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Extracting2LCAProject.r
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Extracting2LCAProject.r
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#REDO SOME STUFF FOR A STANDALONE CODE:
setwd (file.path("D:","FBA","BENTHIS_2020")) # adapt to your need
library(doBy)
years <- 2005:2019
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")
# search in Baltic and North Sea
library(rgdal)
library(raster)
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$F_LEVEL!="MAJOR",] # caution with the MAJOR overidding the over()
fao_areas <- fao_areas[fao_areas$F_SUBDIVIS %in% c('27.3.a.20', '27.3.a.21','27.3.c.22', '27.3.b.23', '27.3.d.24'),]
#-1 Aggregate the results by metier and grid ID
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))
}
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
### DEMERSAL FISHERIES WITH LARGE VESSELS
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
for (y in years){
print(y)
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult
aggResult1 <- aggResult
load(file.path(getwd(), "outputs2020_gns", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult for GNS
aggResult2 <- aggResult
aggResult <- rbind.data.frame(aggResult1, aggResult2)
aggResult$LE_MET_init <- factor(aggResult$LE_MET_init)
levels(aggResult$LE_MET_init) <- gsub("MCD", "CRU", levels(aggResult$LE_MET_init)) # immediate correction to avoid useless historical renaming MCD->CRU
levels(aggResult$LE_MET_init) <- gsub("OTB_CRU_90-119_0_0", "OTB_DEF_90-119_0_0", levels(aggResult$LE_MET_init)) # immediate correction to avoid an artifical split
colnames(aggResult)[ colnames(aggResult) =="LE_MET_init"] <- "LE_MET"
aggResult$LE_MET <- factor(aggResult$LE_MET)
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% "DRB_MOL_>=0_0_0"] <- "DRB_MOL_>0_0_0"
# 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(aggResult[, "CELL_LONG"]), SI_LATI=an(aggResult[, "CELL_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)
aggResult$F_SUBAREA <- idx[,"F_SUBAREA"]
aggResult[is.na(aggResult$F_SUBAREA), "F_SUBAREA"] <- "27.4" # few points on coastline
aggResult$F_DIVISION <- idx[,"F_DIVISION"]
aggResult[is.na(aggResult$F_DIVISION), "F_DIVISION"] <- paste0(aggResult[is.na(aggResult$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
aggResult$F_SUBDIVIS <- idx[,"F_SUBDIVIS"]
## SELECTED AREAS: WESTERN BALTIC AND KATT-SKAGERRAK
#We want to keep fao_areas$F_SUBDIVIS %in% c('27.3.a.20', '27.3.a.21','27.3.c.22', '27.3.b.23', '27.3.d.24')
aggResult <- aggResult[aggResult$F_SUBDIVIS %in% c('27.3.a.20', '27.3.a.21','27.3.c.22', '27.3.b.23', '27.3.d.24'),]
# code small vs large mesh
aggResult$target <- aggResult$LE_MET # init
code <- sapply(strsplit(levels(aggResult$target), split="_"), function(x) x[3])
levels(aggResult$target) <- code
levels(aggResult$target)[levels(aggResult$target) %in% c(">=105","100-119","90-119",">=120","90-104", "110-156", "120-219", ">=220")] <- "LargeMesh"
levels(aggResult$target)[!levels(aggResult$target) %in% "LargeMesh"] <- "SmallMesh"
aggResult$LE_MET <- factor(paste0(aggResult$target, "_", aggResult$F_SUBDIVIS,"_", aggResult$LE_MET))
# debug
aggResult <- aggResult[aggResult$LE_KG_LITRE >0,]
aggResult$KKGallsp <- apply (aggResult[, paste0('LE_KG_', spp)], 1, sum, na.rm=TRUE) /1e3 # in tons
aggResult$KEUROallsp <- apply (aggResult[, paste0('LE_EURO_', spp)], 1, sum, na.rm=TRUE) / 1e3 # in thousands euros
# compute some ratios
#dd <- sweep(aggResult[, paste0('LE_KG_', spp)], 1, aggResult$KKGallsp*1e3, FUN="/")
dd <- sweep(aggResult[, paste0('LE_KG_', spp)], 1, aggResult$effort_mins, FUN="/")
colnames(dd) <- paste0('LE_CPUE_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
aggResult <- cbind.data.frame (aggResult, dd)
aggResult$CPUEallsp <- apply (aggResult[, paste0('LE_CPUE_', spp)], 1, sum, na.rm=TRUE)
dd <- sweep(aggResult[, paste0('LE_KG_', spp)], 1, aggResult$LE_KG_LITRE_FUEL, FUN="/")
colnames(dd) <- paste0('LE_CPUF_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
aggResult <- cbind.data.frame (aggResult, dd)
aggResult$CPUFallsp <- apply (aggResult[, paste0('LE_CPUF_', spp)], 1, sum, na.rm=TRUE)
dd <- sweep(aggResult[, paste0('LE_EURO_', spp)], 1, aggResult$LE_KG_LITRE_FUEL, FUN="/")
colnames(dd) <- paste0('LE_VPUF_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
aggResult <- cbind.data.frame (aggResult, dd)
aggResult$VPUFallsp <- apply (aggResult[, paste0('LE_VPUF_', spp)], 1, sum, na.rm=TRUE)
dd <- sweep(aggResult[, paste0('LE_EURO_', spp)], 1, aggResult$SWEPT_AREA_KM2, FUN="/")
colnames(dd) <- paste0('LE_VPUFSWA_', spp)
dd <- do.call(data.frame,lapply(dd, function(x) replace(x, is.infinite(x) | is.nan(x) , NA)))
aggResult <- cbind.data.frame (aggResult, dd)
aggResult$VPUFSWAallsp <- apply (aggResult[, paste0('LE_VPUFSWA_', spp)], 1, sum, na.rm=TRUE)
dd <-aggResult[, paste0('LE_EURO_', spp)]/aggResult[, 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)))
aggResult <- cbind.data.frame (aggResult, dd)
aggResult$mpriceallsp <- apply (aggResult[, paste0('LE_MPRICE_', spp)], 1, mean, na.rm=TRUE)
idx_cols <- grepl("LE_VPUF_", names(aggResult))
dd <- apply (aggResult[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
aggResult$sp_with_max_vpuf <- gsub("LE_VPUF_", "", names(aggResult[,idx_cols])[dd])
idx_cols <- grepl("LE_CPUE_", names(aggResult))
dd <- apply (aggResult[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
aggResult$sp_with_max_cpue <- gsub("LE_CPUE_", "", names(aggResult[,idx_cols])[dd])
idx_cols <- grepl("LE_CPUF_", names(aggResult))
dd <- apply (aggResult[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x)) [1]
})
aggResult$sp_with_max_cpuf <- gsub("LE_CPUF_", "", names(aggResult[,idx_cols])[dd])
idx_cols <- grepl("LE_VPUFSWA_", names(aggResult))
dd <- apply (aggResult[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
aggResult$sp_with_max_vpufswa <- gsub("LE_VPUFSWA_", "", names(aggResult[,idx_cols])[dd])
agg_by <- "LE_MET"
# aggregate ("sum" if absolute value, "mean" if ratio)
nm <- names(aggResult)
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('effort_mins', 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(aggResult)
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(aggResult)[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.4 <- grep('VPUFSWAallsp', 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.vpuswa <- grep('LE_VPUFSWA_', nm)
idx.col.mprice <- grep('LE_MPRICE_', nm)
idx.col <- c(idx.col.1, idx.col.2, idx.col.3, idx.col.4, idx.col.5, idx.col.cpue,idx.col.cpuf, idx.col.vpuf, idx.col.vpuswa, 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(aggResult)
a_by <- c.listquote( agg_by )
aggResultPerMet2 <- DT[,eval(eq1),by=eval(a_by)]
aggResultPerMet2 <- data.frame(aggResultPerMet2)
colnames(aggResultPerMet2) <- c(agg_by, colnames(aggResult)[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$FPUVallsp)] <- 0
assign(paste0("aggResultPerMet_", y), aggResultPerMet)
cat(paste0("saved for ", y, "\n"))
} # end y
# check
unique(aggResultPerMet$LE_MET)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!###
# capture an export for later doing some quick table
aggResultPerMetAlly <- NULL
for (y in years){
print(y)
dd <- get(paste0("aggResultPerMet_", y))
dd <- cbind.data.frame(Year=y, dd)
aggResultPerMetAlly <- rbind.data.frame(aggResultPerMetAlly, dd)
}
save(aggResultPerMetAlly, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBottContact_2LCAProject.RData", sep="")))
##!!!!!!!!!!!!!!!!!!!!!!##
## A SUMMARY BARPLOT WITH AVERAGE OVER THE PERIOD
##!!!!!!!!!!!!!!!!!!!!!!##
#REDO SOME STUFF FOR A STANDALONE CODE:
setwd (file.path("D:","FBA","BENTHIS_2020")) # adapt to your need
library(doBy)
years <- 2005:2019
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")
load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBottContact_2LCAProject.RData", sep=""))) # aggResultPerMetAlly
library(ggplot2)
### SUMMARIZE LANDINGS AND CPUF ON THE SAME GRAPH.......
variables <- c("KEUROallsp","KKGallsp", "LE_KG_LITRE_FUEL", "CPUFallsp", "VPUFallsp", "FPUCallsp", "FPUVallsp", "VPUFSWAallsp", "mpriceallsp")
prefixes <- c("LE_EURO_","LE_KG_","LE_KG_", "LE_CPUF_", "LE_VPUF_", "LE_KG_", "LE_KG_", "LE_VPUF_", "LE_MPRICE_")
the_names <- c("(z)", "(a)","(b)", "(c)", "(d)", "(e)", "(f)", "(g)", "(h)")
count <- 0
the_agg <- NULL
for(a_variable in variables){
count <- count+1
for (y in years){
#dd <- get(paste0("aggResultPerMet_", y))
dd <- aggResultPerMetAlly[aggResultPerMetAlly$Year==y, ]
# KEEP ONLY MAIN COD AND PLAICE AND HERRING FISHERIES IN THE SELECTED AREAS
dd <- dd[(dd$LE_KG_COD>50000 & (grepl("27.3.c.22", dd$LE_MET) | grepl("27.3.c.24", dd$LE_MET))) |
(dd$LE_KG_PLE>100000 & (grepl("27.3.a.20", dd$LE_MET) | grepl("27.3.a.21", dd$LE_MET))) |
(dd$LE_KG_HER>100000 & (grepl("27.3.a.20", dd$LE_MET) | grepl("27.3.a.21", dd$LE_MET))) ,]
# debug SAN
dd[grepl("LargeMesh",dd$LE_MET) , paste0(prefixes[count], "SAN")] <- 0 # because SAN creates misleading distorsion on plots...
# debug outlier
aggResultPerMetAlly[aggResultPerMetAlly$FPUCallsp>100, c("FPUCallsp", "FPUVallsp")] <- 0
# get percent per stock for sectorisation
PercentThisStk <- dd[paste0(prefixes[count],spp)] / apply(dd[paste0(prefixes[count],spp)], 1, sum, na.rm=TRUE)*100
if(prefixes[count]=="LE_MPRICE_") PercentThisStk <- (dd[paste0(prefixes[count],spp)]*dd[paste0("LE_KG_",spp)]) / apply(dd[paste0(prefixes[count],spp)]*dd[paste0("LE_KG_",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, colnames(VarThisStk))]), id.vars = c("LE_MET",a_variable), variable.name = "Stock")
colnames(long)[colnames(long)==a_variable] <- "Total"
#as.data.frame(long)
long <- long[complete.cases(long),]
long$LE_MET <- paste(long$LE_MET, the_names[count])
head(long[long$LE_MET=="SmallMesh_27.3_OTB_CRU_32-69_0_0 (b)",])
if(y==years[1]){
agg <- cbind.data.frame(as.data.frame(long), year=y)
}
else{
agg <- rbind.data.frame(as.data.frame(agg),
cbind.data.frame(long, year=y))
}
}
the_agg <- rbind.data.frame(as.data.frame(the_agg), as.data.frame(agg))
}
save(the_agg, file=file.path(getwd(), "outputs2020", paste("WestBalticKattegatAggForBottContact_2LCAProject.RData", sep="")))
write.table(the_agg, file=file.path(getwd(), "outputs2020", paste("WestBalticKattegatAggForBottContact_2LCAProject_rev.csv", sep="")), col.names=TRUE, row.names=FALSE, quote=FALSE, sep=";")
## DEM
a_width <- 3200 ; a_height <- 6500
library(ggplot2)
a_comment <- ""
a_unit <- 1
# DEM
the_agg_plot <- as.data.frame(the_agg[grep("LargeMesh",the_agg$LE_MET),])
# a visual fix adding all combi--
dd <- expand.grid(LE_MET=levels(factor(the_agg_plot$LE_MET)), Stock=levels(factor(the_agg_plot$Stock)), year=levels(factor(the_agg_plot$year)))
dd$value <- 0
dd[,"Total"] <- 0
dd <- dd[,colnames(the_agg_plot)]
rownames(the_agg_plot) <- paste0(the_agg_plot$LE_MET,the_agg_plot$Stock,the_agg_plot$year)
rownames(dd) <- paste0(dd$LE_MET,dd$Stock,dd$year)
dd <- dd[!rownames(dd)%in%rownames(the_agg_plot),]
the_agg_plot <- rbind.data.frame(the_agg_plot, dd)
#---
the_agg_plot$LE_MET <- gsub("LargeMesh_", "", the_agg_plot$LE_MET)
a_func_mean <- function (x) mean(x[x!=0 & !is.na(x)])
a_func_cv <- function(x) {sqrt(var(x[x!=0 & !is.na(x)]))/mean(x[x!=0 & !is.na(x)])}
# find order of met
dd <- as.data.frame(the_agg_plot[grep("(a)",the_agg_plot$LE_MET, fixed=TRUE),])
dd$LE_MET <- gsub("\\(a)","", dd$LE_MET)
dd <- aggregate(dd$Total, by=list(dd$LE_MET), a_func_mean)
dd <- orderBy(~ -x,dd)
fleet_segments_ordered <- as.character(dd[,1])
#------------
namefile <- paste0("barplot_mean_fuel_efficiency_", years[1], "-", years[length(years)], a_comment, "_DEM_plot_land_and_FPUC_and_FPUV_2_LCA_project.tif")
#namefile <- paste0("barplot_mean_fuel_efficiency", a_variable, "_", years[1], "-", years[length(years)], a_comment, "_DEM_plot_land_and_CPUF_and_VPUF.tif")
collecting_table <- NULL
collecting_table2019 <- NULL
the_agg_plot0 <- as.data.frame(the_agg_plot[grep("(z)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot0$LE_MET <- gsub("\\(z)","", the_agg_plot0$LE_MET)
the_agg_plot0$LE_MET <- factor(the_agg_plot0$LE_MET, level=fleet_segments_ordered) # reorder
p0 <- ggplot(data=the_agg_plot0, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) + labs(y = "Value (KEuros)", x= "") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + theme(axis.text.x=element_blank()) + guides(fill =guide_legend(ncol=7))
#print(p0)
the_agg_plot1 <- as.data.frame(the_agg_plot[grep("(a)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot1$LE_MET <- gsub("\\(a)","", the_agg_plot1$LE_MET)
the_agg_plot1$LE_MET <- factor(the_agg_plot1$LE_MET, level=fleet_segments_ordered) # reorder
p1 <- ggplot(data=the_agg_plot1, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) + labs(y = "Landings (tons)", x= "") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + theme(axis.text.x=element_blank()) + guides(fill =guide_legend(ncol=7))
#print(p1)
the_agg_plot2 <- as.data.frame(the_agg_plot[grep("(b)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot2$LE_MET <- gsub("\\(b)","", the_agg_plot2$LE_MET)
the_agg_plot2$LE_MET <- factor(the_agg_plot2$LE_MET, level=fleet_segments_ordered) # reorder
the_agg_plot2[the_agg_plot2$LE_MET=="27.3_OTB_CRU_32-69_0_0 ",]
p2 <- ggplot(data=the_agg_plot2, aes(x=LE_MET, y=value/1e3, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) + labs(y ="Fuel (thousands litre)", x= "") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + theme(axis.text.x=element_blank()) + guides(fill =guide_legend(ncol=7))
#print(p2)
the_agg_plot3 <- as.data.frame(the_agg_plot[grep("(c)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot3$LE_MET <- gsub("\\(c)","", the_agg_plot3$LE_MET)
the_agg_plot3$LE_MET <- factor(the_agg_plot3$LE_MET, level=fleet_segments_ordered) # reorder
p3 <- ggplot(data=the_agg_plot3, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) +
labs(y ="CPUF (kg per litre)", x= "") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + theme(axis.text.x=element_blank()) + guides(fill =guide_legend(ncol=7))
#print(p3)
the_agg_plot4 <- as.data.frame(the_agg_plot[grep("(d)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot4$LE_MET <- gsub("\\(d)","", the_agg_plot4$LE_MET)
the_agg_plot4$LE_MET <- factor(the_agg_plot4$LE_MET, level=fleet_segments_ordered) # reorder
the_agg_plot4 <- the_agg_plot4[the_agg_plot4$value<20,] # debug for other
p4 <- ggplot(data=the_agg_plot4, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) +
labs(y = "VPUF (euro per litre)", x= "Fleet-segments") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + guides(fill =guide_legend(ncol=7)) +
theme(axis.text.x=element_blank())
# theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5, size=8))
#print(p4)
the_agg_plot5 <- as.data.frame(the_agg_plot[grep("(e)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot5$LE_MET <- gsub("\\(e)","", the_agg_plot5$LE_MET)
the_agg_plot5$LE_MET <- factor(the_agg_plot5$LE_MET, level=fleet_segments_ordered) # reorder
the_agg_plot5 <- the_agg_plot5[the_agg_plot5$value<1,] # debug for other
p5 <- ggplot(data=the_agg_plot5, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) +
labs(y = "Litre per kg catch", x= "Fleet-segments") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + guides(fill =guide_legend(ncol=7)) +
# theme(axis.text.x=element_blank())
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5, size=8))
the_agg_plot6 <- as.data.frame(the_agg_plot[grep("(f)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot6$LE_MET <- gsub("\\(f)","", the_agg_plot6$LE_MET)
the_agg_plot6$LE_MET <- factor(the_agg_plot6$LE_MET, level=fleet_segments_ordered) # reorder
p6 <- ggplot(data=the_agg_plot6, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) +
labs(y = "Litre per euro catch", x= "Fleet-segments") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + guides(fill =guide_legend(ncol=7)) +
# theme(axis.text.x=element_blank())
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5, size=8))
## ADD-ON revenue per swept area for bottom fishing
the_agg_plot7 <- as.data.frame(the_agg_plot[grep("(g)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot7$LE_MET <- gsub("\\(g)","", the_agg_plot7$LE_MET)
the_agg_plot7 <- the_agg_plot7[!the_agg_plot7$LE_MET=="OTHER_0_0_0 ",] # remove outlier
the_agg_plot7$LE_MET <- factor(the_agg_plot7$LE_MET, level=fleet_segments_ordered) # reorder
p7 <- ggplot(data=the_agg_plot7, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) +
labs(y = "Euro catch per km-sq", x= "Fleet-segments") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + guides(fill =guide_legend(ncol=7)) +
theme(axis.text.x=element_blank())
# theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5, size=8))
print(p7)
### ADD-ON mean price
the_agg_plot8 <- as.data.frame(the_agg_plot[grep("(h)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot8$LE_MET <- gsub("\\(h)","", the_agg_plot8$LE_MET)
the_agg_plot8 <- the_agg_plot8[!the_agg_plot8$LE_MET=="OTHER_0_0_0 ",] # remove outlier
the_agg_plot8[the_agg_plot8$ value>100,"value"] <- 0 # remove outlier
the_agg_plot8$LE_MET <- factor(the_agg_plot8$LE_MET, level=fleet_segments_ordered) # reorder
p8 <- ggplot(data=the_agg_plot8, aes(x=LE_MET, y=value/a_unit, fill=Stock)) + # geom_bar(stat="identity", position=position_dodge())
geom_bar(stat = "summary", fun = a_func_mean) +
labs(y = "Euro catch per kg", x= "Fleet-segments") +
scale_fill_manual(values=some_color_species, name="Species") + theme_minimal() + guides(fill =guide_legend(ncol=7)) +
# theme(axis.text.x=element_blank())
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5, size=8))
print(p8)
# for a plot
tiff(filename=file.path(getwd(), "outputs2020", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggpubr)
ggarrange(p1, p2, p4, p5, ncol=1, heights=c(1,1,1,2),common.legend = TRUE, legend="bottom")
dev.off()
# for a plot
tiff(filename=file.path(getwd(), "outputs2020", "output_plots", "Euro_catch_per_km-sq_or_per_kg_2_LCAproject.tiff"), width = a_width, height = 5000,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggpubr)
ggarrange(p7, p8, ncol=1, heights=c(1),common.legend = TRUE, legend="bottom")
dev.off()
# export underlying data
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot0$LE_MET), var="Value (KEuros)", average=tapply(the_agg_plot0$Total, the_agg_plot0$LE_MET, a_func_mean), cv=tapply(the_agg_plot0$Total, the_agg_plot0$LE_MET, a_func_cv) ))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot1$LE_MET), var="Landings (tons)", average=tapply(the_agg_plot1$Total, the_agg_plot1$LE_MET, a_func_mean), cv=tapply(the_agg_plot1$Total, the_agg_plot1$LE_MET, a_func_cv) ))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot2$LE_MET), var="Fuel (litres)", average=tapply(the_agg_plot2$Total, the_agg_plot2$LE_MET, a_func_mean), cv=tapply(the_agg_plot2$Total, the_agg_plot2$LE_MET, a_func_cv)))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot3$LE_MET), var="CPUF (kg per litre)", average=tapply(the_agg_plot3$Total, the_agg_plot3$LE_MET, a_func_mean), cv=tapply(the_agg_plot3$Total, the_agg_plot3$LE_MET, a_func_cv)))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot4$LE_MET), var="VPUF (euro per litre)", average=tapply(the_agg_plot4$Total, the_agg_plot4$LE_MET, a_func_mean), cv=tapply(the_agg_plot4$Total, the_agg_plot4$LE_MET, a_func_cv)))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot5$LE_MET), var="Litre per kg catch", average=tapply(the_agg_plot5$Total, the_agg_plot5$LE_MET, a_func_mean), cv=tapply(the_agg_plot5$Total, the_agg_plot5$LE_MET, a_func_cv)))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot6$LE_MET), var="Litre per euro catch", average=tapply(the_agg_plot6$Total, the_agg_plot6$LE_MET, a_func_mean), cv=tapply(the_agg_plot6$Total, the_agg_plot6$LE_MET, a_func_cv)))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot7$LE_MET), var="Euro catch per km-sq", average=tapply(the_agg_plot7$Total, the_agg_plot7$LE_MET, a_func_mean), cv=tapply(the_agg_plot7$Total, the_agg_plot7$LE_MET, a_func_cv)))
collecting_table <- rbind.data.frame(collecting_table, cbind.data.frame(type="BottomFishing_LargeMesh", seg=levels(the_agg_plot8$LE_MET), var="Euro catch per kg", average=tapply(the_agg_plot8$Total, the_agg_plot8$LE_MET, a_func_mean), cv=tapply(the_agg_plot8$Total, the_agg_plot8$LE_MET, a_func_cv)))
# export underlying data
write.table(collecting_table,
file=file.path(getwd(), "outputs2020", "output_plots",
paste("barplot_mean_fuel_efficiency_", years[1], "-", years[length(years)], "_DEM__plot_land_and_FPUC_and_FPUV_2_LCA_project.dat")),
row.names=FALSE, quote=FALSE, sep=";")
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
### SMALL VESSELS
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
library(vmstools)
library(maps)
library(mapdata)
if(.Platform$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"
}
#years <- 2012:2019
years <- 2005:2019
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$LE_MET)
if(TRUE){
load(file=file.path(getwd(), "AIS_data", "ais_profile_small_vessels.RData")) # ais_profile
table.fuelcons.per.engine <- read.table(file= file.path(getwd(),"EflaloAndTacsat", "IBM_datainput_engine_consumption.txt"), header=TRUE,sep="")
linear.model <- lm(calc_cons_L_per_hr_max_rpm~ kW2, data=table.fuelcons.per.engine) # conso = a*Kw +b # to guess its fuel consumption at maximal speed
eflalo$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$nbvessel <- 1e3
p3 <- ggplot() + geom_bar(data=eflalo, aes(x=as.character(Year), y=LE_EFF, group=VesselSize, fill=VesselSize), size=1.5, position="stack", stat = "summary", fun = "sum") +
geom_line(data=dd, aes(x=as.character(Year), y=nbvessel, group=VesselSize, color=VesselSize),size=1.5, stat = "summary", fun = "sum") +
geom_line(data=dd, aes(x=as.character(Year), y=litre_fuel, group=1),size=1.2, color=1, linetype = "dashed", stat = "summary", fun = "sum") +
geom_line(data=dd, aes(x=as.character(Year), y=toteuros/100, group=1),size=1.2, color=5, linetype = "dashed", stat = "summary", fun = "sum") +
scale_y_continuous(name = "Trip effort hours; fuel use (litre); euros/100", sec.axis = sec_axis(~./1e3, name = "Nb Vessels") )+
theme_minimal() + theme(axis.text.x=element_text(angle=60,hjust=1,vjust=1)) +
labs(x = "Year") +
scale_color_manual(values=some_color_vessel_size, name="VesselSize") +
scale_fill_manual(values=some_color_vessel_size2) +
guides(fill =guide_legend(ncol=1))
print(p3)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
library(vmstools)
eflalo <- cbind.data.frame(eflalo,
vmstools::ICESrectangle2LonLat(statsq=eflalo$LE_RECT, midpoint=TRUE)
)
eflalo <- eflalo[!is.na(eflalo$SI_LATI),] # some leaks of data there. i.e. total effort is not complete, so do not use it
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
if(per_metier_level6 && !per_vessel_size){
# code F_SUBAREA (time consuming code...)
# Convert all points first to SpatialPoints first
library(rgdal)
library(raster)
an <- function(x) as.numeric(as.character(x))
coords <- SpatialPoints(cbind(SI_LONG=an(eflalo[, "SI_LONG"]), SI_LATI=an(eflalo[, "SI_LATI"])))
fao_areas <- spTransform(fao_areas, CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")) # convert to longlat
projection(coords) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") # a guess!
idx <- over(coords, fao_areas)
eflalo$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$sp_with_max_vpufswa <- gsub("LE_VPUFSWA_", "", names(eflalo[,idx_cols])[dd])
# just for info and a rough approximation
total_kg1 <- tapply(eflalo$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
# HERE a SUBSET FOR SARA H.
eflalo <- eflalo[eflalo$LE_DIV %in% c("3AN", "3AS", "3B"),]
# agg
agg_by <- c("LE_MET", "Year")
# 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
aggResultPerMet2LCA <- aggResultPerMet[,c("LE_MET", "Year", "LE_KG_LITRE_FUEL", "LE_KG_COD", "LE_KG_HER", "LE_KG_PLE", "KKGallsp", "KEUROallsp", "FPUCallsp")]
colnames (aggResultPerMet2LCA)[colnames(aggResultPerMet2LCA) %in% "LE_KG_LITRE_FUEL" ] <- "LITRE_FUEL"
save(aggResultPerMet2LCA, file=file.path(getwd(), "outputs2020_lgbkonly", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosSmallVids",years[1],"-",years[length(years)],"_2LCA.RData", sep="")))
write.table(aggResultPerMet2LCA, file=file.path(getwd(), "outputs2020_lgbkonly", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosSmallVids",years[1],"-",years[length(years)],"_2LCA.csv", sep="")),
col.names=TRUE, row.names=FALSE, sep=";", quote=FALSE)
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
### PELAGIC FISHERIES WITH LARGE VESSELS
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
for (y in years){
load(file.path(getwd(), "outputs2020_pel", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult
aggResult$LE_MET_init <- factor(aggResult$LE_MET_init)
colnames(aggResult)[ colnames(aggResult) =="LE_MET_init"] <- "LE_MET"
##!#!#!#!caution#!#!#!#!
##!! remove the difference among OTM and PTM
aggResult[grepl("PTM",aggResult$LE_MET), "LE_KG_LITRE_FUEL"] <- aggResult[grepl("PTM",aggResult$LE_MET), "LE_KG_LITRE_FUEL"] *2 # because pair trawling
levels(aggResult$LE_MET) <- gsub("OTM", "TM", levels(aggResult$LE_MET))
levels(aggResult$LE_MET) <- gsub("PTM", "TM", levels(aggResult$LE_MET))
##!#!#!#!caution#!#!#!#!
# 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(aggResult[, "CELL_LONG"]), SI_LATI=an(aggResult[, "CELL_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)
aggResult$F_SUBAREA <- idx[,"F_SUBAREA"]
aggResult[is.na(aggResult$F_SUBAREA), "F_SUBAREA"] <- "27.4" # few points on coastline
aggResult$F_DIVISION <- idx[,"F_DIVISION"]
aggResult[is.na(aggResult$F_DIVISION), "F_DIVISION"] <- paste0(aggResult[is.na(aggResult$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
aggResult$F_SUBDIVIS <- idx[,"F_SUBDIVIS"]