R/process_catch-.R

In pacific-hake/hakedataUSA: Extract USA Data for Pacific Hake

#' Process NORPAC catches
#'
#' Summarize and plot NORPAC catches for the at-sea hake fishery. Fleet types
#' are assigned to create summaries by fleet and year.
#'
#' @details # Tribal catches
#' The column `CDQ_CODE` contains strings starting with an upper-case followed
#' by and the last two digits of the the CDQ code of the vessel. The code was
#' created in 1995 and is used to track tribal catches in the hake fishery. The
#' following codes exist:
#' * C51 -- Aleutian Pribilof Island Community Development Association
#' * C52 -- Bristol Bay Economic Development Corporation
#' * C53 -- Central Bering Sea Fishermen's Association
#' * C54 -- Coastal Villages Fishing Cooperative
#' * C55 -- Norton Sound Economic Development Corporation
#' * C56 -- Yukon Delta Fisheries Development Association
#' * R10 -- Research Permit - Alaska Fisheries Science Center Catch Estimation
#' * M01 -- Makah Tribe Whiting Association
#' * R11 -- Research Permit - WA Sea Grant bird/longline interaction
#' * R12 -- Research Permit - WA Sea Grant bird/longline interaction
#'
#' @details # Raw data
#' Raw data consists of sampled and unsampled hauls with the total weight
#' including bycatch weight. For sampled hauls, `EXTRAPOLATED_WEIGHT` is the
#' weight of the catch only. For unsampled hauls that have no knowledge of the
#' amount of bycatch included in the haul, `OFFICIAL_TOTAL_CATCH` must be used
#' along with a estimated bycatch rate to calculate `EXTRAPOLATED_WEIGHT`.
#' Monthly fleet-specific bycatch rates are estimated from the sampled hauls and
#' multiplied by the `OFFICIAL_TOTAL_CATCH` to get an estimate of the amount of
#' bycatch that should be subtracted from `OFFICIAL_TOTAL_CATCH` to get just the
#' weight of Pacific Hake.
#' The raw column names are as follows `colnames(ncatch)`:
#' * "HAULJOIN"
#' * "CRUISE"
#' * "PERMIT"
#' * "VESSEL"
#' * "VESSEL_TYPE"
#' * "HAUL_DATE"
#' * "HAUL"
#' * "DEPLOYMENT_DATE"
#' * "RETRIEVAL_DATE"
#' * "HRS"
#' * "CDQ_CODE"
#' * "OFFICIAL_TOTAL_CATCH"
#' * "HAUL_SAMPLED_BY"
#' * "LATDD_START"
#' * "LONDD_START"
#' * "LATDD_END"
#' * "LONDD_END"
#' * "FISHING_DEPTH_FATHOMS"
#' * "BOTTOM_DEPTH_FATHOMS"
#' * "CATCHER_BOAT_ADFG"
#' * "SPECIES"
#' * "EXTRAPOLATED_WEIGHT"
#'
#' @details # Definitions
#' 1. `OFFICIAL_TOTAL_CATCH` is measured in metric tons (mt) and includes both
#'    retained and discarded species. Thus, this measurement sums the targeted
#'    species, prohibited species, and non-allocated species.
#' 1. `EXTRAPOLATED_WEIGHT` is measured in kilograms (kg) and includes just the
#'    weight of the relevant `SPECIES` included in that row of data, e.g., if
#'    `SPECIES == 206`, then `EXTRAPOLATED_WEIGHT` is the kg of Pacific Hake in
#'    that haul.
#'
#' @details # Assumptions
#' * sampled hauls have an entry in the `SPECIES` column, i.e., hake are
#'   denoted with 206
#' * an unsampled haul is represented by one unique record because there no
#'   additional lines of catch for the bycatch species given it was not sampled
#'
#' @param ncatch A data frame with catch information extracted from the NORPAC
#'   database. The default is to read the `.Rdat` file from the disk using
#'   [get_local()]. Else, users can just pass the R object itself, which would
#'   typically be `hakedata[["ncatch"]]`.
#' @param nyears The number of years for plotting.
#'
#' @import ggplot2 grDevices
#' @export
#' @author Kelli F. Johnson
#'
#' @return Figures and csv files are saved to the disk regarding
#' catch rates and depth (fishing and bottom depths).
#'
#' In the `Catches` directory, the following csv files are saved:
#'   * depth-us-atsea-bottom.csv
#'   * depth-us-atsea-fishing.csv
#'   * us-cp-catch-by-month.csv
#'   * us-ms-catch-by-month.csv
#'   * us-cp-catch-rate-by-month.csv
#'   * us-ms-catch-rate-by-month.csv
#'
process_catch_norpac <- function(ncatch = get_local(file = "norpac_catch.Rdat"),
                                 nyears = 5,
                                 savedir = hakedata_wd()) {
  # Setup the environment
  species <- 206
  args <- list(
    width = 6.5, height = 4.5, pointsize = 10, units = "in", res = 600
  )
  oldop <- options()$warn
  options(warn = -1)
  on.exit(options(warn = oldop), add = TRUE)

  # File management
  fs::dir_create(
    path = fs::path(savedir, "Figures"),
    recurse = TRUE
  )

  outncatch <- ncatch %>%
    dplyr::mutate(
      Date = f_date(RETRIEVAL_DATE, format = "%Y-%m-%d"),
      month = f_date(RETRIEVAL_DATE, format = "%m"),
      Month = f_date(RETRIEVAL_DATE, format = "%b", factor = TRUE),
      year = f_date(RETRIEVAL_DATE, "%Y"),
      # Create catch rate in mt/hr
      crate = EXTRAPOLATED_WEIGHT / 1000 / HRS,
      FISHING_DEPTH_M = FISHING_DEPTH_FATHOMS * fathom_to_meter,
      BOTTOM_DEPTH_M = BOTTOM_DEPTH_FATHOMS * fathom_to_meter,
      vesseltype = f_vessel_type(VESSEL_TYPE),
      Sector = "DomesticAtSea",
      sampled = ifelse(is.na(HAUL_SAMPLED_BY) | HAUL_SAMPLED_BY == 0, 0, 1),
      # Unsampled hauls will have a SPECIES == NA and EXTRAPOLATED_WEIGHT == NA
      SPECIES = ifelse(sampled == 0, species, SPECIES),
      OFFICIAL_TOTAL_CATCHkg = OFFICIAL_TOTAL_CATCH * 1000,
      # ByCatch (kg)
      ByCatch = OFFICIAL_TOTAL_CATCHkg - EXTRAPOLATED_WEIGHT
    ) %>%
    dplyr::group_by(year, month, SPECIES == species, VESSEL_TYPE) %>%
    dplyr::mutate(
      bycatchrate = sum(ifelse(sampled == 1, ByCatch, 0), na.rm = TRUE) /
        sum(ifelse(sampled == 1, OFFICIAL_TOTAL_CATCHkg, 0), na.rm = TRUE),
      Catch.MT = ifelse(
        test = sampled == 0,
        yes = OFFICIAL_TOTAL_CATCHkg * (1 - bycatchrate),
        no = EXTRAPOLATED_WEIGHT
      ) / 1000,
      ByCatch = ifelse(
        test = sampled == 0,
        yes = OFFICIAL_TOTAL_CATCHkg - Catch.MT / 1000,
        no = ByCatch
      ),
      catch = round(Catch.MT, digits = 5)
    ) %>%
    dplyr::ungroup()

  # Find monthly rate for un-sampled tows and multiply times OFFICIAL_CATCH
  # to get the amount of hake in the tow based on average bycatch rate
  # bycatch rates are VESSEL_TYPE specific as of 2019 assessment
  catchout <- outncatch %>%
    dplyr::filter(SPECIES == species) %>%
    dplyr::group_by(Sector, vesseltype, month, year) %>%
    dplyr::summarize(catch = sum(catch, na.rm = TRUE)) %>%
    dplyr::ungroup()

  cp <- catchout[catchout$vesseltype == "CP", -(1:2)]
  utils::write.table(cp %>% dplyr::arrange(year, month),
    file = fs::path(savedir, "us-cp-catch-by-month.csv"),
    sep = ",", row.names = FALSE, quote = FALSE
  )

  ms <- catchout[catchout$vesseltype == "MS", -(1:2)]
  utils::write.table(
    ms %>% dplyr::arrange(year, month),
    file = fs::path(savedir, "us-ms-catch-by-month.csv"),
    sep = ",",
    row.names = FALSE,
    quote = FALSE
  )

  hcatch <- outncatch %>%
    dplyr::filter(SPECIES == species) %>%
    dplyr::mutate(vcolumn = tidyr::replace_na(CATCHER_BOAT_ADFG, var(VESSEL)))
  # Unique vessel (either catcher boat if not NA or mothership)
  keeptheseyears <- utils::tail(1:max(hcatch$year, na.rm = TRUE), nyears)

  #### Figures: depths
  hcatch <- get_confidential(
    hcatch,
    yvar = "vcolumn",
    xvar = c("year")
  )
  stopifnot(
    "Year summaries are not confidential" =
      all(
        stats::aggregate(ngroups ~ year, data = hcatch, unique)[, "ngroups"] > 2
      )
  )
  utils::write.csv(
    x = get_depth_by_year(
      hcatch,
      type = "bottom",
      yrs = keeptheseyears,
      min_depth_cutoff = 0
    ),
    file = file.path(savedir, "depth-us-atsea-bottom.csv"),
    row.names = FALSE
  )
  utils::write.csv(
    x = get_depth_by_year(
      hcatch,
      type = "gear",
      yrs = keeptheseyears,
      min_depth_cutoff = 0
    ),
    file = file.path(savedir, "depth-us-atsea-fishing.csv"),
    row.names = FALSE
  )

  hcatch[, "months"] <- droplevels(factor(hcatch$month,
    levels = 1:12,
    labels = rep(paste(seq(1, 11, by = 2), seq(2, 12, by = 2), sep = "-"),
      each = 2
    )
  ))

  # catch rate
  data_rate <- get_confidential(
    hcatch |>
      dplyr::filter(vesseltype %in% c("CP", "MS")),
    yvar = "vcolumn",
    xvar = c("year", "month", "vesseltype")
  ) |>
    dplyr::filter(ngroups > 2) |>
    tidyr::nest(gg = -"vesseltype") |>
    mutate_at(
      "gg",
      purrr::map,
      function(x) get_rate_by_month(x)
    ) |>
    dplyr::mutate(
      lower_name = tolower(vesseltype),
      name = fs::path(
        savedir,
        glue::glue("us-{lower_name}-catch-rate-by-month.csv")
      ),
      purrr::walk2(
        .x = gg,
        .y = name,
        .f = \(x, y) utils::write.csv(x = x, file = y, row.names = FALSE)
      )
    )
  return(outncatch)
}

#' Print summaries and figures for catches from PacFIN
#'
#' @template pcatch
#' @template nyears
#' @template savedir
#'
#' @return The following files are saved to the disk:
#' * us-shore-catch-by-month.csv
#' * us-research-catch-by-month.csv
#' * us-ti-catch-by-month.csv
#'
process_catch_pacfin <- function(pcatch = get_local(file = "pacfin_catch.Rdat"),
                                 nyears = 5,
                                 savedir = hakedata_wd()) {
  # FLEET XXX is in the hake assessment as shore-based catches,
  # although 1986 differs from data used
  # database  1986 3431.9436
  # assesment 1986 3465.00
  pcatch_by_month_year <- dplyr::group_by(
    pcatch,
    sector, month, year
  ) |>
    dplyr::summarise(catch = sum(MT, na.rm = TRUE)) |>
    dplyr::arrange(sector, year, month) |>
    dplyr::ungroup()

  utils::write.table(
    x = pcatch_by_month_year |>
      dplyr::filter(sector == "USshore") |>
      dplyr::select(-sector) |>
      dplyr::mutate(catch = round(catch, 5)),
    file = file.path(savedir, "us-shore-catch-by-month.csv"),
    sep = ",", quote = FALSE, row.names = FALSE
  )
  utils::write.table(
    x = pcatch_by_month_year |>
      dplyr::filter(sector == "USresearch") |>
      dplyr::select(-sector) |>
      dplyr::mutate(catch = sprintf("%.9f", catch)),
    file = file.path(savedir, "us-research-catch-by-month.csv"),
    sep = ",", quote = FALSE, row.names = FALSE
  )

  # Look at tribal catch in shoreside (already added in above)
  utils::write.table(
    x = pcatch %>%
      dplyr::filter(FLEET == "TI") %>%
      dplyr::group_by(month, year) %>%
      dplyr::summarize(catch = round(sum(MT, na.rm = TRUE), 5)) %>%
      dplyr::arrange(year, month) %>%
      dplyr::ungroup(),
    file = file.path(savedir, "us-ti-catch-by-month.csv"),
    sep = ",", quote = FALSE, row.names = FALSE
  )
}