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A Survivor's Guide to R

A Survivor's Guide to R
An Introduction for the Uninitiated and the Unnerved

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May 2014 | 488 pages | SAGE Publications, Inc
Focusing on developing practical R skills rather than teaching pure statistics, Dr. Kurt Taylor Gaubatz’s A Survivor’s Guide to R provides a gentle yet thorough introduction to R. The book is structured around critical R tasks, and focuses on applied knowledge, rather than abstract concepts. Gaubatz’s easy-to-read approach helps students with little or no background in statistics or programming to develop real-world R skills through straightforward coverage of R objects and functions. Focusing on real-world data, the challenges of dataset construction, and the use of R’s powerful graphing tools, the guide is written in an accessible, sympathetic, even humorous style that ensures students acquire functional R skills they can use in their own projects and carry into their work beyond the classroom.

Chapter 1: Getting Started
Things Your Statistics Class Probably Won't Teach You  
Why R?  
Statistical Modeling  
A Few R Basics  
Saving Your Work  
R Packages  
Help with R Help  
Organization of this Book  
Chapter 2: A Sample Session
Reviewing Your Data  
Data Visualization  
Hypothesis Testing for Fun and Profit  
A Regression Model  
A Nonlinear Model  
Chapter 3: Object Types in R
R Objects And Their Names  
How to Think about Data Objects in R  
R Object Storage Modes  
R Data Object Types  
The Basic Data Objects: Vectors  
The Basic Data Objects: Matrices and Their Indices  
The Basic Data Objects: Data Frames  
The Basic Data Objects: Lists  
A Few Things about Working with Objects  
Object Attributes  
Objects and Environments  
R Object Classes  
The Pseudo Storage Modes  
Date and Time as a Storage Modes  
Coercing Storage Modes  
The Curse of Number-Character-Factor Confusion  
Chapter 4: Getting Your Data Into R
Entering Data  
Creating Data  
Importing Data  
The Read Command: Overview  
The Read Command: Reading from the Clipboard  
The Read Command: Blank Delimited Tables  
The Read Command: Comma Separated Values  
The Read Command: Tab Separated Data  
The Read Command: Fixed-Width Data  
Importing Foreign File Types  
Integrating SQL with R  
Extracting Data from Complex Data Sources  
Web Scraping  
Dealing with Multi-Dimensional Data  
Importing Problematic Characters  
More Resources  
Chapter 5: Reviewing and Summarizing Data
Summary Functions  
Checking A Sample Of Your Data  
Reviewing Data By Categories  
Displaying Data With A Histogram  
Displaying Data With A Scatter Plot  
Scatter Plot Matrices  
Chapter 6: Sorting and Selecting Data
Using Index Values to Select Data  
Using Conditional Values for Selecting  
Using Subset( ) with Variable or Row Names to Select Data  
Splitting a Dataset into Groups  
Splitting Up Continuous Numeric Data  
Sorting And Ordering Data  
Chapter 7: Transforming Data
Creating New Variables  
Editing Data  
Basic Math with R  
R Functions  
Math and Logical Functions in R  
Truncation and Rounding Functions  
The Apply( ) Family of Functions  
Changing Variable Values Conditionally  
Creating New Functions  
Additional R Programming  
Character Strings as Program Elements and Program Elements as Character Strings  
Chapter 8: Text Operations
Some Useful Text Functions  
Finding Things  
Regular Expressions  
Processing Raw Text Data  
Scraping the Web for Fun And Profit  
Chapter 9: Working With Date And Time DataDates in R
Dates in R  
Formatting Dates for R  
Working with POSIX Dates  
Special Date Operations  
Formatting Dates for Output  
Time Series Data  
Creating Moving Averages in Time-Series Data  
Lagged Variables in Time-Series Data  
Differencing Variables in Time-Series Data  
The Limitations of ts Data  
Chapter 10: Data Merging And Aggregation
Dataset Concatenation  
Match Merging  
Keyed Table Look-up Merging  
Aggregating Data  
Transposing and Rotating Datasets  
Chapter 11: Dealing with Missing Data
Reading Data with Missing Values  
Summarizing Missing Values  
The Missing Values Functions  
Recoding Missing Values  
Missing Values And Regression Modeling  
Visualizing Missing Data  
Chapter 12: R Graphics I: The Built-in Plots
Scatter Plots  
Pairs Plots  
Line Plots  
Box Plots  
Histograms, Density Plots, and Bar Charts  
Dot Charts  
Pie Charts  
Mosaic Plots  
Chapter 13: R Graphics II: The Boring Stuff
The Graphics Device  
Graphics Parameters  
The Plot Layout  
Graphic Coordinates in R  
Overlaying Plots  
Multiple Plots  
Chapter 14: R Graphics III: The Fun Stuff--Text
Adding Text  
Setting up a Font  
Titles and Subtitles  
Creating a Legend  
Simple Axes and Axis Labels  
Building More Complex Axes  
Ad-hoc Text  
Chapter 15: R Graphics IV: The Fun Stuff--Shapes
Doing Colors  
Custom Points  
Adding Lines  
Incorporating Images into Plots  
A Final Word about Aesthetics  
Chapter 16 from Here to Where?


Companion Website
R code, graphics, and data are available on the book's companion site.

“The guide is detailed enough that students could practice these operations outside the classroom until they mastered them, which means that more class time can be spent discussing the conceptual issues in statistics.”

Ole J. Forsberg, Oklahoma State University

“R's visualization tools and its powerful graphics capabilities . . . make this book a popular choice for many applications.”

Charlotte Tate, San Francisco State University

“A strength is the author's thorough approach to the code without being . . . dull.  I very much appreciate that the author describes R code idiosyncrasies while keeping the text light.”

Yulan Liang, University of Maryland, Baltimore

“[This book] does an excellent job of guiding readers through pitfalls common to R's data handling idiosyncrasies—pitfalls usually learned after hours of frustration and lamentation. The conversational, and at times humorous, style makes for a readable, enjoyable, and relaxed examination of a powerful computation tool with a steep learning curve. Each chapter is compartmentalized enough to be read separately, but the author includes chapter references . . . to tie the guide together as a whole . . . The author covers the full spectrum, plus, thankfully, quite a bit of  material not usually included in other R introductions . . . The author covers the material in depth with nicely done examples.  I was also very happy to see that the author included a section on programming etiquette in R—very nice.”

A. Dean Monroe, Angelo State University

“I very much appreciate the development of a text primarily devoted to the students and practitioners who are first-time users of R . . . It is a very gentle and easy-to-read introduction to R for anyone who might have been afraid of learning programming language . . . It [is] very easy to read and follow . . . The flow of the topics is logical and natural for teaching any computational language. With a good sense of humor, the text is highly user-friendly.”

Professor David Han, University of Texas, San Antonio

My university and department has yet to allow me to teach R instead of SPSS. However, this is a terrific text, and I hope to see an updated version soon when it becomes clear that we should be teaching and using R instead of, or in conjunction with, SPSS.

Dr Angela Birt
Psychology, Mount St Vincent University
April 7, 2016

Very useful for supporting learning of R. Good introduction and useful for reference.

Dr Mark Ramsden
Department of Sociology, Cambridge University
June 25, 2015

This is a helpful book for students doing data analysis with R.

Dr Katharina Manderscheid
Soziologisches Seminar, University of Lucerne
February 3, 2015

A "Survivor's guide to R" is a nice introduction for the more technical details of R which are essential to make fully use of R's statistical capacities. It is useful for beginners of R who have little or no experience with programming languages. It is easy and nice to read (at least as such a technical topic can be).

Dr Daniel Stahl
Biostatistics and Computing, King's College London
December 17, 2014

good book but not for undergraduates

Dr Tuo Yu Chen
Health and Human Sciences Program, Albany College of Pharmacy and Health Sciences
October 22, 2014
Key features


  • The conversational tone helps new and non-expert users get past the initial anxiety of learning R.
  • Coverage of a wide range of skills—from the most basic issues, such as setting up the software, to more complex material—ensures high-level mastery.
  • Attention to data acquisition and processing promotes the functional skills needed to transition from statistics classes to real-world projects.
  • Procedures and functions are demonstrated rather than simply listed.
  • Frequent warnings about common pitfalls are included, with common errors highlighted.
  • Examples of R procedures are thoroughly annotated and clearly explained.
  • Extensive use of figures makes complex discussions easier to understand.
  • The companion website includes annotated examples that students can cut and paste into their own projects, as well as a number of additional graphical examples to facilitate learning.


Sample Materials & Chapters

Chapter 3

Chapter 14

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