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R in Action, Third Edition

Name: R in Action, Third Edition
Author: Robert I. Kabacoff
ISBN: 9781617296055

by Robert I. Kabacoff

May 2022

Beginner to intermediate

656 pages

18h 50m

English

Manning Publications

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R in Action
Copyright
Praise for the previous edition of R in Action
brief contents
contents
Front matter
prefaceacknowledgmentsabout this bookWhat's new in the third editionWho should read this bookHow this book is organized: A road mapAdvice for data minersAbout the codeliveBook discussion forumabout the authorabout the cover illustration
Part 1. Getting started
1 Introduction to R
1.1 Why use R?1.2 Obtaining and installing R1.3 Working with R1.3.1 Getting started1.3.2 Using RStudio1.3.3 Getting help1.3.4 The workspace1.3.5 Projects1.4 Packages1.4.1 What are packages?1.4.2 Installing a package1.4.3 Loading a package1.4.4 Learning about a package1.5 Using output as input: Reusing results1.6 Working with large datasets1.7 Working through an exampleSummary
2 Creating a dataset
2.1 Understanding datasets2.2 Data structures2.2.1 Vectors2.2.2 Matrices2.2.3 Arrays2.2.4 Data frames2.2.5 Factors2.2.6 Lists2.2.7 Tibbles2.3 Data input2.3.1 Entering data from the keyboard2.3.2 Importing data from a delimited text file2.3.3 Importing data from Excel2.3.4 Importing data from JSON2.3.5 Importing data from the web2.3.6 Importing data from SPSS2.3.7 Importing data from SAS2.3.8 Importing data from Stata2.3.9 Accessing database management systems2.3.10 Importing data via Stat/Transfer2.4 Annotating datasets2.4.1 Variable labels2.4.2 Value labels2.5 Useful functions for working with data objectsSummary
3 Basic data management
3.1 A working example3.2 Creating new variables3.3 Recoding variables3.4 Renaming variables3.5 Missing values3.5.1 Recoding values to missing3.5.2 Excluding missing values from analyses3.6 Date values3.6.1 Converting dates to character variables3.6.2 Going further3.7 Type conversions3.8 Sorting data3.9 Merging datasets3.9.1 Adding columns to a data frame3.9.2 Adding rows to a data frame3.10 Subsetting datasets3.10.1 Selecting variables3.10.2 Dropping variables3.10.3 Selecting observations3.10.4 The subset() function3.10.5 Random samples3.11 Using dplyr to manipulate data frames3.11.1 Basic dplyr functions3.11.2 Using pipe operators to chain statements3.12 Using SQL statements to manipulate data framesSummary

4 Getting started with graphs
4.1 Creating a graph with ggplot24.1.1 ggplot4.1.2 Geoms4.1.3 Grouping4.1.4 Scales4.1.5 Facets4.1.6 Labels4.1.7 Themes4.2 ggplot2 details4.2.1 Placing the data and mapping options4.2.2 Graphs as objects4.2.3 Saving graphs4.2.4 Common mistakesSummary
5 Advanced data management
5.1 A data management challenge5.2 Numerical and character functions5.2.1 Mathematical functions5.2.2 Statistical functions5.2.3 Probability functions5.2.4 Character functions5.2.5 Other useful functions5.2.6 Applying functions to matrices and data frames5.2.7 A solution for the data management challenge5.3 Control flow5.3.1 Repetition and looping5.3.2 Conditional execution5.4 User-written functions5.5 Reshaping data5.5.1 Transposing5.5.2 Converting from wide to long dataset formats5.6 Aggregating dataSummary
Part 2. Basic methods
6 Basic graphs
6.1 Bar charts6.1.1 Simple bar charts6.1.2 Stacked, grouped, and filled bar charts6.1.3 Mean bar charts6.1.4 Tweaking bar charts6.2 Pie charts6.3 Tree maps6.4 Histograms6.5 Kernel density plots6.6 Box plots6.6.1 Using parallel box plots to compare groups6.6.2 Violin plots6.7 Dot plotsSummary
7 Basic statistics
7.1 Descriptive statistics7.1.1 A menagerie of methods7.1.2 Even more methods7.1.3 Descriptive statistics by group7.1.4 Summarizing data interactively with dplyr7.1.5 Visualizing results7.2 Frequency and contingency tables7.2.1 Generating frequency tables7.2.2 Tests of independence7.2.3 Measures of association7.2.4 Visualizing results7.3 Correlations7.3.1 Types of correlations7.3.2 Testing correlations for significance7.3.3 Visualizing correlations7.4 T-tests7.4.1 Independent t-test7.4.2 Dependent t-test7.4.3 When there are more than two groups7.5 Nonparametric tests of group differences7.5.1 Comparing two groups7.5.2 Comparing more than two groups7.6 Visualizing group differencesSummary
Part 3. Intermediate methods
8 Regression
8.1 The many faces of regression8.1.1 Scenarios for using OLS regression8.1.2 What you need to know8.2 OLS regression8.2.1 Fitting regression models with lm()8.2.2 Simple linear regression8.2.3 Polynomial regression8.2.4 Multiple linear regression8.2.5 Multiple linear regression with interactions8.3 Regression diagnostics8.3.1 A typical approach8.3.2 An enhanced approach8.3.3 Multicollinearity8.4 Unusual observations8.4.1 Outliers8.4.2 High-leverage points8.4.3 Influential observations8.5 Corrective measures8.5.1 Deleting observations8.5.2 Transforming variables8.5.3 Adding or deleting variables8.5.4 Trying a different approach8.6 Selecting the “best” regression model8.6.1 Comparing models8.6.2 Variable selection8.7 Taking the analysis further8.7.1 Cross-validation8.7.2 Relative importanceSummary
9 Analysis of variance
9.1 A crash course on terminology9.2 Fitting ANOVA models9.2.1 The aov() function9.2.2 The order of formula terms9.3 One-way ANOVA9.3.1 Multiple comparisons9.3.2 Assessing test assumptions9.4 One-way ANCOVA9.4.1 Assessing test assumptions9.4.2 Visualizing the results9.5 Two-way factorial ANOVA9.6 Repeated measures ANOVA9.7 Multivariate analysis of variance (MANOVA)9.7.1 Assessing test assumptions9.7.2 Robust MANOVA9.8 ANOVA as regressionSummary
10 Power analysis
10.1 A quick review of hypothesis testing10.2 Implementing power analysis with the pwr package10.2.1 T-tests10.2.2 ANOVA10.2.3 Correlations10.2.4 Linear models10.2.5 Tests of proportions10.2.6 Chi-square tests10.2.7 Choosing an appropriate effect size in novel situations10.3 Creating power analysis plots10.4 Other packagesSummary
11 Intermediate graphs
11.1 Scatter plots11.1.1 Scatter plot matrices11.1.2 High-density scatter plots11.1.3 3D scatter plots11.1.4 Spinning 3D scatter plots11.1.5 Bubble plots11.2 Line charts11.3 Corrgrams11.4 Mosaic plotsSummary
12 Resampling statistics and bootstrapping
12.1 Permutation tests12.2 Permutation tests with the coin package12.2.1 Independent two-sample and k-sample tests12.2.2 Independence in contingency tables12.2.3 Independence between numeric variables12.2.4 Dependent two-sample and k-sample tests12.2.5 Going further12.3 Permutation tests with the lmPerm package12.3.1 Simple and polynomial regression12.3.2 Multiple regression12.3.3 One-way ANOVA and ANCOVA12.3.4 Two-way ANOVA12.4 Additional comments on permutation tests12.5 Bootstrapping12.6 Bootstrapping with the boot package12.6.1 Bootstrapping a single statistic12.6.2 Bootstrapping several statisticsSummary
Part 4. Advanced methods
13 Generalized linear models
13.1 Generalized linear models and the glm() function13.1.1 The glm() function13.1.2 Supporting functions13.1.3 Model fit and regression diagnostics13.2 Logistic regression13.2.1 Interpreting the model parameters13.2.2 Assessing the impact of predictors on the probability of an outcome13.2.3 Overdispersion13.2.4 Extensions13.3 Poisson regression13.3.1 Interpreting the model parameters13.3.2 Overdispersion13.3.3 ExtensionsSummary
14 Principal components and factor analysis
14.1 Principal components and factor analysis in R14.2 Principal components14.2.1 Selecting the number of components to extract14.2.2 Extracting principal components14.2.3 Rotating principal components14.2.4 Obtaining principal component scores14.3 Exploratory factor analysis14.3.1 Deciding how many common factors to extract14.3.2 Extracting common factors14.3.3 Rotating factors14.3.4 Factor scores14.3.5 Other EFA-related packages14.4 Other latent variable modelsSummary
15 Time series
15.1 Creating a time-series object in R15.2 Smoothing and seasonal decomposition15.2.1 Smoothing with simple moving averages15.2.2 Seasonal decomposition15.3 Exponential forecasting models15.3.1 Simple exponential smoothing15.3.2 Holt and Holt–Winters exponential smoothing15.3.3 The ets() function and automated forecasting15.4 ARIMA forecasting models15.4.1 Prerequisite concepts15.4.2 ARMA and ARIMA models15.4.3 Automated ARIMA forecasting15.5 Going furtherSummary
16 Cluster analysis
16.1 Common steps in cluster analysis16.2 Calculating distances16.3 Hierarchical cluster analysis16.4 Partitioning-cluster analysis16.4.1 K-means clustering16.4.2 Partitioning around medoids16.5 Avoiding nonexistent clusters16.6 Going furtherSummary
17 Classification
17.1 Preparing the data17.2 Logistic regression17.3 Decision trees17.3.1 Classical decision trees17.3.2 Conditional inference trees17.4 Random forests17.5 Support vector machines17.5.1 Tuning an SVM17.6 Choosing a best predictive solution17.7 Understanding black box predictions17.7.1 Break-down plots17.7.2 Plotting Shapley values17.8 Going furtherSummary
18 Advanced methods for missing data
18.1 Steps in dealing with missing data18.2 Identifying missing values18.3 Exploring missing-values patterns18.3.1 Visualizing missing values18.3.2 Using correlations to explore missing values18.4 Understanding the sources and impact of missing data18.5 Rational approaches for dealing with incomplete data18.6 Deleting missing data18.6.1 Complete-case analysis (listwise deletion)18.6.2 Available case analysis (pairwise deletion)18.7 Single imputation18.7.1 Simple imputation18.7.2 K-nearest neighbor imputation18.7.3 missForest18.8 Multiple imputation18.9 Other approaches to missing dataSummary
Part 5. Expanding your skills
19 Advanced graphs
19.1 Modifying scales19.1.1 Customizing axes19.1.2 Customizing colors19.2 Modifying themes19.2.1 Prepackaged themes19.2.2 Customizing fonts19.2.3 Customizing legends19.2.4 Customizing the plot area19.3 Adding annotations19.4 Combining graphs19.5 Making graphs interactiveSummary
20 Advanced programming
20.1 A review of the language20.1.1 Data types20.1.2 Control structures20.1.3 Creating functions20.2 Working with environments20.3 Non-standard evaluation20.4 Object-oriented programming20.4.1 Generic functions20.4.2 Limitations of the S3 model20.5 Writing efficient code20.5.1 Efficient data input20.5.2 Vectorization20.5.3 Correctly sizing objects20.5.4 Parallelization20.6 Debugging20.6.1 Common sources of errors20.6.2 Debugging tools20.6.3 Session options that support debugging20.6.4 Using RStudio’s visual debugger20.7 Going furtherSummary
21 Creating dynamic reports
21.1 A template approach to reports21.2 Creating a report with R and R Markdown21.3 Creating a report with R and LaTeX21.3.1 Creating a parameterized report21.4 Avoiding common R Markdown problems21.5 Going furtherSummary
22 Creating a package
22.1 The edatools package22.2 Creating a package22.2.1 Installing development tools22.2.2 Creating a package project22.2.3 Writing the package functions22.2.4 Adding function documentation22.2.5 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 file22.2.9 Building and installing the package22.3 Sharing your package22.3.1 Distributing a source package file22.3.2 Submitting to CRAN22.3.3 Hosting on GitHub22.3.4 Creating a package website22.4 Going furtherSummary
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 fileC.2 Excel spreadsheetC.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 programmingF.2 Storing data outside of RAMF.3 Analytic packages for out-of-memory dataF.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

Content preview from R in Action, Third Edition

16 Cluster analysis

This chapter covers

Identifying cohesive subgroups (clusters) of observations
Determining the number of clusters present
Obtaining a nested hierarchy of clusters
Obtaining discrete clusters

Cluster analysis is a data-reduction technique designed to uncover subgroups of observations within a dataset. It allows you to reduce a large number of observations to a much smaller number of clusters or types. A cluster is defined as a group of observations that are more similar to each other than they are to the observations in other groups. This isn’t a precise definition, and that fact has given rise to an enormous variety of clustering methods.

Cluster analysis is widely used in the biological and behavioral sciences, marketing, ...

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R in Action, Third Edition

by Robert I. Kabacoff

16 Cluster analysis

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