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couplingInterpolatedVmsToLandings2020.r
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##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!COUPLE INTERPOLATED VMS WITH CATCH LANDED!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
## Add-on to the BENTHIS WP2 workflow. Therefore possible repetation of some steps
cat("start couplingInterpolatedVmsToLandings2020.r\n")
rm(list=ls())
library(vmstools)
library(maps)
library(mapdata)
library(doBy)
if(.Platform$OS.type == "unix") {
codePath <- file.path("/zhome","fe","8","43283","BENTHIS")
dataPath <- file.path("/zhome","fe","8","43283","BENTHIS","EflaloAndTacsat")
#outPath <- file.path("~","BENTHIS", "outputs")
outPath <- file.path("/zhome","fe","8","43283","BENTHIS", "outputs2020")
polPath <- file.path("/zhome","fe","8","43283","BENTHIS", "BalanceMaps")
##First read in the arguments listed at the command line
args=(commandArgs(TRUE))
##args is now a list of character vectors
## First check to see if arguments are passed.
## Then cycle through each element of the list and evaluate the expressions.
if(length(args)==0){
print("No arguments supplied.")
##supply default values
year1 <- 2012
year2 <- 2019
years <- year1:year2
}else{
for(i in 1:length(args)){
eval(parse(text=args[[i]]))
}
years <- year1:year2
}
}
if(.Platform$OS.type == "windows") {
codePath <- "D:/FBA/BENTHIS_2020/"
dataPath <- "D:/FBA/BENTHIS_2020/EflaloAndTacsat/"
outPath <- file.path("D:","FBA","BENTHIS_2020", "outputs2020")
polPath <- "D:/FBA/BENTHIS/BalanceMaps"
years <- 2005:2019
}
overwrite <- TRUE
if(FALSE){ # do not re-run...this takes ages!
##-----------------------------------
## SPLIT CATCH AMONG THE INTERPOLATED PINGS
##-----------------------------------
#- PER YEAR
for (a_year in years){
cat(paste("Split among interpolated pings", "\n"))
dir.create(file.path(outPath,a_year,"interpolated", "plus"))
# per year, load eflalo data,
load(file.path(dataPath,paste("eflalo_", a_year,".RData", sep=''))); # get the eflalo object
if(a_year>=2016){
eflalo <- formatEflalo(get(paste0("eflalo_", a_year))) # format each of the columns to the specified class
} else{
eflalo <- formatEflalo(get(paste0("eflalo"))) # format each of the columns to the specified class
}
ctry <- "DNK"
eflalo <- eflalo[ grep(ctry, as.character(eflalo$VE_REF)),] # keep the national vessels only.
eflalo$FT_DDATIM <- as.POSIXct(paste(eflalo$FT_DDAT,eflalo$FT_DTIME, sep = " "),
tz = "GMT", format = "%d/%m/%Y %H:%M")
eflalo$FT_LDATIM <- as.POSIXct(paste(eflalo$FT_LDAT,eflalo$FT_LTIME, sep = " "),
tz = "GMT", format = "%d/%m/%Y %H:%M")
eflalo <- orderBy(~VE_REF+FT_DDATIM+FT_REF, data=eflalo)
eflalo$ID <- paste(eflalo$VE_REF,eflalo$FT_REF,sep="")
eflalo$LE_EFF <- an(difftime(eflalo$FT_LDATIM, eflalo$FT_DDATIM, units="hours"))
eflalo$dummy <- 1
eflalo$LE_EFF <- eflalo$LE_EFF / merge(eflalo,aggregate(eflalo$dummy,by=list(eflalo$ID),FUN=sum),by.x="ID",by.y="Group.1",all.x=T)$x
# deprecated. Better to inform fuel cons from the VMS
#table.fuelcons.per.engine <- read.table(file= file.path(dataPath, "IBM_datainput_engine_consumption.txt"), header=TRUE,sep="")
#linear.model <- lm(calc_cons_L_per_hr_max_rpm~ kW2, data=table.fuelcons.per.engine) # conso = a*Kw +b # to guess its fuel consumption at maximal speed
#eflalo$LE_KG_LITRE_FUEL <- predict(linear.model, newdata=data.frame(kW2=as.numeric(as.character(eflalo$VE_KW)))) * eflalo$LE_EFF # Liter per hour * effort this trip in hour
# Gear codes to keep (with assumed severe bottom impact)
gears2keep <- c("TBB","OTT","OTB","SSC","SDN","PTB","DRB")
eflalo <- eflalo[which(eflalo$LE_GEAR %in% gears2keep),]
fls <- dir(file.path(outPath,a_year,"interpolated"))
fls <- fls[fls!="plus"]
lst <- list(); count <- 0
for(iFile in fls){
cat(paste(a_year, "\n"))
cat(paste(iFile, "\n"))
count <- count+1
load(file.path(outPath,a_year,"interpolated",iFile)) # get tacsatIntGearVEREF
a_vid <- tacsatIntGearVEREF$VE_REF [1]
a_gear <- tacsatIntGearVEREF$LE_GEAR[1]
cnm <- colnames(tacsatIntGearVEREF)
cnm <- cnm[!cnm%in%c("LE_KG_LITRE_FUEL")]
if(length(grep("LE_KG", cnm))>1 ) cat('this tacsat object has already been merged!! likely to fail.\n')
# avoid redoing if the outcome file already there for this vessel-gear combination
do_it <-TRUE
if(overwrite==FALSE) if(length(fls)!=0 &&
length(grep(paste("tacsatSweptAreaPlus_",a_vid, "_", a_gear, ".RData", sep=""),fls)!=0)) do_it <- FALSE
if(do_it){
tacsatIntGearVEREF <- tacsatIntGearVEREF[,!colnames(tacsatIntGearVEREF)%in%"LITRE_FUEL"]
colnames(tacsatIntGearVEREF)[colnames(tacsatIntGearVEREF)=="LE_KG_LITRE_FUEL"] <- "LITRE_FUEL" # force renaming to avoid splitAmongPings() to fail
tacsatIntGearVEREF <- splitAmongPings(tacsat=subset(tacsatIntGearVEREF, LE_GEAR == a_gear & VE_REF == a_vid),
eflalo=subset(eflalo, LE_GEAR == a_gear & VE_REF == a_vid),
variable="all",level="day",conserve=T)
# note that we can safely ignore the warning as it corresponds to the 0 catch
# this is because sometimes the declaration of rectangle (in eflalo) does not match the rectangle from VMS points
colnames(tacsatIntGearVEREF)[colnames(tacsatIntGearVEREF)=="LITRE_FUEL"] <- "LE_KG_LITRE_FUEL" # force renaming for back compatibility
# check e.g. for cod
#library(raster)
#plotTools(tacsatIntGearVEREF,level="gridcell", xlim=c(-56,25),ylim=c(45,75),zlim=NULL,log=F, gridcell=c(0.1,0.05), color=NULL, control.tacsat=list(clm="LE_KG_COD"))
#savePlot(file.path(outPath, a_year, "interpolated", "plus", paste("tacsatSweptAreaPlus_",a_vid, "_", a_gear, "_COD.jpeg", sep="")), type="jpeg")
#plotTools(subset(eflalo, LE_GEAR == a_gear & VE_REF == a_vid), level="ICESrectangle",xlim=c(-56,25),ylim=c(45,65), zlim=NULL,log=F,color=NULL,control.eflalo=list(clm="LE_KG_COD"))
#savePlot(file.path(outPath, a_year, "interpolated", "plus", paste("tacsatSweptAreaPlus_",a_vid, "_", a_gear, "_COD_EFLALO.jpeg", sep="")), type="jpeg")
save(tacsatIntGearVEREF, file=file.path(outPath, a_year, "interpolated", "plus",
paste("tacsatSweptAreaPlus_",a_vid, "_", a_gear, ".RData", sep="")),compress=T)
}
} # end a_vessel
} # end a_year
} # end FALSE
if(TRUE){ # do not re-run...this takes ages!
##-----------------------------------
## COMPUTE SWEPT AREA
##-----------------------------------
compute_swept_area <- function(
tacsatIntGearVEREF=tacsatIntGearVEREF,
gear_param_per_metier=gear_param_per_metier,
towedGears=towedGears,
seineGears=seineGears,
VMS_ping_rate_in_hour=VMS_ping_rate_in_hour,
already_informed_width_for=NULL
){
if(is.null(already_informed_width_for)){
tacsatIntGearVEREF <- tacsatIntGearVEREF[,!colnames(tacsatIntGearVEREF) %in%
c('GEAR_WIDTH', 'GEAR_WIDTH_LOWER', 'GEAR_WIDTH_UPPER', 'SWEPT_AREA_KM2', 'SWEPT_AREA_KM2_LOWER', 'SWEPT_AREA_KM2_UPPER')] # remove columns if exists
} else{
tacsatIntGearVEREF <- tacsatIntGearVEREF[,!colnames(tacsatIntGearVEREF) %in%
c('SWEPT_AREA_KM2', 'SWEPT_AREA_KM2_LOWER', 'SWEPT_AREA_KM2_UPPER')] # remove columns if exists
}
if(is.null(already_informed_width_for)){
# MERGE WITH GEAR WIDTH
GearWidth <- tacsatIntGearVEREF[!duplicated(data.frame(tacsatIntGearVEREF$VE_REF,tacsatIntGearVEREF$LE_MET,tacsatIntGearVEREF$VE_KW,tacsatIntGearVEREF$VE_LEN)), ]
GearWidth <- GearWidth[,c('VE_REF','LE_MET','VE_KW', 'VE_LEN') ]
GearWidth$GEAR_WIDTH <- NA
GearWidth$GEAR_WIDTH_LOWER <- NA
GearWidth$GEAR_WIDTH_UPPER <- NA
for (i in 1:nrow(GearWidth)) { # brute force...
kW <- GearWidth$VE_KW[i]
LOA <- GearWidth$VE_LEN[i]
this <- gear_param_per_metier[gear_param_per_metier$a_metier==as.character(GearWidth$LE_MET[i]),]
a <- NULL ; b <- NULL
a <- this[this$param=='a', 'Estimate']
b <- this[this$param=='b', 'Estimate']
GearWidth[i,"GEAR_WIDTH"] <- eval(parse(text= as.character(this[1, 'equ']))) / 1000 # converted in km
a <- this[this$param=='a', 'Estimate']
b <- this[this$param=='b', 'Estimate'] +2*this[this$param=='b', 'Std..Error']
GearWidth[i,"GEAR_WIDTH_UPPER"] <- eval(parse(text= as.character(this[1, 'equ']))) / 1000 # converted in km
a <- this[this$param=='a', 'Estimate']
b <- this[this$param=='b', 'Estimate'] -2*this[this$param=='b', 'Std..Error']
GearWidth[i,"GEAR_WIDTH_LOWER"] <- eval(parse(text= as.character(this[1, 'equ']))) / 1000 # converted in km
}
tacsatIntGearVEREF <- merge(tacsatIntGearVEREF, GearWidth,by=c("VE_REF","LE_MET","VE_KW","VE_LEN"),
all.x=T,all.y=F)
}
# the swept area (note that could work oustide the loop area as well....)
# for the trawlers...
if(tacsatIntGearVEREF$LE_GEAR[1] %in% towedGears){
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- NA
tacsatIntGearVEREF <- orderBy(~SI_DATIM,data=tacsatIntGearVEREF)
a_dist <- distance(c(tacsatIntGearVEREF$SI_LONG[-1],0), c(tacsatIntGearVEREF$SI_LATI[-1],0),
tacsatIntGearVEREF$SI_LONG, tacsatIntGearVEREF$SI_LATI)
a_dist[length(a_dist)] <- rev(a_dist)[2]
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- a_dist * tacsatIntGearVEREF$GEAR_WIDTH
tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER <- a_dist * tacsatIntGearVEREF$GEAR_WIDTH_LOWER
tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER <- a_dist * tacsatIntGearVEREF$GEAR_WIDTH_UPPER
# correct the transition between sequential fishing events
#idx <- which(diff(tacsatIntGearVEREF$SI_DATIM)/60 > 15) # if interval > 15 min then points belong to a different fishing event
# CORRECTION Sep18:
idx <- which( as.numeric(diff(tacsatIntGearVEREF$SI_DATIM), units='mins') > 15) # if interval > 15 min
idx <- c(idx, nrow(tacsatIntGearVEREF)) # to exclude the last observation
tacsatIntGearVEREF[ idx, c('SWEPT_AREA_KM2', 'SWEPT_AREA_KM2_LOWER', 'SWEPT_AREA_KM2_UPPER')] <- NA
}
# for the seiners...
if(tacsatIntGearVEREF$LE_GEAR[1] %in% seineGears){
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- pi*(tacsatIntGearVEREF$GEAR_WIDTH/(2*pi))^2
tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER <- pi*(tacsatIntGearVEREF$GEAR_WIDTH_LOWER/(2*pi))^2
tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER <- pi*(tacsatIntGearVEREF$GEAR_WIDTH_UPPER/(2*pi))^2
haul_duration <- 3 # assumption of a mean duration based from questionnaires to seiners
tacsatIntGearVEREF$SWEPT_AREA_KM2 <- tacsatIntGearVEREF$SWEPT_AREA_KM2 * VMS_ping_rate_in_hour / haul_duration # correction to avoid counting the same circle are several time.
tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER <- tacsatIntGearVEREF$SWEPT_AREA_KM2_LOWER * VMS_ping_rate_in_hour / haul_duration # correction to avoid counting the same circle are several time.
tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER <- tacsatIntGearVEREF$SWEPT_AREA_KM2_UPPER * VMS_ping_rate_in_hour / haul_duration # correction to avoid counting the same circle are several time.
idx <- grep('SSC', as.character(tacsatIntGearVEREF$LE_GEAR))
tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2'] <- tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2'] *1.5 # ad hoc correction to account for the SSC specificities
tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_LOWER'] <- tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_LOWER'] *1.5 # ad hoc correction to account for the SSC specificities
tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_UPPER'] <- tacsatIntGearVEREF[idx, 'SWEPT_AREA_KM2_UPPER'] *1.5 # ad hoc correction to account for the SSC specificities
}
return(tacsatIntGearVEREF)
}
#-----------------------------------------------------------------------------
# Add "gear width-vessel size" relationships table of parameters.
#-----------------------------------------------------------------------------
#gear_param_per_metier <- read.table(file=file.path(dataPath, "estimates_for_gear_param_per_metier.txt"))
# an equivalent is:
gear_param_per_metier <- data.frame(
a_metier=c('OT_CRU','OT_CRU','OT_DMF','OT_DMF','OT_MIX','OT_MIX','OT_MIX_ARA','OT_MIX_ARA','OT_MIX_DMF_BEN','OT_MIX_DMF_BEN','OT_MIX_DMF_PEL','OT_MIX_DMF_PEL','OT_MIX_DPS','OT_MIX_DPS','OT_MIX_NEP','OT_MIX_NEP','OT_MIX_TGS_CTC','OT_MIX_TGS_CTC','OT_MIX_TGS_OCC','OT_MIX_TGS_OCC','OT_SPF','OT_SPF','TBB_CRU','TBB_CRU','TBB_DMF','TBB_DMF','TBB_MOL','TBB_MOL','DRB_MOL','DRB_MOL','SDN_DEM','SDN_DEM','SSC_DEM','SSC_DEM'),
param=c('a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b','a','b'),
Estimate=c(5.10393560454806,0.468985756915913,9.6053549509854,0.433672763959314,10.6607888271164,0.292055014993337,37.5271604597435,0.149004797319136,3.21410379943408,77.981158829069,6.63707197355847,0.770594580782091,26.6738247840508,0.210221545999405,3.92727763464472,35.8253721834011,6.23686411376723,0.767375050454527,0.0192465419797634,119.140335982507,0.965238378524667,68.3889717127507,1.48117115311386,0.457788539321641,0.660086393453441,0.507845311175148,0.953001905566232,0.709356826689359,0.314245137194503,1.24544036138755,1948.83466676682,0.236271746198865,4461.27004311913,0.117589220782479),
Std..Error=c(1.81527145191998,0.0597519960969362,3.98228885098937,0.067572002767068,6.69386377505425,0.104413257104915,10.6717875588847,0.044963446750424,1.67854244656697,40.9297885227685,2.69086696344053,0.126123213329976,5.37466576335144,0.030829495804396,0.928442484509969,21.0228522096513,1.46159830273852,0.0732116002636393,0.000552819642352548,0.510207569180525,0.205245990518183,7.45180177818494,0.278399892100703,0.0346555048025894,0.172902115850281,0.0388684340513048,0.315715856194751,0.138412196798781,0.110027479611801,0.10614681568516,637.25152416296,0.0636712369543136,1665.50234108383,0.118756519107319),
t.value=c(2.81166521907769,7.84887179593252,2.41201864314765,6.41793562718951,1.59262112068153,2.79710664230959,3.51648308708138,3.31390958851994,1.91481830322951,1.90524216331315,2.46651806415295,6.10985527910701,4.96288066244663,6.81884476260001,4.22996329893018,1.70411568450042,4.26715336360309,10.4816046595234,34.8152281598731,233.513462322532,4.70283670871103,9.17750817164227,5.32030074414718,13.2096918492278,3.81768835047121,13.0657517744299,3.01854305657162,5.12495894939517,2.8560604887363,11.733186279291,3.05818753329251,3.71080816866175,2.67863330664306,0.990170658978435),
Pr...t..=c(0.00613312535554725,1.21619365805854e-11,0.021410083292817,2.48114253493853e-07,0.114790848188445,0.00631861326022122,0.000513087659147687,0.0010462790834138,0.0692370736030276,0.0705334706657513,0.0147045751318625,7.39218704723967e-09,1.2637878625965e-05,2.97113026239585e-08,0.000166717383514359,0.097483711710908,0.000314181622785133,5.0948672020349e-10,9.05842416252619e-12,5.10054218622276e-20,0.000204968683311441,5.36482029322678e-08,0.00313939649832079,4.44157761915604e-05,0.000458495488420268,5.11509704563588e-16,0.00678642704689924,5.16047183433098e-05,0.0075895814688592,6.18091407283774e-13,0.00391206507124518,0.000614325243514857,0.0438919330122769,0.367557330382699),
equ=c('DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=(a*LOA)+b','DoS=(a*LOA)+b','DoS=a*(LOA^b)','DoS=a*(LOA^b)','DoS=a*(kW^b)','DoS=a*(kW^b)','DoS=(a*LOA)+b','DoS=(a*LOA)+b','DoS=a*(LOA^b)','DoS=a*(LOA^b)','DoS=(a*kW)+b','DoS=(a*kW)+b','DoS=(a*LOA)+b','DoS=(a*LOA)+b','beamw=a*(kW^b)','beamw=a*(kW^b)','beamw=a*(kW^b)','beamw=a*(kW^b)','beamw=a*(LOA^b)','beamw=a*(LOA^b)','dredgew=a*(LOA^b)','dredgew=a*(LOA^b)','seineropel=a*(kW^b)','seineropel=a*(kW^b)','seineropel=a*(LOA^b)','seineropel=a*(LOA^b)'),
nb_records=c(124,124,39,39,94,94,271,271,48,48,190,190,45,45,53,53,24,24,12,12,19,19,7,7,42,42,22,22,33,33,47,47,8,8)
)
# Gear codes to keep (with assumed severe bottom impact)
gears2keep <- c("TBB","OTT","OTB","SSC","SDN","PTB","DRB")
towedGears <- c("TBB","OTT","OTB","PTB","DRB")
seineGears <- c("SSC","SDN")
VMS_ping_rate_in_hour <- 1 # e.g. 1 hour for Denmark (rev(sort(table(intervalTacsat(sortTacsat(tacsat),level="vessel")$INTV))[1])
spp <- c('LITRE_FUEL', 'COD', 'CSH', 'DAB', 'ELE', 'FLE', 'HAD', 'HER', 'HKE', 'HOM',
'LEM', 'MAC', 'MON', 'MUS', 'NEP', 'NOP', 'PLE', 'POK', 'PRA', 'SAN',
'SOL', 'SPR', 'TUR', 'WHB', 'WIT', 'WHG',
'OTH')
cols2keep <- c("VE_REF", "VE_LEN", "VE_KW", "SI_LATI","SI_LONG","SI_DATE","LE_GEAR","LE_MET","LE_MET_init","SWEPT_AREA_KM2","SWEPT_AREA_KM2_LOWER","SWEPT_AREA_KM2_UPPER", "GEAR_WIDTH", "SI_DATIM", "SI_FT", "FT_REF")
for (a_year in years){
cat(paste(a_year, "\n"))
cat(paste("Compute swept area", "\n"))
fls <- dir(file.path(outPath,a_year,"interpolated", "plus"))
fls <- fls[grep('.RData', fls)]
fls <- fls[!fls %in% paste0("tacsatSweptAreaPlus_",a_year,".RData")]
load(file.path(outPath,a_year,"interpolated", "plus", fls[2])) # get one as an example for the right columns
colkg <- colnames(tacsatIntGearVEREF) [ grep('KG', colnames(tacsatIntGearVEREF)) ]
coleuro <- colnames(tacsatIntGearVEREF) [grep('EURO', colnames(tacsatIntGearVEREF))]
#colums_to_keep <- colnames(tacsatIntGearVEREF) [ ! c(1:ncol(tacsatIntGearVEREF)) %in% c(colkg, coleuro) ]
cols2keep
colkg_to_keep <- c(paste('LE_KG_', spp, sep=''))
coleuro_to_keep <- c(paste('LE_EURO_', spp, sep=''))
coleuro_to_keep <- coleuro_to_keep[!coleuro_to_keep %in% "LE_EURO_LITRE_FUEL"] # remove a useless naming
colkg_to_sum <- colkg[!colkg %in% colkg_to_keep]
coleuro_to_sum <- coleuro[!coleuro %in% coleuro_to_keep]
print(a_year)
load(file=file.path(outPath,a_year,"tacsatActivity.RData"))
# for computing fuel use
table.fuelcons.per.engine <- read.table(file= file.path(dataPath, "IBM_datainput_engine_consumption.txt"), header=TRUE,sep="")
linear.model <- lm(calc_cons_L_per_hr_max_rpm~ kW2, data=table.fuelcons.per.engine) # conso = a*Kw +b # to guess its fuel consumption at maximal speed
fuel_per_h <- function (a,x) a*(x^3) # cubic law
load(file=file.path(outPath,a_year,"steaming_cons_per_VE_REF_FT_REF.RData")) # get steaming_cons_per_VE_REF_FT_REF
lst <- list(); count <- 0 ;vid_with_errors <- NULL
for(iFile in fls){
cat(paste(iFile, "\n"))
count <- count+1
load(file.path(outPath,a_year,"interpolated", "plus", iFile))
# compute the fuel use
nbpoints <- 12 # caution: chek this for your case.
a_vessel <- sapply(strsplit(gsub(".RData","",iFile), split="_"), function(x)x[2])
a_gear <- sapply(strsplit(gsub(".RData","",iFile), split="_"), function(x)x[3])
a_max_vessel_speed <- quantile(as.numeric(as.character(tacsatp[tacsatp$VE_REF==a_vessel, 'SI_SP'])), 0.95) # we assume the towing is done at maximal load
max_consumed <- predict(linear.model, newdata=data.frame(kW2=as.numeric(as.character(tacsatp[tacsatp$VE_REF==a_vessel, 'VE_KW'][1]))))
a <- max_consumed/ (a_max_vessel_speed^3) # scaling factor
if(a_gear%in% c(towedGears,"SSC")){
#for towed gears and SSC: assume full load when dragging the trawl
# fuel use
full_load_factor <- 0.9 # they fish at 90% full load
tacsatIntGearVEREF$LITRE_FUEL_FISHING <- (fuel_per_h(as.numeric(as.character(a)), as.numeric(as.character(a_max_vessel_speed))) * full_load_factor) /nbpoints
tacsatIntGearVEREF$VE_REF_FT_REF <- paste0(tacsatIntGearVEREF$VE_REF,"_",tacsatIntGearVEREF$FT_REF)
nb_fishing_pts_per_VE_REF_FT_REF <- table(tacsatIntGearVEREF$VE_REF_FT_REF) # for dispatching evenly on fishing pts
tacsatIntGearVEREF$FUEL_LITRE_STEAMING <- steaming_cons_per_VE_REF_FT_REF[tacsatIntGearVEREF$VE_REF_FT_REF] / table(tacsatIntGearVEREF$VE_REF_FT_REF)[tacsatIntGearVEREF$VE_REF_FT_REF]
tacsatIntGearVEREF$LE_KG_LITRE_FUEL <- tacsatIntGearVEREF$LITRE_FUEL_FISHING + tacsatIntGearVEREF$FUEL_LITRE_STEAMING
tacsatIntGearVEREF <- tacsatIntGearVEREF[, !colnames(tacsatIntGearVEREF) %in% c("VE_REF_FT_REF", "max_vessel_speed", "max_consumed", "a", "FUEL_LITRE_STEAMING")] # cleaning
}
if(a_gear%in% "SDN"){
#for seiners SDN gears: actual speed is enough and a good proxy
# fuel use
tacsatIntGearVEREF$LITRE_FUEL_FISHING <- fuel_per_h(as.numeric(as.character(a)), (as.numeric(as.character(tacsatIntGearVEREF$SI_SP))))* VMS_ping_rate_in_hour*1.0
tacsatIntGearVEREF$VE_REF_FT_REF <- paste0(tacsatIntGearVEREF$VE_REF,"_",tacsatIntGearVEREF$FT_REF)
nb_fishing_pts_per_VE_REF_FT_REF <- table(tacsatIntGearVEREF$VE_REF_FT_REF) # for dispatching evenly on fishing pts
tacsatIntGearVEREF$FUEL_LITRE_STEAMING <- steaming_cons_per_VE_REF_FT_REF[tacsatIntGearVEREF$VE_REF_FT_REF] / table(tacsatIntGearVEREF$VE_REF_FT_REF)[tacsatIntGearVEREF$VE_REF_FT_REF]
tacsatIntGearVEREF$LE_KG_LITRE_FUEL <- tacsatIntGearVEREF$LITRE_FUEL_FISHING + tacsatIntGearVEREF$FUEL_LITRE_STEAMING
tacsatIntGearVEREF <- tacsatIntGearVEREF[, !colnames(tacsatIntGearVEREF) %in% c("VE_REF_FT_REF", "max_vessel_speed", "max_consumed", "a", "FUEL_LITRE_STEAMING")] # cleaning
}
tacsatIntGearVEREF$LE_KG_OTH <- apply(tacsatIntGearVEREF[,colkg_to_sum], 1, sum, na.rm=TRUE)
tacsatIntGearVEREF$LE_EURO_OTH <- apply(tacsatIntGearVEREF[,coleuro_to_sum], 1, sum, na.rm=TRUE)
# compute the swept area
tacsatIntGearVEREF <- compute_swept_area (tacsatIntGearVEREF, gear_param_per_metier, towedGears, seineGears, VMS_ping_rate_in_hour, already_informed_width_for=NULL)
if(any(tacsatIntGearVEREF$SWEPT_AREA_KM2>100, na.rm = TRUE) ) {
print(paste('check for lat long at 0!! for ', iFile))
vid_with_errors <- c(vid_with_errors, iFile)
tacsatIntGearVEREF[!is.na(tacsatIntGearVEREF$SWEPT_AREA_KM2) & tacsatIntGearVEREF$SWEPT_AREA_KM2>100, c("SWEPT_AREA_KM2", "SWEPT_AREA_KM2_LOWER", "SWEPT_AREA_KM2_UPPER")] <- NA
}
lst[[count]] <- tacsatIntGearVEREF[, c(cols2keep, colkg_to_keep, coleuro_to_keep)]
print(ncol( lst[[count]]))
}
cat(paste("saving....", "\n"))
# caution: the job can get killed silently here if the memory allocated hitting the ceiling...
tacsatSweptArea <- do.call(rbind, lst)
save(tacsatSweptArea, file=file.path(outPath, a_year, "interpolated", "plus",
paste("tacsatSweptAreaPlus_", a_year, ".RData", sep="")),compress=T)
cat(paste("saving....ok", "\n"))
} # end year
} # end FALSE
##-----------------------------------
## GRIDDING
##-----------------------------------
#---------------------------------------------------
# TO DO from the 'tacsatSweptAreaPlus_ objects':
# 1. from the catches, figure out what has been fished for, close to each benthic stations....
# 2. from catches, compute an efficiency indicator: catch per swept area => a way to identify the effective fisheries and priorities areas...
# i.e. what we aim for is high catches with low total swept area.
# Gear codes to keep (with assumed severe bottom impact)
gears2keep <- c("TBB","OTT","OTB","SSC","SDN","PTB","DRB")
towedGears <- c("TBB","OTT","OTB","PTB","DRB")
seineGears <- c("SSC","SDN")
for (a_year in years){
cat(paste(a_year, "\n"))
cat(paste("Gridding", "\n"))
load(file=file.path(outPath, a_year, "interpolated", "plus",
paste("tacsatSweptAreaPlus_", a_year, ".RData", sep="")))
# compute effort in nmin
tacsatSweptArea$effort_mins <- c(0,as.numeric(diff(tacsatSweptArea$SI_DATIM), units='mins'))
idx <- which( tacsatSweptArea$effort_mins>15 & tacsatSweptArea$LE_GEAR %in% towedGears) # if interval > 15 min
tacsatSweptArea[ idx, "effort_mins"] <- NA # exclude change of haul
idx <- which( tacsatSweptArea$effort_mins >75 & tacsatSweptArea$LE_GEAR %in% seineGears) # if interval > 75 min
tacsatSweptArea[ idx, "effort_mins"] <- NA # exclude change of haul
idx <- which( tacsatSweptArea$effort_mins <0) #
tacsatSweptArea[ idx, "effort_mins"] <- NA # exclude change of vessel id
# retrieve the harbour dep from FT_REF (Harb 5 letters in LOCODE)
load(file=file.path(outPath, a_year, "cleanEflalo.RData")) # get tacsatp
tacsatSweptArea$VE_REF_FT_REF <- paste0(tacsatSweptArea$VE_REF,"_",tacsatSweptArea$FT_REF)
eflalo$VE_REF_FT_REF <- paste0(eflalo$VE_REF,"_",eflalo$FT_REF)
dd <- eflalo [!duplicated(eflalo$VE_REF_FT_REF),]
dd <- dd[,c("VE_REF_FT_REF","FT_DHAR")]
rownames(dd) <- dd$VE_REF_FT_REF
tacsatSweptArea$FT_DHAR <- dd[tacsatSweptArea$VE_REF_FT_REF, "FT_DHAR"]
# check
#levels(factor(tacsatSweptArea$FT_DHAR)) [!levels(factor(tacsatSweptArea$FT_DHAR)) %in% levels(vss$Port)]
# vessel size
#12-18, 18-24, 24-40, o40
tacsatSweptArea$VesselSize <- cut(tacsatSweptArea$VE_LEN, breaks=c(0,11.99,17.99,23.99,39.99,100), right=FALSE)
library(vmstools)
xrange <- c(-30,50) # ALL
yrange <- c(30,81) # ALL
#- Set grid
resx <- 1/60 #1 minute
resy <- 1/60 #1 minute
grd <- createGrid(xrange,yrange,resx=1/60,resy=1/60,type="SpatialGrid",exactBorder=T)
#- Grid all tacsatSweptArea data
# Convert all tacsat poins first to SpatialPoints
coords <- SpatialPoints(cbind(SI_LONG=tacsatSweptArea$SI_LONG,SI_LATI=tacsatSweptArea$SI_LATI))
idx <- over(coords,grd)
tacsatSweptArea$grID <- idx
#- Remove records that are not in the study area
tacsatSweptArea <- subset(tacsatSweptArea,is.na(grID)==F)
#-1 Aggregate the results by metier and grid ID (aggregate() can be slow: be patient)
c.listquote <- function (...)
{
args <- as.list(match.call()[-1])
lstquote <- list(as.symbol("list"))
for (i in args) {
if (class(i) == "name" || (class(i) == "call" && i[[1]] !=
"list")) {
i <- eval(substitute(i), sys.frame(sys.parent()))
}
if (class(i) == "call" && i[[1]] == "list") {
lstquote <- c(lstquote, as.list(i)[-1])
}
else if (class(i) == "character") {
for (chr in i) {
lstquote <- c(lstquote, list(parse(text = chr)[[1]]))
}
}
else stop(paste("[", deparse(substitute(i)), "] Unknown class [",
class(i), "] or is not a list()", sep = ""))
}
return(as.call(lstquote))
}
if(FALSE){
# aggregate per VE_REF etc. (useful for DISPLACE North Sea)
library(data.table)
nm <- names(tacsatSweptArea)
idx.col.euro <- grep('LE_EURO_', nm)
idx.col.kg <- grep('LE_KG_', nm)
idx.col.swpt <- grep('SWEPT_AREA_KM2', nm)
idx.col.effectiveeffort <- grep('effort_mins', nm)
idx.col <- c(idx.col.euro, idx.col.kg, idx.col.swpt, idx.col.effectiveeffort)
DT <- data.table(tacsatSweptArea) # library data.table for fast grouping replacing aggregate()
# AGGREGATE PER SPECIES -----> SUM (IF WEIGHT) OR MEAN (IF CPUE)
eq1 <- c.listquote( paste ("sum(",nm[idx.col],",na.rm=TRUE)",sep="") )
tacsatSweptArea.agg <- DT[,eval(eq1),by=list( VE_REF, FT_DHAR, LE_MET, VE_LEN, VE_KW)]
tacsatSweptArea.agg <- data.frame( tacsatSweptArea.agg)
colnames(tacsatSweptArea.agg) <- c("VE_REF", "FT_DHAR", "LE_MET", "VE_LEN", "VE_KW", nm[idx.col.euro], nm[idx.col.kg], nm[idx.col.swpt], nm[idx.col.effectiveeffort])
aggResult<- tacsatSweptArea.agg
#- Add midpoint of gridcell to dataset
#aggResult <- cbind(tacsatSweptArea.agg,CELL_LONG=coordinates(grd)[tacsatSweptArea.agg$grID,1],
# CELL_LATI=coordinates(grd)[tacsatSweptArea.agg$grID,2])
#- Remove records that are not in the study area
#aggResult <- subset(aggResult,is.na(grID)==F)
save(aggResult,file=file.path(outPath, paste("AggregatedSweptAreaPlusPerVidPerMet6PerHarb_", a_year, ".RData", sep="")))
rm(aggResult); gc(TRUE)
}
if(TRUE){
gc()
# aggregate per LE_MET
library(data.table)
nm <- names(tacsatSweptArea)
idx.col.euro <- grep('LE_EURO_', nm)
idx.col.kg <- grep('LE_KG_', nm)
idx.col.swpt <- grep('SWEPT_AREA_KM2', nm)
idx.col.effectiveeffort <- grep('effort_mins', nm)
idx.col <- c(idx.col.euro, idx.col.kg, idx.col.swpt, idx.col.effectiveeffort)
DT <- data.table(tacsatSweptArea) # library data.table for fast grouping replacing aggregate()
# AGGREGATE PER SPECIES -----> SUM (IF WEIGHT) OR MEAN (IF CPUE)
eq1 <- c.listquote( paste ("sum(",nm[idx.col],",na.rm=TRUE)",sep="") )
tacsatSweptArea.agg <- DT[,eval(eq1),by=list(grID, LE_MET)]
tacsatSweptArea.agg <- data.frame( tacsatSweptArea.agg)
colnames(tacsatSweptArea.agg) <- c("grID", "LE_MET", nm[idx.col.euro], nm[idx.col.kg], nm[idx.col.swpt], nm[idx.col.effectiveeffort])
#- Add midpoint of gridcell to dataset
aggResult <- cbind(tacsatSweptArea.agg,CELL_LONG=coordinates(grd)[tacsatSweptArea.agg$grID,1],
CELL_LATI=coordinates(grd)[tacsatSweptArea.agg$grID,2])
#- Remove records that are not in the study area
aggResult <- subset(aggResult,is.na(grID)==F)
save(aggResult,file=file.path(outPath, paste("AggregatedSweptAreaPlus_", a_year, ".RData", sep="")))
rm(aggResult) ; gc(TRUE)
# aggregate per LE_MET_init
library(data.table)
nm <- names(tacsatSweptArea)
idx.col.euro <- grep('LE_EURO_', nm)
idx.col.kg <- grep('LE_KG_', nm)
idx.col.swpt <- grep('SWEPT_AREA_KM2', nm)
idx.col.effectiveeffort <- grep('effort_mins', nm)
idx.col <- c(idx.col.euro, idx.col.kg, idx.col.swpt, idx.col.effectiveeffort)
DT <- data.table(tacsatSweptArea) # library data.table for fast grouping replacing aggregate()
# AGGREGATE PER SPECIES -----> SUM (IF WEIGHT) OR MEAN (IF CPUE)
eq1 <- c.listquote( paste ("sum(",nm[idx.col],",na.rm=TRUE)",sep="") )
tacsatSweptArea.agg <- DT[,eval(eq1),by=list(grID, LE_MET_init)]
tacsatSweptArea.agg <- data.frame( tacsatSweptArea.agg)
colnames(tacsatSweptArea.agg) <- c("grID", "LE_MET_init", nm[idx.col.euro], nm[idx.col.kg], nm[idx.col.swpt], nm[idx.col.effectiveeffort])
gc()
#- Add midpoint of gridcell to dataset
aggResult <- cbind(tacsatSweptArea.agg,CELL_LONG=coordinates(grd)[tacsatSweptArea.agg$grID,1],
CELL_LATI=coordinates(grd)[tacsatSweptArea.agg$grID,2])
#- Remove records that are not in the study area
aggResult <- subset(aggResult,is.na(grID)==F)
save(aggResult,file=file.path(outPath, paste("AggregatedSweptAreaPlusMet6_", a_year, ".RData", sep="")))
rm(aggResult); gc(TRUE)
# DO the plot ordering cell from large revenue to lower revenue and plot cumsum
# aggregate per LE_MET_init & Vessel size
library(data.table)
nm <- names(tacsatSweptArea)
idx.col.euro <- grep('LE_EURO_', nm)
idx.col.kg <- grep('LE_KG_', nm)
idx.col.swpt <- grep('SWEPT_AREA_KM2', nm)
idx.col.effectiveeffort <- grep('effort_mins', nm)
idx.col <- c(idx.col.euro, idx.col.kg, idx.col.swpt, idx.col.effectiveeffort)
DT <- data.table(tacsatSweptArea) # library data.table for fast grouping replacing aggregate()
# AGGREGATE PER SPECIES -----> SUM (IF WEIGHT) OR MEAN (IF CPUE)
eq1 <- c.listquote( paste ("sum(",nm[idx.col],",na.rm=TRUE)",sep="") )
tacsatSweptArea.agg <- DT[,eval(eq1),by=list(grID, LE_MET_init, VesselSize)]
tacsatSweptArea.agg <- data.frame( tacsatSweptArea.agg)
colnames(tacsatSweptArea.agg) <- c("grID", "LE_MET_init", "VesselSize", nm[idx.col.euro], nm[idx.col.kg], nm[idx.col.swpt], nm[idx.col.effectiveeffort])
gc()
#- Add midpoint of gridcell to dataset
aggResult <- cbind(tacsatSweptArea.agg,CELL_LONG=coordinates(grd)[tacsatSweptArea.agg$grID,1],
CELL_LATI=coordinates(grd)[tacsatSweptArea.agg$grID,2])
#- Remove records that are not in the study area
aggResult <- subset(aggResult,is.na(grID)==F)
save(aggResult,file=file.path(outPath, paste("AggregatedSweptAreaPlusMet6AndVsize_", a_year, ".RData", sep="")))
rm(aggResult); gc(TRUE)
# DO the plot ordering cell from large revenue to lower revenue and plot cumsum
} # end FALSE
} # end year
if(FALSE){
##-----------------------------------
## GET SOME EFFORT TIME SERIES
##-----------------------------------
# Gear codes to keep (with assumed severe bottom impact)
gears2keep <- c("TBB","OTT","OTB","SSC","SDN","PTB","DRB")
towedGears <- c("TBB","OTT","OTB","PTB","DRB")
seineGears <- c("SSC","SDN")
aggEffortAndFuelAlly <- NULL
for (a_year in years){
cat(paste(a_year, "\n"))
cat(paste("Effort", "\n"))
rm(tacsatSweptArea) ; gc()
load(file=file.path(outPath, a_year, "interpolated", "plus",
paste("tacsatSweptAreaPlus_", a_year, ".RData", sep="")))
# number of vessels active this year with this selection (to compare with nb in logbooks)
length(unique(tacsatSweptArea$VE_REF))
# number of trips
length(unique(tacsatSweptArea$FT_REF))
# look at the logbooks eflalo:
load(file=file.path(dataPath, paste0("eflalo_",a_year,".RData")))
if(a_year>=2016){
eflalo <- formatEflalo(get(paste0("eflalo_", a_year))) # format each of the columns to the specified class
} else{
eflalo <- formatEflalo(get(paste0("eflalo"))) # format each of the columns to the specified class
}
eflalo <- eflalo[eflalo$LE_GEAR %in% gears2keep, ]
length(unique(eflalo$VE_REF))
length(unique(eflalo$FT_REF))
eflalo$VesselSize <- cut(eflalo$VE_LEN, breaks=c(0,14.99,17.99,23.99,39.99,100), right=FALSE)
lapply(split(eflalo, eflalo$VesselSize), function (x) length(unique(x$VE_REF)))
# compute effort in nmin
tacsatSweptArea$effort_mins <- c(0,as.numeric(diff(tacsatSweptArea$SI_DATIM), units='mins'))
idx <- which( tacsatSweptArea$effort_mins>15 & tacsatSweptArea$LE_GEAR %in% towedGears) # if interval > 15 min
tacsatSweptArea[ idx, "effort_mins"] <- NA # exclude change of haul
idx <- which( tacsatSweptArea$effort_mins >75 & tacsatSweptArea$LE_GEAR %in% seineGears) # if interval > 75 min
tacsatSweptArea[ idx, "effort_mins"] <- NA # exclude change of haul
idx <- which( tacsatSweptArea$effort_mins <0) #
tacsatSweptArea[ idx, "effort_mins"] <- NA # exclude change of vessel id
# vessel size
#15-18, 18-24, 24-40, o40
tacsatSweptArea$VesselSize <- cut(tacsatSweptArea$VE_LEN, breaks=c(0,14.99,17.99,23.99,39.99,100), right=FALSE)
# look at nb of trips per vessel size
lapply(split(tacsatSweptArea, tacsatSweptArea$VesselSize), function (x) length(unique(x$FT_REF)))
lapply(split(tacsatSweptArea, tacsatSweptArea$VesselSize), function (x) length(unique(x$VE_REF)))
# marginal sum of euros
tacsatSweptArea$toteuros <- apply(tacsatSweptArea[,grep("EURO", names(tacsatSweptArea))], 1, sum)
dd <- tacsatSweptArea[,c("VE_REF", "VesselSize", "LE_MET_init", "effort_mins", "toteuros", "LE_KG_LITRE_FUEL")]
dd <- aggregate(dd[,c("effort_mins", "toteuros", "LE_KG_LITRE_FUEL")], list(dd$VE_REF, dd$VesselSize, dd$LE_MET_init), sum, na.rm=TRUE)
colnames(dd) <- c("VE_REF", "VesselSize", "LE_MET", "effective_effort_mins", "toteuros", "litre_fuel")
aggEffortAndFuelAlly <- rbind.data.frame(aggEffortAndFuelAlly, cbind.data.frame(dd, Year=a_year))
}
aggEffortAndFuelAlly0_15m <- aggEffortAndFuelAlly[aggEffortAndFuelAlly$VesselSize=="[0,15)",] # clean up
aggEffortAndFuelAlly <- aggEffortAndFuelAlly[aggEffortAndFuelAlly$VesselSize!="[0,15)",] # clean up
save(aggEffortAndFuelAlly0_15m,file=file.path(outPath, paste("AggregatedEffortAndFuelAlly_BottomFishing0_15m.RData", sep="")))
save(aggEffortAndFuelAlly,file=file.path(outPath, paste("AggregatedEffortAndFuelAlly_BottomFishing.RData", sep="")))
###----------------------
## do a ggplot
load(file=file.path(outPath, paste("AggregatedEffortAndFuelAlly_BottomFishing.RData"))) # aggEffortAlly
library(ggplot2)
some_color_vessel_size <- c("[15,18)"="#FFDB6D", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
p <- ggplot() + geom_bar(data=aggEffortAndFuelAlly, aes(x=as.character(Year), y=effective_effort_mins/60, group=VesselSize, fill=VesselSize), size=1.5, position="stack", stat = "summary", fun = "sum") +
#facet_wrap(. ~ LE_MET, scales = "free_y") +
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
labs(y = "", x = "Year") +
# geom_point(aes(color=VesselSize), size=3) +
scale_fill_manual(values=some_color_vessel_size) +
guides(fill =guide_legend(ncol=1))
print(p)
library(ggplot2)
some_color_vessel_size <- c("[15,18)"="#FFDB6D", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
dd <- aggEffortAndFuelAlly[!duplicated(data.frame(aggEffortAndFuelAlly$VE_REF, aggEffortAndFuelAlly$Year)),]
dd$nbvessel <- 1
a_ylab <- "Nb Vessels"
p2 <- ggplot() +
geom_line(data=dd, aes(x=as.character(Year), y=nbvessel, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum") +
#facet_wrap(. ~ LE_MET, scales = "free_y") +
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
labs(y = a_ylab, x = "Year") +
# geom_point(aes(color=VesselSize), size=3) +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
print(p2)
library(ggplot2)
some_color_vessel_size <- c("[15,18)"="#FFDB6D", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
dd <- aggEffortAndFuelAlly[!duplicated(data.frame(aggEffortAndFuelAlly$VE_REF, aggEffortAndFuelAlly$Year)),]
dd$nbvessel <- 1
a_ylab <- "Fuel use"
p3 <- ggplot() +
geom_line(data=dd, aes(x=as.character(Year), y=litre_fuel/1e6, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum") +
#facet_wrap(. ~ LE_MET, scales = "free_y") +
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
labs(y = a_ylab, x = "Year") +
# geom_point(aes(color=VesselSize), size=3) +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
print(p3)
library(ggplot2)
some_color_vessel_size <- c("[15,18)"="#FFDB6D", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
dd <- aggEffortAndFuelAlly[!duplicated(data.frame(aggEffortAndFuelAlly$VE_REF, aggEffortAndFuelAlly$Year)),]
dd$nbvessel <- 1
a_ylab <- "Income from landings (euros)"
p4 <- ggplot() +
geom_line(data=dd, aes(x=as.character(Year), y=toteuros/1e6, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum") +
#facet_wrap(. ~ LE_MET, scales = "free_y") +
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
labs(y = a_ylab, x = "Year") +
# geom_point(aes(color=VesselSize), size=3) +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
print(p4)
# a trick to combine both info on the same plot i.e. use a secondary y axis
some_color_vessel_size <- c("[15,18)"="#FFDB6D", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
some_color_vessel_size2 <- c("[15,18)"="#ffc207", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
dd <- aggEffortAndFuelAlly[!duplicated(data.frame(aggEffortAndFuelAlly$VE_REF, aggEffortAndFuelAlly$Year)),]
dd$nbvessel <- 1e4
p4 <- ggplot() + geom_bar(data=aggEffortAndFuelAlly, aes(x=as.character(Year), y=effective_effort_mins/60, group=VesselSize, fill=VesselSize), size=1.5, position="stack", stat = "summary", fun = "sum") +
geom_line(data=dd, aes(x=as.character(Year), y=nbvessel, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum") +
geom_line(data=dd, aes(x=as.character(Year), y=litre_fuel/100, group=1),size=1.2, color=1, linetype = "dashed", stat = "summary", fun = "sum") +
geom_line(data=dd, aes(x=as.character(Year), y=toteuros/100, group=1),size=1.2, color=5, linetype = "dashed", stat = "summary", fun = "sum") +
scale_y_continuous(name = "Fished hours effort; or fuel use (litres/100); or euros/100", sec.axis = sec_axis(~./1e4, name = "Nb Vessels") )+
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), axis.title.y=element_text(size=rel(0.8))) +
labs(x = "Year") +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
scale_fill_manual(values=some_color_vessel_size2) +
guides(fill =guide_legend(ncol=1))
print(p4)
# dem
#a_width <- 3000; a_height <- 2300
a_width <- 4000; a_height <- 2500
namefile <- paste0("barplot_and_ts_effort_nb_vessels_", years[1], "-", years[length(years)], "_DEM.tif")
tiff(filename=file.path(outPath, "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
print(p4)
dev.off()
# revised:
load(file=file.path(outPath, paste("AggregatedEffortAndFuelAlly_BottomFishing.RData"))) # aggEffortAlly
some_color_vessel_size <- c("[15,18)"="#FFDB6D", "[18,24)"="#FC4E07", "[24,40)"="#52854C", "[40,100)"="#293352")
dd <- aggEffortAndFuelAlly[!duplicated(data.frame(aggEffortAndFuelAlly$VE_REF, aggEffortAndFuelAlly$Year)),]
dd$nbvessel <- 1
p1 <- ggplot() + geom_bar(data=aggEffortAndFuelAlly, aes(x=as.character(Year), y=effective_effort_mins/60/1000, group=VesselSize, fill=VesselSize), size=1.5, position="stack", stat = "summary", fun = "sum")
p2 <- ggplot() + geom_bar(data=dd, aes(x=as.character(Year), y=nbvessel, group=VesselSize, fill=VesselSize),size=1.5, stat = "summary", fun = "sum")
p3 <- ggplot() + geom_line(data=dd, aes(x=as.character(Year), y=litre_fuel/1e6, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum")
p4 <- ggplot() + geom_line(data=dd, aes(x=as.character(Year), y=toteuros/1e6, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum")
p1 <- p1 + scale_y_continuous(name = "Fishing effort ('000 hours)")+
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), axis.title.y=element_text(size=rel(0.8))) +
labs(x = "Year") +
scale_fill_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
p2 <- p2 + scale_y_continuous(name = "Number of active vessels")+
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), axis.title.y=element_text(size=rel(0.8))) +
labs(x = "Year") +
scale_fill_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
p3 <- p3 + scale_y_continuous(name = "Fuel use (million litres)")+
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), axis.title.y=element_text(size=rel(0.8))) +
labs(x = "Year") +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
p4 <- p4 + scale_y_continuous(name = "Landings (million euros)")+
theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5), axis.title.y=element_text(size=rel(0.8))) +
labs(x = "Year") +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
guides(fill =guide_legend(ncol=1))
save(p1, p2, p3, p4, file="ggplots_for_demersal_nb_vessels.Rdata")
# then do a ggarrange later with the pelagics.....
a_unit <- 1
# paper
a_width <- 8000 ; a_height <- 7500
namefile <- paste0("ggplot.tif")
tiff(filename=file.path("D:","FBA","BENTHIS_2020", "ggplots", "Figure2_revised_nb_vessels_2005-2019_DEM.tiff"), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggpubr)
ggarrange(p1, p2, p3, p4, ncol=1, heights=c(1.5,1.5,1.2,1,1, 1), common.legend = TRUE, legend="right" )
dev.off()
} # end FALSE