Beginning R

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 scriptsBeginning 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.

Auteur

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.

Texte du rabat

Gain better insight into your data using the power of R

While R is very flexible and powerful, it is unlike most of the computer programs you have used. In order to unlock its full potential, this book delves into the language, making it accessible so you can tackle even the most complex of data analysis tasks. Simple data examples are integrated throughout so you can explore the capabilities and versatility of R. Along the way, you'll also learn how to carry out a range of commonly used statistical methods, including Analysis of Variance and Linear Regression. By the end, you'll be able to effectively and efficiently analyze your data and present the results.

Beginning R:

• Discusses how to implement some basic statistical methods such as the t-test, correlation, and tests of association

• Explains how to turn your graphs from merely adequate to simply stunning

• Provides you with the ability to define complex analytical situations

• Demonstrates ways to make and rearrange your data for easier analysis

• Covers how to carry out basic regression as well as complex model building and curvilinear regression

• Shows how to produce customized functions and simple scripts that can automate your workflow

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Résumé
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.

Contenu
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 Loaded Named-Objects 42

Viewing All Objects 42

Viewing Only Matching Names 42

Removing Objects from R 44

Types of Data Items 45

Number Data 45

Text Items 45

Converting Between Number and Text Data 46

The Structure of Data Items 47

Vector Items 48

Data Frames 48

Matrix Objects 49

List Objects 49

Examining Data Structure 49

Working with History Commands 51

Using History Files 52

Viewing the Previous Command History 52

Saving and Recalling Lists of Commands 52

Alternative History Commands in Macintosh OS 52

Editing History Files 53

Saving Your Work in R 54

Saving the Workspace on Exit 54

Saving Data Files to Disk 54

Save Named Objects 54

Save Everything 55

Reading Data Files from Disk 56

Saving Data to Disk as Text…