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Visualize This: The FlowingData Guide To Design, Visualization, And Statistics

Practical data design tips from a data visualization expert of the modern ageData 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 YauOffers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbersDetails 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 IllustratorContains numerous examples and descriptions of patterns and outliers and explains how to show themVisualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing. From the Author: Telling Stories with Data Author Nathan Yau A common mistake in data design is to approach a project with a visual layout before looking at your data. This leads to graphics that lack context and provide little value. Visualize This teaches you a data-first approach. Explore what your data has to say first, and you can design graphics that mean something. Visualization and data design all come easier with practice, and you can advance your skills with every new dataset and project. To begin though, you need a proper foundation and know what tools are available to you (but not let them bog you down). I wrote Visualize This with that in mind. You'll be exposed to a variety of software and code and jump right into real-world datasets so that you can learn visualization by doing, and most importantly be able to apply what you learn to your own data. Three Data Visualization Steps: 1) Ask a Question (Click Graphic to See Larger Version) When you get a dataset, it sometimes is a challenge figuring out where to start, especially when it's a large dataset. Approach your data with a simple curiosity or a question that you want answered, and go from there. 2) Explore Your Data (Click Graphic to See Larger Version) A simple curiosity often leads to more questions, which are a good guide for what stories to dig into. What variables are related to each other? Can you see changes over time? Are there any features in the data that stand out? Find out all you can about your data, because the more you know what's behind the numbers, the better story you can tell. 3) Visualize Your Data (Click Graphic to See Larger Version) Once you know the important parts of your data, you can design graphics the best way you see fit. Use shapes, colors, and sizes that make sense and help tell your story clearly to readers. While the base of your charts and graphs will share many of the same properties – bars, slices, dots, and lines – the final design elements will and should vary by your unique dataset.

Paperback: 384 pages

Publisher: Wiley; 1 edition (July 20, 2011)

Language: English

ISBN-10: 0470944889

ISBN-13: 978-0470944882

Product Dimensions: 7.4 x 0.8 x 9.2 inches

Shipping Weight: 2 pounds (View shipping rates and policies)

Average Customer Review: 4.1 out of 5 stars  See all reviews (77 customer reviews)

Best Sellers Rank: #69,629 in Books (See Top 100 in Books) #31 in Books > Computers & Technology > History & Culture > History #31 in Books > Computers & Technology > Databases & Big Data > Data Modeling & Design #38 in Books > Computers & Technology > Graphics & Design > User Experience & Usability

I was really hoping for a book on how to abstract data sets into visualizations, with concrete programming examples. In other words, "ask yourself these questions about the data; with these answers (or those), the data is best visualized in these formats. Now, let's implement".Instead, I found it to be a kind of "circular" logic (visualize data in good ways is important... here is some data visualized in a good way... now doesn't that show how important it is - and it's cool... btw here is a code snippet). It is almost like the book is just trying to convince me that data visualization can be powerful and cool. I know that - that's why I bought this, I wanted to learn the tools and techniques to determine the best and most innovative way to visualize data sets, not how the author has visualized existing data sets he has dealt with.Interesting enough to borrow if you see it on a friend's desk, but I don't think I'd purchase it again if I had the opportunity.

I really enjoyed this book. It is absolutely beautifully printed and the examples are well made and well explained. There are a couple of things I would have liked to see done a little differently.First, every example uses Adobe Illustrator to make the visualization look as good as they do. In order to complete the exercises, you must have Illustrator. Nathan does explain that it can be obtained at a discount or you can an older version, but it's still a pretty big financial investment. If I hadn't been able to dig up a old copy, Illustrator 9, I would have been out of luck. Even with my outdated copy, not everything worked for me. If he had included at least a couple of examples with the open source Inkscape, this would have been a 5 star rating.The second thing I would have liked to see a little different is more statistical info to go along with the visualizations. We often visualize data to help make decisions. Nathan shows how to display a LOESS line to see the best fit for the curve, but he stops there. Maybe discussing R² ( correlation coefficient) analysis to determine whether the values are are a good match would help me feel better about analyzing the data beyond just visualization.That said, this is an extremely well written book and easily deserves 4 stars. Dig up an old copy of Illustrator (preferably CSx versions) and enjoy this book.

If you are looking for a book on how to use Illustrator and R to create various visual elements, this book may be for you. It shows you examples using various tools, and is more of a guide to creating charts and other elements using those tools. This book is not for people not interested in using R or Illustrator or Excel for that matter. If you are looking for a book that will outline how you should THINK about presenting data that you have, and give you constructive ideas, this book is not for you. The FlowingData blog has a bunch of great tips for that, so that is what I was expecting. However, when I saw a bunch of screenshots and sample code, this is more of a programming guide than a design book.In addition, the first chapter in the preview is not representative of the book, IMO.A more apt title would be "How to use Adobe Illustrator and R to Create Charts".If you are looking for a 'How To' guide, this might very well be your book. I'm rating it low because I was expecting a book that would give me design principles and guides, not show me code samples and tool screenshots.

I have been teaching data management and visualization for 12 years and I have never seen a book that covers visualization so well for such a broad audience. It braids together the very best tools of the trade for scientific data visualization, graphic design concepts and "how-to" advice. It gives a friendly introduction to tools like R, Illustrator, XML, Python (with BeautifulSoup), JSON, etc. (and the toolkit goes on-and-on). Also it gives complete working code examples to show how to scrape data from the web for analysis and visualize the info without swamping the reader with details. It has a HUGE set of references and free tools for getting interesting data-sets (everything from sports to science to politics to health), reformatting data and making graphics that are ready for mass media or scientific publication.There is very little to complain about here except the fact that the author shows off Illustrator instead of its less expensive competitors. I had avoided Illustrator because of cost and the nasty learning curve but now, thanks to this book, I am using it to edit my SAS and R graphics that were "almost perfect." Happily this book has great examples for showing how to manipulate/clean up scientific graphics without getting bogged down in the endless complexity that is Illustrator.So, this is all around beautiful, friendly and worth every cent if you need to make high quality graphics.

This is a nice addition to the books on data visualization. It will be particulary useful for people wanting to learn R (the lingua franca of statisticians) to create good looking visualizations. The writing style is crisp and conversational and is organized around the kind of things one might want the data to communicate: time series, part-to-whole comparisons, relationships, etc. It does not require any expertise in programming or statistics to understand.

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