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A Survivor's Guide to R

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

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

For instructors

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