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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.

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1. Introduction to R programming. 2. Reproducible analysis. 3. Data manipulation. 4. Visualizing and exploring data. 5. Working with large data sets. 6. Supervised learning. 7. Unsupervised learning. 8. More R programming. 9. Advanced R programming. 10. Object oriented programming. 11. Building an R package. 12. Testing and checking. 13. Version control. 14. Profiling and optimizing.

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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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