Compute length frequency distribution from creel interview data

Computes length frequency distributions (count, percent, cumulative percent) from fish length data attached via add_lengths. Supports all fish (type = "catch"), harvested fish (type = "harvest"), and released fish (type = "release").

Usage

summarize_length_freq(design, type = "catch", by = NULL, bin_width = 1)

Arguments

design

A creel_design object with length data attached via add_lengths.

type

Character string specifying which fish to include. One of "catch" (all fish, harvest + release combined), "harvest" (kept fish only), or "release" (released fish only). Default "catch".

by

Optional tidy selector for grouping columns from design$lengths. Common choice: by = species. When NULL, returns a single overall distribution.

bin_width

Positive numeric specifying the width of each length bin in the same units as the length data (typically mm). Default 1.

Value

A data.frame with class c("creel_summary_length_freq", "data.frame") and columns: grouping columns (if any), length_bin (ordered factor), N (integer, fish count per bin), percent (numeric, percent of group total), cumulative_percent (numeric, within group). Only bins with N > 0 are returned. Percent values are rounded to 1 decimal place.

Details

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

Pre-binned release format: When length data was attached with release_format = "binned", release rows have character bin labels (e.g., "350-400") and a count column. This function parses each bin label into a numeric midpoint and expands by count before applying bin_width binning. This allows a consistent bin_width to be applied to both individual and pre-binned data.

See also

add_lengths(), summarize_cws_rates(), summarize_hws_rates()

Other "Reporting & Diagnostics": adjust_nonresponse(), check_completeness(), compare_variance(), flag_outliers(), season_summary(), standardize_species(), summarize_by_angler_type(), summarize_by_day_type(), summarize_by_method(), summarize_by_species_sought(), summarize_by_trip_length(), summarize_cws_rates(), summarize_hws_rates(), 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)
data(example_lengths)
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
)
#> 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%)
d <- add_lengths(d, example_lengths,
  length_uid = interview_id, interview_uid = interview_id,
  species = species, length = length,
  length_type = length_type, count = count,
  release_format = "binned"
)
summarize_length_freq(d, type = "harvest", by = species, bin_width = 25)
#>   species length_bin N percent cumulative_percent
#> 1    bass  [225,250) 1    25.0               25.0
#> 2    bass  [250,275) 1    25.0               50.0
#> 3    bass  [275,300) 2    50.0              100.0
#> 4 panfish  [150,175) 2   100.0              100.0
#> 5 walleye  [375,400) 2    25.0               25.0
#> 6 walleye  [400,425) 3    37.5               62.5
#> 7 walleye  [475,500) 1    12.5               75.0
#> 8 walleye  [500,525) 2    25.0              100.0
summarize_length_freq(d, type = "release", by = species)
#>   species length_bin N percent cumulative_percent
#> 1    bass  [275,276) 4    44.4               44.4
#> 2    bass  [325,326) 5    55.6              100.0
#> 3 panfish  [175,176) 6    66.7               66.7
#> 4 panfish  [225,226) 3    33.3              100.0
#> 5 walleye  [375,376) 2    40.0               40.0
#> 6 walleye  [425,426) 3    60.0              100.0
summarize_length_freq(d, type = "catch")
#>    length_bin N percent cumulative_percent
#> 1   [155,156) 1     2.7                2.7
#> 2   [162,163) 1     2.7                5.4
#> 3   [175,176) 6    16.2               21.6
#> 4   [225,226) 3     8.1               29.7
#> 5   [245,246) 1     2.7               32.4
#> 6   [268,269) 1     2.7               35.1
#> 7   [275,276) 4    10.8               45.9
#> 8   [278,279) 1     2.7               48.6
#> 9   [283,284) 1     2.7               51.3
#> 10  [325,326) 5    13.5               64.8
#> 11  [375,376) 2     5.4               70.2
#> 12  [385,386) 1     2.7               72.9
#> 13  [390,391) 1     2.7               75.6
#> 14  [401,402) 1     2.7               78.3
#> 15  [415,416) 1     2.7               81.0
#> 16  [420,421) 1     2.7               83.7
#> 17  [425,426) 3     8.1               91.8
#> 18  [488,489) 1     2.7               94.5
#> 19  [501,502) 1     2.7               97.2
#> 20  [512,513) 1     2.7               99.9