Horton / Kleinman Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition

2. Auflage 2015
ISBN: 978-1-4822-3737-5
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 313 Seiten

Improve Your Analytical Skills

Incorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book’s simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

New to the Second Edition

- The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows

- New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics

- New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping

- New chapter on simulation that includes examples of data generated from complex models and distributions

- A detailed discussion of the philosophy and use of the knitr and markdown packages for R

- New packages that extend the functionality of R and facilitate sophisticated analyses

- Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots

Easily Find Your Desired Task

Conveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.

Autoren/Hrsg.

Horton, Nicholas J.

Kleinman, Ken

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Data Input and Output

Input
Output
Further resources

Data Management

Structure and metadata

Derived variables and data manipulation
Merging, combining, and subsetting datasets
Date and time variables
Further resources

Examples

Statistical and Mathematical Functions

Probability distributions and random number generation
Mathematical functions
Matrix operations
Examples

Programming and Operating System Interface

Control flow, programming, and data generation
Functions

Interactions with the operating system

Common Statistical Procedures

Summary statistics

Bivariate statistics

Contingency tables

Tests for continuous variables

Analytic power and sample size calculations

Further resources

Examples

Linear Regression and ANOVA

Model fitting

Tests, contrasts, and linear functions of parameters

Model results and diagnostics
Model parameters and results
Further resources

Examples

Regression Generalizations and Modeling

Generalized linear models

Further generalizations

Robust methods

Models for correlated data

Survival analysis

Multivariate statistics and discriminant procedures
Complex survey design

Model selection and assessment
Further resources

Examples

A Graphical Compendium

Univariate plots

Univariate plots by grouping variable

Bivariate plots

Multivariate plots
Special-purpose plots

Further resources

Examples

Graphical Options and Configuration

Adding elements

Options and parameters
Saving graphs

Simulation

Generating data

Simulation applications

Further resources

Special Topics

Processing by group
Simulation-based power calculations

Reproducible analysis and output

Advanced statistical methods

Further resources

Case Studies
Data management and related tasks

Read variable format files

Plotting maps
Data scraping

Text mining

Interactive visualization

Manipulating bigger datasets

Constrained optimization: the knapsack problem

Appendix A: Introduction to R and RStudio
Appendix B: The HELP Study Dataset
Appendix C: References

Appendix D: Indices

Über Autor(innen)

Nicholas J. Horton is a professor of statistics at Amherst College. 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. 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.