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Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.
New to the Third Edition
- Three new chapters on quantile regression, missing values, and Bayesian inference
- Extra material in the logistic regression chapter that describes a regression model for ordered categorical response variables
- Additional exercises
- More detailed explanations of R code
- New section in each chapter summarizing the results of the analyses
- Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics courses
Whether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.
- Sales Rank: #624302 in Books
- Published on: 2014-06-25
- Original language: English
- Number of items: 1
- Dimensions: 9.10" h x .90" w x 6.10" l, 1.40 pounds
- Binding: Paperback
- 456 pages
Review
“I truly appreciate how grounded in practicality this book is―and the way its chapters are structured really underlines this. Furthermore, all the datasets are interesting and vary widely in subject matter. If nothing else, this book is an excellent source of examples one might use to illustrate a variety of statistical techniques. … it offers a lot of good places to start if one wants to analyze data. … The book comes hand-in-hand with an R package, HSAUR3, with all the data and the code used in the text. The book is thus fully reproducible. Overall, it provides a great way for a statistician to get started doing a wide variety of things in the R environment. It would be particularly useful, then, for working statisticians looking to change their software. The book cites all the relevant packages one might need, which is quite nice for those attempting to navigate the vast array of packages freely available, and is quite clear in its presentation of the code. Between this and the datasets, it makes for quite a valuable and enjoyable reference.”
―The American Statistician, August 2015
"… a handy primer for using R to perform standard statistical data analysis. … students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there."
―Kendall Giles on his blog, September 2014
Praise for the Second Edition:
"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."
―International Statistical Review (2011), 79
"… an extensive selection of real data analyzed with [R] … Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. … the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. … This handbook is unusually free of the sort of errors spell checkers do not find."
―MAA Reviews, April 2011
Most helpful customer reviews
3 of 3 people found the following review helpful.
Primer for statistical data analysis with R
By Kendall Giles
With the explosion of the amount of data available, the need for stronger data analysis tools than Excel has grown. Fortunately one of the best data analysis tools, R, is also free. This book, A Handbook of Statistical Analyses using R, Third Edition, by Torsten Hothorn and Brian Everitt, provides a handy primer for using R to perform standard statistical data analysis.
As suggested by the title, the book is not meant to teach you data analysis. Rather, it is a *handbook* of methods of data analysis using R. After an introductory chapter for those completely unfamiliar with R, each chapter focuses on applying a specific statistical technique to data using R.
Each chapter has a similar structure, and the teaching method uses a data-first approach. The Analysis of Variance chapter, for example, begins with a sample dataset and a short case study to give context. A brief summary of the analysis of variance statistical technique is given, followed by the commands for how to perform analysis of variance in R using the case study data. For each R command, expected outputs are given so that you can verify what you are doing if you are following along on your own computer. A summary of the analysis findings are given (i.e., how to interpret the data), along with final comments. End of chapter exercises are also given so the reader can explore further.
The list of chapter titles are:
1 An Introduction to R
2 Data Analysis Using Graphical Displays
3 Simple Inference
4 Conditional Inference
5 Analysis of Variance
6 Simple and Multiple Linear Regression
7 Logistic Regression and Generalized Linear Models
8 Density Estimation
9 Recursive Partitioning
10 Scatterplot Smoothers and Additive Models
11 Survival Analysis
12 Quantile Regression
13 Analyzing Longitudinal Data I
14 Analyzing Longitudinal Data II
15 Simultaneous Inference and Multiple Comparisons
16 Missing Values
17 Meta-Analysis
18 Bayesian Inference
19 Principle Component Analysis
20 Multidimensional Scaling
21 Cluster Analysis
Note that every example, graphic, dataset, and output in the book can be reproduced using a free R package the user can download.
Who's this book for? It's not meant to teach you statistics, so it's best if you are already familiar with statistical data analysis approaches. You don't have to be an expert though--the short theory section for each statistical method covered in the book gives you the main points, and you can learn main concepts by working through the case study and seeing the R commands used in the context of the given case study problem. Note however that there is a lot more to R than is covered in this book, so don't expect this to be a comprehensive book on how to use R.
So, students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there.
3 of 3 people found the following review helpful.
Most important statistical techniques in R examples
By Reluctant Reader
ISBN 1482204584 (A Handbook of Statistical Analyses using R, 3rd ed.) is an excellent compact reference on R not for beginners in programming or statistics even though it starts with the brief introductions to R and data analysis. They are followed by chapters on inference, variance, regression, density, partitioning, smoothers, longitudinal data, comparisons, meta-analysis, principal component, scaling, and clustering, as in the 2nd ed., and additionally on quantile regression and missing values not in the the 2nd ed..
The text full of useful examples well teaching R to solve problems of explained statistical techniques and involved math described individually in separate chapters. The descriptions are systematic explanations in the tutorial style: introduction of data, R example to purify data, and R example to find the data significance. The book is well printed on good paper, but the relatively flimsy soft covers are prone to creasing. It could be complemented by Introduction to Scientific Programming and Simulation Using R.
1 of 1 people found the following review helpful.
Superior to online tutorials, but just as easy
By Peter B. Nelson
"A Handbook of Statistical Analyses using R", joins a growing list of related books on my shelf, including Data Science for Business: What you need to know about data mining and data-analytic thinking, Doing Data Science: Straight Talk from the Frontline, Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst and Data Analysis with Open Source Tools. Each of those has unique qualities, but HSAUR, as the authors consistently refer to it, stands out.
To start with, HSAUR is relatively expensive for a paperback, over fifty dollars, suggesting it is intended for the higher-priced academic market. The text implies this, with a more in-depth treatment of its subject matter and a section of exercises at the end of each chapter - an implication the authors confirm in the preface.
One of the best things about this book is its companion 'R' package, on CRAN, called HSAUR3. For those who don't know, many people consider the greatest strength of 'R' to be its open-source community-supplied extensions, called 'packages', typically hosted on the central repository dedicated for this purpose at cran.r-project.org.
It is common enough for authors to supply supplemental material for download, but by providing a CRAN package Hothorn and Everitt have made the process perfectly seamless for 'R' users. With one command I can import the book's data into an 'R' session, then perform all the same analyses whilst reading. This synergistic approach makes for an especially powerful learning experience, much superior to reading alone, and much simpler than downloading separate datasets, extracting, storing and loading them.
If you search for HSAUR3 on the cran.r-project.org site you will readily find their title page. Once there, you can open 22 PDF's, one for each chapter plus one for the entire book. This helps make up for the fact that, as of this writing, there is no "Look Inside" feature at Amazon.com (although I did find quite an extensive preview at books.google.com if you care you search there). Those same PDF's can be opened within 'R' by a single command, and even edited. From there, copying source code is trivial.
For the sake of this review, I'd like to focus on one thing: logistic regression, which is especially useful for categorical prediction. Compared to the other books in my Data Science library, HSAUR is particularly complete. Not only does it provide seven pages of introduction, overview and theory but goes on to provide 22 pages of practical examples. In fact, the authors work through five case studies: blood screening, women's role in society, colonic polyps, driving and back pain, and happiness in China. Each case study is given a slightly different treatment to illustrate various strategies for using logistic regression on different sorts of data. See for yourself: go to CRAN and get the chapter PDF (called a 'vignette') and read all five case studies - all that's missing there is the seven-page introduction and overview.
No other book in my library gives such a thorough treatment of logistic regression, and it's hard to imagine what more could be said outside of a book dedicated to the subject. In this it far surpasses any other treatment I've found online. As an open-source project, 'R' is famous for having ample free self-help resources. A google search for "learning R" returns 375000 hits, a vast trove of resources varying from entire books for free download, to dedicated websites, to videos, to massive open online courses. No one alive could review all those resources, but I can say I've spent a fair amount of time looking for anything that can elaborate on logistic regression, and nothing I've yet found online gives as good an overview as the chapter in HSAUR. It is varied and complete, without being obtuse. I suppose if I knew where to look, I could have found the chapter excerpt hosted on CRAN, so I'm not saying there's nothing out there. But I am saying that HSAUR does a great job of combining a traditional textbook format with the ease and interactivity of online learning. It's great to be able to read in bed at night, then take the book downstairs and try a few examples in the morning.
Overall, it's a joy to use, and it's not hard to see why some instructors would assign it as a textbook. I envy the lucky students in those courses.
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