NEFSC Northwest Atlantic Spring and Fall

# Get rid of memory limits -----------------------------------------------------
options(future.globals.maxSize = 1 * 1024^4) # Allow up to 1 TB for globals

# Install Libraries ------------------------------------------------------------

# Here we list all the packages we will need for this whole process
# We'll also use this in our works cited page.
PKG <- c(
  "surveyresamplr",
  "dplyr",
  "tidyr",
  "purrr",
  "ggplot2",
  "future.callr",
  "flextable", 
  "sdmTMB"
)

pkg_install <- function(p) {
  if (!require(p, character.only = TRUE)) {
    install.packages(p)
  }
  require(p, character.only = TRUE)
}
base::lapply(unique(PKG), pkg_install)

Run scenarios

### Define study species -------------------------------------------------------

spp_list <-
  data.frame(
    srvy = "NWA_SPRING",
    common_name = c(
      "Atlantic herring", "black sea bass", "Atlantic cod",
      "American lobster", "longfin squid", "mackerel",
      "monkfish", "red hake", "scup",
      "sea scallop", "silver hake", "summer flounder",
      "winter flounder"
    ),
    file_name0 = c(
      "AtlanticHerring", "BlackSeaBass", "Cod",
      "Lobster", "LongfinSquid", "Mackerel",
      "Monkfish", "RedHake", "Scup",
      "SeaScallop", "SilverHake", "SummerFlounder",
      "WinterFlounder"
    ),
    filter_lat_lt = NA,
    filter_lat_gt = NA,
    filter_depth = NA,
    model_fn = "total_catch_wt_kg ~ 0 + factor(year)",
    model_family = "delta_gamma",
    model_anisotropy = TRUE,
    model_spatiotemporal = "iid, iid"
  ) |>
  dplyr::mutate(
    file_name = gsub(pattern = " ", replacement = "_", x = (tolower(common_name)))
  )

spp_list <- spp_list[1:3, ]

### Load grid data -------------------------------------------------------------

grid_yrs <- replicate_df(surveyresamplr::noaa_nefsc_pred_grid_depth, "year", unique(catch$year))

### Variables ------------------------------------------------------------------

seq_from <- 0.2
seq_to <- 1.0
seq_by <- 0.2
tot_dataframes <- 13
replicate_num <- 3

Spring

# Set directories --------------------------------------------------------------

wd <- paste0(here::here(), "/vignettes/")
dir_out <- paste0(wd, "output/")
dir_final <- paste0(dir_out, "NWA_SPRING_0results/")

### Load survey data -----------------------------------------------------------

catch <- surveyresamplr::noaa_nefsc_catch |> dplyr::filter(srvy == "FALL")

### Variables ------------------------------------------------------------------

srvy <- "NWA_SPRING"

### Run ------------------------------------------------------------------------

purrr::map(
  1:nrow(spp_list),
  ~ clean_and_resample(spp_list[.x, ],
    catch, seq_from, seq_to, seq_by,
    tot_dataframes, replicate_num, grid_yrs, dir_out,
    test = TRUE
  )
)

### Plot indices --------------------------------------------------------------

out <- plot_results(srvy = srvy, dir_out = dir_out, dir_final = dir_final)

out$plots

Parameter output:

i <- 1
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

i <- 1 + i
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

i <- 1 + i
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

i <- 1 + i
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

Fall

# Set directories --------------------------------------------------------------

wd <- paste0(here::here(), "/vignettes/")
dir_out <- paste0(wd, "output/")
dir_final <- paste0(dir_out, "NWA_FALL_0results/")

### Load survey data -----------------------------------------------------------

catch <- surveyresamplr::noaa_nefsc_catch %>% dplyr::filter(srvy == "FALL") # pulled data again to get 2024

### Variables ------------------------------------------------------------------

srvy <- "NWA_FALL"

### Run ------------------------------------------------------------------------

purrr::map(
  1:nrow(spp_list),
  ~ clean_and_resample(spp_list[.x, ],
    catch, seq_from, seq_to, seq_by,
    tot_dataframes, replicate_num, grid_yrs, dir_out,
    test = TRUE
  )
)

### Plot indices --------------------------------------------------------------

out <- plot_results(srvy = srvy, dir_out = dir_out, dir_final = dir_final)

out$plots

Parameter output:

i <- 1
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

i <- 1 + i
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

i <- 1 + i
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()

i <- 1 + i
print(names(out$tables)[i])
head(out$tables[i][[1]]) %>%
  flextable::flextable()