## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 4,
  out.width = "100%"
)

## ----load---------------------------------------------------------------------
library(tidycreel)
library(ggplot2)

## ----design-no-counts---------------------------------------------------------
# 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")

## ----design-with-counts, warning = FALSE--------------------------------------
design <- add_counts(design, example_counts)

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

## ----schedule-plot------------------------------------------------------------
# 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")

## ----effort-ungrouped, warning = FALSE----------------------------------------
data("example_interviews")

design <- add_interviews(
  design,
  example_interviews,
  catch       = catch_total,
  effort      = hours_fished,
  trip_status = trip_status
)

effort <- estimate_effort(design)

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

## ----effort-grouped, warning = FALSE------------------------------------------
effort_by_type <- estimate_effort(design, by = day_type)

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

## ----cpue, warning = FALSE----------------------------------------------------
cpue <- estimate_catch_rate(design)

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

## ----length-dist--------------------------------------------------------------
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)

autoplot(ld, theme = "creel")

## ----combine, eval = FALSE----------------------------------------------------
# # Requires patchwork
# library(patchwork)
# plot_design(design) + autoplot(effort)

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

## ----manual-theme-------------------------------------------------------------
# Access individual colors
pal <- creel_palette()
pal[["primary"]]

# 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")