Using R for Data Management, Statistical Analysis, and Graphics
CRC Press – 2010 – 297 pages
Quick and Easy Access to Key Elements of Documentation
Includes worked examples across a wide variety of applications, tasks, and graphics
Using R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation and vast number of add-on packages. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics.
Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and R syntax. Demonstrating the R code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book’s website.
Helping to improve your analytical skills, this book lucidly summarizes the aspects of R most often used by statistical analysts. New users of R will find the simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.
This book is an excellent reference resource. Used this way, it can be helpful for years to come for both experienced and novice users. The organization of the material makes it easy to find the relevant piece of information either by topic (from the table of contents) or using one of the indexes. The task entries are self-contained. Users with experience in technical computing may use it as a quick starter in R, as well.
—Georgi N. Boshnakov, Journal of Applied Statistics, June 2012
This book provides a concise reference and annotated examples for R … . It is needed because R does not come with a coordinated manual … It is much easier to find information in Horton and Kleinman’s book because of their more detailed indices and table of contents. … Horton and Kleinman have succeeded very well in their goal of providing a concise reference manual and annotated examples. If you know the statistics (or can look them up) and have some experience using R, it is an extremely useful reference, and it has become my most consulted R book. … it would be an excellent reference for those wanting look up the syntax of a command together with an example of how to use it. It is also very useful if you cannot remember the command and want to know how to do it in R.
—Paul H. Geissler, The American Statistician, November 2011
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
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
Structure and meta-data
Derived variables and data manipulation
Merging, combining, and subsetting datasets
Date and time variables
Interactions with the operating system
Probability distributions and random number generation
Control flow, programming, and data generation
Common Statistical Procedures
Two sample tests for continuous variables
Linear Regression and ANOVA
Model comparison and selection
Tests, contrasts, and linear functions of parameters
Model parameters and results
Regression Generalizations and Multivariate Statistics
Generalized linear models
Models for correlated data
Further generalizations to regression models
Multivariate statistics and discriminant procedures
A compendium of useful plots
Options and parameters
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
Appendix: The HELP Study Dataset
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.