Modern Data Analytics for Forestry and Natural Resources
Efficient Coding, Data Analysis, and AI-Assisted Debugging
Preface
This open textbook teaches data analysis for forestry and natural resources using free, modern, openly licensed tools: Microsoft Excel (Chapters 1–3), R with the tidyverse (Chapters 4–9), and Quarto for reproducible reporting, an integrated case study, and presentations. Spatial analysis with the sf and leaflet packages is available as an optional extension. Every dataset used is openly licensed Canadian, BC, or Ontario government data. The book is designed to bridge from Excel to R for learners who have never written code before, while preparing them for advanced study in statistical modelling, GIS, and reproducible research.
It is the primary (free) text for FRST 232 — Computer Applications in Forestry and a pre-learning and refresher resource for FRST 531. The front-matter pages that follow — land acknowledgment, Indigenous data sovereignty, the accessibility statement, the EDI commitment, and How to use this book — are part of the resource, not boilerplate; please read them before the chapters.
See the Authors and Acknowledgements pages for the full project team and funding acknowledgement, and the Datasets Used in This Book page for every dataset and its download link.
Errata and contributing
This book is openly licensed. We welcome:
- Bug reports — open an issue on the GitHub repository.
- Accessibility concerns — see the Accessibility statement; we treat these as high priority.
- EDI feedback — see the EDI commitment; we welcome correction.
- Pull requests with corrections, improvements, or additions — under the same CC BY 4.0 (content) / MIT (code) licences.
The canonical version lives at https://github.com/subornaa/modern-computing-forestry.
Licence
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material for any purpose, even commercially), under the following terms: Attribution — you must give appropriate credit, provide a link to the license, and indicate if changes were made.
The code (R scripts, Quarto sources, JavaScript in the quiz files) is additionally licensed under the MIT licence.