R in Action, Second Edition

Robert I. Kabacoff

• Publisher: Manning Publications
• Year: 2015
• ISBN:
  9781617291388
• Topic: Informatik
• Subtopic: Informatik Allgemein

R in Action, Second Edition presents both the R language and the examples that make it so useful for business developers. Focusing on practical solutions, the book offers a crash course in statistics and covers elegant methods for dealing with messy and incomplete data that are difficult to analyze using traditional methods. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. And this expanded second edition includes new chapters on time series analysis, cluster analysis, and classification methodologies, including decision trees, random forests, and support vector machines.

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’s free, open source software, available 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 is all about becoming familiar 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 out 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. But 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’s 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’ll 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 I, you’ll be thoroughly familiar with programming in the R environment. You’ll have the skills needed to enter or access your data, clean it up, and prepare it for further analyses. You’ll also have experience creating, customizing, and saving a variety of graphs.

Chapter 1. Introduction to R

This chapter covers

• Installing R
• Understanding the R language
• Running programs

How we analyze data has changed dramatically in recent years. With the advent of personal computers and the internet, the sheer volume of data we have available has grown enormously. Companies have terabytes of data about the consumers they interact with, and governmental, academic, and private research institutions have extensive archival and survey data on every manner of research topic. Gleaning information (let alone wisdom) from these massive stores of data has become an industry in itself. At the same time, presenting the information in easily accessible and digestible ways has become increasingly challenging.

The science of data analysis (statistics, psychometri...