Sanitize Gleaning Catch Inputs

Drops (sets to NA) values that fall outside physically plausible bounds. Defensive against data-entry slips and unit/decimal-separator confusion (e.g. a single 10 kg bucket recorded as bucket_weight = 13000, or a full bucket count of 7000). Bounds are deliberately conservative: they remove only the unambiguously impossible. Stricter, taxon-aware checks belong downstream — this only protects the reshaped output from extreme outliers.

Drops (sets to NA) values that fall outside physically plausible bounds. Defensive against data-entry slips and unit/decimal-separator confusion (e.g. a single 10 kg bucket recorded as bucket_weight = 13000, or a full bucket count of 7000). Bounds are deliberately conservative: they remove only the unambiguously impossible. Stricter, taxon-aware checks belong downstream — this only protects the reshaped output from extreme outliers.

Usage

sanitize_gleaning_inputs(df)

sanitize_gleaning_inputs(df)

Arguments

df

A data frame containing any subset of the columns above.

Value

The input with out-of-range values replaced by NA.

The input with out-of-range values replaced by NA.

Details

Bounds:

• n_individuals in 0-10000 — a bucket of small shells can hold thousands; counts above this are almost certainly errors. Recorded zeros are kept (a true "none in this size class").

• unit_weight_kg in 0-100 — nominal containers are 5–50 kg.

• n_containers in 0-200 — hand gleaning rarely exceeds a few full containers; very large counts are data slips.

Columns absent from the input are left untouched.

Bounds:

• n_individuals in 0-10000 — a bucket of small shells can hold thousands; counts above this are almost certainly errors. Recorded zeros are kept (a true "none in this size class").

• unit_weight_kg in 0-100 — nominal containers are 5–50 kg.

• n_containers in 0-200 — hand gleaning rarely exceeds a few full containers; very large counts are data slips.

Columns absent from the input are left untouched.