R in Action, Third Edition
Robert I. Kabacoff
  • MEAP began July 2019
  • Publication in Summer 2020 (estimated)
  • ISBN 9781617296055
  • 625 pages (estimated)
  • printed in black & white
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If you are looking for the definitive guide to get you up and running with R, this is the book you need!

Jean-François Morin
Built specifically for statistical computing and graphics, the R language, along with its amazing collection of libraries and tools, is one of the most powerful tools you can use to tackle data analysis for business, research, and other data-intensive domains. R in Action, Third Edition takes you hands-on with R, focusing on practical solutions and real-world applications that are most relevant to business developers. This revised and expanded third edition, covers the new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package.
Table of Contents detailed table of contents

Part 1: Getting Started

1 Introduction to R

1.1 Why use R?

1.2 Obtaining and installing R

1.3 Working with R

1.3.1 Getting started

1.3.2 Using RStudio

1.3.3 Getting help

1.3.4 The workspace

1.3.5 Projects

1.4 Packages

1.4.1 What are packages?

1.4.2 Installing a package

1.4.3 Loading a package

1.4.4 Learning about a package

1.5 Using output as input: reusing results

1.6 Working with large datasets

1.7 Working through an example

1.8 Summary

2 Creating a dataset

2.1 Understanding datasets

2.2 Data structures

2.2.1 Vectors

2.2.2 Matrices

2.2.3 Arrays

2.2.4 Data frames

2.2.5 Factors

2.2.6 Lists

2.2.7 Tibbles

2.3 Data input

2.3.1 Entering data from the keyboard

2.3.2 Importing data from a delimited text file

2.3.3 Importing data from Excel

2.3.4 Importing data from XML

2.3.5 Importing data from the Web

2.3.6 Importing data from SPSS

2.3.7 Importing data from SAS

2.3.8 Importing data from Stata

2.3.9 Accessing database management systems (DBMSs)

2.3.10 Importing data via Stat/Transfer

2.4 Annotating datasets

2.4.1 Variable labels

2.4.2 Value labels

2.5 Useful functions for working with data objects

2.6 Summary

3 Basic data management

3.1 A working example

3.2 Creating new variables

3.3 Recoding variables

3.4 Renaming variables

3.5 Missing values

3.5.1 Recoding values to missing

3.5.2 Excluding missing values from analyses

3.6 Date values

3.6.1 Converting dates to character variables

3.6.2 Going further

3.7 Type conversions

3.8 Sorting data

3.9 Merging datasets

3.9.1 Adding columns to a data frame

3.9.2 Adding rows to a data frame

3.10 Subsetting datasets

3.10.1 Selecting variables

3.10.2 Dropping variables

3.10.3 Selecting observations

3.10.4 The subset() function

3.10.5 Random samples

3.11 Using dplyr to manipulate data frames

3.11.1 Basic dplyr functions

3.11.2 Using pipe operators to chain statements

3.12 Using SQL statements to manipulate data frames

3.13 Summary

4 Getting started with graphs

5 Advanced data management

Part 2: Basic Methods

6 Basic Graphs

7 Basic Statistics

Part 3: Intermediate Methods

8 Regression

9 Analysis of Variance

10 Power Analysis

11 Intermediate Graphs

12 Resampling statistics and bootstrapping

Part 4: Advanced Methods

13 Generalized Linear Models

14 Principal Components and Factor Analysis

15 Time Series

16 Clustering

17 Classification

18 Advanced methods for missing data

Part 5: Expanding Your Skills

19 Advanced Graphics with ggplot2

20 Advanced R Programming

21 Creating a Package


Appendix A: Version control with git

Appendix B: Customizing the startup environment

Appendix C: Exporting data from R

Appendix D: Matrix Algebra in R

Appendix E: Packages used in this book

Appendix F: Working with large datasets

Appendix G: Updating an R installation

About the Technology

The R language is the most powerful platform you can choose for modern data analysis. Free and open source, R’s community has created thousands of modules to tackle challenges from data-crunching to presentation. R’s graphical capabilities are also state-of-the-art, with a comprehensive and powerful feature set available for data visualization. R runs on all major operating systems and is used by businesses, researchers, and organizations worldwide.

About the book

R in Action, Third Edition teaches you to use the R language, including the popular tidyverse packages, through hands-on examples relevant to scientific, technical, and business developers. Focusing on practical solutions to real-world data challenges, R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualisations. In this revised and expanded third edition, new coverage has been added for R’s state-of-the-art graphing capabilities with the ggplot2 package.

What's inside

  • A complete learning resource for R and tidyverse
  • Clean, manage, and analyze data with R
  • Use the ggplot2 package for graphs and visualizations
  • Techniques for debugging programs and creating packages

About the reader

This book is designed for readers who need to solve practical data analysis problems using the R language and tools. Some background in mathematics and statistics is helpful, but no prior experience with R or computer programming is required

About the author

Dr. Robert I Kabacoff is a professor of quantitative analytics at Wesleyan University, and a seasoned data scientist with more than 20 years of experience providing statistical programming and data analytic support in business, healthcare, and government settings. He has taught both undergraduate and graduate courses in data analysis and statistical programming and manages the Quick-R website at statmethods.net and the R for Data Visualization website at rkabacoff.github.io/datavis.

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