Beginning R - The Statistical Programming Language by Gardener, Mark [Paperback]

Description: Conquer the complexities of this open source statistical language R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming. * R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex * This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used * Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs * Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression * Provides beginning programming instruction for those who want to write their own scripts Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence. Dr. Mark Gardener is an ecologist, lecturer, and writer working in the UK. He is currently self-employed and runs courses in ecology, data analysis, and R for a variety of organizations. Introduction xxi Chapter 1: Introducing R: What It Is and How to Get It 1 Chapter 2: Starting Out: Becoming Familiar with R 25 Chapter 3: Starting Out: Working With Objects 65 Chapter 4: Data: Descriptive Statistics and Tabulation 107 Chapter 5: Data: Distrib ution 151 Chapter 6: Si mple Hypothesis Testing 181 Chapter 7: Introduction to Graphical Analysis 215 Chapter 8: Formula Notation and Complex Statistic s 263 Chapter 9: Manipulating Data and Extracting Components 295 Chapter 10: Regression (Li near Modeling) 327 Chapter 11: More About Graphs 363 Chapter 12: Writing Your Own Scripts: Beginning to Program 415 Appendix: Answers to Exerci ses 433 Index 461

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