The R Book, 3rd Edition

Table of contents

1. Cover
2. Title Page
3. Copyright
4. List of Tables
5. Preface
6. Acknowledgments
7. About the Companion Website
8. 1 Getting Started
   1. 1.1 Navigating the book
   2. 1.2 vs. RStudio
   3. 1.3 Installing and RStudio
   4. 1.4 Using RStudio
   5. 1.5 The Comprehensive Archive Network
   6. 1.6 Packages in
   7. 1.7 Getting help in
   8. 1.8 Good housekeeping
   9. 1.9 Linking to other computer languages
   10. References
9. 2 Technical Background
   1. 2.1 Mathematical functions
   2. 2.2 Matrices
   3. 2.3 Calculus
   4. 2.4 Probability
   5. 2.5 Statistics
   6. Reference
10. 3 Essentials of the Language
    1. 3.1 Calculations
    2. 3.2 Naming objects
    3. 3.3 Factors
    4. 3.4 Logical operations
    5. 3.5 Generating sequences
    6. 3.6 Class membership
    7. 3.7 Missing values, infinity, and things that are not numbers
    8. 3.8 Vectors and subscripts
    9. 3.9 Working with logical subscripts
    10. 3.10 Vector functions
    11. 3.11 Matrices and arrays
    12. 3.12 Random numbers, sampling, and shuffling
    13. 3.13 Loops and repeats
    14. 3.14 Lists
    15. 3.15 Text, character strings, and pattern matching
    16. 3.16 Dates and times in
    17. 3.17 Environments
    18. 3.18 Writing functions
    19. 3.19 Structure of objects
    20. 3.20 Writing from to a file
    21. 3.21 Tips for writing code
    22. References
11. 4 Data Input and Dataframes
    1. 4.1 Working directory
    2. 4.2 Data input from files
    3. 4.3 Data input directly from the web
    4. 4.4 Built‐in data files
    5. 4.5 Dataframes
    6. 4.6 Using the match () function in dataframes
    7. 4.7 Adding margins to a dataframe
12. 5 Graphics
    1. 5.1 Plotting principles
    2. 5.2 Plots for single variables
    3. 5.3 Plots for showing two numeric variables
    4. 5.4 Plots for numeric variables by group
    5. 5.5 Plots showing two categorical variables
    6. 5.6 Plots for three (or more) variables
    7. 5.7 Trellis graphics
    8. 5.8 Plotting functions
    9. References
13. 6 Graphics in More Detail
    1. 6.1 More on colour
    2. 6.2 Changing the look of graphics
    3. 6.3 Adding items to plots
    4. 6.4 The grammar of graphics and ggplot2
    5. 6.5 Graphics cheat sheet
    6. References
14. 7 Tables
    1. 7.1 Tabulating categorical or discrete data
    2. 7.2 Tabulating summaries of numeric data
    3. 7.3 Converting between tables and dataframes
    4. Reference
15. 8 Probability Distributions in
    1. 8.1 Probability distributions: the basics
    2. 8.2 Probability distributions in
    3. 8.3 Continuous probability distributions
    4. 8.4 Discrete probability distributions
    5. 8.5 The central limit theorem
    6. References
16. 9 Testing
    1. 9.1 Principles
    2. 9.2 Continuous data
    3. 9.3 Discrete and categorical data
    4. 9.4 Bootstrapping
    5. 9.5 Multiple tests
    6. 9.6 Power and sample size calculations
    7. 9.7 A table of tests
    8. References
17. 10 Regression
    1. 10.1 The simple linear regression model
    2. 10.2 The multiple linear regression model
    3. 10.3 Understanding the output
    4. 10.4 Fitting models
    5. 10.5 Checking model assumptions
    6. 10.6 Using the model
    7. 10.7 Further types of regression modelling
    8. References
18. 11 Generalised Linear Models
    1. 11.1 How GLMs work
    2. 11.2 Count data and GLMs
    3. 11.3 Count table data and GLMs
    4. 11.4 Proportion data and GLMs
    5. 11.5 Binary Response Variables and GLMs
    6. 11.6 Bootstrapping a GLM
    7. References
19. 12 Generalised Additive Models
    1. 12.1 Smoothing example
    2. 12.2 Straightforward examples of GAMs
    3. 12.3 Background to using GAMs
    4. 12.4 More complex GAM examples
    5. References
20. 13 Mixed‐Effect Models
    1. 13.1 Regression with categorical covariates
    2. 13.2 An alternative method: random effects
    3. 13.3 Common data structures where random effects are useful
    4. 13.4 packages to deal with mixed effects models
    5. 13.5 Examples of implementing random effect models
    6. 13.6 Generalised linear mixed models
    7. 13.7 Alternatives to mixed models
    8. References
21. 14 Non‐linear Regression
    1. 14.1 Example: modelling deer jaw bone length
    2. 14.2 Example: grouped data
    3. 14.3 Self‐starting functions
    4. 14.4 Further considerations
    5. References
22. 15 Survival Analysis
    1. 15.1 Handling survival data
    2. 15.2 The survival and hazard functions
    3. 15.3 Modelling survival data
    4. References
23. 16 Designed Experiments
    1. 16.1 Factorial experiments
    2. 16.2 Pseudo‐replication
    3. 16.3 Contrasts
    4. References
24. 17 Meta‐Analysis
    1. 17.1 Elements of a meta‐analysis
    2. 17.2 Meta‐analysis in
    3. 17.3 Examples
    4. 17.4 Meta‐analysis of categorical data
    5. References
25. 18 Time Series
    1. 18.1 Moving average
    2. 18.2 Blowflies
    3. 18.3 Seasonal data
    4. 18.4 Multiple time series
    5. 18.5 Some theoretical background
    6. 18.6 ARIMA example
    7. 18.7 Simulation of time series
    8. Reference
26. 19 Multivariate Statistics
    1. 19.1 Visualising data
    2. 19.2 Multivariate analysis of variance
    3. 19.3 Principal component analysis
    4. 19.4 Factor analysis
    5. 19.5 Cluster analysis
    6. 19.6 Hierarchical cluster analysis
    7. 19.7 Discriminant analysis
    8. 19.8 Neural networks
    9. References
27. 20 Classification and Regression Trees
    1. 20.1 How CARTs work
    2. 20.2 Regression trees
    3. 20.3 Classification trees
    4. 20.4 Looking for patterns
    5. References
28. 21 Spatial Statistics
    1. 21.1 Spatial point processes
    2. 21.2 Geospatial statistics
    3. References
29. 22 Bayesian Statistics
    1. 22.1 Components of a Bayesian Analysis
    2. 22.2 Bayesian analysis in
    3. 22.3 Examples
    4. 22.4 MCMC for a model with binomial errors
    5. References
30. 23 Simulation Models
    1. 23.1 Temporal dynamics
    2. 23.2 Spatial simulation models
    3. 23.3 Temporal and spatial dynamics: random walk
    4. References
31. Index
32. End User License Agreement