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data.R
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# Initial formatting of the data
library(icesTAF)
library(icesFO)
library(dplyr)
mkdir("data")
# load species list
species_list <- read.taf("bootstrap/data/FAO_ASFIS_species/species_list.csv")
sid <- read.taf("bootstrap/data/ICES_StockInformation/sid.csv")
# 1: ICES official catch statistics
hist <- read.taf("bootstrap/data/ICES_nominal_catches/ICES_historical_catches.csv")
official <- read.taf("bootstrap/data/ICES_nominal_catches/ICES_2006_2019_catches.csv")
prelim <- read.taf("bootstrap/data/ICES_nominal_catches/ICES_preliminary_catches.csv")
catch_dat <-
format_catches(2022, "Bay of Biscay and the Iberian Coast",
hist, official, NULL, species_list, sid)
write.taf(catch_dat, dir = "data", quote = TRUE)
# 2: SAG
sag_sum <- read.taf("bootstrap/data/SAG_data/SAG_summary.csv")
sag_refpts <- read.taf("bootstrap/data/SAG_data/SAG_refpts.csv")
sag_status <- read.taf("bootstrap/data/SAG_data/SAG_status.csv")
#DGS has a custom ref point for F
sag_complete$FMSY[which(sag_complete$FishStock == "dgs.27.nea")] <- 0.0429543
sag_complete$MSYBtrigger[which(sag_complete$FishStock == "dgs.27.nea")] <- 336796
sag_complete$StockSize[which(sag_complete$FishStock == "dgs.27.nea")] <- sag_complete$TBiomass[which(sag_complete$FishStock == "dgs.27.nea")]
clean_sag <- format_sag(sag_complete, sid)
clean_sag <- unique(clean_sag)
clean_status <- format_sag_status(status, 2022, "Biscay")
write.taf(clean_sag, dir = "data", quote = TRUE)
write.taf(clean_status, dir = "data", quote = TRUE)
# 3: STECF effort and landings
# effort <- read.taf("bootstrap/initial/data/FDI effort by country.csv", check.names = TRUE)
# names(effort)
# effort$Sub.region <- tolower(effort$Sub.region)
# unique(effort$Sub.region)
# effort_BI <- dplyr::filter(effort, grepl("27.8.a|27.8.b|27.8.c|27.8.d|27.8.e|
# 27.9.a|27.9.b", Sub.region))
landings1 <- read.taf("bootstrap/initial/data/Landings_2014.csv", check.names = TRUE)
landings2 <- read.taf("bootstrap/initial/data/Landings_2015.csv", check.names = TRUE)
landings3 <- read.taf("bootstrap/initial/data/Landings_2016.csv", check.names = TRUE)
landings4 <- read.taf("bootstrap/initial/data/Landings_2017.csv", check.names = TRUE)
landings5 <- read.taf("bootstrap/initial/data/Landings_2018.csv", check.names = TRUE)
landings6 <- read.taf("bootstrap/initial/data/Landings_2019.csv", check.names = TRUE)
landings7 <- read.taf("bootstrap/initial/data/Landings_2020.csv", check.names = TRUE)
landings <- rbind(landings1, landings2, landings3, landings4, landings5, landings6, landings7)
names(landings)
landings$Sub.region <- tolower(landings$Sub.region)
landings_BI <- dplyr::filter(landings, grepl("27.8.a|27.8.b|27.8.c|27.8.d|27.8.e|
27.9.a|27.9.b", Sub.region))
# need to group gears, Sarah help.
unique(landings_BI$Gear.Type)
unique(effort_BI$Gear.Type)
landings_BI <- dplyr::mutate(landings_BI, gear_class = case_when(
grepl("TBB", Gear.Type) ~ "Beam trawl",
grepl("DRB|DRH|HMD", Gear.Type) ~ "Dredge",
grepl("GNS|GND|GTN|LHP|LLS|FPN|GTR|FYK|LLD|SDN|LTL|LNB", Gear.Type) ~ "Static/Gill net/LL",
grepl("OTT|OTB|PTB|SSC|SB|SPR|SV", Gear.Type) ~ "Otter trawl/seine",
grepl("PTM|OTM|PS", Gear.Type) ~ "Pelagic trawl/seine",
grepl("FPO", Gear.Type) ~ "Pots",
grepl("NK|NO|LHM", Gear.Type) ~ "other",
is.na(Gear.Type) ~ "other",
TRUE ~ "other"
)
)
effort_BI <- dplyr::mutate(effort_BI, gear_class = case_when(
grepl("TBB", Gear.Type) ~ "Beam trawl",
grepl("DRB|DRH|HMD", Gear.Type) ~ "Dredge",
grepl("GNS|GND|GTN|LHP|LLS|FPN|GTR|FYK|LLD|SDN|LTL|LNB", Gear.Type) ~ "Static/Gill net/LL",
grepl("OTT|OTB|PTB|SSC|SB|SPR|SV", Gear.Type) ~ "Otter trawl/seine",
grepl("PTM|OTM|PS", Gear.Type) ~ "Pelagic trawl/seine",
grepl("FPO", Gear.Type) ~ "Pots",
grepl("NK|NO|LHM", Gear.Type) ~ "other",
is.na(Gear.Type) ~ "other",
TRUE ~ "other"
)
)
#Check there are no NAs
unique(landings_BI[c("Gear.Type", "gear_class")])
unique(effort_BI[c("Gear.Type", "gear_class")])