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.
For many students around the world, educational resources are hardly affordable. Therefore, I have has decided that this book should remain an independent, non-profit, open-access project. You can read it at:
- https://deepr.gagolewski.com/ (a browser-friendly version)
- https://deepr.gagolewski.com/deepr.pdf (PDF)
You can also order a paper copy.
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 this project.
Consider citing this book as: Gagolewski M. (2024), Deep R Programming, Melbourne, DOI: 10.5281/zenodo.7490464, ISBN: 978-0-6455719-2-9, URL: https://deepr.gagolewski.com/.
Any remarks and bug fixes are appreciated. Please submit them via this repository's Issues tracker. Thank you.
Dr habil. Marek Gagolewski is currently a Senior Lecturer in Data Science/Applied AI at Deakin University in Melbourne, Australia and an Associate Professor at the Systems Research Institute of the Polish Academy of Sciences.
His research interests are related to data science, in particular: modelling complex phenomena, developing usable, general-purpose algorithms, studying their analytical properties, and finding out how people use, misuse, understand, and misunderstand methods of data analysis in research, commercial, and decision-making settings.
He's an author of 90+ publications, including journal papers in outlets such as Proceedings of the National Academy of Sciences (PNAS), Journal of Statistical Software, The R Journal, Information Fusion, International Journal of Forecasting, Statistical Modelling, Physica A: Statistical Mechanics and its Applications, Information Sciences, Knowledge-Based Systems, IEEE Transactions on Fuzzy Systems, and Journal of Informetrics.
In his "spare" time, he writes books for his students
(check out Minimalist Data Wrangling with Python)
and develops open-source software for data analysis, such as
stringi (one of the most often downloaded
R packages) and
genieclust (a fast and robust
hierarchical clustering algorithm in both Python and R).
Copyright (C) 2022–2024, 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).