Introduction to Forestry Data Analysis with R
2025-11-05
Preface
The role of the forester and natural resource professional has expanded. Data, analysis, and interpretation are now more central to our work than ever before, and today’s practitioners are expected to move confidently between field measurements and analytical workflows. With unprecedented volumes of information on forested systems produced through inventories, sensor networks, remote sensing platforms, and long-term monitoring programs, practitioners are increasingly called upon to act as data analysts—wrangling, visualizing, and defending quantitative results to support sound management decisions.
This book is for readers on two paths: students beginning their analytical training and practitioners who want to modernize and streamline their workflows. Our goal is to build proficiency in R and the tidyverse ecosystem, and to connect familiar estimators and inventory principles with clear, repeatable, and reproducible data analysis pipelines. Whether readers are new to these tools or seeking more efficient ways to implement classical methods, the emphasis is on transparent workflows that adapt to common estimation settings.
Chapters 1 through 9 form an introduction to practical computing with R. We start from base R—objects, vectors, data frames, indexing, functions, and I/O—and build toward pipe-based workflows. Along the way, we develop data wrangling with dplyr (verbs and joins), reshaping with tidyr, iteration, small reusable functions, visualization with ggplot2, and reproducible project habits that scale from a single stand to large inventories. The aim is pragmatic: readable code, consistent structure, and workflows that make real forest data easier to organize, check, and explain.
Chapters 10 through 13 turn to forest inventory and estimation. Here we build directly on the computing habits established earlier and apply well-established statistical tools—simple random sampling, systematic designs, stratification, cluster and multistage sampling, and ratio and regression estimators. The mathematical foundations of these methods were developed by generations of forest biometricians and applied statisticians whose work continues to guide the profession. Our contribution is to bring these classical ideas forward through clear illustrations, transparent calculation paths, and reproducible workflows that help both students and practitioners apply enduring principles to modern datasets and meet contemporary analytical demands.
Our hope is that the material in this book helps readers develop an adaptable codebase, a clear workflow mindset, and the confidence to think with data—as analysts, decision-makers, and stewards of forested landscapes.
Acknowledgments
During the long process of writing this book, we have benefited greatly from the generous review, technical comments, contributed material, and encouragement of Anthony D’Amato, Ken Desmarais, Mike Eckley, Lutz Fehrmann, Ed Green, Kim Iles, Malcolm Itter, Annika Kangas, Braeden Klaty, David Orwig, Elliot Shannon, and Aaron Weiskittel.