with data or numeric visualization or need to understand complex data better.

  16. 4 out of 5

    Jake Losh

    Visualize This: The FlowingData Guide to Design, Visualization, and Statistics is a worthy effort to make a primer on data visualization. You'll learn all the tricks of the trade for finding data, cleaning data, making a graphic and cleaning the graphic to make it fit to print. If you're already a data nut or a fan of Nathan Yau's blog, you'll likely enjoy the ride. In some sense, though, the book tries to do too much in too small a space. Aside from the core content revolving around data viz, yo Visualize This: The FlowingData Guide to Design, Visualization, and Statistics is a worthy effort to make a primer on data visualization. You'll learn all the tricks of the trade for finding data, cleaning data, making a graphic and cleaning the graphic to make it fit to print. If you're already a data nut or a fan of Nathan Yau's blog, you'll likely enjoy the ride. In some sense, though, the book tries to do too much in too small a space. Aside from the core content revolving around data viz, you'll also get lengthy tutorials on how to write scripts (i.e., program) and how to use Adobe Illustrator. It demonstrates nicely how the role of an infographic maker really straddles multiple departments and disciplines, but it also muddles the core of the book somewhat. I found myself constantly skimming through these parts. It would have been great if those parts had been left to the appendices or suggested readings pages. I applaud Nathan Yau for this ambitious undertaking and would highly recommend the book to any of his blog's frequent readers, but would caution that those without any programming or graphic design background be patient and take it slow when reading the book. That's probably best for all of us, in any event.

  17. 4 out of 5

    Gina

    Yau is best when he talks about data and how to acquire it and about how to present various types of data. He is fixated on the notion that people need to code their own visualizations, preferably using R, an open source program that is quite good but not for the faint of heart. The documentation is spotty, and while I gamely carried out multiple exercises from the book, there were errors in the coding one was instructed to use, and multiple gaps that assumed readers would have greater knowledge Yau is best when he talks about data and how to acquire it and about how to present various types of data. He is fixated on the notion that people need to code their own visualizations, preferably using R, an open source program that is quite good but not for the faint of heart. The documentation is spotty, and while I gamely carried out multiple exercises from the book, there were errors in the coding one was instructed to use, and multiple gaps that assumed readers would have greater knowledge than they might. Small pullout boxes indicating where to go online to get additional information were not always helpful. I was reminded of a book I reviewed for the New York Times at the outset of the internet, just before graphical user interfaces burst on the scene. It was a well done book, too, but made obsolete almost as soon as the review was published. I suspect the same is happening now with visualizations, as new "black box" sites come online every month allowing users to create visualizations from existing datasets, without have to get down and get messy with code. Not that there's anything wrong with code, just that most people do not have time for it.

  18. 4 out of 5

    Yahia El gamal

    The idea of writing this book is really good. Having a book to fill the gap between heavy-on-code tutorials of a visualization tool(s) and purely theoretical, conceptual, have-no-idea-how-to-create-those-examples books. But the book fails in the former side. I would have at least given it 4 stars if it used a proper set of tools. The book is using base-r for plotting (which no one who is taking visualization seriously should use in the presence of ggplot). And is using Flash (Action Script stuff) The idea of writing this book is really good. Having a book to fill the gap between heavy-on-code tutorials of a visualization tool(s) and purely theoretical, conceptual, have-no-idea-how-to-create-those-examples books. But the book fails in the former side. I would have at least given it 4 stars if it used a proper set of tools. The book is using base-r for plotting (which no one who is taking visualization seriously should use in the presence of ggplot). And is using Flash (Action Script stuff) for interactive graph (Which is already dead, and no one who is taking interactive visualization seriously should use in the presence of D3). I would urge the author to rewrite the book using proper tools, it would be an invaluable book. Having that said, the book is very useful. I think it made me think a little more clearly about the readability and interpretability of the graphics I produce. I will move to Tufte's The Visual Display of Quantitive Data and see what it has to offer. I recommended this book with a a note, don't follow the code, implement the same graphics with proper tools and you will learn a lot.

  19. 5 out of 5

    Ariadna73

    10-19-2011: This book is a wealth of good resources for visualization. I felt like a kid in a candy store. It must be read in front of a computer with internet connection. There are so many different places where we can find data and ideas to visualize it! I loved this book! 09-18-2011: This book has been a nice surprise: I was expecting another boring recount of graphics and enless tables; but this one is really well written and entertaining. I have been reading it with real attention and I have 10-19-2011: This book is a wealth of good resources for visualization. I felt like a kid in a candy store. It must be read in front of a computer with internet connection. There are so many different places where we can find data and ideas to visualize it! I loved this book! 09-18-2011: This book has been a nice surprise: I was expecting another boring recount of graphics and enless tables; but this one is really well written and entertaining. I have been reading it with real attention and I have not skipped a single paragraph since the begining. I'll see if it continues this wonderful way... Check my blog entry out: http://lunairereadings.blogspot.com/2...

  20. 4 out of 5

    Chris

    Decided to quickly read this over the weekend. The last few chapters are relatively good with some quick examples on how to use R and Python to produce visualizations. There is also a good example of SVG-style graphics and with very limited skill set. You could easily set up many of those graphics by manipulating a few XML files. The first part of this book was ok. This book is more practical, which I find better than the book data point, which is just theory. Nathan Yau also presents his backgr Decided to quickly read this over the weekend. The last few chapters are relatively good with some quick examples on how to use R and Python to produce visualizations. There is also a good example of SVG-style graphics and with very limited skill set. You could easily set up many of those graphics by manipulating a few XML files. The first part of this book was ok. This book is more practical, which I find better than the book data point, which is just theory. Nathan Yau also presents his background in data journalism a bit better in this book. Overall, not a bad book, though intermediate Python and R programmers will probably not pick that much up (maybe a few libraries).

  21. 4 out of 5

    David

    Great beginner text, but potentially a little dated now (flash anyone?). The narrative and observations however are well communicated and timeless. A little put off by the constant jumping between tool sets. includes R and python (horray!) but also provis, illustrator and tableau and *cough* action script and flash. Further complicated by the fact he keeps pushing the reader to take the output of all these tools and 'perk them up' in illustrator at the end of every chapter. 2009 was a while ago, Great beginner text, but potentially a little dated now (flash anyone?). The narrative and observations however are well communicated and timeless. A little put off by the constant jumping between tool sets. includes R and python (horray!) but also provis, illustrator and tableau and *cough* action script and flash. Further complicated by the fact he keeps pushing the reader to take the output of all these tools and 'perk them up' in illustrator at the end of every chapter. 2009 was a while ago, but I'm sure a decent graphic output could have been produced by keeping a project within whichever random tool he happened to start in...

  22. 5 out of 5

    Leslie

    Great hands on book about data visualization. It helps to know a little bit about stats and visualization before starting, but this book does a great job of explaining how to put some very advanced and interesting visualizations together, and how to use various software and programming languages to get the visualizations you want. As someone who isn't a programmer, this is was a super helpful guide in getting started in the world of R in particular. Love it. Will keep and reference again for man Great hands on book about data visualization. It helps to know a little bit about stats and visualization before starting, but this book does a great job of explaining how to put some very advanced and interesting visualizations together, and how to use various software and programming languages to get the visualizations you want. As someone who isn't a programmer, this is was a super helpful guide in getting started in the world of R in particular. Love it. Will keep and reference again for many years to come.

  23. 4 out of 5

    Ji

    It's probably an OK book for people who has not done any data visualization before. But then, even so, you might want to pick up another book that is better organized in the basics and foundational knowledge than this one, which is pieced together by examples where no obvious logic can be detected on how they are really put together, even with those chapter titles. I recommend anybody from any level to try to skip this book. It's no better than the blog posts that are already freely available at It's probably an OK book for people who has not done any data visualization before. But then, even so, you might want to pick up another book that is better organized in the basics and foundational knowledge than this one, which is pieced together by examples where no obvious logic can be detected on how they are really put together, even with those chapter titles. I recommend anybody from any level to try to skip this book. It's no better than the blog posts that are already freely available at flowingdata.com.

  24. 5 out of 5

    Brian Kelly

    Recently started reading this. It's a good book though a little bit dated at this point. My main issue is that it's pretty much a workbook - you're meant to use apps and write programs as you read the book and information about how to better display different types of information is sprinkled through that. I would've liked that information to be more separate from the actual "exercises". This made it hard to read on the train.

  25. 4 out of 5

    S.M.

    I'd recommend this to anyone who is working with large data sets and needs a quick intro into good visualization techniques. Of course, it is not a substitute for years of work with R or gnuplot but does a great job of explaining what a good data visualization is expected to be. Oh and did I mention I own a signed copy of the book? :D

  26. 4 out of 5

    Derrick Schneider

    This book is a great hands-on start to the world of data visualization. Yau provides exercises that use a wide variety of tools, giving breadth that allows you to be smarter about which toolset you use for any given visualization task. Along the way, he also discusses the theory behind different visualizations -- when to use them and how.

  27. 4 out of 5

    Mick Bordet

    A very easy read, but after racing through it in two days, I know that this is a book that will also be used as a reference. Some very useful insights into producing clear visualisations and interesting starter code for various systems. I did skip over the sections on using flash, but everything else was very useful.

  28. 4 out of 5

    Tyler Simonds

    Outdated already. I started this book, and the first major chunk is just about different programs and websites related to data visualization. I spent over an hour installing Python and troubleshooting because the author's directions weren't very clear and didn't quite work. (Though back in 2011 it may have been easier.) Got stuck :P

  29. 4 out of 5

    Maria

    A very good beginner's guide to data visualization, with an easy-to-read and fun writing style. I liked that the author touches all basic concepts with concrete examples and I think this book can be a good source of inspiration for digging deeper later on. The programming code is also very useful, as well as the tips and notes that the writer gives.

  30. 4 out of 5

    Eric Bell

    Nice book on data visualization, but could have been much shorter, and contained much less code. We need a book written like this book that shares less about the how to the obtain the data and more on the thinking about and visualizing the data.

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