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Calculate key fishery metrics by landing site and month in normalized long format

get_fishery_metrics_long.Rd

Summarizes fishery data to extract main characteristics including catch rates, gear usage, species composition, CPUE and RPUE by gear type. Returns data in a fully normalized long format for maximum interoperability and analytical flexibility.

Usage

get_fishery_metrics_long(data = NULL)

Arguments

data

A dataframe containing fishery landing data with columns: submission_id, landing_date, landing_site, gear, no_of_fishers, fish_category, catch_kg, and total_catch_price

Value

A dataframe in long format with columns:

  • landing_site: Name of the landing site

  • year_month: First day of the month

  • metric_type: Type of metric (e.g., "avg_fishers_per_trip", "cpue", "rpue", "species_pct")

  • metric_value: Numeric value of the metric

  • gear_type: Type of fishing gear (for gear-specific metrics, NA for site-level metrics)

  • species: Fish species name (for species-specific metrics, NA for other metrics)

  • rank: Rank order (for ranked metrics like top species, NA for others)

Details

The function creates a fully normalized dataset where each row represents a single metric observation. This format enables:

  • Easy filtering by metric type, gear, or species

  • Flexible aggregation and comparison across dimensions

  • Database-friendly structure for storage and querying

  • Simplified visualization and statistical analysis

Examples

if (FALSE) { # \dontrun{
fishery_metrics <- get_fishery_metrics_long(data = valid_data)

# Filter for CPUE metrics only
cpue_data <- fishery_metrics %>% filter(metric_type == "cpue")

# Filter for RPUE metrics only
rpue_data <- fishery_metrics %>% filter(metric_type == "rpue")

# Get predominant gear by site
main_gear <- fishery_metrics %>% filter(metric_type == "predominant_gear")
} # }