Data Visualization: A Practical Introduction

While generally clear, there there are enough cases where Healy uses terms & notations before explaining them, and at least one where they're not explained _after_ they're used either. If there were a more comprehensive index, or a section explaining these, it wouldn't be a problem, but there isn't.There's also one case where the text associated with a figure is both inaccurate and essentially "left as an execise for the reader.The example I'll give is from p.47, but it's not the first instance of this problem -- just the straw that broke my back.FWIW, I'm not a newbie: I've been a programmer for 40 years, and have worked in at least a dozen high-level languages, including APL, FORTRAN, COBOL, Pascal, C/C++, Java, Lisp, Prolog, and Perl. Some of these (e.g., Perl & APL) have pretty exotic sytaxes, so I'm not unfamiliar with compact and hieroglyphic notation. I'm not bragging -- after 40 years, I'd be a slacker if I hadn't accumulated a decent amount of experience -- just saying that I do have (at least in theory) enough background that I don't think I'm just being stupid.In this example, Healy has presented a data frame, and then a tibble created from it. He says "Look carefully at the top and bottom of the output to see what additional information the tibble class gives you over and above the data frame version."The only _substantive_ difference at the top is a row between the first row (var names) and the first observation that reads as follows:<fct> <fct> <dbl> <dbl>"dbl" is easy, but WTF is fct? "function"? "fact"? looks like string data (perished, survived; male, female), but my intuition says that we have two defined enumerated data types, each with two values ... but ...I should not have to rely on guesswork: the meaning of <fct> should be easily accessible.There's no index entry for "fct"; I see one for "facet" -- is this a facet? I won't find out for another 30 pages. If this were the Kindle version, I could search for <fct>, and for fct if I find nothing for <fct> -- but (a) this if the paperback and (b) I shouldn't _have to_ search for the meaning of a notation in an example.Returning to the tibble, there is _nothing_ added at the bottom: the last row is the same as the last row of the data frame (modulo the addition of a decimal point after 344, the value of the "n" variable).I haven't decided yet whether to return it, but I'm put off by sloppy proofreading & copy-editing, partly because it's unprofessional, but largely because I'm a publisher and have _done_ proofing & copy-editing for 6 books, and I am scrupulous about it (and have at least 2 other people check me before I release a title). It's hard, and time-consuming, but to have an otherwise decent book marred by random glitches like this decreases the value & utility of the book.If I can find errata for this book, and they address the many glitches I've found, I may keep it; o/w I'll wait for the 2nd edition.

The world of data visualization has always been a bit bifurcated; you could look at wonderful pretty pictures of graphs and learn theory, or you could learn statistical software packages, but connecting the two was left as an exercise for the oft-confused reader. This book is beautiful, and full of exceptionally clear and practically useful graphs, but it also walks through all the steps of getting up and running with visual scientific communication, from installing R through downloading data sets through plain-text manuscript generation. Every element of this book is an incredibly important component of what beginning researchers need to learn to communicate scientific ideas, and having them rolled into a single attractive and carefully composed package is a delight and fairly revelatory. I want -- but realize I will not get -- all technical books to find as elegant a balance as this one does.

In the preface to the Data Visualization: A Practical Introduction author Kieran Healy writes: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.

This book is the first I've seen (ranging from the Edward Tufte gospels to O'Reilly handbooks) to strike a solid balance between both the theory and practice of data visualization. While the practical examples and code are for use with R and GGPlot (preferred platforms across a wide swath of the scientific community anyways), the principles you'll learn, both about handling data and about the "visual display of quantitative information" will obtain in any setting and data visualization platform.

I first came across this book on the web (a slight earlier version is available free for viewing but not download). It was so helpful on some topics that I ordered the hard copy. Although I have many introductory books on ggplot2, this is significant addition to that group.The reason is that the author provides a narrative that is easy to read and that focuses on the basic logic of the gg structure (rather than just the syntax). This orientation can be helpful even when the user already knows the syntax (mostly) because it helps create a mental framework that enables more creative use of ggplot2 beyond examples that are provided in the many books available.I recommend the book even if you have other intro books already and even if you already have basic capabilities in using ggplot2. It is up-to-date and adds a perspective that expands both appreciation and and facility with ggplot2.

Very good for someone with enough insight to understand it and a desire to learn more about this fascinating, practical topic.

Don’t buy the Kindle version - there are download limitations which they don’t tell you about

The book itself is well written: good pacing, clear prose and excellent examples. Only one warning: examples rely on manipulating data using additional R packages only from the "tidyverse" series - whereas quite a few of the manipulation are better illustrated using the inbuilt R commands.The problem is the print quality. Even though R can produce vector graphics (SVG, even mentioned in the book) this book's illustrations were rendered in a compressed format at a low resolution leading to quite poor fuzzy graphics and muted colours. This is simply unforgiveable in a book all about how to present the best graphics using R.Finally my copy had a number of pages with ink smudges resulting from a problem during the print run - also a big issue in a book all about presentation quality.

This book is for the people who need to work with graphics and data. Specially if you are going to use the language “R”. Its very easy to follow. Its not a book about “programming” although you will have to write code if you want to follow the examples. The author use the popular R package ggplot2 . A Good book

Llego en el tiempo establecido y el contenido es bueno, ya que hay ejemplos de gráficos bastante atractivos.