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Generate a validation summary report

Source: R/validation-report.R
validation_report.Rd

Runs validate_creel_data() on counts and/or interviews, aggregates the results into a human-readable summary tibble (one row per table x check type), and optionally detects unrecognised species values via standardize_species().

Usage

validation_report(
  counts = NULL,
  interviews = NULL,
  species_col = 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.

interviews

A data frame of interview observations, or NULL.

species_col

Character scalar. If non-NULL and interviews is provided, calls standardize_species() on this column and appends a species_coverage row showing the fraction of rows successfully matched to an AFS code. Default NULL (no species check).

na_threshold

Passed to validate_creel_data(). Default 0.10.

date_range

Passed to validate_creel_data(). Default c(as.Date("1970-01-01"), as.Date("2100-12-31")).

Value

An object of class creel_validation_report - a data frame with columns:

table

Source table: "counts", "interviews", or "species".

check

Check type (e.g. "na_rate", "date_range").

n_pass

Number of columns with "pass" status.

n_warn

Number of columns with "warn" status.

n_fail

Number of columns with "fail" status.

detail

Comma-separated list of flagged columns, or "all ok".

Details

The returned object is a creel_validation_report - a data frame with a custom print method that renders a colour-coded cli summary. It can be exported with write_estimates().

See also

write_estimates()

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_creel_data(), validate_design(), validate_incomplete_trips(), write_estimates()

Examples

if (FALSE) { # \dontrun{
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", "")
)
rpt <- validation_report(counts, interviews, species_col = "species")
print(rpt)
} # }