English | 2022 | ISBN: 978-1617296055 | 656 Pages | PDF, EPUB | 37 MB

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.

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.

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.

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