PART 1 GETTING STARTED 1
1 Introduction to R 3
2 Creating a dataset 20
3 Basic data management 46
4 Getting started with graphs 68
5 Advanced data management 88
PART 2 BASIC METHODS 115
6 Basic graphs 117
7 Basic statistics 147
PART 3 INTERMEDIATE METHODS 177
8 Regression 179
9 Analysis of variance 221
10 Power analysis 249
11 Intermediate graphs 265
12 Resampling statistics and bootstrapping 293
PART 4 ADVANCED METHODS 313
13 Generalized linear models 315
14 Principal components and factor analysis 333
15 Time series 355
16 Cluster analysis 386
17 Classification 409
18 Advanced methods for missing data 434
PART 5 EXPANDING YOUR SKILLS 457
19 Advanced graphs 459
20 Advanced programming 491
21 Creating dynamic reports 525
22 Creating a package 543

 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.

 Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

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.