Tabulate interviews by angler type and month

Counts the number of interviews for each angler type within each calendar month. Angler type is taken from the column set via add_interviews(angler_type = ...).

Usage

summarize_by_angler_type(design)

Arguments

design

A creel_design object with interviews attached and angler_type column set via add_interviews(angler_type = ...).

Value

A data.frame with class c("creel_summary_angler_type", "data.frame") and columns: month, angler_type, N, percent.

Details

Interview-based summary, not pressure-weighted. This function tabulates raw interview records without applying survey weighting by sampling effort or effort stratum. For pressure-weighted extrapolated estimates, use estimate_catch_rate or estimate_harvest_rate.

See also

Other "Reporting & Diagnostics": adjust_nonresponse(), check_completeness(), compare_variance(), flag_outliers(), season_summary(), standardize_species(), summarize_by_day_type(), summarize_by_method(), summarize_by_species_sought(), summarize_by_trip_length(), summarize_cws_rates(), summarize_hws_rates(), summarize_length_freq(), summarize_refusals(), summarize_successful_parties(), summarize_trips(), summary.creel_estimates(), validate_creel_data(), validate_design(), validate_incomplete_trips(), validation_report(), write_estimates()

Examples

data(example_calendar)
data(example_interviews)
d <- creel_design(example_calendar, date = date, strata = day_type)
d <- add_interviews(d, example_interviews,
  catch = catch_total, effort = hours_fished, harvest = catch_kept,
  trip_status = trip_status, angler_type = angler_type
)
#> 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%)
summarize_by_angler_type(d)
#>   month angler_type  N percent
#> 1  June        bank 13    59.1
#> 2  June        boat  9    40.9