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Validate creel survey data frames

Source: R/validate-creel-data.R
validate_creel_data.Rd

Runs field-level schema and quality checks on counts and/or interview data frames, returning a tidy results tibble with a pass/warn/fail verdict per column check. A print method renders a colour-coded cli summary.

Usage

validate_creel_data(
  counts = NULL,
  interviews = NULL,
  na_threshold = 0.1,
  date_range = c(as.Date("1970-01-01"), as.Date("2100-12-31"))
)

Arguments

counts

A data frame of count (effort) observations, or NULL to skip.

interviews

A data frame of interview observations, or NULL to skip.

na_threshold

Numeric scalar in \([0, 1]\). Columns with an NA rate above this threshold receive a "warn" status. Default 0.10.

date_range

A length-2 Date vector giving the earliest and latest plausible dates. Default c(as.Date("1970-01-01"), as.Date("2100-12-31")).

Value

An object of class creel_data_validation - a tibble with columns:

table

Which input was checked: "counts" or "interviews".

column

Column name.

check

Short check label (e.g. "na_rate", "negative_values", "type").

status

"pass", "warn", or "fail".

detail

Human-readable detail string.

Details

Checks performed for every column:

  • Type check - column class is reported.

  • NA rate - warns if \(>\) na_threshold (default 0.10) of values are NA.

Additional checks based on detected column role:

  • Date columns - values must fall within date_range (defaults to 1970-01-01 - 2100-12-31); warns on future dates.

  • Numeric columns - warns if any value is negative (effort/count should be \(\ge 0\)).

  • Character/factor columns - warns if any value is an empty string.

See also

validate_creel_schedule() for schedule-specific validation.

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_length_freq(), summarize_refusals(), summarize_successful_parties(), summarize_trips(), summary.creel_estimates(), validate_design(), validate_incomplete_trips(), validation_report(), write_estimates()

Examples

counts <- data.frame(
  date     = as.Date(c("2024-06-01", "2024-06-02")),
  day_type = c("weekday", "weekend"),
  count    = c(10L, NA_integer_)
)
interviews <- data.frame(
  date      = as.Date(c("2024-06-01", "2024-06-02")),
  fish_kept = c(2L, -1L),
  species   = c("walleye", "")
)
res <- validate_creel_data(counts, interviews)
print(res)
#> 
#> ── Creel Data Validation ───────────────────────────────────────────────────────
#> 15 pass | 3 warn | 0 fail
#> 
#> 
#> ── Table: counts ──
#> 
#> ✔ date
#> ✔ type: class: Date
#> ✔ na_rate: 0 / 2 NA (0%)
#> ✔ date_range: all within 1970-01-01 - 2100-12-31
#> ✔ day_type
#> ✔ type: class: character
#> ✔ na_rate: 0 / 2 NA (0%)
#> ✔ empty_strings: none
#> ! count
#> ✔ type: class: integer
#> ⚠ na_rate: 1 / 2 NA (50%)
#> ✔ negative_values: none
#> 
#> 
#> ── Table: interviews ──
#> 
#> ✔ date
#> ✔ type: class: Date
#> ✔ na_rate: 0 / 2 NA (0%)
#> ✔ date_range: all within 1970-01-01 - 2100-12-31
#> ! fish_kept
#> ✔ type: class: integer
#> ✔ na_rate: 0 / 2 NA (0%)
#> ⚠ negative_values: 1 negative value(s)
#> ! species
#> ✔ type: class: character
#> ✔ na_rate: 0 / 2 NA (0%)
#> ⚠ empty_strings: 1 empty string(s)
#>