Deep R Programming#

Deep R Programming by Marek Gagolewski 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.
Please spread the news about it by sharing the above URLs with your mates, peers, or students. Any bug/typo reports/fixes are appreciated. Please submit them via this project’s GitHub repository. Thank you.
Consider citing this book as: Gagolewski M. (2023), Deep R Programming, Zenodo, Melbourne, DOI: 10.5281/zenodo.7490464, ISBN: 978-0-6455719-2-9, URL: https://deepr.gagolewski.com/.
You can order a printed copy from Amazon: AU CA DE ES FR IT JP NL PL SE UK US. Note that I receive 0% revenue from sales (price = cost of printing + distributor fee). Let me know if you know a vendor who can deliver this book to some geographic regions more cheaply.
Make sure to check out my other open-access book, Minimalist Data Wrangling with Python [26].
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).
Start here
Deep
Deeper
Deepest
Appendix