English | 2022 | ISBN: 978-1484281543 | 539 Pages | PDF, EPUB | 24 MB

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. Youâ€™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

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