Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.

Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.

Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well.

Source code is available at github.com/Apress/beg-data-science-r4.

What You Will Learn

• Perform data science and analytics using statistics and the R programming language
• Visualize and explore data, including working with large data sets found in big data
• Build an R package
• Test and check your code
• Practice version control
• Profile and optimize your code

Who This Book Is For

Those with some data science or analytics background, but not necessarily experience with the R programming language.

Table of Contents

Chapter 1: Introduction to R Programming

Chapter 2: Reproducible Analysis

Chapter 3: Data Manipulation

Chapter 4: Visualizing Data

Chapter 5: Working with Large Data Sets

Chapter 6: Supervised Learning

Chapter 7: Unsupervised Learning

Chapter 8: Project 1: Hitting the Bottle

Chapter 9: Deeper into R Programming

Chapter 10: Working with Vectors and Lists

Chapter 11: Functional Programming

Chapter 12: Object-Oriented Programming

Chapter 13: Building an R Package

Chapter 14: Testing and Package Checking

Chapter 15: Version Control

Chapter 16: Profiling and Optimizing

Chapter 17: Project 2: Bayesian Linear Regression

About the Author

Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. His background is in math and computer science but for the last decade his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species.