Beginning R: The Statistical Programming Language [NOOK Book]

Overview

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 ...

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Beginning R: The Statistical Programming Language

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Overview

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.

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Product Details

  • ISBN-13: 9781118239377
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 5/24/2012
  • Sold by: Barnes & Noble
  • Format: eBook
  • Edition number: 1
  • Pages: 432
  • Sales rank: 557,258
  • File size: 8 MB

Meet the Author

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.

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Read an Excerpt

Beginning R

The Statistical Programming Language
By Mark Gardener

John Wiley & Sons

Copyright © 2012 John Wiley & Sons, Ltd
All right reserved.

ISBN: 978-1-1181-6430-3


Chapter One

Introducing R: What It Is and How to Get It

WHAT YOU WILL LEARN IN THIS CHAPTER:

* Discovering what R is

* How to get the R program

* How to install R on your computer

* How to start running the R program

* How to use the help system and find help from other sources

* How to get additional libraries of commands

R is more than just a program that does statistics. It is a sophisticated computer language and environment for statistical computing and graphics. R is available from the R-Project for Statistical Computing website (www.r-project.org), and following is some of its introductory material:

R is an open-source (GPL) statistical environment modeled after S and S-Plus. The S language was developed in the late 1980s at AT&T labs. The R project was started by Robert Gentleman and Ross Ihaka (hence the name, R) of the Statistics Department of the University of Auckland in 1995. It has quickly gained a widespread audience. It is currently maintained by the R core-development team, a hard-working, international team of volunteer developers. The R project webpage is the main site for information on R. At this site are directions for obtaining the software, accompanying packages, and other sources of documentation.

R is a powerful statistical program but it is first and foremost a programming language. Many routines have been written for R by people all over the world and made freely available from the R project website as "packages." However, the basic installation (for Linux, Windows or Mac) contains a powerful set of tools for most purposes.

Because R is a computer language, it functions slightly differently from most of the programs that users are familiar with. You have to type in commands, which are evaluated by the program and then executed. This sounds a bit daunting to many users, but the R language is easy to pick up and a lot of help is available. It is possible to copy and paste in commands from other applications (for example: word processors, spreadsheets, or web browsers) and this facility is very useful, especially if you keep notes as you learn. Additionally, the Windows and Macintosh versions of R have a graphical user interface (GUI) that can help with some of the basic tasks.

R can deal with a huge variety of mathematical and statistical tasks, and many users find that the basic installation of the program does everything they need. However, many specialized routines have been written by other users and these libraries of additional tools are available from the R website. If you need to undertake a particular type of analysis, there is a very good chance that someone before you also wanted to do that very thing and has written a package that you can download to allow you to do it.

R is open source, which means that it is continually being reviewed and improved. R runs on most computers—installations are available for Windows, Macintosh, and Linux. It also has good interoperability, so if you work on one computer and switch to another you can take your work with you.

R handles complex statistical approaches as easily as more simple ones. Therefore once you know the basics of the R language, you can tackle complex analyses as easily as simple ones (as usual it is the interpretation of results that can be the really hard bit).

GETTING THE HANG OF R

R is unlike most current computer programs in that you must type commands into the console window to carry out most tasks you require. Throughout the text, the use of these commands is illustrated, which is indeed the point of the book.

Where a command is illustrated in its basic form, you will see a fixed width font to mimic the R display like so:

help.start()

When the use of a particular command is illustrated, you will see the user-typed input illustrated by beginning the lines with the > character, which mimics the cursor line in the R console window like so:

> data1 = c(3, 5, 7, 5, 3, 2, 6, 8, 5, 6, 9)

Lines of text resulting from your actions are shown without the cursor character, once again mimicking the output that you would see from R itself:

> data1 [1] 3 5 7 5 3 2 6 8 5 6 9

So, in the preceding example the first line was typed by the user and resulted in the output shown in the second line. Keep these conventions in mind as you are reading this chapter and they will come into play as soon as you have R installed and are ready to begin using it!

The R Website

The R website at www.r-project.org is a good place to visit to obtain the R program. It is also a good place to look for help items and general documentation as well as additional libraries of routines. If you use Windows or a Mac, you will need to visit the site to download the R program and install it. You can also find installation files for many Linux versions on the R website.

The R website is split into several parts; links to each section are on the main page of the site. The two most useful for beginners are the Documentation and Download sections.

In the Documentation section (see Figure 1-1) a Manuals link takes you to many documents contributed to the site by various users. Most of these are in HTML and PDF format. You can access these and a variety of help guides under Manuals d Contributed Documentation. These are especially useful for helping the new user to get started. Additionally, a large FAQ section takes you to a list that can help you find answers to many question you might have. There is also a Wiki, and although this is still a work in progress, it is a good place to look for information on installing R on Linux systems.

In the Downloads section you will find the links from which you can download R. The following section goes into more detail on how to do this.

Downloading and Installing R from CRAN

The Comprehensive R Archive Network (CRAN) is a network of websites that host the R program and that mirror the original R website. The benefit of having this network of websites is improved download speeds. For all intents and purposes, CRAN is the R website and holds downloads (including old versions of software) and documentation (e.g. manuals, FAQs). When you perform searches for R-related topics on the internet, adding CRAN (or R) to your search terms increases your results. To get started downloading R, you'll want to perform the following steps:

1. Visit the main R web page (www.r-project.org); you see a Getting Started box with a link to download R (see Figure 1-2). Click that link and you are directed to select a local CRAN mirror site from which to download R.

2. The starting page of the CRAN website appears once you have selected your preferred mirror site. This page has a Software section on the left with several links. Choose the R Binaries link to install R on your computer (see Figure 1-3). You can also click the link to Packages, which contains libraries of additional routines. However, you can install these from within R so you can just ignore the Packages link for now. The Other link goes to a page that lists software available on CRAN other than the R base distribution and regular contributed extension packages. This link is also unnecessary for right now and can be ignored as well.

3. Once you click the R Binaries link you move to a simple directory containing folders for a variety of operating system (see Figure 1-4). Select the appropriate operating system on which you will be downloading R and follow the link to a page containing more information and the installation files that you require.

The details for individual operating systems vary, so the following sections are split into instructions for each of Windows, Macintosh, and Linux.

Installing R on Your Windows Computer

The install files for Windows come bundled in an .exe file, which you can download from the windows folder (refer to Figure 1-4). Downloading the .exe file is straightforward (see Figure 1-5), and you can install R simply by double-clicking the file once it is on your computer.

Run the installer with all the default settings and when it is done you will have R installed.

Versions of Windows post XP require some of additional steps to make R work properly. For Vista or later you need to alter the properties of the R program so that it runs with Administrator privileges. To do so, follow these steps:

1. Click the Windows button (this used to be labeled Start).

2. Select Programs.

3. Choose the R folder.

4. Right-click the R program icon to see an options menu (see Figure 1-6).

5. Select Properties from the menu. You will then see a new options window.

6. Under the Compatibility tab, tick the box in the Privilege Level section (see Figure 1-7) and click OK.

7. Run R by clicking the Programs menu, shortcut, or quick-launch icon like any other program. If the User Account Control window appears (see Figure 1-8), select Yes and R runs as normal.

Now R is set to run with administrator access and will function correctly. This is important, as you see later. R will save your data items and a history of the commands you used to the disk and it cannot do this without the appropriate access level.

Installing R on Your Macintosh Computer

The install files for OS X come bundled in a DMG file, which you can download from the macosx folder (refer to Figure 1-4).

Once the file has downloaded it may open as a disk image or not (depending how your system is set up). Once the DMG file opens you can double-click the installer file and installation will proceed (see Figure 1-9). Installation is fairly simple and no special options are required. Once installed, you can run R from Applications and place it in the dock like any other program.

Installing R on Your Linux Computer

If you are using a Linux OS, R runs through the Terminal program. Downloadable install files are available for many Linux systems on the R website (see Figure 1-10). The website also contains instructions for installation on several versions of Linux. Many Linux systems also support a direct installation via the Terminal.

The major Linux systems allow you to install the R program directly from the Terminal, and R files are kept as part of their software repositories. These repositories are not always very up-to-date however, so if you want to install the very latest version of R, look on the CRAN website for instructions and an appropriate install file. The exact command to install direct from the Terminal varies slightly from system to system, but you will not go far wrong if you open the Terminal and type R into it. If R is not installed (the most likely scenario), the Terminal may well give you the command you need to get it (see Figure 1-11)!

In general, a command along the following lines will usually do the trick:

sudo apt-get install r-base-core

In Ubuntu 10.10, for example, this installs everything you need to get started. In other systems you may need two elements to install, like so:

sudo apt-get install r-base r-base-dev

The basic R program and its components are built from the r-base part. For many purposes this is enough, but to gain access to additional libraries of routines the r-base-dev part is needed. Once you run these commands you will connect to the Internet and the appropriate files will be downloaded and installed.

Once R is installed it can be run through the Terminal program, which is found in the Accessories part of the Applications menu. In Linux there is no GUI, so all the commands must be typed into the Terminal window.

RUNNING THE R PROGRAM

Once R is installed you can run it in a variety of ways:

* In Windows the program works like any other—you may have a desktop shortcut, a quick launch icon, or simply get to it via the Start button and the regular program list.

* On a Macintosh the program is located in the Applications folder and you can drag this to the dock to create a launcher or create an alias in the usual manner.

* On Linux the program is launched via the Terminal program, which is located in the Accessories section of the Applications menu.

Once the R program starts up you are presented with the main input window and a short introductory message that appears a little different on each OS:

* In Windows a few menus are available at the top as shown in Figure 1-12.

* On the Macintosh OS X, the welcome message is the same (see Figure 1-13). In this case you also have some menus available and they are broadly similar to those in the Windows version. You also see a few icons; these enable you to perform a few tasks but are not especially useful. Under these icons is a search box, which is useful as an alternative to typing in help commands (you look at getting help shortly).

* In Linux systems there are no icons and the menu items you see relate to the Terminal program rather than R itself (see Figure 1-14).

R is a computer language, and like any other language you must learn the vocabulary and the grammar to make yourself understood and to carry out the tasks you want. Getting to know where help is available is a good starting point, and that is the subject of the next section.

FINDING YOUR WAY WITH R

Finding help when you are starting out can be a daunting prospect. A lot of material is available for help with R and tracking down the useful information can take a while. (Of course, this book is a good starting point!) In the following sections you see the most efficient ways to access some of the help that is available, including how to access additional libraries that you can use to deal with the tasks you have.

Getting Help via the CRAN Website and the Internet

The R website is a good place to find material that supports your learning of R. Under the Manuals link are several manuals available in HTML or as PDF. You'll also find some useful beginner's guides in the Contributed Documentation section. Different authors take different approaches, and you may find one suits you better than another. Try a few and see how you get on. Additionally, preferences will change as your command of the system develops. There is also a Wiki on the R website that is a good reference forum, which is continually updated.

The Help Command in R

R contains a lot of built-in help, and how this is displayed varies according to which OS you are using and the options (if any) that you set. The basic command to bring up help is:

help(topic)

Simply replace topic with the name of the item you want help on. You can also save a bit of typing by prefacing the topic with a question mark, like so:

?topic

You can also access the help system via your web browser by typing:

help.start()

This brings up the top-level index page where you can use the Search Engine & Keywords hyperlink to find what you need. This works for all the different operating systems. Of course, you need to know what command you are looking for to begin with. If you are not quite sure, you can use the following command:

apropos('partword')

This searches through the help files for matches to the word you typed, you replace 'partword' with the text you want to search for. Note that unlike the previous help() command you do need the quotes (single or double quotes are fine as long as they match).

Help for Windows Users

The Windows default help generally works fine (see Figure 1-15), but the Index and Search tabs only work within the section you are in, and it is not possible to get to the top level in the search hierarchy. If you return to the main command window and type in another help command, a new window opens so it is not possible to scroll back through entries unless they are in the same section.

Once you are done with your help window, you can close it by clicking the red X button.

Help for Macintosh Users

In OS X the default help appears in a separate window as HTML text (see Figure 1-16). The help window acts like a browser and you can use the arrow buttons to return to previous topics if you follow hyperlinks. You can also type search terms into the search box.

Scrolling to the foot of the help entry enables you to jump to the index for that section (Figure 1-17). Once at the index you can jump further up the hierarchy to reach other items.

The top level you can reach is identical to the HTML version of the help that you get if you type the help.start() command (see Figure 1-18), except that it is in a dedicated help window rather than your browser.

Once you are finished you can close the window in the usual manner by clicking the red button. If you return to the main command window and type another help item, the original window alters to display the new help. You can return to the previous entries using the arrow buttons at the top of the help window.

(Continues...)



Excerpted from Beginning R by Mark Gardener Copyright © 2012 by John Wiley & Sons, Ltd. Excerpted by permission of John Wiley & Sons. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

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Table of Contents

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 Files 57

Writing Vector Objects to Disk 58

Writing Matrix and Data Frame Objects to Disk 58

Writing List Objects to Disk 59

Converting List Objects to Data Frames 60

Summary 61

Chapter 3: Starting Out: Working With Objects 65

Manipulating Objects 65

Manipulating Vectors 66

Selecting and Displaying Parts of a Vector 66

Sorting and Rearranging a Vector 68

Returning Logical Values from a Vector 70

Manipulating Matrix and Data Frames 70

Selecting and Displaying Parts of a Matrix or Data Frame 71

Sorting and Rearranging a Matrix or Data Frame 74

Manipulating Lists 76

Viewing Objects within Objects 77

Looking Inside Complicated Data Objects 77

Opening Complicated Data Objects 78

Quick Looks at Complicated Data Objects 80

Viewing and Setting Names 82

Rotating Data Tables 86

Constructing Data Objects 86

Making Lists 87

Making Data Frames 88

Making Matrix Objects 89

Re-ordering Data Frames and Matrix Objects 92

Forms of Data Objects: Testing and Converting 96

Testing to See What Type of Object You Have 96

Converting from One Object Form to Another 97

Convert a Matrix to a Data Frame 97

Convert a Data Frame into a Matrix 98

Convert a Data Frame into a List 99

Convert a Matrix into a List 100

Convert a List to Something Else 100

Summary 104

Chapter 4: Data: Descriptive Statistics and Tabulation 107

Summary Commands 108

Summarizing Samples 110

Summary Statistics for Vectors 110

Summary Commands With Single Value Results 110

Summary Commands With Multiple Results 113

Cumulative Statistics 115

Simple Cumulative Commands 115

Complex Cumulative Commands 117

Summary Statistics for Data Frames 118

Generic Summary Commands for Data Frames 119

Special Row and Column Summary Commands 119

The apply() Command for Summaries on Rows or Columns 120

Summary Statistics for Matrix Objects 120

Summary Statistics for Lists 121

Summary Tables 122

Making Contingency Tables 123

Creating Contingency Tables from Vectors 123

Creating Contingency Tables from Complicated Data 123

Creating Custom Contingency Tables 126

Creating Contingency Tables from Matrix Objects 128

Selecting Parts of a Table Object 130

Converting an Object into a Table 132

Testing for Table Objects 133

Complex (Flat) Tables 134

Making “Flat” Contingency Tables 134

Making Selective “Flat” Contingency Tables 138

Testing “Flat” Table Objects 139

Summary Commands for Tables 139

Cross Tabulation 142

Testing Cross-Table (xtabs) Objects 144

A Better Class Test 144

Recreating Original Data from a Contingency Table 145

Switching Class 146

Summary 147

Chapter 5: Data: Distrib ution 151

Looking at the Distribution of Data 151

Stem and Leaf Plot 152

Histograms 154

Density Function 158

Using the Density Function to Draw a Graph 159

Adding Density Lines to Existing Graphs 160

Types of Data Distribution 161

The Normal Distribution 161

Other Distributions 164

Random Number Generation and Control 166

Random Numbers and Sampling 168

The Shapiro-Wilk Test for Normality 171

The Kolmogorov-Smirnov Test 172

Quantile-Quantile Plots 174

A Basic Normal Quantile-Quantile Plot 174

Adding a Straight Line to a QQ Plot 174

Plotting the Distribution of One Sample Against Another 175

Summary 177

Chapter 6: Si mple Hypothesis Testing 181

Using the Student’s t-test 181

Two-Sample t-Test with Unequal Variance 182

Two-Sample t-Test with Equal Variance 183

One-Sample t-Testing 183

Using Directional Hypotheses 183

Formula Syntax and Subsetting Samples in the t-Test 184

The Wilcoxon U-Test (Mann-Whitney) 188

Two-Sample U-Test 189

One-Sample U-Test 189

Using Directional Hypotheses 189

Formula Syntax and Subsetting Samples in the U-test 190

Paired t- and U-Tests 193

Correlation and Covariance 196

Simple Correlation 197

Covariance 199

Significance Testing in Correlation Tests 199

Formula Syntax 200

Tests for Association 203

Multiple Categories: Chi-Squared Tests 204

Monte Carlo Simulation 205

Yates’ Correction for 2 n 2 Tables 206

Single Category: Goodness of Fit Tests 206

Summary 210

Chapter 7: Introduction to Graphical Analysis 215

Box-whisker Plots 215

Basic Boxplots 216

Customizing Boxplots 217

Horizontal Boxplots 218

Scatter Plots 222

Basic Scatter Plots 222

Adding Axis Labels 223

Plotting Symbols 223

Setting Axis Limits 224

Using Formula Syntax 225

Adding Lines of Best-Fit to Scatter Plots 225

Pairs Plots (Multiple Correlation Plots) 229

Line Charts 232

Line Charts Using Numeric Data 232

Line Charts Using Categorical Data 233

Pie Charts 236

Cleveland Dot Charts 239

Bar Charts 245

Single-Category Bar Charts 245

Multiple Category Bar Charts 250

Stacked Bar Charts 250

Grouped Bar Charts 250

Horizontal Bars 253

Bar Charts from Summary Data 253

Copy Graphics to Other Applications 256

Use Copy/Paste to Copy Graphs 257

Save a Graphic to Disk 257

Windows 257

Macintosh 258

Linux 258

Summary 259

Chapter 8: Formula Notation and Complex Statistic s 263

Examples of Using Formula Syntax for Basic Tests 264

Formula Notation in Graphics 266

Analysis of Variance (ANOVA) 268

One-Way ANOVA 268

Stacking the Data before Running Analysis of Variance 269

Running aov() Commands 270

Simple Post-hoc Testing 271

Extracting Means from aov() Models 271

Two-Way ANOVA 273

More about Post-hoc Testing 275

Graphical Summary of ANOVA 277

Graphical Summary of Post-hoc Testing 278

Extracting Means and Summary Statistics 281

Model Tables 281

Table Commands 283

Interaction Plots 283

More Complex ANOVA Models 289

Other Options for aov() 290

Replications and Balance 290

Summary 292

Chapter 9: Manipulating Data and Extracting Components 295

Creating Data for Complex Analysis 295

Data Frames 296

Matrix Objects 299

Creating and Setting Factor Data 300

Making Replicate Treatment Factors 304

Adding Rows or Columns 306

Summarizing Data 312

Simple Column and Row Summaries 312

Complex Summary Functions 313

The rowsum() Command 314

The apply() Command 315

Using tapply() to Summarize Using a Grouping Variable 316

The aggregate() Command 319

Summary 323

Chapter 10: Regression (Li near Modeling) 327

Simple Linear Regression 328

Linear Model Results Objects 329

Coefficients 330

Fitted Values 330

Residuals 330

Formula 331

Best-Fit Line 331

Similarity between lm() and aov() 334

Multiple Regression 335

Formulae and Linear Models 335

Model Building 337

Adding Terms with Forward Stepwise Regression 337

Removing Terms with Backwards Deletion 339

Comparing Models 341

Curvilinear Regression 343

Logarithmic Regression 344

Polynomial Regression 345

Plotting Linear Models and Curve Fitting 347

Best-Fit Lines 348

Adding Line of Best-Fit with abline() 348

Calculating Lines with fitted() 348

Producing Smooth Curves using spline() 350

Confidence Intervals on Fitted Lines 351

Summarizing Regression Models 356

Diagnostic Plots 356

Summary of Fit 357

Summary 359

Chapter 11: More About Graphs 363

Adding Elements to Existing Plots 364

Error Bars 364

Using the segments() Command for Error Bars 364

Using the arrows() Command to Add Error Bars 368

Adding Legends to Graphs 368

Color Palettes 370

Placing a Legend on an Existing Plot 371

Adding Text to Graphs 372

Making Superscript and Subscript Axis Titles 373

Orienting the Axis Labels 375

Making Extra Space in the Margin for Labels 375

Setting Text and Label Sizes 375

Adding Text to the Plot Area 376

Adding Text in the Plot Margins 378

Creating Mathematical Expressions 379

Adding Points to an Existing Graph 382

Adding Various Sorts of Lines to Graphs 386

Adding Straight Lines as Gridlines or Best-Fit Lines 386

Making Curved Lines to Add to Graphs 388

Plotting Mathematical Expressions 390

Adding Short Segments of Lines to an Existing Plot 393

Adding Arrows to an Existing Graph 394

Matrix Plots (Multiple Series on One Graph) 396

Multiple Plots in One Window 399

Splitting the Plot Window into Equal Sections 399

Splitting the Plot Window into Unequal Sections 402

Exporting Graphs 405

Using Copy and Paste to Move a Graph 406

Saving a Graph to a File 406

Windows 406

Macintosh 406

Linux 406

Using the Device Driver to Save a Graph to Disk 407

PNG Device Driver 407

PDF Device Driver 407

Copying a Graph from Screen to Disk File 408

Making a New Graph Directly to a Disk File 408

Summary 410

Chapter 12: Writing Your Own Scripts: Beginning to Program 415

Copy and Paste Scripts 416

Make Your Own Help File as Plaintext 416

Using Annotations with the # Character 417

Creating Simple Functions 417

One-Line Functions 417

Using Default Values in Functions 418

Simple Customized Functions with Multiple Lines 419

Storing Customized Functions 420

Making Source Code 421

Displaying the Results of Customized Functions and Scripts 421

Displaying Messages as Part of Script Output 422

Simple Screen Text 422

Display a Message and Wait for User Intervention 424

Summary 428

Appendix: Answers to Exerci ses 433

Index 461

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