Visualising Creel Survey Designs and Results

tidycreel ships three ggplot2-based plotting functions that cover the main inspection points in a creel survey workflow:

Function	When to use
`plot_design()`	Inspect stratum sample sizes and count distributions
`autoplot(schedule)`	Review the survey calendar tile-by-tile
`autoplot(estimates)`	Visualise effort or CPUE estimates with CIs

library(tidycreel)
library(ggplot2)

1 Inspect the design with `plot_design()`

Before attaching counts

Once you have built a creel_design object from a calendar, plot_design() shows the number of sampled days per stratum.

# Build a design from the bundled example calendar
data("example_counts")
cal <- unique(example_counts[, c("date", "day_type")])
design <- creel_design(cal, date = date, strata = day_type)

plot_design(design, title = "Sample sizes by stratum")

The bar chart makes it immediately clear whether weekday and weekend strata are balanced. A tall weekday bar with a short weekend bar signals that estimates for the weekend stratum will carry higher uncertainty.

After attaching counts

Once counts are attached, plot_design() switches to a jitter + crossbar display showing the raw count distribution per stratum.

design <- add_counts(design, example_counts)

plot_design(design, title = "Count distribution by stratum")

The crossbar shows the mean with a 95 % normal CI across sampled days. Large within-stratum spread (many jittered points far from the crossbar) suggests high day-to-day variability and the need for more sampling days.

2 Review the survey calendar with `autoplot()`

autoplot.creel_schedule() renders a monthly tile calendar from a creel_schedule object — the same object produced by generate_schedule().

# Generate a three-month schedule sampling 40 % of days
schedule <- generate_schedule(
  start_date    = "2024-06-01",
  end_date      = "2024-08-31",
  n_periods     = 2,
  sampling_rate = 0.4,
  seed          = 42
)

autoplot(schedule, title = "2024 Summer Creel Schedule")

Blue tiles are sampled weekdays; red tiles are sampled weekends; grey tiles are unsampled days. Month panels stack vertically so the full season is visible at a glance.

3 Visualise estimates with `autoplot()`

autoplot.creel_estimates() draws a point-and-errorbar plot from any creel_estimates object.

Ungrouped effort estimate

data("example_interviews")

design <- add_interviews(
  design,
  example_interviews,
  catch       = catch_total,
  effort      = hours_fished,
  trip_status = trip_status
)
#> ℹ No `n_anglers` provided — assuming 1 angler per interview.
#> ℹ Pass `n_anglers = <column>` to use actual party sizes for angler-hour
#>   normalization.
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)

effort <- estimate_effort(design)

autoplot(effort, title = "Total angler-effort estimate")

Grouped effort estimate

Passing by = day_type estimates effort separately for each stratum. The resulting plot maps the grouping variable to both the x-axis and point colour.

effort_by_type <- estimate_effort(design, by = day_type)

autoplot(effort_by_type, title = "Angler-effort by day type")

Weekend effort is substantially higher than weekday effort — a typical pattern in summer recreational fisheries.

CPUE estimate

cpue <- estimate_catch_rate(design)
#> ℹ Using complete trips for CPUE estimation
#>   (n=17, 77.3% of 22 interviews) [default]

autoplot(cpue, title = "Walleye CPUE (catch per angler-hour)")

Combining plots

All three functions return standard ggplot objects, so they compose naturally with + (ggplot2 operators) or side-by-side using patchwork if that package is installed.

# Requires patchwork
library(patchwork)
plot_design(design) + autoplot(effort)

Summary

Function	Input class	Returns
`plot_design(design)`	`creel_design`	bar chart (no counts) or jitter+crossbar (counts)
`autoplot(schedule)`	`creel_schedule`	monthly tile calendar
`autoplot(estimates)`	`creel_estimates`	point-and-errorbar plot

All plots accept a title = argument and return a ggplot object for further customisation with standard ggplot2 + syntax.

1 Inspect the design with plot_design()