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A Survivor's Guide to R provides a gentle, but thorough, introduction to R. It is an ideal supplement to any introductory statistics text or a practical field guide for those who want to use the powerful R language for statistical analysis in their own research. A Survivor's Guide to R focuses on providing students with the real-world R skills that are often hard to get to in statistics classes: basic data management and manipulation, and working with R graphics. A Survivor's Guide to R is designed to get students with little or no background in statistics or programming started on R within the context of a statistics class, and to ensure that they have acquired functional R skills that they can continue to use as they move on to their own projects.
A Survivor's Guide to R begins with a straightforward approach to understanding R objects, and then moves systematically through the use of R to transform, sort, and aggregate data; to work with complex textual and date/time data; and to effectively build on R's default graphics capabilities to produce highly customized and effective graphics. It focuses on working with real-world data, with – on reading data in different formats and the challenges of missing data. A Survivor's Guide to R is intended for those with little to no statistics or programming experience – students and other new users who are likely to find their first encounter with R more than a little intimidating. It is written in an accessible and sympathetic style that makes minimal assumptions about user skills, and provides frequent warnings about common pitfalls that must be avoided along the road to R mastery. .
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
Factors
Coercing Storage Modes
The Curse of Number-Character-Factor Confusion
Conclusions
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
Conclusions
Chapter 13: R Graphics II: The Boring Stuff
The Graphics Device
Graphics Parameters
The Plot Layout
Graphic Coordinates in R
Overlaying Plots
Multiple Plots
Conclusions
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
Shapes
Incorporating Images into Plots
A Final Word about Aesthetics
Chapter 16 from Here to Where?
"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
