Deep R Programming#
Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambitious students, professionals, and researchers with the knowledge and skills to become independent users of this potent environment so that they can tackle any problem related to data wrangling and analytics, numerical computing, statistics, and machine learning.
Although available online, it is a whole course, and should be read from the beginning to the end. In particular, refer to the Preface for general introductory remarks.
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 seeking knowledge for knowledge’s sake.
You can also order a paper copy.
Any bug/typo reports/fixes are appreciated. Please submit them via this project’s GitHub repository. Thank you.
Copyright (C) 2022–2023 by Marek Gagolewski. Some rights reserved. This material is published 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 (*)
- 17. Lazy evaluation (**)