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Using R for Data Management, Statistical Analysis, and Graphics


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Table of Contents

Introduction to R
Running R and sample session
Using the R Commander graphical interface
Learning R and getting help
Fundamental structures: Objects, classes, and related concepts
Built-in and user-defined functions
Add-ons: Libraries and packages
Support and bugs

Data Management
Structure and meta-data
Derived variables and data manipulation
Merging, combining, and subsetting datasets
Date and time variables
Interactions with the operating system
Mathematical functions
Matrix operations
Probability distributions and random number generation
Control flow, programming, and data generation
Further resources
HELP examples

Common Statistical Procedures
Summary statistics
Contingency tables
Bivariate statistics
Two sample tests for continuous variables
Further resources
HELP examples

Linear Regression and ANOVA
Model fitting
Model comparison and selection
Tests, contrasts, and linear functions of parameters
Model diagnostics
Model parameters and results
Further resources
HELP examples

Regression Generalizations and Multivariate Statistics
Generalized linear models
Models for correlated data
Survival analysis
Further generalizations to regression models
Multivariate statistics and discriminant procedures
Further resources
HELP examples

A compendium of useful plots
Adding elements
Options and parameters
Saving graphs
Further resources
HELP examples

Advanced Applications
Power and sample size calculations
Simulations and data generation
Data management and related tasks
Read geocoded data and draw maps
Data scraping and visualization
Account for missing data using multiple imputation
Propensity score modeling
Empirical problem solving
Further resources

Appendix: The HELP Study Dataset

Subject Index
R Index

About the Author

Nicholas J. Horton is an associate professor in the Department of Mathematics and Statistics at Smith College in Northampton, Massachusetts. His research interests include longitudinal regression models and missing data methods, with applications in psychiatric epidemiology and substance abuse research. Ken Kleinman is an associate professor in the Department of Population Medicine at Harvard Medical School in Boston, Massachusetts. His research deals with clustered data analysis, surveillance, and epidemiological applications in projects ranging from vaccine and bioterrorism surveillance to observational epidemiology to individual-, practice-, and community-randomized interventions.


! The interesting aspect of the book is that it does not only describe the basic statistics and graphics function of the basic R system but it describes the use of 40 additional available from the CRAN website. The website contains also the R code to install all the packages that contain the described features. In summary, the book is a useful complement to introductory statistics books and lectures ! Those who know R might get additional hints on new features of statistical analyses. --International Statistical Review (2011), 79

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