R in Action: Data analysis and graphics with R and Tidyverse

R in Action, Third Edition
brief contents
contents
preface
acknowledgments
about this book
	What\'s new in the third edition
	Who should read this book
	How this book is organized: A road map
	Advice for data miners
	About the code
	liveBook discussion forum
about the author
about the cover illustration
Part 1 Getting started
	1 Introduction to R
		1.1 Why use R?
		1.2 Obtaining and installing R
		1.3 Working with R
			1.3.1 Getting started
			1.3.2 Using RStudio
			1.3.3 Getting help
			1.3.4 The workspace
			1.3.5 Projects
		1.4 Packages
			1.4.1 What are packages?
			1.4.2 Installing a package
			1.4.3 Loading a package
			1.4.4 Learning about a package
		1.5 Using output as input: Reusing results
		1.6 Working with large datasets
		1.7 Working through an example
		Summary
	2 Creating a dataset
		2.1 Understanding datasets
		2.2 Data structures
			2.2.1 Vectors
			2.2.2 Matrices
			2.2.3 Arrays
			2.2.4 Data frames
			2.2.5 Factors
			2.2.6 Lists
			2.2.7 Tibbles
		2.3 Data input
			2.3.1 Entering data from the keyboard
			2.3.2 Importing data from a delimited text file
			2.3.3 Importing data from Excel
			2.3.4 Importing data from JSON
			2.3.5 Importing data from the web
			2.3.6 Importing data from SPSS
			2.3.7 Importing data from SAS
			2.3.8 Importing data from Stata
			2.3.9 Accessing database management systems
			2.3.10 Importing data via Stat/Transfer
		2.4 Annotating datasets
			2.4.1 Variable labels
			2.4.2 Value labels
		2.5 Useful functions for working with data objects
		Summary
	3 Basic data management
		3.1 A working example
		3.2 Creating new variables
		3.3 Recoding variables
		3.4 Renaming variables
		3.5 Missing values
			3.5.1 Recoding values to missing
			3.5.2 Excluding missing values from analyses
		3.6 Date values
			3.6.1 Converting dates to character variables
			3.6.2 Going further
		3.7 Type conversions
		3.8 Sorting data
		3.9 Merging datasets
			3.9.1 Adding columns to a data frame
			3.9.2 Adding rows to a data frame
		3.10 Subsetting datasets
			3.10.1 Selecting variables
			3.10.2 Dropping variables
			3.10.3 Selecting observations
			3.10.4 The subset() function
			3.10.5 Random samples
		3.11 Using dplyr to manipulate data frames
			3.11.1 Basic dplyr functions
			3.11.2 Using pipe operators to chain statements
		3.12 Using SQL statements to manipulate data frames
		Summary
	4 Getting started with graphs
		4.1 Creating a graph with ggplot2
			4.1.1 ggplot
			4.1.2 Geoms
			4.1.3 Grouping
			4.1.4 Scales
			4.1.5 Facets
			4.1.6 Labels
			4.1.7 Themes
		4.2 ggplot2 details
			4.2.1 Placing the data and mapping options
			4.2.2 Graphs as objects
			4.2.3 Saving graphs
			4.2.4 Common mistakes
		Summary
	5 Advanced data management
		5.1 A data management challenge
		5.2 Numerical and character functions
			5.2.1 Mathematical functions
			5.2.2 Statistical functions
			5.2.3 Probability functions
			5.2.4 Character functions
			5.2.5 Other useful functions
			5.2.6 Applying functions to matrices and data frames
			5.2.7 A solution for the data management challenge
		5.3 Control flow
			5.3.1 Repetition and looping
			5.3.2 Conditional execution
		5.4 User-written functions
		5.5 Reshaping data
			5.5.1 Transposing
			5.5.2 Converting from wide to long dataset formats
		5.6 Aggregating data
		Summary
Part 2 Basic methods
	6 Basic graphs
		6.1 Bar charts
			6.1.1 Simple bar charts
			6.1.2 Stacked, grouped, and filled bar charts
			6.1.3 Mean bar charts
			6.1.4 Tweaking bar charts
		6.2 Pie charts
		6.3 Tree maps
		6.4 Histograms
		6.5 Kernel density plots
		6.6 Box plots
			6.6.1 Using parallel box plots to compare groups
			6.6.2 Violin plots
		6.7 Dot plots
		Summary
	7 Basic statistics
		7.1 Descriptive statistics
			7.1.1 A menagerie of methods
			7.1.2 Even more methods
			7.1.3 Descriptive statistics by group
			7.1.4 Summarizing data interactively with dplyr
			7.1.5 Visualizing results
		7.2 Frequency and contingency tables
			7.2.1 Generating frequency tables
			7.2.2 Tests of independence
			7.2.3 Measures of association
			7.2.4 Visualizing results
		7.3 Correlations
			7.3.1 Types of correlations
			7.3.2 Testing correlations for significance
			7.3.3 Visualizing correlations
		7.4 T-tests
			7.4.1 Independent t-test
			7.4.2 Dependent t-test
			7.4.3 When there are more than two groups
		7.5 Nonparametric tests of group differences
			7.5.1 Comparing two groups
			7.5.2 Comparing more than two groups
		7.6 Visualizing group differences
		Summary
Part 3 Intermediate methods
	8 Regression
		8.1 The many faces of regression
			8.1.1 Scenarios for using OLS regression
			8.1.2 What you need to know
		8.2 OLS regression
			8.2.1 Fitting regression models with lm()
			8.2.2 Simple linear regression
			8.2.3 Polynomial regression
			8.2.4 Multiple linear regression
			8.2.5 Multiple linear regression with interactions
		8.3 Regression diagnostics
			8.3.1 A typical approach
			8.3.2 An enhanced approach
			8.3.3 Multicollinearity
		8.4 Unusual observations
			8.4.1 Outliers
			8.4.2 High-leverage points
			8.4.3 Influential observations
		8.5 Corrective measures
			8.5.1 Deleting observations
			8.5.2 Transforming variables
			8.5.3 Adding or deleting variables
			8.5.4 Trying a different approach
		8.6 Selecting the “best” regression model
			8.6.1 Comparing models
			8.6.2 Variable selection
		8.7 Taking the analysis further
			8.7.1 Cross-validation
			8.7.2 Relative importance
		Summary
	9 Analysis of variance
		9.1 A crash course on terminology
		9.2 Fitting ANOVA models
			9.2.1 The aov() function
			9.2.2 The order of formula terms
		9.3 One-way ANOVA
			9.3.1 Multiple comparisons
			9.3.2 Assessing test assumptions
		9.4 One-way ANCOVA
			9.4.1 Assessing test assumptions
			9.4.2 Visualizing the results
		9.5 Two-way factorial ANOVA
		9.6 Repeated measures ANOVA
		9.7 Multivariate analysis of variance (MANOVA)
			9.7.1 Assessing test assumptions
			9.7.2 Robust MANOVA
		9.8 ANOVA as regression
		Summary
	10 Power analysis
		10.1 A quick review of hypothesis testing
		10.2 Implementing power analysis with the pwr package
			10.2.1 T-tests
			10.2.2 ANOVA
			10.2.3 Correlations
			10.2.4 Linear models
			10.2.5 Tests of proportions
			10.2.6 Chi-square tests
			10.2.7 Choosing an appropriate effect size in novel situations
		10.3 Creating power analysis plots
		10.4 Other packages
		Summary
	11 Intermediate graphs
		11.1 Scatter plots
			11.1.1 Scatter plot matrices
			11.1.2 High-density scatter plots
			11.1.3 3D scatter plots
			11.1.4 Spinning 3D scatter plots
			11.1.5 Bubble plots
		11.2 Line charts
		11.3 Corrgrams
		11.4 Mosaic plots
		Summary
	12 Resampling statistics and bootstrapping
		12.1 Permutation tests
		12.2 Permutation tests with the coin package
			12.2.1 Independent two-sample and k-sample tests
			12.2.2 Independence in contingency tables
			12.2.3 Independence between numeric variables
			12.2.4 Dependent two-sample and k-sample tests
			12.2.5 Going further
		12.3 Permutation tests with the lmPerm package
			12.3.1 Simple and polynomial regression
			12.3.2 Multiple regression
			12.3.3 One-way ANOVA and ANCOVA
			12.3.4 Two-way ANOVA
		12.4 Additional comments on permutation tests
		12.5 Bootstrapping
		12.6 Bootstrapping with the boot package
			12.6.1 Bootstrapping a single statistic
			12.6.2 Bootstrapping several statistics
		Summary
Part 4 Advanced methods
	13 Generalized linear models
		13.1 Generalized linear models and the glm() function
			13.1.1 The glm() function
			13.1.2 Supporting functions
			13.1.3 Model fit and regression diagnostics
		13.2 Logistic regression
			13.2.1 Interpreting the model parameters
			13.2.2 Assessing the impact of predictors on the probability of an outcome
			13.2.3 Overdispersion
			13.2.4 Extensions
		13.3 Poisson regression
			13.3.1 Interpreting the model parameters
			13.3.2 Overdispersion
			13.3.3 Extensions
		Summary
	14 Principal components and factor analysis
		14.1 Principal components and factor analysis in R
		14.2 Principal components
			14.2.1 Selecting the number of components to extract
			14.2.2 Extracting principal components
			14.2.3 Rotating principal components
			14.2.4 Obtaining principal component scores
		14.3 Exploratory factor analysis
			14.3.1 Deciding how many common factors to extract
			14.3.2 Extracting common factors
			14.3.3 Rotating factors
			14.3.4 Factor scores
			14.3.5 Other EFA-related packages
		14.4 Other latent variable models
		Summary
	15 Time series
		15.1 Creating a time-series object in R
		15.2 Smoothing and seasonal decomposition
			15.2.1 Smoothing with simple moving averages
			15.2.2 Seasonal decomposition
		15.3 Exponential forecasting models
			15.3.1 Simple exponential smoothing
			15.3.2 Holt and Holt–Winters exponential smoothing
			15.3.3 The ets() function and automated forecasting
		15.4 ARIMA forecasting models
			15.4.1 Prerequisite concepts
			15.4.2 ARMA and ARIMA models
			15.4.3 Automated ARIMA forecasting
		15.5 Going further
		Summary
	16 Cluster analysis
		16.1 Common steps in cluster analysis
		16.2 Calculating distances
		16.3 Hierarchical cluster analysis
		16.4 Partitioning-cluster analysis
			16.4.1 K-means clustering
			16.4.2 Partitioning around medoids
		16.5 Avoiding nonexistent clusters
		16.6 Going further
		Summary
	17 Classification
		17.1 Preparing the data
		17.2 Logistic regression
		17.3 Decision trees
			17.3.1 Classical decision trees
			17.3.2 Conditional inference trees
		17.4 Random forests
		17.5 Support vector machines
			17.5.1 Tuning an SVM
		17.6 Choosing a best predictive solution
		17.7 Understanding black box predictions
			17.7.1 Break-down plots
			17.7.2 Plotting Shapley values
		17.8 Going further
		Summary
	18 Advanced methods for missing data
		18.1 Steps in dealing with missing data
		18.2 Identifying missing values
		18.3 Exploring missing-values patterns
			18.3.1 Visualizing missing values
			18.3.2 Using correlations to explore missing values
		18.4 Understanding the sources and impact of missing data
		18.5 Rational approaches for dealing with incomplete data
		18.6 Deleting missing data
			18.6.1 Complete-case analysis (listwise deletion)
			18.6.2 Available case analysis (pairwise deletion)
		18.7 Single imputation
			18.7.1 Simple imputation
			18.7.2 K-nearest neighbor imputation
			18.7.3 missForest
		18.8 Multiple imputation
		18.9 Other approaches to missing data
		Summary
Part 5 Expanding your skills
	19 Advanced graphs
		19.1 Modifying scales
			19.1.1 Customizing axes
			19.1.2 Customizing colors
		19.2 Modifying themes
			19.2.1 Prepackaged themes
			19.2.2 Customizing fonts
			19.2.3 Customizing legends
			19.2.4 Customizing the plot area
		19.3 Adding annotations
		19.4 Combining graphs
		19.5 Making graphs interactive
		Summary
	20 Advanced programming
		20.1 A review of the language
			20.1.1 Data types
			20.1.2 Control structures
			20.1.3 Creating functions
		20.2 Working with environments
		20.3 Non-standard evaluation
		20.4 Object-oriented programming
			20.4.1 Generic functions
			20.4.2 Limitations of the S3 model
		20.5 Writing efficient code
			20.5.1 Efficient data input
			20.5.2 Vectorization
			20.5.3 Correctly sizing objects
			20.5.4 Parallelization
		20.6 Debugging
			20.6.1 Common sources of errors
			20.6.2 Debugging tools
			20.6.3 Session options that support debugging
			20.6.4 Using RStudio’s visual debugger
		20.7 Going further
		Summary
	21 Creating dynamic reports
		21.1 A template approach to reports
		21.2 Creating a report with R and R Markdown
		21.3 Creating a report with R and LaTeX
			21.3.1 Creating a parameterized report
		21.4 Avoiding common R Markdown problems
		21.5 Going further
		Summary
	22 Creating a package
		22.1 The edatools package
		22.2 Creating a package
			22.2.1 Installing development tools
			22.2.2 Creating a package project
			22.2.3 Writing the package functions
			22.2.4 Adding function documentation
			22.2.5 Adding a general help file (optional)
			22.2.6 Adding sample data to the package (optional)
			22.2.7 Adding a vignette (optional)
			22.2.8 Editing the DESCRIPTION file
			22.2.9 Building and installing the package
		22.3 Sharing your package
			22.3.1 Distributing a source package file
			22.3.2 Submitting to CRAN
			22.3.3 Hosting on GitHub
			22.3.4 Creating a package website
		22.4 Going further
		Summary
afterword Into the rabbit hole
appendix A Graphical user interfaces
appendix B Customizing the startup environment
appendix C Exporting data from R
	C.1 Delimited text file
	C.2 Excel spreadsheet
	C.3 Statistical applications
appendix D Matrix algebra in R
appendix E Packages used in this book
appendix F Working with large datasets
	F.1 Efficient programming
	F.2 Storing data outside of RAM
	F.3 Analytic packages for out-of-memory data
	F.4 Comprehensive solutions for working with enormous datasets
appendix G Updating an R installation
	G.1 Automated installation (Windows only)
	G.2 Manual installation (Windows and macOS)
	G.3 Updating an R installation (Linux)
references
index
	Symbols
	Numerics
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
	W
	X
	Z