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
`autoplot(length_dist)`	Visualise weighted length-frequency distributions
`theme_creel()` / `creel_palette()`	Apply consistent package-wide styling

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() draws point-and-errorbar plots from creel_estimates objects and histogram-style bar charts from creel_length_distribution objects.

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)")

Weighted Length Distributions

est_length_distribution() produces weighted estimates of the population length frequency. autoplot() renders this as a histogram-style bar chart.

data("example_lengths")

design <- add_lengths(
  design,
  example_lengths,
  length_uid    = interview_id,
  interview_uid = interview_id,
  species       = species,
  length        = length,
  length_type   = length_type,
  count         = count,
  release_format = "binned"
)

ld <- est_length_distribution(design, by = species, bin_width = 25)

autoplot(ld, theme = "creel")

4 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)

5 Customising Appearance with `theme_creel()`

tidycreel provides a built-in theme and color palette to ensure your plots match the package’s visual style. These are designed for clean, publication-ready output.

Using the `theme = "creel"` argument

The autoplot() methods for estimates and schedules include a theme argument. Setting this to "creel" applies theme_creel() and uses the package’s primary colors automatically.

autoplot(cpue, theme = "creel", title = "CPUE with theme = 'creel'")

Manual Customisation

You can also apply theme_creel() manually to any ggplot object, including those returned by plot_design(). The creel_palette() function provides access to the individual hex codes.

# Access individual colors
pal <- creel_palette()
pal[["primary"]]
#> [1] "#0b3b5e"

# Apply theme and colors manually
ggplot(example_counts, aes(x = day_type, y = effort_hours)) +
  geom_boxplot(fill = pal[["light"]], color = pal[["primary"]]) +
  theme_creel() +
  labs(title = "Manual Plot with tidycreel Styles")

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
`autoplot(length_dist)`	`creel_length_distribution`	histogram-style bar chart
`theme_creel()`	N/A	ggplot2 theme object
`creel_palette()`	N/A	named character vector of hex colors

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()