The R Primer

The R Primer

by Claus Thorn Ekstrom
The R Primer

The R Primer

by Claus Thorn Ekstrom

eBook

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Overview

Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software.

Rather than explore the many options available for every command as well as the ever-increasing number of packages, the book focuses on the basics of data preparation and analysis and gives examples that can be used as a starting point. The numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production. Each example is self-contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated. While base R is used throughout, other functions or packages are listed if they cover or extend the functionality.

After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R. Additional topics and R code are available from the book’s supporting website at www.statistics.life.ku.dk/primer/


Product Details

ISBN-13: 9781439862087
Publisher: CRC Press
Publication date: 08/29/2011
Sold by: Barnes & Noble
Format: eBook
Pages: 299
File size: 3 MB

About the Author

Claus Thorn Ekstrøm is an associate professor of statistics in the Department of Basic Sciences and Environment and leader of the Center for Applied Bioinformatics at the University of Copenhagen. His research interests include genetic marker error detection, simulation-based inference, image analysis, and the analysis of microarray DNA chips, metabolic profiles, and quantitative traits for complex human families.

Table of Contents

Importing Data
Reading spreadsheets
Importing data from other statistical software programs
Exporting data

Manipulating Data
Working with data frames
Factors
Transforming variables

Statistical Analyses
Descriptive statistics
Linear models
Generalized linear models
Methods for analysis of repeated measurements
Specific methods
Model validation
Contingency tables
Agreement
Multivariate methods
Resampling statistics and bootstrapping
Robust statistics
Non-parametric methods
Survival analysis

Graphics
High-level plots
More advanced graphics
Working with graphics

R
Getting information
R packages
The R workspace

Bibliography

Index

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