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

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.

Unrecorded grouping values

A length record whose value for a by column was not recorded is reported under "Unknown", sorted last, rather than dropped. Dropping it removed the record from the distribution entirely, taking its weight with it – and a binned release row carries a count rather than one fish, so six dropped rows cost eleven fish on the shipped example data. The ungrouped total was never affected, which is what kept this invisible.

"Unknown" labels the absence; nothing is imputed and it is never a category anyone recorded. sum(N) equals the number of fish the lengths frame describes, grouped or not.

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_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_refusals(), summarize_successful_parties(), 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_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
)
#> 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_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