Efnisyfirlit

• Cover
• Title page
• Copyright
• Contents
• Preface
• Introduction to the second edition
• What this book is about
• How the book is organized
• Why R?
• Updates
• Acknowledgements
• Chapter 1: Getting and Getting Acquainted with R
• 1.1 Getting started
• 1.2 Getting R
• 1.2.1 Linux/Unix
• 1.2.2 Windows
• 1.2.3 Macintosh
• 1.3 Getting RStudio
• 1.4 Let’s play
• 1.5 Using R as a giant calculator (the size of your computer)
• 1.5.1 From the Simple to the Slightly More Complex
• 1.5.2 Functions Take Arguments
• 1.5.3 Now for Something Really Important
• 1.5.4 How Does R Do Stuff with Vectors?
• 1.6 Your first script
• 1.6.1 The Script Pane
• 1.6.2 How Do I Make R Do Stuff, You Ask?
• 1.6.3 Two More Bits of RStudio Magic
• 1.7 Intermezzo remarks
• 1.8 Important functionality: packages
• 1.8.1 Using the New Functions that Come in a Package
• 1.9 Getting help
• 1.10 A mini-practical—some in-depth play
• 1.11 Some more top tips and hints for a successful first (and more) R experience
• 1.11.1 Saving and the Workspace Option
• 1.11.2 Some Nice Things about RStudio
• Appendix 1a Mini-tutorial solutions
• Appendix 1b File extensions and operating systems
• Chapter 2: Getting Your Data into R
• 2.1 Getting data ready for R
• 2.1.1 Setting Up Your Own Data
• An exercise in data preparation
• 2.1.2 Someone Else’s Data?
• 2.2 Getting your data into R
• 2.2.1 Importing Data, Preparation
• 2.2.2 Method 1: The Import Dataset Tool
• 2.2.3 Method 2: The file.choose() Function
• 2.2.4 Method 3: Dancing with the Import Devil
• 2.2.5 Method 4: Put Your Data in the Same Place as Your Script
• 2.3 Checking that your data are your data
• 2.3.1 Your First Interaction with dplyr
• 2.4 Basic troubleshooting while importing data
• 2.5 Summing up
• Appendix Advanced activity: dealing with untidy data
• Chapter 3: Data Management, Manipulation, and Exploration with dplyr
• 3.1 Summary statistics for each variable
• 3.1.1 The Compensation Data
• 3.1.2 The summary() Function
• 3.2 dplyr Verbs
• 3.3 Subsetting
• 3.3.1 select()
• 3.3.2 slice()
• 3.3.3 filter()
• Logical operators and booleans
• Using filter()
• 3.3.4 Making Sure You Can Use the Subset of Data
• 3.3.5 What Should my Script Look Like Now?
• 3.4 Transforming
• 3.4.1 mutate()
• 3.5 Sorting
• 3.5.1 arrange()
• 3.6 Mini-summary and two top tips
• 3.7 Calculating summary statistics about groups of your data
• 3.7.1 Overview of Summarization
• 3.7.2 Method 1: Nested, No Pipe
• 3.7.3 Method 2: Pipe, No Nesting
• 3.7.4 Summarizing and Extending Summarization
• 3.8 What have you learned … lots
• Appendix 3a Comparing classic methods and dplyr
• Appendix 3b Advanced dplyr
• Chapter 4: Visualizing Your Data
• 4.1 The first step in every data analysis—making a picture
• 4.2 ggplot2: a grammar for graphics
• 4.2.1 Making a Picture—Scatterplots
• 4.2.2 Interpretation, then Customize
• 4.2.3 That Grey Background
• 4.3 Box-and-whisker plots
• 4.3.1 A Moment of Interpretive Contemplation …
• 4.4 Distributions: making histograms of numeric variables
• 4.4.1 A Nifty Tool: Facets
• 4.5 Saving your graphs for presentation, documents, etc.
• 4.6 Closing remarks
• Chapter 5: Introducing Statistics in R
• 5.1 Getting started doing statistics in R
• 5.1.1 Getting Ready for Some Statistics
• 5.2 χ2 contingency table analysis
• 5.2.1 The Data: Ladybirds
• 5.2.2 Organizing the Data for Plotting and Analysis
• 5.2.3 New ggplot() Detail
• 5.2.4 Fixing the Colours
• 5.2.5 Interpreting the Graph (Guess the Answer Before We Do Stats)
• 5.2.6 Making the χ2 Test
• 5.2.7 From Data to Statistics: An Overview
• 5.3 Two-sample t-test
• 5.3.1 The T-Test Data
• 5.3.2 The First Step: Plot Your Data
• 5.3.3 The Two-Sample T-Test Analysis
• 5.3.4 T-Test Summary
• 5.4 Introducing … linear models
• 5.5 Simple linear regression
• 5.5.1 Getting and Plotting the Data
• 5.5.2 Interpreting the Figure: Biological Insight
• 5.5.3 Making a Simple Linear Regression Happen
• 5.5.4 Assumptions First
• 5.5.5 Now the Interpretation
• 5.5.6 From Stats Back to Figure
• 5.6 Analysis of variance: the one-way ANOVA
• 5.6.1 Getting and Plotting the Data
• 5.6.2 Construct the Anova
• 5.6.3 Check the Assumptions
• 5.6.4 Making an Inference from a One-Way Anova
• 5.6.5 Treatment Contrasts
• 5.7 Wrapping up
• Appendix Getting packages not on CRAN
• Chapter 6: Advancing Your Statistics in R
• 6.1 Getting started with more advanced statistics
• 6.2 The two-way ANOVA
• 6.2.1 The Cow (Mooooo!) Growth Data
• 6.2.2 Step 1: dplyr Summary Data
• 6.2.3 Step 2: ggplot() Interaction Plot
• 6.2.4 Interpreting the Figure: Biological Insight
• 6.2.5 Constructing the Two-Way Anova
• 6.2.6 Examine the Assumptions
• 6.2.7 Model Output and More Biology
• The ANOVA table
• The summary table
• 6.2.8 Stats Back to Graphics
• 6.3 Analysis of covariance (ANCOVA)
• 6.3.1 The limpet Reproduction Data
• 6.3.2 Always Start with a Picture
• 6.3.3 Interpreting the Figure—It’s All about Lines
• Let’s go a bit deeper
• 6.3.4 Constructing the Ancova
• 6.3.5 Assumptions First, So Say the Limpets
• 6.3.6 Interpretation: The anova() Table
• 6.3.7 Interpretation: The summary() Table
• 6.3.8 Putting the Lines Onto the Figure
• 6.3.9 The Final Picture Using ggplot()
• That ggplot code…
• 6.4 Overview: an Analysis Workflow
• Chapter 7: Getting Started with Generalized Linear Models
• 7.1 Introduction
• 7.1.1 Counts and Proportions and the GLM
• 7.1.2 Key Terms for GLM Models
• 7.2 Counts and rates—Poisson GLMs
• 7.2.1 Counting Sheep—The Data and Question
• 7.3 Doing it wrong
• 7.3.1 Doing it Wrong: Diagnosing the Problems
• 7.3.2 The Poisson Distribution—A Solution
• 7.4 Doing it right—the Poisson GLM
• 7.4.1 Anatomy of a GLM
• The family
• The linear predictor
• The link function
• 7.4.2 Doing it Right—Actually Fitting the Model
• 7.4.3 Doing it Right—The Diagnostics
• 7.4.4 Doing it Right—anova() and summary()
• 7.4.5 Making a Beautiful Graph
• 7.5 When a Poisson GLM isn’t good for counts
• 7.5.1 Overdispersion
• 7.5.2 Zero Inflation
• 7.5.3 Transformations Ain’t All Bad
• 7.6 Summary, and beyond simple Poisson regression
• 7.6.1 The Link Function Rules
• 7.6.2 The Workflow Stays the Same
• 7.6.3 Binomial Models?
• Chapter 8: Pimping Your Plots: Scales and Themes in ggplot2
• 8.1 What you already know about graphs
• 8.2 Preparation
• 8.2.1 Did You Know …?
• 8.3 What you may want to customize
• 8.4 Axis labels, axis limits, and annotation
• 8.4.1 Putting Custom Text Inside the Figure
• 8.5 Scales
• 8.6 The theme
• 8.6.1 Some theme() Syntax About the Panels and Gridlines
• 8.6.2 Some theme() Syntax About Axis Titles and Tick Marks
• 8.6.3 Discrete-Axis Customizations
• 8.6.4 Some theme() Syntax about Customizing Legends/Keys
• 8.7 Summing up
• Chapter 9: Closing Remarks: Final Comments and Encouragement
• General Appendices
• Appendix 1 Data Sources
• Appendix 2 Further Reading
• A.2.1 Online
• A.2.2 Print
• Appendix 3 R Markdown
• Index

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