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10_subset_of_analysis_for_qc.R
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#'------------------------------------------------------------------------------
#
# Script to extract and process VMS and logbook data for ICES VMS data call
# 2: Linking TACSAT and EFLALO data ----
#
#'------------------------------------------------------------------------------
library(dplyr)
library(vmstools)
setwd("C:/Users/MD09/OneDrive - CEFAS/projects/datacalls/ices/2024")
source("global-subset.R")
yearsToSubmit = 2009:2023
year = 2023
# Looping through the years to submit
for(year in yearsToSubmit){
tacsat_raw <-
load(
file.path(
dataPath,
paste0("tacsat-", year, ".RData")
)); #- data is saved as tacsat_2009, tacsat_2010 etc
eflalo_raw <-
load(
file.path(
dataPath,
paste0("eflalo-", year, ".RData") ) )
print(paste0("Start loop for year ", year))
#'----------------------------------------------------------------------------
# 2.1.0 load TACSAT and EFLALO data from file ----
#'----------------------------------------------------------------------------
# load(file = paste0(outPath, paste0("/cleanEflalo",year,".RData")) )
# Save 'tacsatp' to a file named "tacsatMerged<year>.RData" in the 'outPath' directory
load(
file = file.path(outPath, paste0("tacsatMerged", year, ".RData"))
)
tacsatprects = tacsatp
tacsatprects$LE_RECT_VMS = ICESrectangle(dF = tacsatp |> as.data.frame())
tacsat_ICES_rect_coincidence = tacsatprects |> filter ( LE_RECT == LE_RECT_VMS) |> dim ( )
#VMS RECT == LOGBOOK RECT : 1046574
tacsat_ICES_rect_not_coincidence = tacsatprects |> filter ( LE_RECT != LE_RECT_VMS) |> dim ( )
#VMS RECT != LOGBOOK RECT : 645448
print(paste0("finish QC1 for", year))
#####################################################################
### Identify Fishing Activity - TACSAT/VMS records as fishing #####
#####################################################################
# start by correctly formatting the level 5 metier
tacsatp$LE_L5MET <- sapply(strsplit(tacsatp$LE_MET, "_"), function(x) paste(x[1:2], collapse = "_"))
## Join the TACSAT data with the speed array ranges to identify fishing VMS records ('f') and not fishing records ( steaming, 's')
join_q = join_by(LE_L5MET, between ( SI_SP, min, max) )
tacsatp = tacsatp |> left_join( fishing_speed_met5_array , by = join_q )
tacsatp = tacsatp |> mutate ( SI_STATE = ifelse ( is.na (min) & is.na (max) , 's', 'f')) |> dplyr::select( -colnames (fishing_speed_met5_array ))
#-------------------------------------------------------------------------------------------------
## QC 2: Get statistics on the number of VMS records fishing /no fishing
#-------------------------------------------------------------------------------------------------
tacsatp_fishing_steaming = tacsatp |> group_by(SI_STATE ) |> summarise ( n = n())
save(
tacsatp_fishing_steaming,
file = file.path(outPath, paste0("qc2_tacsatp_fishing_steaming_", year, ".RData"))
)
print(paste0("finish QC2 for", year))
##############################################################################
#### QC 1.1 Get teh ICES rectangles coincidence after select only fishing ##############
tacsatp_fishing = tacsatp |> filter( SI_STATE == 'f' )
tacsatp_fishing$LE_RECT_VMS = ICESrectangle(dF = tacsatp_fishing |> as.data.frame())
tacsat_f_ICES_rect_coincidence = tacsatp_fishing |> filter ( LE_RECT == LE_RECT_VMS) |> dim ( )
#VMS RECT == LOGBOOK RECT : 1046574
tacsat_f_ICES_rect_not_coincidence = tacsatp_fishing |> filter ( LE_RECT != LE_RECT_VMS) |> dim ( )
tacsat_eflalo_ICES_R_report = data.frame(ref = c ("VMS records with LB coincidence ICES R.",
"VMS records with LB not coincidence ICES R.",
"VMS Fishing records with LB coincidence ICES R.",
"VMS Fishing records with LB not coincidence ICES R."),
records = c( tacsat_ICES_rect_coincidence[1],
tacsat_ICES_rect_not_coincidence[1],
tacsat_f_ICES_rect_coincidence[1],
tacsat_f_ICES_rect_not_coincidence[1])) |>
add_row(ref = "propotion of coincidence / total" ,
records = round (( tacsat_ICES_rect_coincidence[1] / ( tacsat_ICES_rect_not_coincidence[1] + tacsat_ICES_rect_coincidence[1]) ), 2) * 100 ) |>
add_row(ref = "propotion of fishing coincidence / total" ,
records = round (( tacsat_f_ICES_rect_coincidence[1] / ( tacsat_f_ICES_rect_not_coincidence[1] + tacsat_f_ICES_rect_coincidence[1]) ), 2) * 100 )
save(
tacsat_eflalo_ICES_R_report,
file = file.path(outPath, paste0("qc1_tacsat_eflalo_ICES_R_report", year, ".RData"))
)
}
#QC2 END -------------------------------------------------------------------------------------------------------------
# Looping through the years to submit
for(year in yearsToSubmit){
print(paste0("Start loop for year ", year))
# Save 'tacsatp' to a file named "tacsatMerged<year>.RData" in the 'outPath' directory
load(
file = file.path(outPath, paste0("tacsatMerged", year, ".RData"))
)
load(
file = file.path(outPath, paste0("tacsatEflalo", year, ".RData"))
)
load(file = paste0(outPath, paste0("/cleanEflalo",year,".RData")) )
eflaloM <- subset(eflalo, FT_REF %in% unique(tacsatp$FT_REF))
#-------------------------------------------------------------------------------------------------
## QC 3: Get the summary of dispatched landing values to VMS records
#-------------------------------------------------------------------------------------------------
tot_kg_eflalo = eflalo |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloM = eflaloM |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloTacsat = tacsatEflalo |> summarise(total = sum ( LE_KG_TOT)) |> pull()
summary_landings_tacsatEflalo = data.frame ( ref = c( "total_landings_kg_eflalo","total_landings_kg_eflaloM", "total_landings_kg_tacsat_eflalo" ) ,
total = c( tot_kg_eflalo, tot_kg_eflaloM , tot_kg_eflaloTacsat ) ) |>
add_row ( ref = "eflaloM - tacsatEflalo total kg", total = tot_kg_eflaloM - tot_kg_eflaloTacsat )
save(
summary_landings_tacsatEflalo,
file = file.path(outPath, paste0("qc3_summary_landings_tacsatEflalo", year, ".RData"))
)
print(paste0("finish QC3 for", year))
#QC3 END -------------------------------------------------------------------------------------------------------------
}
##########################################################
###Analysis of the EFLALO/TACSAT with SplitAmongPings versions
##############################################################
##########################################################
### SplitAmongPings2 ICES WG 2024 #####################
##############################################################
yearsToSubmit = 2009:2023
## Load the Fishing Speed Arrays from the AD-HOC speed profile analysis
fish_array = "C:\\Users\\RM12\\OneDrive - CEFAS\\Roi\\projects\\datacalls\\ices\\march_2024\\ICES-VMS-and-Logbook-Data-Call_Cefas\\Results"
load(file = file.path(fish_array, paste0("fishing_speed_met5_array_2024.RData")) )
# Looping through the years to submit
for(year in yearsToSubmit){
print(year)
#####################################################################
### Identify Fishing Activity - TACSAT/VMS records as fishing #####
#####################################################################
load(file = paste0(outPath, paste0("/cleanEflalo",year,".RData")) )
load(file = file.path(outPath, paste0("tacsatMerged", year, ".RData") ) )
# start by correctly formatting the level 5 metier
tacsatp$LE_L5MET <- sapply(strsplit(tacsatp$LE_MET, "_"), function(x) paste(x[1:2], collapse = "_"))
## Join the TACSAT data with the speed array ranges to identify fishing VMS records ('f') and not fishing records ( steaming, 's')
join_q = join_by(LE_L5MET, between ( SI_SP, min, max) )
tacsatp = tacsatp |> left_join( fishing_speed_met5_array , by = join_q )
tacsatp = tacsatp |> mutate ( SI_STATE = ifelse ( is.na (min) & is.na (max) , 's', 'f')) |> select( -colnames (fishing_speed_met5_array ))
# 2.2 Dispatch landings of merged eflalo at the ping scale
# ----------------------------------------------------------------------------
# Split eflalo into two data frames based on the presence of FT_REF in tacsatp
eflaloNM <- subset(eflalo, !FT_REF %in% unique(tacsatp$FT_REF))
eflaloM <- subset(eflalo, FT_REF %in% unique(tacsatp$FT_REF))
message(sprintf("%.2f%% of the eflalo data not in tacsat\n", (nrow(eflaloNM) / (nrow(eflaloNM) ))))
# Convert SI_STATE to binary (0/1) format
tacsatp$SI_STATE <- ifelse(tacsatp$SI_STATE == "f", 1, 0)
# Filter rows where SI_STATE is 1
# tacsatEflalo <- tacsatp[tacsatp$SI_STATE == 1,]
tacsatp <- tacsatp[tacsatp$SI_STATE == 1,]
tacsatp <- tacsatp[!is.na(tacsatp$INTV),]
## Remove the LE_RECT column in tacsatp that was assigned form logbook
tacsatp$LE_RECT = NULL
#Set catch date to be equal to SI_DATE
## THIS IS ONLY A REQUIREMENT FOR RUN SPLITAMONGPINGS2
tacsatp <- as.data.frame(tacsatp)
# Distribute landings among pings, first by day, metier and trip; then by metier and trip; then by trip
tacsatEflalo <- splitAmongPings2(tacsatp, eflaloM) # was originally splitAmongPings2(tacsatp, eflalo)
tacsatEflalo_res = tacsatEflalo[[1]]
save(
tacsatEflalo_res,
file = file.path(outPath, paste0("tacsatEflalo", year, ".RData"))
)
if ( year == yearsToSubmit[1]) {
stats_all = tacsatEflalo[[2]]
stats_all = stats_all |> mutate ( year_a = year )
} else {
stats_year = tacsatEflalo[[2]] |> mutate ( year_a = year )
print( stats_year)
stats_all = rbind( stats_all , stats_year )
}
#-------------------------------------------------------------------------------------------------
## QC 3: Get the summary of dispatched landing values to VMS records
#-------------------------------------------------------------------------------------------------
tot_kg_eflalo = eflalo |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloM = eflaloM |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloTacsat = tacsatEflalo_res |> summarise(total = sum ( LE_KG_TOT)) |> pull()
summary_landings_tacsatEflalo = data.frame ( ref = c( "total_landings_kg_eflalo","total_landings_kg_eflaloM", "total_landings_kg_tacsat_eflalo" ) ,
total = c( tot_kg_eflalo, tot_kg_eflaloM , tot_kg_eflaloTacsat ) ) |>
add_row ( ref = "eflaloM - tacsatEflalo total kg", total = tot_kg_eflaloM - tot_kg_eflaloTacsat )
save(
summary_landings_tacsatEflalo,
file = file.path(outPath, paste0("qc3_summary_landings_tacsatEflalo", year, ".RData"))
)
}
save(
stats_all,
file = file.path(outPath, paste0("tacsatEflalo_stats_sap2_all_years.RData"))
)
##########################################################
### SplitAmongPings 0.77 ##############################
##############################################################
yearsToSubmit = 2009:2023
## Load the Fishing Speed Arrays from the AD-HOC speed profile analysis
fish_array = "C:\\Users\\RM12\\OneDrive - CEFAS\\Roi\\projects\\datacalls\\ices\\march_2024\\ICES-VMS-and-Logbook-Data-Call_Cefas\\Results"
load(file = file.path(fish_array, paste0("fishing_speed_met5_array_2024.RData")) )
# Looping through the years to submit
for(year in yearsToSubmit){
#year = 2023
print(year)
#####################################################################
### Identify Fishing Activity - TACSAT/VMS records as fishing #####
#####################################################################
load(file = paste0(outPath, paste0("/cleanEflalo",year,".RData")) )
load(file = file.path(outPath, paste0("tacsatMerged", year, ".RData") ) )
# start by correctly formatting the level 5 metier
tacsatp$LE_L5MET <- sapply(strsplit(tacsatp$LE_MET, "_"), function(x) paste(x[1:2], collapse = "_"))
## Join the TACSAT data with the speed array ranges to identify fishing VMS records ('f') and not fishing records ( steaming, 's')
join_q = join_by(LE_L5MET, between ( SI_SP, min, max) )
tacsatp = tacsatp |> left_join( fishing_speed_met5_array , by = join_q )
tacsatp = tacsatp |> mutate ( SI_STATE = ifelse ( is.na (min) & is.na (max) , 's', 'f')) |> dplyr::select( -colnames (fishing_speed_met5_array ))
# 2.2 Dispatch landings of merged eflalo at the ping scale
# ----------------------------------------------------------------------------
# Split eflalo into two data frames based on the presence of FT_REF in tacsatp
eflaloNM <- subset(eflalo, !FT_REF %in% unique(tacsatp$FT_REF))
eflaloM <- subset(eflalo, FT_REF %in% unique(tacsatp$FT_REF))
message(sprintf("%.2f%% of the eflalo data not in tacsat\n", (nrow(eflaloNM) / (nrow(eflaloNM) ))))
# Filter rows where SI_STATE is 1
# tacsatEflalo <- tacsatp[tacsatp$SI_STATE == 1,]
# Convert SI_STATE to binary (0/1) format
tacsatp$SI_STATE <- ifelse(tacsatp$SI_STATE == "f", 1, 0)
tacsatp <- tacsatp[tacsatp$SI_STATE == 1,]
tacsatp <- tacsatp[!is.na(tacsatp$INTV),]
## Remove the LE_RECT column in tacsatp that was assigned form logbook
tacsatp$LE_RECT = NULL
#Set catch date to be equal to SI_DATE
## THIS IS ONLY A REQUIREMENT FOR RUN SPLITAMONGPINGS2
tacsatp <- as.data.frame(tacsatp)
tacsatEflalo_0p77 <-
splitAmongPings_0p77(
tacsat = tacsatp,
eflalo = eflalo,
variable = "all",
level = c("day","ICESrectangle", "trip"),
conserve = TRUE,
conserve_ML2 = TRUE,
by = "INTV"
)
tacsatEflalo = tacsatEflalo_0p77[[1]]
stats_splitamongpings = tacsatEflalo_0p77[[2]] |> mutate(year = year )
stats_vms_splitamongpings = tacsatEflalo_0p77[[3]] |> mutate(year = year )
save(
tacsatEflalo ,
file = file.path(outPath, paste0("tacsatEflalo_0p77_", year, ".RData"))
)
print( paste0("Saved the result of SplitAmongPings_", year))
save(
stats_splitamongpings ,
file = file.path(outPath, paste0("tacsatEflalo_0p77_", year, "_summary_split_kg.RData"))
)
save(
stats_vms_splitamongpings ,
file = file.path(outPath, paste0("tacsatEflalo_0p77_", year, "_summary_split_vms.RData"))
)
print( paste0("Saved the stats of SplitAmongPings_", year))
#-------------------------------------------------------------------------------------------------
## QC 3: Get the summary of dispatched landing values to VMS records
#-------------------------------------------------------------------------------------------------
tot_kg_eflalo = eflalo |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloM = eflaloM |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloNM = eflaloNM |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloTacsat = tacsatEflalo |> summarise(total = sum ( LE_KG_TOT, na.rm = T)) |> pull()
summary_landings_tacsatEflalo = data.frame ( ref = c( "total_landings_kg_eflalo","total_landings_kg_eflaloM","total_landings_kg_eflaloNM", "total_landings_kg_tacsat_eflalo" ) ,
total = c( tot_kg_eflalo, tot_kg_eflaloM , tot_kg_eflaloNM , tot_kg_eflaloTacsat ) ) |>
add_row ( ref = "eflaloM - tacsatEflalo total kg", total = round(tot_kg_eflaloM - tot_kg_eflaloTacsat, 2 ) ) |>
add_row ( ref = "eflalo - tacsatEflalo total kg", total = round(tot_kg_eflalo - tot_kg_eflaloTacsat, 2) )
########## ADD THE
save(
summary_landings_tacsatEflalo,
file = file.path(outPath, paste0("splitAmongPings_comparison/qc3_summary_landings_tacsatEflalo_0p77_", year, ".RData"))
)
print( paste0("Saved the summary stats of tacsatEflalo_", year))
}
##########################################################
### SplitAmongPings 0.76 ############################
##############################################################
yearsToSubmit = 2009:2023
## Load the Fishing Speed Arrays from the AD-HOC speed profile analysis
fish_array = "Y:\\FISHERIES M MoU\\Working_Area\\spatial_fisheries_data\\ices_datacalls\\ices_vms_lb_datacall_2024\\data/"
load(file = file.path(fish_array, paste0("fishing_speed_met5_array_2024.RData")) )
temp_res_local = 'C:\\Users\\RM12\\OneDrive - CEFAS\\Roi\\projects\\datacalls\\ices\\march_2024\\ICES-VMS-and-Logbook-Data-Call_Cefas\\Results\\'
# Looping through the years to submit
for(year in yearsToSubmit){
print(paste ("year analysis: " ,year) )
#####################################################################
### Identify Fishing Activity - TACSAT/VMS records as fishing #####
#####################################################################
load(file = paste0(temp_res_local, paste0("cleanEflalo",year,".RData")) )
load(file = file.path(temp_res_local, paste0("tacsatMerged", year, ".RData") ) )
# start by correctly formatting the level 5 metier
tacsatp$LE_L5MET <- sapply(strsplit(tacsatp$LE_MET, "_"), function(x) paste(x[1:2], collapse = "_"))
## Join the TACSAT data with the speed array ranges to identify fishing VMS records ('f') and not fishing records ( steaming, 's')
join_q = join_by(LE_L5MET, between ( SI_SP, min, max) )
tacsatp = tacsatp |> left_join( fishing_speed_met5_array , by = join_q )
tacsatp = tacsatp |> mutate ( SI_STATE = ifelse ( is.na (min) & is.na (max) , 's', 'f')) |> select( -colnames (fishing_speed_met5_array ))
# 2.2 Dispatch landings of merged eflalo at the ping scale
# ----------------------------------------------------------------------------
# Split eflalo into two data frames based on the presence of FT_REF in tacsatp
eflaloNM <- subset(eflalo, !FT_REF %in% unique(tacsatp$FT_REF))
eflaloM <- subset(eflalo, FT_REF %in% unique(tacsatp$FT_REF))
message(sprintf("%.2f%% of the eflalo data not in tacsat\n", (nrow(eflaloNM) / (nrow(eflaloNM) ))))
# Convert SI_STATE to binary (0/1) format
tacsatp$SI_STATE <- ifelse(tacsatp$SI_STATE == "f", 1, 0)
# Filter rows where SI_STATE is 1
# tacsatEflalo <- tacsatp[tacsatp$SI_STATE == 1,]
tacsatp <- tacsatp[tacsatp$SI_STATE == 1,]
tacsatp <- tacsatp[!is.na(tacsatp$INTV),]
#Set catch date to be equal to SI_DATE
## THIS IS ONLY A REQUIREMENT FOR RUN SPLITAMONGPINGS2
tacsatp$LE_CDAT <- tacsatp$SI_DATE
tacsatp <- as.data.frame(tacsatp)
##### splitamongpings 0p76
tacsatEflalo_0p76 <-
splitAmongPings_0p76(
tacsat = tacsatp,
eflalo = eflalo,
variable = "all",
level = "day",
conserve = TRUE,
by = "INTV"
)
save(
tacsatEflalo_0p76,
file = file.path(outPath, paste0("tacsatEflalo_0p76_", year, ".RData"))
)
#-------------------------------------------------------------------------------------------------
## QC 3: Get the summary of dispatched landing values to VMS records using splitamongpings 0p76
#-------------------------------------------------------------------------------------------------
length(which(!level %in% c("day","ICESrectangle","trip")))>0
tot_kg_eflalo = eflalo |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloM = eflaloM |> summarise(total = sum ( LE_KG_TOT) ) |> pull()
tot_kg_eflaloTacsat = tacsatEflalo_0p76 |> summarise(total = sum ( LE_KG_TOT, na.rm = T)) |> pull()
summary_landings_tacsatEflalo = data.frame ( ref = c( "total_landings_kg_eflalo","total_landings_kg_eflaloM", "total_landings_kg_tacsat_eflalo" ) ,
total = c( tot_kg_eflalo, tot_kg_eflaloM , tot_kg_eflaloTacsat ) ) |>
add_row ( ref = "eflaloM - tacsatEflalo total kg", total = tot_kg_eflaloM - tot_kg_eflaloTacsat )
########## ADD THE
save(
summary_landings_tacsatEflalo,
file = file.path(outPath, paste0("splitAmongPings_comparison/qc3_summary_landings_tacsatEflalo_0p76_", year, ".RData"))
)
}
##############################################################################################
##########################################################
### Grid to C-Square SAP077 #################################
##############################################################
yearsToSubmit = 2009:2023
year = 2023
# Looping through the years to submit
for(year in yearsToSubmit){
load(file = paste0(outPath,paste0("/tacsatEflalo_0p77_", year, ".RData")))
# Calculate the c-square based on longitude and latitude
tacsatEflalo$Csquare <- CSquare(tacsatEflalo$SI_LONG, tacsatEflalo$SI_LATI, degrees = 0.05)
table1_sap0p77 = tacsatEflalo
table1_sap0p77 |> colnames()
## Run this line ONLY to analyse the location with 0 landings aportioned by SAP0p77
### table1_sap0p77 = table1_sap0p77 |> filter ( LE_KG_TOT == 0 )
table1_sap0p77 = table1_sap0p77 |> mutate( kg_0 = ifelse ( LE_KG_TOT == 0, '0_kg_vms' , 'no0_kg_vms' ) )
table1_agg_sap0p77 <- table1_sap0p77 %>%
# Separate LE_MET into met4 and met5, dropping extra pieces
separate(col = LE_MET, c("MetierL4", "MetierL5"), sep = '_', extra = "drop", remove = FALSE) %>%
# Group by several variables
group_by(SI_YEAR, Csquare , MetierL4, MetierL5, kg_0) %>%
# Summarise the grouped data
summarise(
No_Records = n(),
AverageFishingSpeed = mean(SI_SP),
FishingHour = sum(INTV, na.rm = TRUE),
AverageInterval = mean(INTV, na.rm = TRUE),
AverageVesselLength = mean(VE_LEN, na.rm = TRUE),
AveragekW = mean(VE_KW, na.rm = TRUE),
TotWeight = sum(LE_KG_TOT, na.rm = TRUE),
TotValue = sum(LE_EURO_TOT, na.rm = TRUE),
.groups = "drop"
) %>%
as.data.frame()
table1_agg_sap0p77_coord = table1_agg_sap0p77 |>
cbind ( CSquare2LonLat(table1_agg_sap0p77$Csquare , 0.05) ) |>
st_as_sf(coords = c( "SI_LONG", "SI_LATI"), remove = F) |>
st_set_crs(value = 4326) |>
filter( SI_LATI |> between ( 40, 70), SI_LONG |> between ( -15, 20))
load(file = paste0(outPath,paste0("/tacsatEflalo_SAP2_", year, ".RData")))
# Calculate the c-square based on longitude and latitude
tacsatEflalo$Csquare <- CSquare(tacsatEflalo$SI_LONG, tacsatEflalo$SI_LATI, degrees = 0.05)
table1_sap2 = tacsatEflalo
table1_sap2 |> colnames()
table1_agg_sap2 <- table1_sap2 %>%
# Separate LE_MET into met4 and met5, dropping extra pieces
separate(col = LE_MET, c("MetierL4", "MetierL5"), sep = '_', extra = "drop", remove = FALSE) %>%
# Group by several variables
group_by(SI_YEAR, Csquare , MetierL4, MetierL5) %>%
# Summarise the grouped data
summarise(
No_Records = n(),
AverageFishingSpeed = mean(SI_SP),
FishingHour = sum(INTV, na.rm = TRUE),
AverageInterval = mean(INTV, na.rm = TRUE),
AverageVesselLength = mean(VE_LEN, na.rm = TRUE),
AveragekW = mean(VE_KW, na.rm = TRUE),
TotWeight = sum(LE_KG_TOT, na.rm = TRUE),
TotValue = sum(LE_EURO_TOT, na.rm = TRUE),
.groups = "drop"
) %>%
as.data.frame()
}
### Join both tables to compare the reuslts of SAP functions
table1_comparisons = table1_agg_sap2 |> left_join(table1_agg_sap0p77,
by = join_by(SI_YEAR, Csquare , MetierL4, MetierL5) )
table1_comparisons |> colnames()
tabtable1_comparisons_diff = table1_comparisons |>
mutate( n_records_diff = No_Records.x- No_Records.y ,
effort_h = FishingHour.x - FishingHour.y ,
le_kg_diff = round(TotWeight.x - TotWeight.y, 2) )
tabtable1_comparisons_diff |>
group_by(MetierL4, MetierL5) |>
summarise(hours = mean( effort_h ) ,kg = mean ( abs ( le_kg_diff)), kg_max = max(le_kg_diff ) , kg_min = min(le_kg_diff ) ) |>
arrange(desc( kg)) |>
as.data.frame()
tabtable1_comparisons_diff_coord = tabtable1_comparisons_diff |>
cbind ( CSquare2LonLat(tabtable1_comparisons_diff$Csquare , 0.05) ) |>
st_as_sf(coords = c( "SI_LONG", "SI_LATI"), remove = F) |> st_set_crs(value = 4326) |>
filter( SI_LATI |> between ( 40, 70), SI_LONG |> between ( -15, 20)) |>
mutate ( le_kg_diff_class = cut(le_kg_diff, breaks = seq ( from = -2000000, to = 1250000, by = 250000) ) )
tabtable1_comparisons_diff_coord |> filter( le_kg_diff_class |> is.na())
tabtable1_comparisons_diff_coord |> st_drop_geometry( ) |>
ggplot() + geom_histogram(aes(log( le_kg_diff)) )
tabtable1_comparisons_diff_coord |> st_drop_geometry( ) |>
ggplot() + geom_histogram(aes(n_records_diff) )
ggplot(tabtable1_comparisons_diff_coord ) +
geom_sf(aes( color = le_kg_diff_class), size = 0.5) +
scale_color_discrete( ) + facet_wrap(~ le_kg_diff_class)
?scale_color_discrete
tabtable1_comparisons_diff_coord |> distinct(SI_YEAR)
getwd()
### Get the data with no SAR for non mobile bottom contact gears
con = dbConnect(odbc::odbc(), "geofish_dev" )
csquares = st_read( dsn = con,
layer = DBI::Id ( schema = 'marine_statistical_grids' ,
table = 'c_squares_all_pol_marine_fao_div_eez_ices_ospar_uk_regions') ) #,
#query = "select * from marine_statistical_grids.c_squares_all_pol_marine_fao_div limit 10 ")
## Comparison between result of SAP2 and SAP0p77
csquares_an = csquares |> filter( c_square %in% tabtable1_comparisons_diff_coord$Csquare )
tabtable1_comparisons_diff_coord_cs = csquares_an |> inner_join(tabtable1_comparisons_diff_coord |> st_drop_geometry() , by = join_by(c_square == Csquare))
st_write(tabtable1_comparisons_diff_coord_cs, dsn = ".\\Results\\splitAmongPings_comparison\\maps_csquares\\splitamongpings_0p77_2_comparisons.gpkg", layer = "sap2_vs_sap0p77_csquares_diff", append = FALSE)
### SAP0p77 results to CSquares
csquares_sap0p77 = csquares |> filter( c_square %in% table1_agg_sap0p77$Csquare )
table1_agg_sap0p77_diff_coord_cs = csquares_sap0p77 |> inner_join(table1_agg_sap0p77_coord |> st_drop_geometry() , by = join_by(c_square == Csquare))
table1_agg_sap0p77_diff_coord_cs |> st_drop_geometry() |> distinct(uk_marine_zone )
table1_agg_sap0p77_diff_coord_cs |> filter (uk_marine_zone == 'England' & TotWeight )
st_write(table1_agg_sap0p77_diff_coord_cs, dsn = ".\\Results\\splitAmongPings_comparison\\maps_csquares\\splitamongpings_0p77_2_comparisons.gpkg", layer = "table1_agg_sap0p77_diff_coord_cs", append = FALSE)
### SAP0p77 results to CSquares - Difference between cels with VMS with effort by 0 kg
table1_agg_sap0p77pivot = table1_agg_sap0p77 |>
pivot_wider(names_from = kg_0, values_from = - c ( SI_YEAR ,Csquare , MetierL4, MetierL5, kg_0 ) ,values_fill = 0 )
table1_agg_sap0p77pivot_diff_kg_0 =
table1_agg_sap0p77pivot |>
mutate ( vms_kg_vs_0_kg = No_Records_no0_kg_vms - No_Records_0_kg_vms ,
hours_kg_vs_0_kg = FishingHour_no0_kg_vms - FishingHour_0_kg_vms ) |>
mutate ( vms_kg_and_0_kg = No_Records_no0_kg_vms + No_Records_0_kg_vms ,
hours_kg_and_0_kg = FishingHour_no0_kg_vms + FishingHour_0_kg_vms ) |>
filter (!is.na ( vms_kg_vs_0_kg) )
table1_agg_sap0p77pivot_diff_kg_0_tot =
table1_agg_sap0p77pivot_diff_kg_0|>
group_by(SI_YEAR , Csquare , MetierL4 ) |>
mutate(vms_no0_kg_vms = sum ( No_Records_no0_kg_vms, na.rm = T) ,
vms_0_kg_vms = sum ( No_Records_0_kg_vms, na.rm = T) ,
FishingHour_no0_kg_vms = sum ( FishingHour_no0_kg_vms, na.rm = T) ,
FishingHour_0_kg_vms = sum ( FishingHour_0_kg_vms, na.rm = T) ) |>
reframe(vms_kg_vs_0_kg_tot = sum ( vms_kg_vs_0_kg, na.rm = T),
hours_kg_vs_0_kg_tot = sum(hours_kg_vs_0_kg, na.rm = T),
vms_kg_and_0_kg_tot = sum ( vms_kg_and_0_kg, na.rm = T),
hours_kg_and_0_kg_tot = sum(hours_kg_and_0_kg, na.rm = T),
vms_no0_kg_vms_prop = round ( ( vms_no0_kg_vms/vms_kg_and_0_kg_tot ) , 2) ,
FishingHour_no0_kg_vms_prop = round ( ( FishingHour_no0_kg_vms/hours_kg_and_0_kg_tot ) , 2)
) |>
as.data.frame()
summary(table1_agg_sap0p77pivot_diff_kg_0_tot)
table1_agg_sap0p77pivot_diff_kg_0_tot |> filter ( vms_no0_kg_vms_prop > 1)
### save the general aggregated dataset product. No comaprison stats, etc
csquares_sap0p77 = csquares |> filter( c_square %in% table1_agg_sap0p77pivot_diff_kg_0_tot$Csquare )
table1_agg_sap0p77_diff_coord_cs = csquares_sap0p77 |> inner_join(table1_agg_sap0p77_coord |> st_drop_geometry() , by = join_by(c_square == Csquare))
table1_agg_sap0p77_diff_coord_cs |> st_drop_geometry() |> distinct(uk_marine_zone )
table1_agg_sap0p77_diff_coord_cs |> filter (uk_marine_zone == 'England' & TotWeight )
st_write(table1_agg_sap0p77_diff_coord_cs, dsn = ".\\Results\\splitAmongPings_comparison\\maps_csquares\\splitamongpings_0p77_2_comparisons.gpkg", layer = "table1_agg_sap0p77_diff_coord_cs", append = FALSE)
### save the general aggregated dataset product. No comparison stats, etc
csquares_sap0p77 = csquares |> filter( c_square %in% table1_agg_sap0p77$Csquare )
csquares_sap0p77 = csquares_sap0p77 |> distinct(c_square, uk_marine_zone, icesname , geom )
table1_agg_sap0p77_diff_coord_cs = csquares_sap0p77 |> inner_join(table1_agg_sap0p77_coord |> st_drop_geometry() , by = join_by(c_square == Csquare))
table1_agg_sap0p77_diff_coord_cs |> st_drop_geometry() |> distinct(uk_marine_zone )
table1_agg_sap0p77_diff_coord_cs |> filter (uk_marine_zone == 'England' & TotWeight )
st_write(table1_agg_sap0p77_diff_coord_cs, dsn = ".\\Results\\splitAmongPings_comparison\\maps_csquares\\splitamongpings_0p77_2_comparisons.gpkg", layer = "table1_agg_sap0p77_diff_coord_cs", append = FALSE)
### save the general aggregated dataset product. comparison of effort and records with 0 kg allocated
csquares_sap0p77 = csquares |> filter( c_square %in% table1_agg_sap0p77$Csquare )
csquares_sap0p77 = csquares_sap0p77 |> distinct(c_square, uk_marine_zone, icesname , geom )
table1_agg_sap0p77_diff_kg_0_coord_cs = csquares_sap0p77 |> inner_join(table1_agg_sap0p77pivot_diff_kg_0_tot |> st_drop_geometry() , by = join_by(c_square == Csquare))
summary ( table1_agg_sap0p77_diff_kg_0_coord_cs)
table1_agg_sap0p77_diff_kg_0_coord_cs |> st_drop_geometry() |> distinct(uk_marine_zone )
table1_agg_sap0p77_diff_kg_0_coord_cs |> filter (uk_marine_zone == 'England' & TotWeight )
st_write(table1_agg_sap0p77_diff_kg_0_coord_cs, dsn = ".\\Results\\splitAmongPings_comparison\\maps_csquares\\splitamongpings_0p77_2_comparisons.gpkg", layer = "table1_table1_agg_sap0p77_diff_kg_0_coord_cs", append = FALSE)
##############################################################################################################################################
#'------------------------------------------------------------------------------
# 2.3 Add information to tacsatEflalo ----
#'------------------------------------------------------------------------------
# If you already have cleaned tacsatEflalo files elsewhere,
# change file location below, and make sure data is called tacsatEflalo
# Loop trough years to submit
for(year in yearsToSubmit){
print(paste0("Start loop for year ",year))
load(file = paste0(outPath,"tacsatEflalo",year,".RData"))
# 2.3.1 Assign c-square, year, month, quarter, area and create table 1
# ------------------------------------------------------------------
# Add habitat and bathymetry to the tacsatEflalo file
tacsatEflalo <- tacsatEflalo |>
sf::st_as_sf(coords = c("SI_LONG", "SI_LATI"), remove = F) |>
sf::st_set_crs(4326) |>
st_join(eusm, join = st_intersects) |>
st_join(bathy, join = st_intersects) |>
mutate(geometry = NULL) |>
data.frame()
# Calculate the c-square based on longitude and latitude
tacsatEflalo$Csquare <- CSquare(tacsatEflalo$SI_LONG, tacsatEflalo$SI_LATI, degrees = 0.05)
# Extract the year and month from the date-time
tacsatEflalo$Year <- year(tacsatEflalo$SI_DATIM)
tacsatEflalo$Month <- month(tacsatEflalo$SI_DATIM)
# Calculate the kilowatt-hour and convert interval to hours
### CEFAS INTV is already in hours so not need to transform
tacsatEflalo$kwHour <- tacsatEflalo$VE_KW * tacsatEflalo$INTV
tacsatEflalo$INTV <- tacsatEflalo$INTV
# Add the calculated gear width to each fishing point
tacsatEflalo$GEARWIDTH <- add_gearwidth(tacsatEflalo)
# Add surface swept area(m2) for each point in the tacsateflalo
tacsatEflalo$SA_M2 <- add_swept_area(tacsatEflalo)
# Add subsurface swept area(m2) for each point in the tacsateflalo
tacsatEflalo$SA_M2_subsurface <- add_subsurface_swept_area(tacsatEflalo)
# Check if logical
tacsatEflalo[,.(min = min(GEARWIDTH), max = max(GEARWIDTH)), by = .(LE_MET)]
save(
tacsatEflalo,
file = file.path(outPath, paste0("tacsatEflalo_hab_depth", year, ".RData"))
)
}
# Housekeeping
rm(speedarr, tacsatp, tacsatEflalo,
eflalo, eflaloM, eflaloNM)
#----------------
# End of file
#----------------
#----------------
# DATA SUBMISSION QC
#----------------
library(dplyr)