Modern Creel Survey Analysis in R
Design, estimation, diagnostics, and reporting with tidycreel
creel survey, fisheries, design-based inference, R, tidycreel, Quarto
This book is substantially complete — all chapters are drafted and the full pipeline renders — but it remains a work in progress. Content, code examples, and tidycreel function coverage are still being refined, and some sections may change as the companion package evolves. Feedback and corrections are welcome via the issue tracker.
0.1 Overview
Modern Creel Survey Analysis in R guides readers through contemporary approaches to creel survey analysis using R. The book is designed as a companion to tidycreel, but it is broader than a package manual. Rather than documenting functions one at a time, it focuses on the practical and statistical reasoning needed to move from creel survey data to defensible estimates.
Creel surveys occupy a challenging space between field logistics, survey design, data management, statistical estimation, and management reporting. Analysts often work with data collected under real-world conditions: changing access patterns, incomplete interviews, variable trip lengths, stratified sampling designs, nonresponse, and reporting deadlines. This book treats those complications as part of the analysis, not as distractions from it.
The emphasis is on design-based reasoning, clear estimands, reproducible data structures, transparent uncertainty estimation, and interpretation that stays within the domain actually covered by the survey design. Throughout the book, estimation is framed around a simple question: what quantity are we trying to estimate, for what population, over what domain, and under what sampling design?
The book is intended for analysts, managers, students, and reviewers who need to understand how creel estimates are produced and how they should be interpreted. It is especially aimed at readers who need to connect field data to estimates of angler effort, catch, harvest, trip characteristics, catch rates, harvest rates, and related quantities.
Because this is a methods-first book, examples emphasize workflow, assumptions, diagnostics, and interpretation. Code is included where it helps clarify an analysis, but the goal is not simply to show how to run a function. The goal is to make analytical decisions visible enough that they can be checked, reproduced, revised, and explained.
tidycreel provides the computational framework for many of the workflows described here. Some tasks are currently supported directly by package functions; others require partial package support, manual analyst decisions, or future development. The book makes those distinctions explicit so readers can understand what is automated, what still requires judgment, and where the package is expected to grow.
Quarto book Design-based inference Editorial but practical Companion to tidycreel
0.2 License
This book is licensed under the Creative Commons Attribution 4.0 International License.
You are free to share and adapt the material for any purpose, provided appropriate credit is given to the author. Please cite the book as:
Chizinski, C. 2026. Modern Creel Survey Analysis in R: Design, Estimation, Diagnostics, and Reporting with tidycreel.