R in action: data analysis and graphics with R

Table of content
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

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.