About the Book

R in Action, Second Edition teaches you how to use the R language by presenting examples relevant to scientific, technical and business developers. Focusing on practical solutions, the book offers a crash course in statistics, including elegant methods for dealing with messy and incomplete data. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on forecasting, data mining and dynamic report writing.

About the Author

Dr. Rob Kabacoff is a seasoned researcher and teacher who specializes in data analysis. He also maintains the popular Quick-R website at statmethods.net.

Table of Contents:
Part 1 Getting started 1 Introduction to R 1.1 Why use R? 1.2 Obtaining and installing R 1.3 Working with R 1.4 Packages 1.5 Batch processing 1.6 Using output as input: reusing results 1.7 Working with large datasets 1.8 Working through an example 1.9 Summary 2 Creating a dataset 2.1 Understanding datasets 2.2 Data structures 2.3 Data input 2.4 Annotating datasets 2.5 Useful functions for working with data objects 2.6 Summary 3 Getting started with graphs 3.1 Working with graphs 3.2 A simple example 3.3 Graphical parameters 3.4 Adding text, customized axes and legends 3.5 Combining graphs 3.6 Summary 4 Basic data management 4.1 A working example 4.2 Creating new variables 4.3 Recoding variables 4.4 Renaming variables 4.5 Missing values 4.6 Date values 4.7 Type conversions 4.8 Sorting data 4.9 Merging datasets 4.10 Subsetting datasets 4.11 Using SQL statements to manipulate data frames 4.12 Summary 5 Advanced data management 5.1 A data-management challenge 5.2 Numerical and character functions 5.3 A solution for the data-management challenge 5.4 Control flow 5.5 User-written functions 5.6 Aggregation and reshaping 5.7 Summary Part 2 Basic methods 6 Basic graphs 6.1 Bar plots 6.2 Pie charts 6.3 Histograms 6.4 Kernel density plots 6.5 Box plots 6.6 Dot plots 6.7 Summary 7 Basic statistics 7.1 Descriptive statistics 7.2 Frequency and contingency tables 7.3 Correlations 7.4 T-tests 7.5 Nonparametric tests of group differences 7.6 Visualizing group differences 7.7 Summary Part 3 Intermediate methods 8 Regression 8.1 The many faces of regression 8.2 OLS regression 8.3 Regression diagnostics 8.4 Unusual observations 8.5 Corrective measures 8.6 Selecting the “best” regression model 8.7 Taking the analysis further 8.8 Summary 9 Analysis of variance 9.1 A crash course on terminology 9.2 Fitting ANOVA models 9.3 One-way ANOVA 9.4 One-way ANCOVA 9.5 Two-way factorial ANOVA 9.6 Repeated measures ANOVA 9.7 Multivariate analysis of variance (MANOVA) 9.8 ANOVA as regression 9.9 Summary 10 Power analysis 10.1 A quick review of hypothesis testing 10.2 Implementing power analysis with the pwr package 10.3 Creating power analysis plots 10.4 Other packages 10.5 Summary 11 Intermediate graphs 11.1 Scatter plots 11.2 Line charts 11.3 Corrgrams 11.4 Mosaic plots 11.5 Summary 12 Resampling statistics and bootstrapping 12.1 Permutation tests 12.2 Permutation tests with the coin package 12.3 Permutation tests with the lmPerm package 12.4 Additional comments on permutation tests 12.5 Bootstrapping 12.6 Bootstrapping with the boot package 12.7 Summary Part 4 Advanced methods 13 Generalized linear models 13.1 Generalized linear models and the glm() function 13.2 Logistic regression 13.4 Poisson regression 13.5 Summary 14 Principal components and factor analysis 14.1 Principal components and factor analysis in R 14.2 Principal components 14.3 Exploratory factor analysis 14.4 Other latent variable models 14.5 Summary 15 Time series 15.1 Creating a time-series object in R 15.2 Smoothing and seasonal decomposition 15.3 Exponential forecasting models 15.4 ARIMA forecasting models 15.5 Going further 15.6 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 16.5 Avoiding nonexistent clusters 16.6 Summary 17 Classification 17.1 Preparing the data 17.2 Logistic regression 17.3 Decision trees 17.4 Random forests 17.5 Support vector machines 17.6 Choosing a best predictive solution 17.7 Using the rattle package for data mining 17.8 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 18.4 Understanding the sources and impact of missing data 18.5 Rational approaches for dealing with incomplete data 18.6 Complete-case analysis (listwise deletion) 18.7 Multiple imputation 18.8 Other approaches to missing data 18.9 Summary Part 5 Expanding your skills 19 Advanced graphics with ggplot2 19.1 The four graphics systems in R 19.2 An introduction to the ggplot2 package 19.3 Specifying the plot type with geoms 19.4 Grouping 19.5 Faceting 19.6 Adding smoothed lines 19.7 Modifying the appearance of ggplot2 graphs 19.8 Saving graphs 19.9 Summary 20 Advanced programming 20.1 A review of the language 20.2 Working with environments 20.3 Object-oriented programming 20.4 Writing efficient code 20.5 Debugging 20.6 Going further 20.7 Summary 21 Creating a package 21.1 Nonparametric analysis and the npar package 21.2 Developing the package 21.3 Creating the package documentation 21.4 Building the package 21.5 Going further 21.6 Summary 22 Creating dynamic reports 22.1 A template approach to reports 22.2 Creating dynamic reports with R and Markdown 22.3 Creating dynamic reports with R and LaTeX 22.4 Creating dynamic reports with R and Open Document 22.5 Creating dynamic reports with R and Microsoft Word 22.6 Summary