Beginning Data Science in R

1. Front Matter

   Pages i-xxvii

   PDF

2. Introduction to R Programming

   • Thomas Mailund

   Pages 1-28

3. Reproducible Analysis

   • Thomas Mailund

   Pages 29-44

4. Data Manipulation

   • Thomas Mailund

   Pages 45-73

5. Visualizing Data

   • Thomas Mailund

   Pages 75-111

6. Working with Large Datasets

   • Thomas Mailund

   Pages 113-124

7. Supervised Learning

   • Thomas Mailund

   Pages 125-167

8. Unsupervised Learning

   • Thomas Mailund

   Pages 169-204

9. More R Programming

   • Thomas Mailund

   Pages 205-231

10. Advanced R Programming

    • Thomas Mailund

    Pages 233-256

11. Object Oriented Programming

    • Thomas Mailund

    Pages 257-267

12. Building an R Package

    • Thomas Mailund

    Pages 269-280

13. Testing and Package Checking

    • Thomas Mailund

    Pages 281-286

14. Version Control

    • Thomas Mailund

    Pages 287-301

15. Profiling and Optimizing

    • Thomas Mailund

    Pages 303-346

16. Back Matter

    Pages 347-352

    PDF

Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. 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 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. 


This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. 


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.

• R
• programming
• statistics
• data science
• big data
• machine learning
• deep learning
• ai
• cloud
• analytics
• coding
• software

• Aarhus, Denmark

  Thomas Mailund

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.

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