Deep R Programming
Deep R Programming is a comprehensive course on one of the most popular languages in data science (statistical computing, graphics, machine learning, data wrangling and analytics). It introduces the base language in-depth and is aimed at ambitious students, practitioners, and researchers who would like to become independent users of this powerful environment.
This early draft is distributed in the hope that it will be useful.
For many students around the world, educational resources are hardly affordable. Therefore, I have decided that this book should remain an independent, non-profit, open-access project (available both in PDF and HTML forms). Whilst, for some people, the presence of a “designer tag” from a major publisher might still be a proxy for quality, it is my hope that this publication will prove useful to those who seek knowledge for knowledge’s sake.
Please spread the news about it by sharing the above URLs with your mates, peers, or students. Thank you.
Any bug/typos reports/fixes are appreciated. Although available online, this is a whole course, and should be read from the beginning to the end. Please refer to the Preface for general introductory remarks and design philosophy.
Copyright (C) 2022–2023 by Marek Gagolewski. Some rights reserved.
This material is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).
- 1. Introduction
- 2. Numeric Vectors
- 3. Logical Vectors
- 4. Lists and Attributes
- 5. Vector Indexing
- 6. Character Vectors
- 7. Functions
- 8. Flow of Execution
- 9. Designing Functions
- 10. S3 Classes
- 11. Matrices and Other Arrays
- 12. Data Frames
- 13. 🚧🚧 Graphics
- 14. 🚧🚧 Interfacing Compiled Code (*)
- 15. 🚧 Unevaluated Expressions (**)
- 16. 🚧🚧 Environments and Evaluation (**)