Synopsis
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.
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.
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
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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