My official endorsement (literally, it's on the ):
Through apt use of analogies, hands-on exercises, and abundant opportunities to gMy official endorsement (literally, it's on the ):
Through apt use of analogies, hands-on exercises, and abundant opportunities to get coding, this book delivers on its promise to give a reader without a background in statistics or programming the tools necessary for understanding and conducting real-world statistical inference and data analysis.
With an emphasis on learning new concepts first "by hand," before turning to the code, it would make a particularly useful classroom companion. However, the "learning checks" provided throughout also make it a great guide for self-study.
Students and teachers alike will benefit from this thoughtful introduction, as it addresses even the smallest of details that can trip beginners up, and keep them from getting to the more fruitful parts of data analysis.
Because RJ Andrews is immensely talented, the beauty of Info We Trust: How to Inspire the World with Data (the book itself) may trick Because RJ Andrews is immensely talented, the beauty of Info We Trust: How to Inspire the World with Data (the book itself) may trick you into thinking it is to be put on a table (coffee or otherwise) for casual consumption � and, don't get me wrong, it can be! But, in so doing, you would be denying yourself something that is so much greater than the sum of its parts. It is as Andrews writes of the art of data-storytelling:
Do not allow the allure of the reward to supersede the journey itself.
Everything about this book has been artfully considered and crafted. The bibliography (which brings together so many great works I've loved, but would never have connected without Andrews' keen eye) is its own data visualization of sorts. “What is this magical wonderland of which you speak so highly?� you may be asking yourself at this point. And I stutter, because it's hard to describe something that has already captured so much in so little space (which I mean in the best of ways). It is, as its title suggests, inspiration (in the Latin, past-participle sense of breathe life into, excite, enflame), but also practical wisdom. The narrative, a trip through the British Museum, say, is interwoven with things you as a data storyteller will also need to know � artifacts to be taken home with you. You're not told that "information overload" is something to be avoided, Andrews lets you � leads you � to discover this by his side:
Satiation occurs from repeated exposure to too much of the same thing. Distraction occurs when you are overrun with stimuli begging for your gaze.
Andrews' use of marginalia and gives you both simple lessons to take away, and insights into where to find out more (I told you, the bibliography is magnificent). That hand-drawn feeling demonstrates just how much care was taken with you, dear reader, in mind.
You will turn the final page both sated, and energized and equipped to go forth and do more....more
My main goal is to introduce you to both the ideas and the methods of data visualization in a sensible, comprehensible, reproducible way.
Well, mission accomplished. The book is at once enormously readable, and sufficiently technically detailed as to make it easy to implement the principles introduced.
The book itself is also beautifully designed. The use of figures and margin notes give you a sense of being guided through the ideas rather than just being told what they are. I've had lots of fun going back to some of my own visualizations made with R and ggplot2 and improving them based on what I learned here.
I absolutely recommend this to beginners and experts alike. Healy gives you everything you'd need to know if you're starting from scratch, but in such a way as to not slow things down for the more experienced reader. For that reason, it would also make a great book for a course on applied use of R....more
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