tidycreel

Tidy Interface for Creel Survey Design, Estimation, and Reporting

tidycreel provides a pipe-friendly interface for creel survey design, data management, estimation, visualisation, and reporting. Built on the survey package for design-based inference, it lets fisheries biologists work in creel vocabulary — dates, strata, counts, effort, catch, lengths, schedules — without managing survey-package internals directly.

Installation

Install the development version from GitHub:

# install.packages("pak")
pak::pak("chrischizinski/tidycreel")

# or with devtools
devtools::install_github("chrischizinski/tidycreel")

Survey Types

Instantaneous Count

Stratified effort estimation from periodic angler counts.

Getting Started →

Bus-Route

PPS site selection with Horvitz-Thompson estimators and enumeration expansion.

Bus-Route vignette →

Ice Fishing

Degenerate bus-route design with certainty site sampling.

Ice Fishing vignette →

Camera-Monitored

Counter and ingress-egress preprocessing plus camera effort indexing.

Camera Survey vignette →

Aerial Survey

Single-overflight effort estimation with calibrated open-hours scaling.

Aerial vignette → | GLMM variant →

Key Capabilities

• Design-based inference — wraps the survey package; biologists write creel vocabulary, not survey-package internals.
• Single design entry point — creel_design() dispatches to the correct workflow for each survey type.
• Core estimation surface — effort, catch rate, harvest rate, release rate, and total-catch estimators share a common API.
• Extended estimation methods — includes camera effort indexing, weighted length distributions, and variance comparison helpers.
• Validation and cleaning — field-level validation, species standardisation, data-validation summaries, and toy datasets for examples.
• Planning and QA — schedule generation, count-window planning, completeness checks, sample-size tools, and power calculators.
• Visualisation and reporting — autoplot() methods, theme_creel(), creel_palette(), and a flexdashboard report template scaffold.
• Documentation and onboarding — glossary, workflow vignettes, statistical-method articles, and a pkgdown site.

Quick Start

Instantaneous Count Survey

library(tidycreel)

# 1. Define survey structure with tidy selectors
design <- creel_design(example_calendar, date = date, strata = day_type)

# 2. Attach count observations
design <- add_counts(design, example_counts)

# 3. Estimate effort with design-based variance
estimate_effort(design)

Bus-Route Survey

# Probability-proportional-to-size site selection
design <- creel_design(
  example_calendar,
  date = date,
  strata = day_type,
  survey_type = "bus_route",
  sampling_frame = my_site_frame,
  site = site_id
)

design <- add_interviews(design, interview_data,
  catch = catch_total,
  effort = hours_fished,
  harvest = catch_kept
)

estimate_catch_rate(design)

Where to Start

If you want to…	Start here
Learn the package vocabulary	Glossary
See the main end-to-end workflow	Getting Started
Plan a season before sampling starts	Survey Design Toolbox
Understand plotting and output styling	Visualisation and theme_creel()
Build a report/dashboard	Install the package, then open R Markdown > From Template > Creel Dashboard in RStudio

Functions at a Glance

Survey Design

• creel_design() — single entry point; dispatches on survey_type.
• add_counts(), add_interviews(), add_catch(), add_lengths(), add_sections() — attach observation data.

Estimation

• estimate_effort() — total effort with Taylor linearization, bootstrap, or jackknife variance.
• estimate_catch_rate(), estimate_harvest_rate(), estimate_release_rate() — ratio-based interview estimators.
• estimate_total_catch(), estimate_total_harvest(), estimate_total_release() — totals via delta-method propagation.
• est_effort_camera(), est_length_distribution() — camera effort indexing and weighted size-structure estimation.

Validation and Diagnostics

• validate_creel_data(), validation_report(), standardize_species() — clean and validate real-world creel inputs.
• validate_incomplete_trips(), validate_design(), check_completeness(), compare_variance(), adjust_nonresponse() — QA and estimator diagnostics.

Planning and Reporting

• generate_schedule(), generate_bus_schedule(), generate_count_times(), attach_count_times() — planning/scheduling helpers.
• creel_n_effort(), creel_n_cpue(), creel_power(), cv_from_n() — sample-size and power tools.
• season_summary(), summary.creel_estimates(), write_estimates() — report-ready summary/export helpers.
• autoplot.creel_estimates(), autoplot.creel_schedule(), autoplot.creel_length_distribution(), theme_creel(), creel_palette() — visualisation helpers.

Vignettes

Get Started

Vignette	Description
Getting Started	Core workflow: design → counts → effort estimation
Glossary	Plain-language guide to tidycreel terms and concepts

Survey Types

Vignette	Description
Bus-Route Surveys	PPS site selection with Horvitz-Thompson estimators
Ice Fishing	Certainty-site (degenerate bus-route) design
Camera Surveys	Counter and ingress-egress preprocessing, camera effort
Aerial Surveys	Single-overflight effort with calibrated open-hours scaling
Aerial GLMM	Negative-binomial GLMM aerial effort (Askey 2018)

Reporting & Planning

Vignette	Description
Survey Design Toolbox	Sample-size, power, scheduling, and pre-season planning tools
Survey Scheduling	Count windows, schedules, validation, and completeness checks
Visualisation	Plotting patterns and output styling with theme_creel()
Interview Estimation	CPUE, catch, and harvest from interview data
Incomplete Trips	When and how to use mean-of-ratios and TOST validation
Replicate Designs	Variance workflows and replicate-design reasoning

License

MIT License — see LICENSE.md for details.