Using R for data management, statistical analysis, and graphics

✍ Scribed by Nicholas J Horton; Ken Kleinman

• Publisher: CRC Press
• Year: 2011
• Tongue: English
• Leaves: 296
• Category: Library

✦ Synopsis

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 unwieldly 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 dataset from the HELP study. They also provides several case studies of more complex applications. Datasets and code are available for download on the book's Web site. --

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. --Book Jacket. Read more...

✦ Table of Contents

Content: Introduction to R Installation 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 Input Output 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 Graphics 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
Abstract:

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. Read more...