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Copy pathfuelEfficiencyGraphs2020_VidWithLogbooksOnly.r
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fuelEfficiencyGraphs2020_VidWithLogbooksOnly.r
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rm(list=ls())
library(vmstools)
library(maps)
library(mapdata)
if(.Platform$OS.type == "windows") {
codePath <- "D:/FBA/BENTHIS_2020/"
dataPath <- "D:/FBA/BENTHIS_2020/EflaloAndTacsat/"
outPath <- file.path("D:","FBA","BENTHIS_2020", "outputs2020_lgbkonly")
polPath <- "D:/FBA/BENTHIS/BalanceMaps"
#a_year <- 2005
#a_year <- 2006
#a_year <- 2007
#a_year <- 2008
#a_year <- 2009
#a_year <- 2010
#a_year <- 2011
#a_year <- 2012
#a_year <- 2013
#a_year <- 2014
#a_year <- 2015
#a_year <- 2016
#a_year <- 2017
#a_year <- 2018
##a_year <- 2019
}
#years <- 2012:2019
years <- 2005:2019
if(FALSE){
eflalo_res <- NULL
for (a_year in years) { # on WINDOWS system...
dir.create(file.path(outPath))
dir.create(file.path(outPath, a_year))
cat(paste("Year", a_year, "\n"))
library(vmstools)
load(file.path(dataPath,paste("eflalo_", a_year,".RData", sep=''))); # get the eflalo object
if(a_year>=2016){
eflalo <- formatEflalo(get(paste0("eflalo_", a_year))) # format each of the columns to the specified class
} else{
eflalo <- formatEflalo(get(paste0("eflalo"))) # format each of the columns to the specified class
}
# marginal sums
idx <- kgeur(colnames(eflalo))
eflalo$LE_KG_SPECS <- rowSums(eflalo[,grep("LE_KG_",colnames(eflalo))],na.rm=T)
eflalo$LE_EURO_SPECS <- rowSums(eflalo[,grep("LE_EURO_",colnames(eflalo))],na.rm=T)
# Remove non-unique trip numbers
eflalo <- eflalo[!duplicated(paste(eflalo$LE_ID,eflalo$LE_CDAT,sep="-")),]
# Remove impossible time stamp records
eflalo$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$FT_LTIME, sep = " "),
tz = "GMT", format = "%d/%m/%Y %H:%M")
eflalo <- eflalo[!(is.na(eflalo$FT_DDATIM) |is.na(eflalo$FT_LDATIM)),] # some leaks of data there.
# Remove trip starting before 1st Jan
# year <- min(year(eflalo$FT_DDATIM)) # deprecated?
eflalo <- eflalo[eflalo$FT_DDATIM>=strptime(paste(a_year,"-01-01 00:00:00",sep=''),
"%Y-%m-%d %H:%M"),]
# Remove records with arrival date before departure date
eflalop <- eflalo
eflalop$FT_DDATIM <- as.POSIXct(paste(eflalo$FT_DDAT, eflalo$FT_DTIME, sep=" "),
tz="GMT", format="%d/%m/%Y %H:%M")
eflalop$FT_LDATIM <- as.POSIXct(paste(eflalo$FT_LDAT, eflalo$FT_LTIME, sep=" "),
tz="GMT", format="%d/%m/%Y %H:%M")
idx <- which(eflalop$FT_LDATIM >= eflalop$FT_DDATIM)
eflalo <- eflalo[idx,]
# compute effort of the trip
eflalo <- subset(eflalo,FT_REF != 0)
eflalo <- orderBy(~VE_REF+FT_DDATIM+FT_REF, data=eflalo)
eflalo$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)$x
eflalo <- cbind.data.frame(eflalo, Year=a_year)
if (a_year==years[1]) { eflalo_res <- eflalo }
cols <- unique(colnames(eflalo)[colnames(eflalo) %in% colnames(eflalo_res)],
colnames(eflalo_res)[ colnames(eflalo_res) %in% colnames(eflalo)])
if (a_year>years[1]) {
eflalo_res <- rbind.data.frame(eflalo_res[,cols], eflalo[,cols])
}
} # end y
# keep all vessels with < 12m in length (i.e. most of them without VMS)
eflalo <- eflalo_res
eflalo_small_vids <- eflalo_res[as.numeric(as.character(eflalo$VE_LEN)) <= 11.99,]
save(eflalo, file=file.path(dataPath ,paste("eflalo_ally_",years[1],"-",years[length(years)],".RData", sep='')))
save(eflalo_small_vids, file=file.path(dataPath ,paste("eflalo_small_vids_ally_",years[1],"-",years[length(years)],".RData", sep='')))
} # end FALSE
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#load(file=file.path(dataPath ,paste("eflalo_small_vids_ally_",years[1],"-",years[length(years)],".RData", sep='')))
#eflalo <- eflalo_small_vids
load(file=file.path(dataPath ,paste("eflalo_ally_",years[1],"-",years[length(years)],".RData", sep='')))
eflalo <- eflalo
spp <- c("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")
color_species <- c("#E69F00","hotpink","#56B4E9","#F0E442", "green", "#0072B2", "mediumorchid4", "#CC79A7",
"indianred2", "#EEC591", "#458B00", "#F0F8FF", "black", "#e3dcbf", "#CD5B45", "lightseagreen",
"#6495ED", "#CDC8B1", "#00FFFF", "#8B0000", "#008B8B", "#A9A9A9", "#76a5c4", "red", "yellow", "blue")
some_color_species<- c("COD"="#E69F00", "CSH"="hotpink", "DAB"="#56B4E9", "ELE"="#F0E442", "FLE"="green",
"HAD"="#0072B2", "HER"="mediumorchid4", "HKE"="#CC79A7","HOM"="indianred2", "LEM"="#EEC591",
"MAC"="#458B00", "MON"="#F0F8FF", "MUS"="black", "NEP"="#e3dcbf", "NOP"="#CD5B45", "PLE"="lightseagreen",
"POK"="#6495ED", "PRA"="#CDC8B1", "SAN"="#00FFFF", "SOL"="#8B0000", "SPR"="#008B8B", "TUR"="#A9A9A9", "WHB"="#76a5c4",
"WIT"="red", "WHG"="yellow", "OTH"="blue",
"COC"="#108291", "OYF"="#6a9110", "LUM"="red", "SAL"="#c2a515") # specific to small vids
per_metier_level6 <- TRUE
per_vessel_size <- FALSE
per_region <- TRUE
# search in Baltic and North Sea
library(rgdal)
fao_areas <- readOGR(file.path(getwd(), "FAO_AREAS", "FAO_AREAS.shp"))
fao_areas <- fao_areas[ grepl("27", fao_areas$F_AREA) & fao_areas$F_SUBAREA %in% c("27.3", "27.4", "27.2") & fao_areas$F_LEVEL!="MAJOR",] # caution with the MAJOR overidding the over()
eflalo$VesselSize <- cut(eflalo$VE_LEN, breaks=c(0,11.99,17.99,23.99,39.99,100), right=FALSE) # ideally, should have been right=TRUE...
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
eflalo <- eflalo[eflalo$VesselSize=="[0,12)",]
eflalo <- eflalo[!is.na(eflalo$VesselSize),]
eflalo$LE_MET <- factor(eflalo$LE_MET)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# fuel comsumption
if(FALSE){
# ad hoc
# from there decrease the maximal fuel cons by a conversion factor assuming the engine is not switch on all the time:
#1. All trawling and Flyshooting (OTB, OTM, TBB, DRB, SSC, etc.): Engine load/conversion factor of 90% for the duration of the trip (Assumed as an average across shorter or longer periods of steaming, trawling, setting or hauling)
#2. All passive gears (GNS, Pots, lines, etc.): Engine load/conversion factor of 50% for the duration of the trip (Assumed as an average across shorter or longer periods of steaming, setting or hauling)
#3. Danish seiners (SDN): Engine load/conversion factor of 67% for the duration of the trip (Assumed as an average across shorter or longer periods of steaming, setting or hauling)
# TO DO:
eflalo$convfactor <- 1
convfactor <- c("OTB"=0.9,"GN"=0.5,"SSC"=0.67,"GTR"=0.5,"PTM"=0.9,"DRB"=0.9,"MIS"=0.5,"GNS"=0.5,"FPN"=0.5,"LL"=0.5,"UNK"=0.5,"SDN"=0.67,"LHP"=0.5,"LLS"=0.5,
"GND"=0.5,"PTB"=0.9,"OTM"=0.9,"LLD"=0.5,"FYK"=0.5,"FPO"=0.5,"LH"=0.5,"OTT"=0.9,"TBB"=0.9,"FIX"=0.5,"LX"=0.5,"DRH"=0.5,"OFG"=0.5,"ZZZ"=0.5,
"GTN"=0.5,"DRC"=0.5,"LTL"=0.5,"TBN"=0.9,"AHB"=0.5,"BMS"=0.5,"DRO"=0.5,"KLM"=0.5,"BRJ"=0.5)
eflalo$convfactor <- convfactor[as.character(eflalo$LE_GEAR)]
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$LE_EFF * eflalo$convfactor # Liter per hour * effort this trip in hour
} # end FALSE
if(FALSE){
# use AIS data to deduce fuel cons from a typical speed profile (per DCF level6) as we don´t have speed info for small vessels in logbooks
# (we also dont have AIS data for all small vessels becuase no mandatory) and deduce and apply typical fuel cons per hour (computed from kW and speed) to each trip.
# this is assuning a mean kW for each segment which might be a rough assumption given the kW is driving very much the fuel consumption rate.
# for towed gears, this is also assuming 0.9 of max speed when within a certain interval in speed (i.e. when towing...) (see GetTripFuelConsFromAISdata.R for details)
load(file=file.path(getwd(), "AIS_data", "fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m.RData")) # get fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m (but a rough average)
eflalo$fuel_cons_in_trip_level_6 <- fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m[ as.character(eflalo$LE_MET) ]
eflalo$LE_MET5 <- substr(as.character(eflalo$LE_MET), 1,7)
fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m <- cbind.data.frame(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m, LE_MET5= substr(names(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m), 1,7) )
fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m <- tapply(fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m[,1], fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m$LE_MET5, mean, na.rm=TRUE)
eflalo$fuel_cons_in_trip_level_5 <- fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m[eflalo$LE_MET5 ]
eflalo$LE_MET4 <- substr(as.character(eflalo$LE_MET), 1,3)
fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m <- cbind.data.frame(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m, LE_MET4= substr(names(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m), 1,3) )
fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m <- tapply(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m[,1], fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m$LE_MET4, mean, na.rm=TRUE)
fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m <- c(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m, LHP=as.numeric(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m["LLD"]), GND=as.numeric(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m["GNS"])) # a small fix to avoid loosing vessels
eflalo$fuel_cons_in_trip_level_4 <- fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m[eflalo$LE_MET4 ]
# take the best estimate first, then, if NAs, fill the gaps with lower levels
eflalo$fuel_cons_in_trip_level <- eflalo$fuel_cons_in_trip_level_6
eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level"] <- eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level_5"]
eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level"] <- eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level_4"]
# check
unique(eflalo[is.na(eflalo$fuel_cons_in_trip_level_4) ,"VE_REF"]) # ideally, we should lost 0 vessels
eflalo$LE_KG_LITRE_FUEL <- as.numeric(as.character(eflalo$fuel_cons_in_trip_level)) * eflalo$LE_EFF
} # end FALSE
if(TRUE){
load(file=file.path(getwd(), "AIS_data", "ais_profile_small_vessels.RData")) # ais_profile
table.fuelcons.per.engine <- read.table(file= file.path(getwd(),"EflaloAndTacsat", "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$max_consumed_per_h <- predict(linear.model, newdata=data.frame(kW2=as.numeric(as.character(eflalo$VE_KW))))
fuel_per_h <- function (a,x) a*(x^3) # cubic law, x is vessel speed
eflalo$a <- NA
eflalo$fuelcons_per_h <- NA
eflalo$LE_MET <- as.character(eflalo$LE_MET)
eflalo$max_consumed_per_h <- as.numeric(as.character(eflalo$max_consumed_per_h ))
ais_profile$max_vessel_speed <- as.numeric(as.character(ais_profile$max_vessel_speed))
ais_profile$Speed <- as.numeric(as.character(ais_profile$Speed))
ais_profile$Prop <- as.numeric(as.character(ais_profile$Prop))
ais_profile$Level6 <- as.character(ais_profile$Level6)
ais_profile$Weight <- ais_profile[, "Speed"] * ais_profile[, "Prop"]
library(data.table)
ais_profile_dt <- ais_profile[,c('Level6',"Speed", "Prop")]
wide_prop <- data.table::dcast(setDT(ais_profile_dt), Level6 ~ Speed, value.var="Prop")
wide_prop <- as.data.frame(wide_prop)
rownames(wide_prop) <- wide_prop$Level6
wide_prop[is.na(wide_prop)] <-0
eflalo <- cbind.data.frame(eflalo, wide_prop[eflalo$LE_MET,])
maxspeed_per_metier <- ais_profile[!duplicated(ais_profile$Level6),c("Level6", "max_vessel_speed")]
rownames(maxspeed_per_metier) <- maxspeed_per_metier$Level6
eflalo <- cbind.data.frame(eflalo, max_vessel_speed=maxspeed_per_metier[eflalo$LE_MET,"max_vessel_speed"])
eflalo$a <- eflalo$max_consumed_per_h / (eflalo$max_vessel_speed^3)
cubic_speed <- matrix(as.numeric(colnames(wide_prop[,-1]))^3, ncol=length(colnames(wide_prop[,-1])), nrow=nrow(eflalo), byrow=TRUE)
prop_per_speed_bin <- eflalo[,c(colnames(wide_prop[,-1]))]
fuelcons_max_per_speed <- sweep(cubic_speed, 1, eflalo$a, FUN="*")
colnames(fuelcons_max_per_speed) <- colnames(wide_prop[,-1])
# Assume max vessel consumption when towing for towed gears (i.e. within a Speed interval assuming fishing) because of the dragging resistance of the towed net
towedGears <- c("OTB", "PTB", "DRB", "OTM", "PTM", "SSC", "SDN")
eflalo$Gear <- substr(eflalo$Level6, 1,3)
idx <- eflalo$Gear %in% c(towedGears)
fuelcons_max_per_speed [, c("1.5", "2.5", "3.5")] <- eflalo [,"max_consumed_per_h"] *0.9 #assume 90% full load when a towed net is fishing
eflalo$fuelcons_per_h <- apply(fuelcons_max_per_speed*prop_per_speed_bin, 1, sum)
# => doing a weighted average of fuel comsumption with standardized vessel speed profile as weight (then lower than assuming vessel always at max_speed)
# back up if some missing metiers in the sampled AIS vessels....
load(file=file.path(getwd(), "AIS_data", "fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m.RData")) # get fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m (but a rough average)
eflalo$LE_MET5 <- substr(as.character(eflalo$LE_MET), 1,7)
fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m <- cbind.data.frame(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m, LE_MET5= substr(names(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m), 1,7) )
fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m <- tapply(fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m[,1], fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m$LE_MET5, mean, na.rm=TRUE)
eflalo$fuel_cons_in_trip_level_5 <- fuel_cons_in_trip_per_level5_per_hour_for_vessels_under_12m[eflalo$LE_MET5 ]
eflalo$LE_MET4 <- substr(as.character(eflalo$LE_MET), 1,3)
fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m <- cbind.data.frame(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m, LE_MET4= substr(names(fuel_cons_in_trip_per_level6_per_hour_for_vessels_under_12m), 1,3) )
fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m <- tapply(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m[,1], fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m$LE_MET4, mean, na.rm=TRUE)
fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m <- c(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m, LHP=as.numeric(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m["LLD"]), GND=as.numeric(fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m["GNS"])) # a small fix to avoid loosing vessels
eflalo$fuel_cons_in_trip_level_4 <- fuel_cons_in_trip_per_level4_per_hour_for_vessels_under_12m[eflalo$LE_MET4 ]
# take the best estimate first, then, if NAs, fill the gaps with lower levels
eflalo$fuel_cons_in_trip_level <- eflalo$fuel_cons_in_trip_level_6
eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level"] <- eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level_5"]
eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level"] <- eflalo[is.na(eflalo$fuel_cons_in_trip_level),"fuel_cons_in_trip_level_4"]
# check
unique(eflalo[is.na(eflalo$fuel_cons_in_trip_level_4) ,"VE_REF"]) # ideally, we should lost 0 vessels
eflalo$LE_KG_LITRE_FUEL <- as.numeric(as.character(eflalo$fuel_cons_in_trip_level)) * eflalo$LE_EFF
}
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
eflalo <- eflalo[!is.na(eflalo$VesselSize),]
eflalo$VesselSize <- factor(eflalo$VesselSize )
eflalo <- eflalo[!is.na(eflalo$LE_KG_LITRE_FUEL),] # loss of a bunch of few vessels
## PLOT TIME SERIES OF TRIP EFFORT AND NB OF VESSELS
# marginal sum of euros
eflalo$toteuros <- apply(eflalo[,grep("EURO", names(eflalo))], 1, sum, na.rm=TRUE)
dd <- eflalo[,c("VE_REF", "VesselSize", "LE_MET", "LE_EFF", "toteuros", "LE_KG_LITRE_FUEL", "Year")]
dd <- aggregate(dd[,c("LE_EFF", "toteuros", "LE_KG_LITRE_FUEL")], list(dd$VE_REF, dd$VesselSize, dd$LE_MET, dd$Year), sum, na.rm=TRUE)
colnames(dd) <- c("VE_REF", "VesselSize", "LE_MET", "Year", "trip_effort_hours", "toteuros", "litre_fuel")
# a trick to combine both info on the same plot i.e. use a secondary y axis
library(ggplot2)
#some_color_vessel_size <- c("(0,12]"="#999999", "(12,18]"="#FFDB6D", "(18,24]"="#FC4E07", "(24,40]"="#52854C", "(40,100]"="#293352")
# some_color_vessel_size2 <- c("(0,12]"="#999999", "(12,18]"="#ffc207", "(18,24]"="#c93e05", "(24,40]"="#416a3c", "(40,100]"="#293d52")
some_color_vessel_size <- c("[0,12)"="#999999", "[12,18)"="#FFDB6D", "[18,24)"="#c93e05", "[24,40)"="#52854C", "[40,100)"="#293352")
some_color_vessel_size2 <- c("[0,12)"="#999999", "[12,18)"="#ffc207", "[18,24)"="#FC4E07", "[24,40)"="#416a3c", "[40,100)"="#293d52")
dd <- dd[!duplicated(data.frame(dd$VE_REF, dd$Year)),]
dd$nbvessel <- 1e3
p3 <- ggplot() + geom_bar(data=eflalo, aes(x=as.character(Year), y=LE_EFF, 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, 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 = "Trip effort hours; fuel use (litre); euros/100", sec.axis = sec_axis(~./1e3, name = "Nb Vessels") )+
theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) +
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(p3)
# lgbkonly
#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)], "_lgbkonly.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
print(p3)
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
library(vmstools)
eflalo <- cbind.data.frame(eflalo,
vmstools::ICESrectangle2LonLat(statsq=eflalo$LE_RECT, midpoint=TRUE)
)
eflalo <- eflalo[!is.na(eflalo$SI_LATI),] # some leaks of data there. i.e. total effort is not complete, so do not use it
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# find metiers lvl6-Vsize to merge
if(per_metier_level6 && per_vessel_size){
# code F_SUBAREA (time consuming code...)
# Convert all points first to SpatialPoints first
library(rgdal)
library(raster)
an <- function(x) as.numeric(as.character(x))
coords <- SpatialPoints(cbind(SI_LONG=an(eflalo[, "SI_LONG"]), SI_LATI=an(eflalo[, "SI_LATI"])))
fao_areas <- spTransform(fao_areas, CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")) # convert to longlat
projection(coords) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") # a guess!
idx <- over(coords, fao_areas)
eflalo$F_SUBAREA <- idx[,"F_SUBAREA"]
eflalo[is.na(eflalo$F_SUBAREA), "F_SUBAREA"] <- "27.4" # few points on coastline
eflalo$F_DIVISION <- idx[,"F_DIVISION"]
eflalo[is.na(eflalo$F_DIVISION), "F_DIVISION"] <- paste0(eflalo[is.na(eflalo$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
#
eflalo$LE_MET <- factor(eflalo$LE_MET)
levels(eflalo$LE_MET) <- gsub("MCD_90-119_0_0", "DEF_90-119_0_0", levels(eflalo$LE_MET)) # immediate correction to avoid an artifical split
levels(eflalo$LE_MET) <- gsub("_MCD_>=120", "_DEF_>=120", levels(eflalo$LE_MET)) # immediate correction to avoid an artifical split
# code small vs large mesh
eflalo$target <- factor(eflalo$LE_MET) # init
code <- sapply(strsplit(levels(eflalo$target), split="_"), function(x) x[3])
levels(eflalo$target) <- code
levels(eflalo$target)[levels(eflalo$target) %in% c(">=105","100-119","90-119",">=120","90-104", ">=220", "70-89", ">=157", ">=156", "110-156", "120-219", "90-99")] <- "LargeMesh"
levels(eflalo$target)[!levels(eflalo$target) %in% "LargeMesh"] <- "SmallMesh"
dd <- tapply(eflalo$LE_EFF, paste0(eflalo$target, "_", eflalo$F_SUBAREA, "_", eflalo$LE_MET, "_", eflalo$VesselSize), sum)
pel <- dd[grep("SmallMesh",names(dd))]
pel <- pel[order(pel, decreasing=TRUE)]
oth_mets_pel <- names(pel)[cumsum(pel)/sum(pel)>.75]
oth_mets_pel <- c(oth_mets_pel, "NA_27.3_No_Matrix6_(0,12]", "NA_27.4_No_Matrix6_(0,12]")
dem <- dd[grep("LargeMesh",names(dd))]
dem <- dem[order(dem, decreasing=TRUE)]
oth_mets_dem <- names(dem)[cumsum(dem)/sum(dem)>.75]
oth_mets_dem <- c(oth_mets_dem, "NA_27.3_No_Matrix6_(0,12]", "NA_27.4_No_Matrix6_(0,12]")
eflalo$LE_MET_init <- eflalo$LE_MET
eflalo$LE_MET <- factor(paste0(eflalo$target, "_", eflalo$F_SUBAREA, "_", eflalo$LE_MET, "_", eflalo$VesselSize))
levels(eflalo$LE_MET)[levels(eflalo$LE_MET) %in% oth_mets_pel] <- "SmallMesh_OTHER_0_0_0"
levels(eflalo$LE_MET)[levels(eflalo$LE_MET) %in% oth_mets_dem] <- "LargeMesh_OTHER_0_0_0"
}
if(per_metier_level6 && !per_vessel_size){
# code F_SUBAREA (time consuming code...)
# Convert all points first to SpatialPoints first
library(rgdal)
library(raster)
an <- function(x) as.numeric(as.character(x))
coords <- SpatialPoints(cbind(SI_LONG=an(eflalo[, "SI_LONG"]), SI_LATI=an(eflalo[, "SI_LATI"])))
fao_areas <- spTransform(fao_areas, CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")) # convert to longlat
projection(coords) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") # a guess!
idx <- over(coords, fao_areas)
eflalo$F_SUBAREA <- idx[,"F_SUBAREA"]
eflalo[is.na(eflalo$F_SUBAREA), "F_SUBAREA"] <- "27.4" # few points on coastline
eflalo$F_DIVISION <- idx[,"F_DIVISION"]
eflalo[is.na(eflalo$F_DIVISION), "F_DIVISION"] <- paste0(eflalo[is.na(eflalo$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
#
eflalo$LE_MET <- factor(eflalo$LE_MET)
levels(eflalo$LE_MET) <- gsub("MCD_90-119_0_0", "DEF_90-119_0_0", levels(eflalo$LE_MET)) # immediate correction to avoid an artifical split
levels(eflalo$LE_MET) <- gsub("_MCD_>=120", "_DEF_>=120", levels(eflalo$LE_MET)) # immediate correction to avoid an artifical split
# code small vs large mesh
eflalo$target <- factor(eflalo$LE_MET) # init
code <- sapply(strsplit(levels(eflalo$target), split="_"), function(x) x[3])
levels(eflalo$target) <- code
levels(eflalo$target)[levels(eflalo$target) %in% c(">=105","100-119","90-119",">=120","90-104", ">=220", "70-89", ">=157", ">=156", "110-156", "120-219", "90-99")] <- "LargeMesh"
levels(eflalo$target)[!levels(eflalo$target) %in% "LargeMesh"] <- "SmallMesh"
dd <- tapply(eflalo$LE_EFF, paste0(eflalo$target, "_", eflalo$F_SUBAREA, "_", eflalo$LE_MET), sum)
pel <- dd[grep("SmallMesh",names(dd))]
pel <- pel[order(pel, decreasing=TRUE)]
oth_mets_pel <- names(pel)[cumsum(pel)/sum(pel)>.70]
oth_mets_pel <- c(oth_mets_pel, "NA_27.3_No_Matrix6", "NA_27.4_No_Matrix6")
dem <- dd[grep("LargeMesh",names(dd))]
dem <- dem[order(dem, decreasing=TRUE)]
oth_mets_dem <- names(dem)[cumsum(dem)/sum(dem)>.75]
oth_mets_dem <- c(oth_mets_dem, "NA_27.3_No_Matrix6", "NA_27.4_No_Matrix6")
eflalo$LE_MET_init <- eflalo$LE_MET
eflalo$LE_MET <- factor(paste0(eflalo$target, "_", eflalo$F_SUBAREA, "_", eflalo$LE_MET))
levels(eflalo$LE_MET)[levels(eflalo$LE_MET) %in% oth_mets_pel] <- "SmallMesh_OTHER_0_0_0"
levels(eflalo$LE_MET)[levels(eflalo$LE_MET) %in% oth_mets_dem] <- "LargeMesh_OTHER_0_0_0"
}
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# OTH species
spp <- gsub("LE_KG_", "", colnames(eflalo)[grepl("LE_KG", colnames(eflalo))])
spp <- spp[!spp %in% c("SPECS", "LITRE_FUEL")]
dd <- apply(eflalo[,paste0("LE_EURO_",spp)], 2, sum, na.rm=TRUE)
dd <- dd[order(dd, decreasing=TRUE)]
main_species <-gsub("LE_EURO_", "", names(dd[cumsum(dd)/sum(dd)<0.93]) )
oth_species <- gsub("LE_EURO_", "", names(dd[cumsum(dd)/sum(dd)>=0.93]) )
eflalo$LE_EURO_OTH <- apply(eflalo[,paste0("LE_EURO_",oth_species)], 1, sum, na.rm=TRUE)
eflalo$LE_KG_OTH <- apply(eflalo[,paste0("LE_KG_",oth_species)], 1, sum, na.rm=TRUE)
spp <- c(main_species, "OTH")
eflalo <- eflalo[,!colnames(eflalo) %in% paste0("LE_EURO_",oth_species)]
eflalo <- eflalo[,!colnames(eflalo) %in% paste0("LE_KG_",oth_species)]
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# compute some ratios (to plot across spp)
eflalo$KKGallsp <- apply (eflalo[, paste0('LE_KG_', spp)], 1, sum, na.rm=TRUE) /1e3 # in tons
eflalo$KEUROallsp <- apply (eflalo[, paste0('LE_EURO_', spp)], 1, sum, na.rm=TRUE) / 1e3 # in thousands euros
dd <- sweep(eflalo[, paste0('LE_KG_', spp)], 1, eflalo$LE_EFF, FUN="/")
colnames(dd) <- paste0('LE_CPUE_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
eflalo <- cbind.data.frame (eflalo, dd)
eflalo$CPUEallsp <- apply (eflalo[, paste0('LE_CPUE_', spp)], 1, sum, na.rm=TRUE)
dd <- sweep(eflalo[, paste0('LE_KG_', spp)], 1, eflalo$LE_KG_LITRE_FUEL, FUN="/")
colnames(dd) <- paste0('LE_CPUF_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
eflalo <- cbind.data.frame (eflalo, dd)
eflalo$CPUFallsp <- apply (eflalo[, paste0('LE_CPUF_', spp)], 1, sum, na.rm=TRUE)
dd <- sweep(eflalo[, paste0('LE_EURO_', spp)], 1, eflalo$LE_KG_LITRE_FUEL, FUN="/")
colnames(dd) <- paste0('LE_VPUF_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
eflalo <- cbind.data.frame (eflalo, dd)
eflalo$VPUFallsp <- apply (eflalo[, paste0('LE_VPUF_', spp)], 1, sum, na.rm=TRUE)
dd <- eflalo[, paste0('LE_EURO_', spp)]/ eflalo[, paste0('LE_KG_', spp)]
colnames(dd) <- paste0('LE_MPRICE_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
eflalo <- cbind.data.frame (eflalo, dd)
eflalo$mpriceallsp <- apply (eflalo[, paste0('LE_MPRICE_', spp)], 1, mean, na.rm=TRUE)
idx_cols <- grepl("LE_VPUF_", names(eflalo))
dd <- apply (eflalo[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
eflalo$sp_with_max_vpuf <- gsub("LE_VPUF_", "", names(eflalo[,idx_cols])[dd])
idx_cols <- grepl("LE_CPUE_", names(eflalo))
dd <- apply (eflalo[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
eflalo$sp_with_max_cpue <- gsub("LE_CPUE_", "", names(eflalo[,idx_cols])[dd])
idx_cols <- grepl("LE_CPUF_", names(eflalo))
dd <- apply (eflalo[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x)) [1]
})
eflalo$sp_with_max_cpuf <- gsub("LE_CPUF_", "", names(eflalo[,idx_cols])[dd])
idx_cols <- grepl("LE_VPUFSWA_", names(eflalo))
dd <- apply (eflalo[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
eflalo$sp_with_max_vpufswa <- gsub("LE_VPUFSWA_", "", names(eflalo[,idx_cols])[dd])
# capture an export for quickmap2020.r
if(per_metier_level6 && !per_vessel_size){
save(eflalo, file=file.path(getwd(), "outputs2020_lgbkonly", paste("AggregatedEflaloWithSmallVids",years[1],"-",years[length(years)],".RData", sep="")))
}
# just for info and a rough approximation
total_kg1 <- tapply(eflalo$LE_KG_SPECS, list(eflalo$LE_MET), sum, na.rm=TRUE)
total_kg2 <- tapply(eflalo$LE_KG_SPECS, list(eflalo$LE_MET, as.character(eflalo$sp_with_max_cpue)), sum, na.rm=TRUE)
an_order <- total_kg1 [order(total_kg1, decreasing=TRUE)]
xx<- round(total_kg2[names(an_order),]) # cumul over period
# agg
agg_by <- c("Year","LE_MET")
# aggregate ("sum" if absolute value, "mean" if ratio)
nm <- names(eflalo)
library(data.table)
# for sum
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('LE_EFF', nm)
idx.col.kkg <- grep('KKGallsp', nm, fixed=TRUE)
idx.col.keuro <- grep('KEUROallsp', nm, fixed=TRUE)
idx.col <- c(idx.col.euro, idx.col.kg, idx.col.swpt, idx.col.effectiveeffort, idx.col.kkg, idx.col.keuro)
DT <- data.table(eflalo)
eq1 <- c.listquote( paste ("sum(",nm[idx.col],",na.rm=TRUE)",sep="") )
a_by <- c.listquote( agg_by )
aggResultPerMet <- DT[,eval(eq1),by=eval(a_by)]
aggResultPerMet <- data.frame(aggResultPerMet)
colnames(aggResultPerMet) <- c(agg_by, colnames(eflalo)[idx.col] )
library(doBy)
aggResultPerMet <- orderBy (as.formula(paste("~ ", paste(agg_by, collapse="+"))), data=aggResultPerMet) # order to ensure same order when collating....
# for average
idx.col.1 <- grep('CPUEallsp', nm, fixed=TRUE)
idx.col.2 <- grep('CPUFallsp', nm, fixed=TRUE)
idx.col.3 <- grep('VPUFallsp', nm, fixed=TRUE)
idx.col.5 <- grep('mpriceallsp', nm, fixed=TRUE)
idx.col.cpue <- grep('LE_CPUE_', nm)
idx.col.cpuf <- grep('LE_CPUF_', nm)
idx.col.vpuf <- grep('LE_VPUF_', nm)
idx.col.mprice <- grep('LE_MPRICE_', nm)
idx.col <- c(idx.col.1, idx.col.2, idx.col.3, idx.col.5, idx.col.cpue,idx.col.cpuf, idx.col.vpuf, idx.col.mprice)
a_mean <- function(x, na.rm) mean(x[x!=0], na.rm=na.rm) # modify the mean() so that 0 are first removed....
eq1 <- c.listquote( paste ("a_mean(",nm[idx.col],",na.rm=TRUE)",sep="") )
DT <- data.table(eflalo)
a_by <- c.listquote( agg_by )
aggResultPerMet2 <- DT[,eval(eq1),by=eval(a_by)]
aggResultPerMet2 <- data.frame(aggResultPerMet2)
colnames(aggResultPerMet2) <- c(agg_by, colnames(eflalo)[idx.col] )
aggResultPerMet2 <- orderBy (as.formula(paste("~ ", paste(agg_by, collapse="+"))), data=aggResultPerMet2) # order to ensure same order when collating....
# collate
aggResultPerMet <- cbind(aggResultPerMet, aggResultPerMet2[,-c(1:length(agg_by))])
# then do some estimates, caution, after the aggregation for those ones
# litre per kilo catch
aggResultPerMet$FPUCallsp <- aggResultPerMet$LE_KG_LITRE_FUEL/(aggResultPerMet$KKGallsp*1000)
aggResultPerMet$FPUCallsp [is.infinite(aggResultPerMet$FPUCallsp)] <- 0
# litre per euro catch
aggResultPerMet$FPUVallsp <- aggResultPerMet$LE_KG_LITRE_FUEL/(aggResultPerMet$KEUROallsp*1000)
aggResultPerMet$FPUVallsp [is.infinite(aggResultPerMet$FPUVallsp)] <- 0
# debug outlier e.g. SAL?
aggResultPerMet[aggResultPerMet$FPUCallsp>50, c("FPUCallsp", "FPUVallsp")] <- 50
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!###
## QUICK & EASY TABLES
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!###
# capture an export for later doing some quick table
aggResultPerMetAlly <- aggResultPerMet
#save(aggResultPerMetAlly, file=file.path(getwd(), "outputs2020_lgbkonly", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosSmallVids",years[1],"-",years[length(years)],".RData", sep="")))
#save(aggResultPerMetAlly, file=file.path(getwd(), "outputs2020_lgbkonly", paste("aggResultPerMetAllyMet6AndRatiosSmallVids",years[1],"-",years[length(years)],".RData", sep="")))
if(per_metier_level6 && per_vessel_size){
load(file=file.path(getwd(), "outputs2020_lgbkonly", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosSmallVids",years[1],"-",years[length(years)],".RData", sep=""))) # aggResultPerMetAlly
# add a fuel efficiency metric
aggResultPerMetAlly$LPUE <- aggResultPerMetAlly$LE_KG_LITRE_FUEL / (aggResultPerMetAlly$LE_EFF)
aggResultPerMetAlly$LE_MESH_GROUP <- NA
aggResultPerMetAlly[grepl("LargeMesh",aggResultPerMetAlly$LE_MET), "LE_MESH_GROUP"] <- "LargeMesh"
aggResultPerMetAlly[grepl("SmallMesh",aggResultPerMetAlly$LE_MET), "LE_MESH_GROUP"] <- "SmallMesh"
aggResultPerMetAlly$LE_MET <- gsub("LargeMesh_", "", aggResultPerMetAlly$LE_MET)
aggResultPerMetAlly$LE_MET <- gsub("SmallMesh_", "", aggResultPerMetAlly$LE_MET)
### plot LPUE
library(ggplot2)
dd <- aggResultPerMetAlly[,c("Year", "LPUE", "LE_MET", "LE_MESH_GROUP")]
dd <- dd[!(grepl("OTHER",aggResultPerMetAlly$LE_MET) | grepl("NA",aggResultPerMetAlly$LE_MET)),]
dd <- dd[dd$LE_MET %in%names(table(dd$LE_MET))[table(dd$LE_MET)==length(years)] ,] # keep complete ts only
a_lpue_plot <- ggplot(dd, aes(x=as.character(Year), y=as.numeric(LPUE), group=as.character(LE_MET))) +
geom_line(aes(color=LE_MET), size=1.5) +
facet_wrap(. ~ LE_MESH_GROUP, scales = "free_y", ncol=1) + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) +
labs(y = "Fuel efficiency (Litre fuel per hour)", x = "Year") +
scale_colour_manual(values=some_color_seg, name="Fleet-segment") + guides(fill =guide_legend(ncol=1)) +
xlab("")
print(a_lpue_plot)
# for paper:
a_width <- 6000; a_height <- 4000
namefile <- paste0("ts_LPUE_", years[1], "-", years[length(years)], "_PEL_DEM.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggpubr)
ggarrange(a_lpue_plot, ncol=1, common.legend = TRUE, legend="right")
dev.off()
}
if(per_metier_level6 && !per_vessel_size){
load(file=file.path(getwd(), "outputs2020_lgbkonly", paste("aggResultPerMetAllyMet6AndRatiosSmallVids",years[1],"-",years[length(years)],".RData", sep=""))) # aggResultPerMetAlly
}
library(doBy)
spp <- colnames(aggResultPerMetAlly) [grep("LE_EURO_", colnames(aggResultPerMetAlly))] ; spp <- gsub("LE_EURO_", "", spp) ; spp <- spp [!spp=="SPECS"]
# split fuel
PercentThisStk <- aggResultPerMetAlly[paste0("LE_KG_",spp)] / apply(aggResultPerMetAlly[paste0("LE_KG_",spp)], 1, sum, na.rm=TRUE)*100
colnames(PercentThisStk) <- paste0("Percent_",spp)
aggResultPerMetAlly <- cbind.data.frame (aggResultPerMetAlly, PercentThisStk)
VarThisStk <- sweep(PercentThisStk[,colnames(PercentThisStk)]/100, 1, aggResultPerMetAlly[,"LE_KG_LITRE_FUEL"], FUN="*")
colnames(VarThisStk) <- paste0("LE_LITRE_",spp)
aggResultPerMetAlly <- cbind.data.frame (aggResultPerMetAlly, VarThisStk)
# split effort
VarThisStk <- sweep(PercentThisStk[,colnames(PercentThisStk)]/100, 1, aggResultPerMetAlly[,"LE_EFF"], FUN="*")
colnames(VarThisStk) <- paste0("LE_EFFORT_",spp)
aggResultPerMetAlly <- cbind.data.frame (aggResultPerMetAlly, VarThisStk)
# a quick informative table (for kg)
nm <- colnames(aggResultPerMetAlly)
tot <- aggregate(aggResultPerMetAlly[, c(paste0("LE_KG_", spp), "LE_KG_LITRE_FUEL") ], list(aggResultPerMetAlly$LE_MET), mean) # annual average
tot <- orderBy(~ - LE_KG_LITRE_FUEL, tot)
tot[,-1] <- round(tot[,-1]/1e6,2) # millions litres or thousand tons or euros
head(tot, 5)
# same but for euros
aggResultPerMetAlly$LE_EURO_LITRE_FUEL <- aggResultPerMetAlly$LE_KG_LITRE_FUEL # a tip for ordering
nm <- colnames(aggResultPerMetAlly)
tot <- aggregate(aggResultPerMetAlly[, c(paste0("LE_EURO_", spp),"LE_EURO_LITRE_FUEL")], list(aggResultPerMetAlly$LE_MET), mean) # annual average
tot <- orderBy(~ - LE_EURO_LITRE_FUEL, tot)
tot[,-1] <- round(tot[,-1]/1e6,2) # millions litres or thousand tons or euros
head(tot, 5)
# same but for litre fuel
aggResultPerMetAlly$LE_LITRE_FUEL <- aggResultPerMetAlly$LE_KG_LITRE_FUEL # a tip for ordering
nm <- colnames(aggResultPerMetAlly)
tot <- aggregate(aggResultPerMetAlly[, c(paste0("LE_LITRE_", spp), "LE_LITRE_FUEL")], list(aggResultPerMetAlly$LE_MET), mean) # annual average
tot <- orderBy(~ - LE_LITRE_FUEL, tot)
tot[,-1] <- round(tot[,-1]/1e6,2) # millions litres or thousand tons or euros
head(tot, 5)
nm <- colnames(aggResultPerMetAlly)
sum_y_kg <- aggregate(aggResultPerMetAlly[, grepl("LE_KG_", nm)], list(Year=aggResultPerMetAlly$Year), sum) # annual average
nm <- colnames(sum_y_kg)
average_y_kg <- apply(sum_y_kg[, grepl("LE_KG_", nm)], 2, mean) # annual average
info1 <- round(average_y_kg[order(average_y_kg, decreasing=TRUE)]/1e6,2)
nm <- colnames(aggResultPerMetAlly)
sum_y_euros <- aggregate(aggResultPerMetAlly[, grepl("LE_EURO_", nm)], list(Year=aggResultPerMetAlly$Year), sum) # annual average
nm <- colnames(sum_y_euros)
average_y_euros <- apply(sum_y_euros[, grepl("LE_EURO_", nm)], 2, mean) # annual average
round(average_y_euros[order(average_y_euros, decreasing=TRUE)]/1e6,2)
info2 <- round(average_y_euros[order(average_y_euros, decreasing=TRUE)]/1e6,2)
nm <- colnames(aggResultPerMetAlly)
sum_y_litres <- aggregate(aggResultPerMetAlly[, grepl("LE_LITRE_", nm)], list(Year=aggResultPerMetAlly$Year), sum, na.rm=TRUE) # annual average
nm <- colnames(sum_y_litres)
average_y_litres <- apply(sum_y_litres[, grepl("LE_LITRE_", nm)], 2, mean) # annual average
round(average_y_litres[order(average_y_litres, decreasing=TRUE)]/1e6,2)
info3 <- round(average_y_litres[order(average_y_litres, decreasing=TRUE)]/1e6,2)
nm <- colnames(aggResultPerMetAlly)
sum_y_hoursatsea <- aggregate(aggResultPerMetAlly[, grepl("LE_EFFORT_", nm)], list(Year=aggResultPerMetAlly$Year), sum, na.rm=TRUE) # annual average
nm <- colnames(sum_y_hoursatsea)
average_y_hoursatsea <- apply(sum_y_hoursatsea[, grepl("LE_EFFORT_", nm)], 2, mean) # annual average
round(average_y_hoursatsea[order(average_y_hoursatsea, decreasing=TRUE)]/1e3,2)
info4 <- round(average_y_hoursatsea[order(average_y_hoursatsea, decreasing=TRUE)]/1e3,3) # thousands of hours at sea
spp <- sapply(strsplit(as.character(names(info3)), split="_"), function(x) x[3]) # give the order on the plot
spp <- spp[spp!="FUEL"]
a_summary <- rbind.data.frame(info1[paste0("LE_KG_", spp)], info2[paste0("LE_EURO_", spp)], info3[paste0("LE_LITRE_", spp)], info4[paste0("LE_EFFORT_", spp)] )
colnames(a_summary) <- spp
rownames(a_summary) <- c("Thousands tons", "Millions euros", "Millions litres", "Thousands hours at sea")
a_summary
#=> supplementary data
# dem
library(data.table)
long1 <- melt(setDT(sum_y_kg[,c("Year", paste0("LE_KG_", spp))]), id.vars = c("Year"), variable.name = "Var")
long1$value <- long1$value /1e6 # thousand tons
long2 <- melt(setDT(sum_y_euros[,c("Year", paste0("LE_EURO_", spp))]), id.vars = c("Year"), variable.name = "Var")
long2$value <- long2$value /1e6 # millions
long3 <- melt(setDT(sum_y_litres[,c("Year", paste0("LE_LITRE_", spp))]), id.vars = c("Year"), variable.name = "Var")
long3$value <- long3$value /1e6 # millions
long4 <- melt(setDT(sum_y_hoursatsea[,c("Year", paste0("LE_EFFORT_", spp))]), id.vars = c("Year"), variable.name = "Var")
long4$value <- long4$value /1e3 # thousands hours
long <- rbind.data.frame(long1, long2, long3, long4)
long$Species <- sapply(strsplit(as.character(long$Var), split="_"), function(x) x[3])
long$VarType <- factor(sapply(strsplit(as.character(long$Var), split="_"), function(x) x[2]))
levels(long$VarType) <- c( "Thousands hours at sea", "Millions Euros", "Thousand Tons", "Millions Litres")
long$Species <- with(long, reorder(Species, value, median)) # reorder
long$Species <- factor(long$Species, levels=rev(levels(long$Species))) # reverse
var_names <- c( "Thousands hours at sea"="Thousands hours at sea", "Millions Euros"="Millions Euros", "Thousand Tons"="Thousand Tons", "Millions Litres"="Millions Litres")
a_width <- 2000; a_height <- 5500
namefile <- paste0("barplot_fuel_efficiency_smallvids_per_species",years[1],"-",years[length(years)],".tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggplot2)
p <- ggplot(data=long[!long$Species %in% c("TODO"),], aes(x=Species, y=value)) +
geom_boxplot(outlier.size = -1, fill='#A4A4A4', color="black") + scale_color_grey() +
facet_wrap(~VarType, scales="free", ncol=1, labeller=as_labeller(var_names), strip.position = "left") +
labs(y = "", x = "Species") +
theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1, size=12), strip.background = element_blank(),strip.placement = "outside")
print(p)
dev.off()
#write.table(a_summary, "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# plot sectored per species
long <- NULL
agg <- NULL
variables <- c("LE_KG_LITRE_FUEL", "CPUEallsp", "CPUFallsp", "VPUFallsp", "KKGallsp", "KEUROallsp")
prefixes <- c("LE_KG_", "LE_CPUE_", "LE_CPUF_", "LE_VPUF_", "LE_KG_", "LE_EURO_")
count <- 0
for(a_variable in variables){
count <- count+1
dd <- get(paste0("aggResultPerMetAlly"))
# get percent per stock for sectorisation
PercentThisStk <- dd[paste0(prefixes[count],spp)] / apply(dd[paste0(prefixes[count],spp)], 1, sum, na.rm=TRUE)*100
colnames(PercentThisStk) <- paste0("Percent_",spp)
dd <- cbind.data.frame (dd, PercentThisStk)
VarThisStk <- sweep(dd[,colnames(PercentThisStk)]/100, 1, dd[,a_variable], FUN="*")
colnames(VarThisStk) <- spp
dd <- cbind.data.frame (dd, VarThisStk)
# reshape
library(data.table)
long <- melt(setDT(dd[,c("LE_MET",a_variable, "Year", colnames(VarThisStk))]), id.vars = c("LE_MET",a_variable, "Year"), variable.name = "Stock")
#as.data.frame(long)
long <- long[complete.cases(long),]
# filtering the ratios:
# a quick informative table (for kg) for filtering out the ratios that are misleading because low catch kg
if(a_variable %in% c("CPUEallsp", "CPUFallsp", "VPUFallsp")){
nm <- colnames(aggResultPerMetAlly)
tot <- aggregate(aggResultPerMetAlly[, grepl("LE_KG_", nm)], list(aggResultPerMetAlly$LE_MET), mean) # annual average
tot <- orderBy(~ - LE_KG_LITRE_FUEL, tot)
tot[,-1] <- round(tot[,-1]) # kg
head(tot, 5)
colnames(tot)[1] <- "LE_MET"
a_long_for_filter <- melt(setDT(tot[,c("LE_MET", paste0("LE_KG_", spp))]), id.vars = c("LE_MET"), variable.name = "Var2", value.name="value2")
a_long_for_filter$Var2 <- gsub("LE_KG_", "", a_long_for_filter$Var2)
long <- merge(long, a_long_for_filter, by.x=c("LE_MET", "Stock"), by.y=c("LE_MET", "Var2"))
long <- long[long$value2>500,] # here the actual filtering....i.e. keep only seg and value when total catch kg is > threshold in kg
long <- as.data.frame(long)
long <- long[,c("LE_MET","Stock", a_variable, "Year", "value")]
}
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
library(ggplot2)
if(a_variable=="LE_KG_LITRE_FUEL") {a_ylab <- "Fuel use (litre)"; ylims=c(0,max(as.data.frame(long)[,a_variable],100000))}
if(a_variable=="CPUEallsp") {a_ylab <- "CPUE (kg per effort)"; ylims=c(0,max(as.data.frame(long)[,a_variable],15))}
if(a_variable=="CPUFallsp") {a_ylab <- "CPUF (kg per litre)"; ylims=c(0,max(as.data.frame(long)[,a_variable],15))}
if(a_variable=="VPUFallsp") {a_ylab <- "VPUF (euro per litre)"; ylims=c(0,max(as.data.frame(long)[,a_variable],15))}
if(a_variable=="VPUFSWAallsp") {a_ylab <- "VPUFSWA (euro per swept area)"; ylims=c(0,max(as.data.frame(long)[,a_variable],100000))}
if(a_variable=="KKGallsp") {a_ylab <- "Landings (tons)"; ylims=c(0,max(as.data.frame(long)[,a_variable],100000))}
if(a_variable=="KEUROallsp") {a_ylab <- "Landings (keuros)"; ylims=c(0,max(as.data.frame(long)[,a_variable],100000))}
a_width <- 9000 ; a_height <- 4000
a_comment <- "" ; if(per_metier_level6) a_comment <- "_met6"; if(per_vessel_size) a_comment <- paste0(a_comment,"_vsize") ; if(per_region) a_comment <- paste0(a_comment,"_region")
# dem
namefile <- paste0("barplot_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_DEM.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
the_agg <- long[grep("LargeMesh",long$LE_MET),]
the_agg$LE_MET <- gsub("LargeMesh_", "", the_agg$LE_MET)
p <- ggplot(data=the_agg, aes(x=LE_MET, y=value, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat="identity") + labs(y = a_ylab, x = "Fleet-segments") + #ylim(ylims[1], ylims[2]) +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1)) + facet_grid(. ~ Year) + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1, size=12))
print(p)
dev.off()
# pel
namefile <- paste0("barplot_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_PEL.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
the_agg <- long[grep("SmallMesh",long$LE_MET),]
the_agg$LE_MET <- gsub("SmallMesh_", "", the_agg$LE_MET)
p <- ggplot(data=the_agg, aes(x=LE_MET, y=value, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat="identity") + labs(y = a_ylab, x = "Fleet-segments") + #ylim(ylims[1], ylims[2]) +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1)) + facet_grid(. ~ Year) + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1, size=12))
print(p)
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
a_width <- 9000 ; a_height <- 4000
library(ggplot2)
# dem
namefile <- paste0("ts_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_DEM.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
the_agg <- long[grep("LargeMesh",long$LE_MET),]
the_agg$LE_MET <- gsub("LargeMesh_", "", the_agg$LE_MET)
p <- ggplot(the_agg, aes(x=as.character(Year), y=value, group=Stock)) + facet_wrap(. ~ LE_MET, scales = "free_y") + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) + labs(y = a_ylab) +
geom_line(aes(color=Stock), size=1.5) + labs(y = a_ylab, x = "Year") + geom_point(aes(color=Stock), size=3) +
scale_color_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1)) +
xlab("") # + ylim(ylims[1], ylims[2])
print(p)
dev.off()
# pel
namefile <- paste0("ts_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_PEL.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
the_agg <- long[grep("SmallMesh",long$LE_MET),]
the_agg$LE_MET <- gsub("SmallMesh_", "", the_agg$LE_MET)
p <- ggplot(the_agg, aes(x=as.character(Year), y=value, group=Stock)) + facet_wrap(. ~ LE_MET, scales = "free_y") + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) + labs(y = a_ylab) +
geom_line(aes(color=Stock), size=1.5) + labs(y = a_ylab, x = "Year") + geom_point(aes(color=Stock), size=3) +
scale_color_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1)) +
xlab("") # + ylim(ylims[1], ylims[2])
print(p)
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!##
a_width <- 9000 ; a_height <- 4000
library(ggplot2)
# dem
namefile <- paste0("ts_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_DEM_areaplot.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
the_agg <- long[grep("LargeMesh",long$LE_MET),]
# a visual fix adding all combi--
dd <- expand.grid(LE_MET=levels(factor(the_agg$LE_MET)), Stock=levels(factor(the_agg$Stock)), Year=levels(factor(the_agg$Year)))
dd$value <- 0
dd[,a_variable] <- 0
dd <- dd[,colnames(the_agg)]
rownames(the_agg) <- paste0(the_agg$LE_MET,the_agg$Stock,the_agg$Year)
rownames(dd) <- paste0(dd$LE_MET,dd$Stock,dd$Year)
dd <- dd[!rownames(dd)%in%rownames(the_agg),]
the_agg <- rbind.data.frame(the_agg, dd)
#---
the_agg$LE_MET <- gsub("LargeMesh_", "", the_agg$LE_MET)
p <- ggplot(the_agg, aes(x=as.character(Year), y=value, group=Stock)) + facet_wrap(. ~ LE_MET, scales = "free_y") + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) + labs(y = a_ylab) +
geom_area(aes( fill=Stock)) + labs(y = a_ylab, x = "Year") +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1)) +
xlab("") # + ylim(ylims[1], ylims[2])
print(p)
dev.off()
# pel
namefile <- paste0("ts_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_PEL_areaplot.tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
the_agg <- long[grep("SmallMesh",long$LE_MET),]
# a visual fix adding all combi--
dd <- expand.grid(LE_MET=levels(factor(the_agg$LE_MET)), Stock=levels(factor(the_agg$Stock)), Year=levels(factor(the_agg$Year)))
dd$value <- 0
dd[,a_variable] <- 0
dd <- dd[,colnames(the_agg)]
rownames(the_agg) <- paste0(the_agg$LE_MET,the_agg$Stock,the_agg$Year)
rownames(dd) <- paste0(dd$LE_MET,dd$Stock,dd$Year)
dd <- dd[!rownames(dd)%in%rownames(the_agg),]
the_agg <- rbind.data.frame(the_agg, dd)
#---
the_agg$LE_MET <- gsub("SmallMesh_", "", the_agg$LE_MET)
p <- ggplot(the_agg, aes(x=as.character(Year), y=value, group=Stock)) +
facet_wrap(. ~ LE_MET, scales = "free_y") + theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) + labs(y = a_ylab) +
geom_area(aes(fill=Stock)) + labs(y = a_ylab, x = "Year") +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1)) +
xlab("")
print(p)
dev.off()
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} # end a_variable
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