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Informationen zum Autor 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. Klappentext Conquer the complexities of this open source statistical languageR 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 scriptsBeginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence. Zusammenfassung 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. Inhaltsverzeichnis Introduction xxi Chapter 1: Introducing R: What It Is and How to Get It 1 Getting the Hang of R 2 The R Website 3 Downloading and Installing R from CRAN 3 Installing R on Your Windows Computer 4 Installing R on Your Macintosh Computer 7 Installing R on Your Linux Computer 7 Running the R Program 8 Finding Your Way with R 10 Getting Help via the CRAN Website and the Internet 10 The Help Command in R 10 Help for Windows Users 11 Help for Macintosh Users 11 Help for Linux Users 13 Help For All Users 13 Anatomy of a Help Item in R 14 Command Packages 16 Standard Command Packages 16 What Extra Packages Can Do for You 16 How to Get Extra Packages of R Commands 18 How to Install Extra Packages for Windows Users 18 How to Install Extra Packages for Macintosh Users 18 How to Install Extra Packages for Linux Users 19 Running and Manipulating Packages 20 Loading Packages 21 Windows-Specific Package Commands 21 Macintosh-Specific Package Commands 21 Removing or Unloading Packages 22 Summary 22 Chapter 2: Starting Out: Becoming Familiar with R 25 Some Simple Math 26 Use R Like a Calculator 26 Storing the Results of Calculations 29 Reading and Getting Data into R 30 Using the combine Command for Making Data 30 Entering Numerical Items as Data 30 Entering Text Items as Data 31 Using the scan Command for Making Data 32 Entering Text as Data 33 Using the Clipboard to Make Data 33 Reading a File of Data from a Disk 35 Reading Bigger Data Files 37 The read.csv() Command 37 Alternative Commands for Reading Data in R 39 Missing Values in Data Files 40 Viewing Named Objects 41 Viewing Previously...
List of contents
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
