R is the most powerful tool you can use for statistical analysis. This definitive guide smooths R’s steep learning curve with practical solutions and real-world applications for commercial environments.

In R in Action, Third Edition you will learn how to:

Set up and install R and RStudio

Clean, manage, and analyze data with R

Use the ggplot2 package for graphs and visualizations

Solve data management problems using R functions

Fit and interpret regression models

Test hypotheses and estimate confidence

Simplify complex multivariate data with principal components and exploratory factor analysis

Make predictions using time series forecasting

Create dynamic reports and stunning visualizations

Techniques for debugging programs and creating packages

R in Action, Third Edition makes learning R quick and easy. That’s why thousands of data scientists have chosen this guide to help them master the powerful language. Far from being a dry academic tome, every example you’ll encounter in this book is relevant to scientific and business developers, and helps you solve common data challenges. R expert Rob Kabacoff takes you on a crash course in statistics, from dealing with messy and incomplete data to creating stunning visualizations. This revised and expanded third edition contains fresh coverage of the new tidyverse approach to data analysis and R’s state-of-the-art graphing capabilities with the ggplot2 package.

About the technology

Used daily by data scientists, researchers, and quants of all types, R is the gold standard for statistical data analysis. This free and open source language includes packages for everything from advanced data visualization to deep learning. Instantly comfortable for mathematically minded users, R easily handles practical problems without forcing you to think like a software engineer.

About the book

R in Action, Third Edition teaches you how to do statistical analysis and data visualization using R and its popular tidyverse packages. In it, you’ll investigate real-world data challenges, including forecasting, data mining, and dynamic report writing. This revised third edition adds new coverage for graphing with ggplot2, along with examples for machine learning topics like clustering, classification, and time series analysis.

What's inside

Clean, manage, and analyze data

Use the ggplot2 package for graphs and visualizations

Techniques for debugging programs and creating packages

A complete learning resource for R and tidyverse

About the reader

Requires basic math and statistics. No prior experience with R needed.

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.

Table of Contents

PART 1 GETTING STARTED

1 Introduction to R

2 Creating a dataset

3 Basic data management

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 Cluster analysis

17 Classification

18 Advanced methods for missing data

PART 5 EXPANDING YOUR SKILLS

19 Advanced graphs

20 Advanced programming

21 Creating dynamic reports

22 Creating a package