Tabulate successful parties by angler type and species sought
Source:R/creel-summaries.R
summarize_successful_parties.RdA party is "successful" when its total catch of the species it was seeking
(species_sought) is greater than zero. That total follows the model
add_catch documents: the pair's "caught" row when it has
one, and otherwise harvested + released, because a "caught" row
is optional. Returns counts of successful and total parties for each angler
type x species sought combination.
Arguments
- design
A
creel_designobject with interviews attached (includingangler_typeandspecies_soughtcolumns) and catch data attached viaadd_catch.
Value
A data.frame with class
c("creel_summary_successful_parties", "data.frame") and columns:
angler_type, species_sought, N_successful (integer,
NA where success is not determinable), N_total (integer),
percent (numeric, 1 decimal, NA likewise).
Details
A pair that records its own "caught" row keeps it even when that row
is zero — a recorded catch of none is data, not an absence, and does not fall
back to the dispositions.
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.
Unrecorded grouping values
An interview whose angler_type or species_sought was not
recorded is reported under "Unknown", sorted last, rather than
dropped, so sum(N_total) always equals the number of interviews
attached to the design.
The two are not equivalent. A party is successful when it caught some of the
species it sought, so where the sought species is unrecorded there is
nothing to compare the catch against and success is not
determinable: those rows report NA for N_successful and
percent, never 0, which would assert that the parties failed.
An unrecorded angler type leaves success perfectly determinable –
only the reporting group is unknown – so those rows carry real counts. So
does a sought species genuinely recorded as "Unknown": that is
a real answer, not a missing one, and it keeps its own counts.
See also
Other "Reporting & Diagnostics":
adjust_nonresponse(),
check_completeness(),
compare_variance(),
flag_outliers(),
season_summary(),
standardize_species(),
summarize_boat_composition(),
summarize_by_angler_type(),
summarize_by_county(),
summarize_by_day_type(),
summarize_by_method(),
summarize_by_species_sought(),
summarize_by_trip_length(),
summarize_by_zip(),
summarize_cws_rates(),
summarize_hws_rates(),
summarize_length_freq(),
summarize_refusals(),
summarize_trips(),
summary.creel_estimates(),
tidy.creel_estimates(),
validate_creel_data(),
validate_design(),
validate_incomplete_trips(),
validation_report(),
write_estimates()
Examples
data(example_calendar)
data(example_interviews)
data(example_catch)
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,
species_sought = species_sought
)
#> Warning: ! No `n_anglers` provided — assuming 1 angler per interview.
#> ℹ Pass `n_anglers = <column>` to use actual party sizes for angler-hour
#> normalization.
#> ℹ If the interviews really are one angler each, pass `n_anglers = 1` to state
#> that and silence this warning.
#> ℹ Added 22 interviews: 17 complete (77%), 5 incomplete (23%)
d <- add_catch(d, example_catch,
catch_uid = interview_id, interview_uid = interview_id,
species = species, count = count, catch_type = catch_type
)
summarize_successful_parties(d)
#> angler_type species_sought N_total N_successful percent
#> 1 bank bass 5 1 20.0
#> 2 bank panfish 3 1 33.3
#> 3 bank walleye 5 4 80.0
#> 4 boat bass 1 1 100.0
#> 5 boat panfish 2 0 0.0
#> 6 boat walleye 6 3 50.0