R in Action

Description

R in Action, Third Edition MEAP V07 Copyright Welcome letter Brief contents Chapter 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 1.8 Summary Chapter 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 XML 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 (DBMSs) 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 2.6 Summary Chapter 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 3.13 Summary Chapter 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 Exporting graphs 4.2.4 Common mistakes 4.3 Summary Chapter 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 5.3 A solution for the data-management challenge 5.4 Control flow 5.4.1 Repetition and looping 5.4.2 Conditional execution 5.5 User-written functions 5.6 Reshaping data 5.6.1 Transpose 5.6.2 Converting between wide to long dataset formats 5.7 Aggregating data 5.8 Summary Chapter 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 6.8 Summary Chapter 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 7.7 Summary Chapter 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 8.8 Summary Chapter 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 9.9 Summary Chapter 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 10.5 Summary Chapter 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 11.5 Summary Chapter 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 12.7 Summary Chapter 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 13.4 Summary Chapter 14: Principal components and factor analysis 14.1 Principal components and factor analysis in R 14.2 Principal componentsxe "PCA (principal components analysis)" 14.2.1 Selecting the number of components to extract 14.2.2 Extracting principal componentsxe "principal components:extracting" 14.2.3 Rotating principal components 14.2.4 Obtaining principal components 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 14.5 Summary Chapter 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 15.6 Summary