tidycreel ships three ggplot2-based plotting functions that cover the
main inspection points in a creel survey workflow:
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 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")
data("example_catch")
# Species catch is required to group a distribution BY species: the totals are
# scaled onto the reported catch, and only this table records catch per species
# (the interview-level column is the all-species total).
design <- add_catch(
design,
example_catch,
catch_uid = interview_id,
interview_uid = interview_id,
species = species,
count = count,
catch_type = catch_type
)
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)
#> Warning: ! Length totals were rescaled onto the reported catch.
#> ℹ Measured fish (weighted): 37; reported: 93 -- a factor of 2.51.
#> ℹ estimate, se and the confidence bounds describe the REPORTED catch, estimated
#> from the measured subsample. Shares (percent) are unaffected.
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 so
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")
