you’ll know how to use random numbers to simulate some distributions.
Finally, we show you some of the basics of using linear models (for example, linear regression and analysis of variance). We also show you how to use R to predict the values of new data using models that you’ve fitted to your data.
They say that a picture is worth a thousand words. This is certainly the case when you want to share your results with other people. In this part, you discover how to create basic and more sophisticated plots to visualize your data. We move on from bar charts and line charts, and show you how to present cuts of your data using facets.
In this part, we show you how to do ten things in R that you probably use Microsoft Excel for at the moment (for example, how to do the equivalent of pivot tables and lookup tables). We also give you ten tips for working with packages that are not part of base R.
Icons Used in This Book
As you read this book, you’ll find little pictures in the margins. These pictures, or icons, mark certain types of text:
Beyond the Book
R For Dummies includes the following goodies online for easy download:
✔ Cheat Sheet: You can find the Cheat Sheet for this book here:
✔ Extras: We provide a few extra articles here:
✔ Example code: We provide the example code for the book here:
If we have updates to the content of the book, look here for it:
Where to Go from Here
There’s only one way to learn R: Use it! In this book, we try to make you familiar with the usage of R, but you’ll have to sit down at your PC and start playing around with it yourself. Crack the book open so the pages don’t flip by themselves, and start hitting the keyboard!
Part I
Getting Started with R Programming
✔ Introducing R programming concepts.
✔ Creating your first script.
✔ Making clear, legible code.
✔ Visit www.dummies.com for great Dummies content online.
Chapter 1
Introducing R: The Big Picture
▶ Discovering the benefits of R
▶ Identifying some programming concepts that make R special
With an estimated worldwide user base of more than 2 million people, the R language has rapidly grown and extended since its origin as an academic demonstration language in the 1990s.
Some people would argue – and we think they’re right – that R is much more than a statistical programming language. It’s also
✔ A very powerful tool for all kinds of data processing and manipulation
✔ A community of programmers, users, academics, and practitioners
✔ A tool that makes all kinds of publication-quality graphics and data visualizations
✔ A collection of freely distributed add-on packages
✔ A versatile toolbox for extensive automation of your work
In this chapter, we fill you in on the benefits of R, as well as its unique features and quirks.
Tracing the history of R
Ross Ihaka and Robert Gentleman developed R as a free software environment for their teaching classes when they were colleagues at the University of Auckland in New Zealand. Because they were both familiar with S, a programming language for statistics, it seemed natural to use similar syntax in their own work. After Ihaka and Gentleman announced their software on the S-news mailing list, several people became interested and started to collaborate with them, notably Martin Mächler.
Currently, a group of 21 people has rights to modify the central archive of source code (http://www.r-project.org/contributors.html). This group is referred to as the R Core Team. In addition, many other people have contributed new code and bug fixes to the project.
Here are some milestone dates in the development of R:
✔ Early 1990s: The development of R began.
✔ August 1993: The software was announced on the S-news mailing list. Since then, a set of active R mailing lists has been created. The web page at www.r-project.org/mail.html
