Part 1. Getting started
Welcome to R in Action! R is one of the most popular platforms for data analysis and visualization currently available. It is free, open-source software, with versions for Windows, Mac OS X, and Linux operating systems. This book will provide you with the skills needed to master this comprehensive software, and apply it effectively to your own data.
The book is divided into four sections. Part I covers the basics of installing the software, learning to navigate the interface, importing data, and massaging it into a useful format for further analysis.
Chapter 1 will familiarize you with the R environment. The chapter begins with an overview of R and the features that make it such a powerful platform for modern data analysis. After briefly describing how to obtain and install the software, the user interface is explored through a series of simple examples. Next, you’ll learn how to enhance the functionality of the basic installation with extensions (called contributed packages), that can be freely downloaded from online repositories. The chapter ends with an example that allows you to test your new skills.
Once you’re familiar with the R interface, the next challenge is to get your data into the program. In today’s information-rich world, data can come from many sources and in many formats. Chapter 2 covers the wide variety of methods available for importing data into R. The first half of the chapter introduces the data structures R uses to hold data and describes how to input data manually. The second half discusses methods for importing data from text files, web pages, spreadsheets, statistical packages, and databases.
From a workflow point of view, it would probably make sense to discuss data management and data cleaning next. However, many users approach R for the first time out of an interest in its powerful graphics capabilities. Rather than frustrating that interest and keeping you waiting, we dive right into graphics in chapter 3. The chapter reviews methods for creating graphs, customizing them, and saving them in a variety of formats. The chapter describes how to specify the colors, symbols, lines, fonts, axes, titles, labels, and legends used in a graph, and ends with a description of how to combine several graphs into a single plot.
Once you’ve had a chance to try out R’s graphics capabilities, it is time to get back to the business of analyzing data. Data rarely comes in a readily usable format. Significant time must often be spent combining data from different sources, cleaning messy data (miscoded data, mismatched data, missing data), and creating new variables (combined variables, transformed variables, recoded variables) before the questions of interest can be addressed. Chapter 4 covers basic data management tasks in R, including sorting, merging, and subsetting datasets, and transforming, recoding, and deleting variables.
Chapter 5 builds on the material in chapter 4. It covers the use of numeric (arithmetic, trigonometric, and statistical) and character functions (string subsetting, concatenation, and substitution) in data management. A comprehensive example is used throughout this section to illustrate many of the functions described. Next, control structures (looping, conditional execution) are discussed and you will learn how to write your own R functions. Writing custom functions allows you to extend R’s capabilities by encapsulating many programming steps into a single, flexible function call. Finally, powerful methods for reorganizing (reshaping) and aggregating data are discussed. Reshaping and aggregation are often useful in preparing data for further analyses.
After having completed part 1, you will be thoroughly familiar with programming in the R environment. You will have the skills needed to enter and access data, clean it up, and prepare it for further analyses. You will also have experience creating, customizing, and saving a variety of graphs.