R in Action: Data Analysis and Graphics with R (英语) 平装 – 2011年6月15日
Dr. Robert Kabacoff has more than 20 years of experience providing research and statistical consultation to organizations in health care, financial services, manufacturing, behavioral sciences, government, and academia. He is a former professor of psychology at Nova Southeastern University in Florida, where he taught graduate courses in quantitative methods and statistical programming. For the past two years, he has managed Quick-R, a popuar R tutorial website.
I have read about R graphics from many sources. But I was not very confident of myself. But after reading chapter 3 of the book, I have started building really nice graphs.
I wish I read this book before.
I have a small complain however. The book (the back cover page) says I am eligible to receive a free soft copy of the book. But I do not find the link anywhere in the website stated in the book. Hope the publisher takes care of it.
That said, however, be forewarned that as with other texts, you should not expect to find all of your answers about R in this book. In my opinion, Manning publications are typically written in a format that fits well with the agile learning method with which I have grown accustomed during my consulting career. The author introduces topics along the way, sometimes more piecemeal that I would like, but his style forced me to explore other resources for more detail, bringing familiarity to other available resources. The number of plugin statistical packages for R has grown exponentially over the years (there are now over 2500), for example, so no book, not even "R in a Nutshell: A Desktop Quick Reference (Second Edition)", which I purchased recently, should be expected to be a one-stop shop.
This text quickly brought me up to speed with R language basics working with data sets, and introduced me to specifics with regard to R statistical methods and visualizations. Using R 2.15.0 for Windows, starting with the small data sets the author provides with which to run his examples, as well as sample data sets that the R language itself provides, I soon found myself working with larger data sets that the City of Chicago makes publicly available via its website, followed by using R at work. Your comfort level will be greater or lesser depending on your experience working with data.
As someone new to R, but not new to working with data, I especially appreciated the first five chapters that encompass the first of four parts in the book ("Introduction to R", "Creating a Dataset", "Getting Started with Graphs", "Basic Data Management", and "Advanced Data Management"). Like it or not, but as with any language, most data work revolves around first getting it into the correct format. After these first five chapters, the author walks the reader through basic graphs and statistics, followed by intermediate methods such as regression and analysis of variance (ANOVA), and advanced methods such as generalized linear models and more advanced graphics than was covered earlier in the book.
Glad I didn't buy it for reading on a Kindle as it gets too awkward for bookmaking important pages that you need to revisit.