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fuelEfficiencyGraphs2020.r
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setwd (file.path("D:","FBA","BENTHIS_2020")) # adapt to your need
# aggregation per metier
# aggregation per metier per region
# overlay with D:\FBA\BENTHIS_2020\FAO_AREAS
# and compare with AER fleet segmentation and footprint estimates:
# in D:\FBA\ADVICES\STECF\AERdata\STECF 19-06 - AER - Economic and Transversal data tables
# "2019-08_STECF 19-06 - EU Fleet Landings species_FAO sub-region_all years.csv"
# separate pelagic vs demersal
# panel plot for years
# independent workflow for passive gears (just using (Danish) eflalo files)
library(doBy)
library(rgdal)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#-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))
}
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#years <- 2012:2019
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")
per_metier_level6 <- TRUE
per_vessel_size <- FALSE
per_region <- TRUE
# search in Baltic and North Sea
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$F_LEVEL!="MAJOR",] # caution with the MAJOR overidding the over()
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#---
# find metiers lvl6 to merge
if(per_metier_level6 && !per_vessel_size){
res <- NULL
for (y in years){
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult for towed gears (trawl and dredge) & seines
aggResult1 <- aggResult
load(file.path(getwd(), "outputs2020_gns", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult for GNS
aggResult2 <- aggResult
aggResult <- rbind.data.frame(aggResult1, aggResult2)
aggResult$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$LE_MET_init)) # immediate correction to avoid an artifical split
# debug
aggResult <- aggResult[aggResult$LE_KG_LITRE >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$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
# code small vs large mesh
aggResult$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$target) %in% "LargeMesh"] <- "SmallMesh"
dd <- tapply(aggResult$effort_mins, paste0(aggResult$target, "_", aggResult$F_SUBAREA, "_", aggResult$LE_MET_init), sum)
res <- rbind.data.frame(res, cbind.data.frame(names(dd), dd))
}
pel <- res[grep("SmallMesh",res[,1]),]
pel <- aggregate(pel$dd, list(pel[,1]), sum)
pel <- orderBy(~ -x, pel)
oth_mets_pel <- as.character(pel[cumsum(pel[,2])/sum(pel[,2])>.9,1]) # 75% in effort in pel
dem <- res[grep("LargeMesh",res[,1]),]
dem <- aggregate(dem$dd, list(dem[,1]), sum)
dem <- orderBy(~ -x, dem)
oth_mets_dem <- as.character(dem[cumsum(dem[,2])/sum(dem[,2])>.95,1]) # 75% in effort in dem
# but keep two exeption anyway to get some representation for SSC and GNS
#LargeMesh_27.3_SSC_DEF_>=120_0_0
oth_mets_dem <- oth_mets_dem[!oth_mets_dem %in% c('LargeMesh_27.3_SSC_DEF_>=120_0_0')]
# met to keep
oth_mets <- c(oth_mets_dem, oth_mets_pel, "27.3_No_Matrix6_[12,18)","27.4_No_Matrix6", "27.3_No_Matrix6","27.4_No_Matrix6", paste0("NA","_") )
oth_mets_27_3 <- oth_mets[grepl("27.3", oth_mets)]
oth_mets_27_4 <- oth_mets[grepl("27.4", oth_mets)]
}
#----
#---
# find metiers lvl6-Vsize to merge
if(per_metier_level6 && per_vessel_size){
res <- NULL
for (y in years){
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlusMet6AndVsize_",y,".RData") )) # aggResult for towed gears (trawl and dredge) & seines
aggResult1 <- aggResult
load(file.path(getwd(), "outputs2020_gns", paste0("AggregatedSweptAreaPlusMet6AndVsize_",y,".RData") )) # aggResult for GNS
aggResult2 <- aggResult
aggResult <- rbind.data.frame(aggResult1, aggResult2)
aggResult$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$LE_MET_init)) # immediate correction to avoid an artifical split
# debug
aggResult <- aggResult[aggResult$LE_KG_LITRE >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$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
# code small vs large mesh
aggResult$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$target) %in% "LargeMesh"] <- "SmallMesh"
dd <- tapply(aggResult$effort_mins, paste0(aggResult$target, "_", aggResult$F_SUBAREA, "_", aggResult$LE_MET_init, "_", aggResult$VesselSize), sum)
res <- rbind.data.frame(res, cbind.data.frame(names(dd), dd))
}
pel <- res[grep("SmallMesh",res[,1]),]
pel <- aggregate(pel$dd, list(pel[,1]), sum)
pel <- orderBy(~ -x, pel)
oth_mets_pel <- as.character(pel[cumsum(pel[,2])/sum(pel[,2])>.75,1]) # 75% in effort in pel
dem <- res[grep("LargeMesh",res[,1]),]
dem <- aggregate(dem$dd, list(dem[,1]), sum)
dem <- orderBy(~ -x, dem)
oth_mets_dem <- as.character(dem[cumsum(dem[,2])/sum(dem[,2])>.75,1]) # 75% in effort in dem
# but keep two exeption anyway to get some representation for SSC and GNS
#LargeMesh_27.3_SSC_DEF_>=120_0_0
oth_mets_dem <- oth_mets_dem[!oth_mets_dem %in% c('LargeMesh_27.4_SSC_DEF_>=120_0_0_[24,40)', 'LargeMesh_27.4_SSC_DEF_>=120_0_0_[18,24)', 'LargeMesh_27.3_PTB_DEF_>=105_1_110_[12,18)')]
# met to keep
oth_mets <- c(oth_mets_dem, oth_mets_pel, "27.3_No_Matrix6_[12,18)","27.4_No_Matrix6_[12,18)", "27.3_No_Matrix6_[18,24)","27.4_No_Matrix6_[18,24)", paste0("NA","_",levels(aggResult$VesselSize)) )
oth_mets_27_3 <- oth_mets[grepl("27.3", oth_mets)]
oth_mets_27_4 <- oth_mets[grepl("27.4", oth_mets)]
}
#----
# aggregation per metier this year
for (y in years){
if(!per_metier_level6 && !per_vessel_size){
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlus_",y,".RData") )) # aggResult
aggResult$LE_MET_init <- factor(aggResult$LE_MET)
levels(aggResult$LE_MET_init) <- gsub("MCD", "CRU", levels(aggResult$LE_MET_init)) # immediate correction to avoid useless historical renaming of MCD->CRU
#correct some met:
levels(aggResult$LE_MET)[levels(aggResult$LE_MET)=="OT_CRU"] <- "OT_MIX_NEP"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET)=="OT_MIX_DMF_PEL"] <- "OT_DMF_PEL"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET)=="OT_DEM_PEL"] <- "OT_DMF_PEL"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET)=="OT_DMF_PEL"] <- "OT_DMF"
aggResult <- aggResult[!is.na(aggResult$LE_MET),]
}
if(per_metier_level6 && !per_vessel_size) {
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult
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
# dd <- tapply(aggResult$effort_mins, aggResult$LE_MET_init, sum)
# dd <- dd[order(-dd)]
# names(dd[cumsum(dd)/sum(dd)>.99])
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% oth_mets_27_3] <- "27.3_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% oth_mets_27_4] <- "27.4_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% "No_matrix6"] <- "OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% "NA"] <- "OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% "DRB_MOL_>=0_0_0"] <- "DRB_MOL_>0_0_0"
# 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$LE_MET <- paste0(aggResult$target,"_",aggResult$LE_MET)
}
if(per_metier_level6 && !per_vessel_size) {
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult
aggResult1 <- aggResult
load(file.path(getwd(), "outputs2020_gns", paste0("AggregatedSweptAreaPlusMet6_",y,".RData") )) # aggResult for GNS
aggResult2 <- aggResult
aggResult <- rbind.data.frame(aggResult1, aggResult2)
aggResult$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$LE_MET_init)) # immediate correction to avoid an artifical split
# dd <- tapply(aggResult$effort_mins, aggResult$LE_MET_init, sum)
# dd <- dd[order(-dd)]
# names(dd[cumsum(dd)/sum(dd)>.99])
colnames(aggResult)[ colnames(aggResult) =="LE_MET_init"] <- "LE_MET"
aggResult$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
# 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$F_SUBAREA,"_", aggResult$LE_MET))
# manage the OTHER mets
smallmesh_oth_mets_27_3 <- oth_mets_27_3[grepl("SmallMesh",oth_mets_27_3)]
smallmesh_oth_mets_27_4 <- oth_mets_27_4[grepl("SmallMesh",oth_mets_27_4)]
largemesh_oth_mets_27_3 <- oth_mets_27_3[grepl("LargeMesh",oth_mets_27_3)]
largemesh_oth_mets_27_4 <- oth_mets_27_4[grepl("LargeMesh",oth_mets_27_4)]
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% smallmesh_oth_mets_27_3] <- "SmallMesh_27.3_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% smallmesh_oth_mets_27_4] <- "SmallMesh_27.4_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% largemesh_oth_mets_27_3] <- "LargeMesh_27.3_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% largemesh_oth_mets_27_4] <- "LargeMesh_27.4_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% oth_mets[!oth_mets %in% c(smallmesh_oth_mets_27_3, smallmesh_oth_mets_27_4, largemesh_oth_mets_27_3, largemesh_oth_mets_27_4)]] <- "LargeMesh_OTHER_0_0_0"
levels(aggResult$LE_MET)[grepl("NA", levels(aggResult$LE_MET)) ] <- "NA_OTHER_0_0_0"
# caution - a small fix for an annoying renaming of segment
aggResult$LE_MET <- factor(aggResult$LE_MET)
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% "LargeMesh_27.4_OTB_CRU_>=120_0_0"] <- "LargeMesh_27.4_OTB_DEF_>=120_0_0"
aggResult$LE_MET <- factor(aggResult$LE_MET)
}
if(per_metier_level6 && per_vessel_size) {
load(file.path(getwd(), "outputs2020", paste0("AggregatedSweptAreaPlusMet6AndVsize_",y,".RData") )) # aggResult for towed gears (trawl and dredge) & seines
aggResult1 <- aggResult
load(file.path(getwd(), "outputs2020_gns", paste0("AggregatedSweptAreaPlusMet6AndVsize_",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
# dd <- tapply(aggResult$effort_mins, aggResult$LE_MET_init, sum)
# dd <- dd[order(-dd)]
# names(dd[cumsum(dd)/sum(dd)>.99])
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$F_DIVISION), "F_SUBAREA"],".a") # few points on coastline
# code small vs large mesh
aggResult$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$target) %in% c(">=105","100-119","90-119",">=120","90-104", "110-156")] <- "LargeMesh"
levels(aggResult$target)[!levels(aggResult$target) %in% "LargeMesh"] <- "SmallMesh"
aggResult$LE_MET <- factor(paste0(aggResult$target, "_", aggResult$F_SUBAREA,"_", aggResult$LE_MET, "_", aggResult$VesselSize))
# manage the OTHER mets
smallmesh_oth_mets_27_3 <- oth_mets_27_3[grepl("SmallMesh",oth_mets_27_3)]
smallmesh_oth_mets_27_4 <- oth_mets_27_4[grepl("SmallMesh",oth_mets_27_4)]
largemesh_oth_mets_27_3 <- oth_mets_27_3[grepl("LargeMesh",oth_mets_27_3)]
largemesh_oth_mets_27_4 <- oth_mets_27_4[grepl("LargeMesh",oth_mets_27_4)]
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% smallmesh_oth_mets_27_3] <- "SmallMesh_27.3_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% smallmesh_oth_mets_27_4] <- "SmallMesh_27.4_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% largemesh_oth_mets_27_3] <- "LargeMesh_27.3_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% largemesh_oth_mets_27_4] <- "LargeMesh_27.4_OTHER_0_0"
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% oth_mets[!oth_mets %in% c(smallmesh_oth_mets_27_3, smallmesh_oth_mets_27_4, largemesh_oth_mets_27_3, largemesh_oth_mets_27_4)]] <- "LargeMesh_OTHER_0_0_0"
levels(aggResult$LE_MET)[grepl("NA", levels(aggResult$LE_MET)) ] <- "NA_OTHER_0_0_0"
# caution - a small fix for an annoying renaming of segment
aggResult$LE_MET <- factor(aggResult$LE_MET)
levels(aggResult$LE_MET)[levels(aggResult$LE_MET) %in% "LargeMesh_27.4_OTB_CRU_>=120_0_0_[24,40)"] <- "LargeMesh_27.4_OTB_DEF_>=120_0_0_[24,40)"
aggResult$LE_MET <- factor(aggResult$LE_MET)
}
#head(aggResult)
#range(aggResult$effort_mins)
# debug divide by 0 or almost 0
aggResult <- aggResult[aggResult$LE_KG_LITRE >0,]
aggResult[, paste0('LE_KG_', spp)] [aggResult[, paste0('LE_KG_', spp)] <0.1] <- 0
aggResult[, paste0('LE_EURO_', spp)] [aggResult[, paste0('LE_EURO_', spp)] <1] <- 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)
# check
#aggResult[aggResult$LE_MET=="SmallMesh_27.3_OTB_CRU_32-69_0_0",]
#bb <- which(aggResult[aggResult$LE_MET=="SmallMesh_27.3_OTB_CRU_32-69_0_0","LE_MPRICE_PLE"]>10)
# vv <- aggResult[aggResult$LE_MET=="SmallMesh_27.3_OTB_CRU_32-69_0_0",]
#vv[bb,]
idx_cols <- grepl("LE_VPUF_", names(aggResult))
dd <- apply (aggResult[,idx_cols], 1, function (x) {
idx <- which.max(as.numeric(x))[1]
})
aggResult$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])
# capture an export for quickmap2020.r (and DISPLACE NorthSea)
if(per_metier_level6 && per_vessel_size) {
save(aggResult, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForBottContact_", y, ".RData", sep="")))
# caution: cpues in kg per minute here
}
if(per_metier_level6 && !per_vessel_size) {
save(aggResult, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBottContact_", y, ".RData", sep="")))
}
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)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!###
## QUICK & EASY TABLES AND PLOTS
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!###
# 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)
}
#OLD: if(per_metier_level6 && !per_vessel_size) save(aggResultPerMetAlly, file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosBottContact.RData", sep="")))
if(per_metier_level6 && !per_vessel_size) save(aggResultPerMetAlly, file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContactAndGNS.RData", sep="")))
if(per_metier_level6 && per_vessel_size) save(aggResultPerMetAlly, file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosBottContactAndGNS.RData", sep="")))
#-----------------
#-----------------
# caution: always same color for seg
some_color_seg <- c("#7FC97F", "#BEAED4", "#FDC086", "#FFFF99", "#386CB0", "#F0027F", "#BF5B17", "#666666", "#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666", "#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F",
"#FF7F00", "#CAB2D6", "#6A3D9A", "#FFFF99", "#B15928", "#FBB4AE", "#B3CDE3", "#CCEBC5", "#DECBE4", "#FED9A6", "#FFFFCC", "#E5D8BD", "#FDDAEC", "#F2F2F2", "#B3E2CD", "#FDCDAC", "#CBD5E8", "#F4CAE4", "#E6F5C9", "#FFF2AE", "#F1E2CC", "#CCCCCC", "#E41A1C",
"#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69",
"#FCCDE5", "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
load(file=file.path(getwd(), "outputs2020_pel", paste("aggResultPerMetAllyMet6AndRatiosForPel.RData", sep=""))) # aggResultPerMetAlly
met_set_1 <- friendly_met_names(aggResultPerMetAlly)
load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContactAndGNS.RData", sep=""))) # aggResultPerMetAlly
met_set_2 <- friendly_met_names(aggResultPerMetAlly)
names(some_color_seg) <- unique(c(met_set_1, met_set_2))
some_color_seg <- some_color_seg[!is.na(names(some_color_seg))]
#load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosBottContactAndGNS.RData", sep=""))) # aggResultPerMetAlly
load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContactAndGNS.RData", sep=""))) # aggResultPerMetAlly
# add a fuel efficiency metric
aggResultPerMetAlly$FPUE <- aggResultPerMetAlly$LE_KG_LITRE_FUEL / (aggResultPerMetAlly$effort_mins/60)
aggResultPerMetAlly$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 FPUE
library(ggplot2)
dd <- aggResultPerMetAlly[,c("Year", "FPUE", "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$LE_MET)==length(years)] ,] # keep complete ts only
dd$met_desc <- friendly_met_names(dd) # the ones used for color seg
a_fpue_plot <- ggplot(dd, aes(x=as.character(Year), y=as.numeric(FPUE), group=as.character(met_desc))) +
geom_line(aes(color=met_desc), size=1.5) +
facet_wrap(. ~ LE_MESH_GROUP, scales = "free_y", ncol=1) + theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
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_fpue_plot)
# for paper:
a_width <- 6000; a_height <- 4000
namefile <- paste0("ts_FPUE_", years[1], "-", years[length(years)], "_DEM_PEL_rev.tif")
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(a_fpue_plot, ncol=1, common.legend = TRUE, legend="right")
dev.off()
#-----------------
#-----------------
load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContactAndGNS.RData", sep=""))) # aggResultPerMetAlly
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")
unique(aggResultPerMetAlly$LE_MET)
# split fuel
PercentThisStk <- aggResultPerMetAlly[paste0("LE_KG_",spp)] / apply(aggResultPerMetAlly[paste0("LE_KG_",spp)], 1, sum, na.rm=TRUE)*100
colnames(PercentThisStk) <- paste0("Percent_",spp)
aggResultPerMetAlly <- cbind.data.frame (aggResultPerMetAlly, PercentThisStk)
VarThisStk <- sweep(PercentThisStk[,colnames(PercentThisStk)]/100, 1, aggResultPerMetAlly[,"LE_KG_LITRE_FUEL"], FUN="*")
colnames(VarThisStk) <- paste0("LE_LITRE_",spp)
aggResultPerMetAlly <- cbind.data.frame (aggResultPerMetAlly, VarThisStk)
# split effort
VarThisStk <- sweep(PercentThisStk[,colnames(PercentThisStk)]/100, 1, aggResultPerMetAlly[,"effort_mins"], FUN="*")
colnames(VarThisStk) <- paste0("LE_EFFORT_",spp)
aggResultPerMetAlly <- cbind.data.frame (aggResultPerMetAlly, VarThisStk)
# a quick informative table (for kg)
nm <- colnames(aggResultPerMetAlly)
tot <- aggregate(aggResultPerMetAlly[, grepl("LE_KG_", nm)], list(aggResultPerMetAlly$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[, grepl("LE_EURO_", nm)], 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$LE_KG_LITRE_FUEL # a tip for ordering
nm <- colnames(aggResultPerMetAlly)
tot <- aggregate(aggResultPerMetAlly[, grepl("LE_LITRE_", nm)], list(aggResultPerMetAlly$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$Year), sum, na.rm=TRUE) # annual average
nm <- colnames(sum_y_litres)
average_y_litres <- apply(sum_y_litres[, grepl("LE_LITRE_", nm)], 2, mean) # annual average
round(average_y_litres[order(average_y_litres, decreasing=TRUE)]/1e6,2)
info3 <- round(average_y_litres[order(average_y_litres, decreasing=TRUE)]/1e6,2)
nm <- colnames(aggResultPerMetAlly)
sum_y_hoursatsea <- aggregate(aggResultPerMetAlly[, grepl("LE_EFFORT_", nm)]/60, list(Year=aggResultPerMetAlly$Year), sum, na.rm=TRUE) # annual average
nm <- colnames(sum_y_hoursatsea)
average_y_hoursatsea <- apply(sum_y_hoursatsea[, grepl("LE_EFFORT_", nm)]/60, 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(ggplot2)
a_width <- 2000; a_height <- 5500
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$Species))) # reverse
var_names <- c( "Thousands hours at sea"="Thousands hours at sea", "Millions Euros"="Millions Euros", "Thousand Tons"="Thousand Tons", "Millions Litres"="Millions Litres")
p <- ggplot(data=long[!long$Species %in% c( "MAC", "HOM", "WHB", "ELE"),], 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=90,hjust=1,vjust=0.5, size=8), strip.background = element_blank(),strip.placement = "outside")
namefile <- paste0("barplot_fuel_efficiency_bottcontact_per_species-2005-2019_rev.tif")
tiff(filename=file.path(getwd(), "outputs2020", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
print(p)
dev.off()
#write.table(a_summary, "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
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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")
# caution: always same color for seg
some_color_seg <- c("#7FC97F", "#BEAED4", "#FDC086", "#FFFF99", "#386CB0", "#F0027F", "#BF5B17", "#666666", "#1B9E77", "#D95F02", "#7570B3", "#E7298A", "#66A61E", "#E6AB02", "#A6761D", "#666666", "#A6CEE3", "#1F78B4", "#B2DF8A", "#33A02C", "#FB9A99", "#E31A1C", "#FDBF6F",
"#FF7F00", "#CAB2D6", "#6A3D9A", "#FFFF99", "#B15928", "#FBB4AE", "#B3CDE3", "#CCEBC5", "#DECBE4", "#FED9A6", "#FFFFCC", "#E5D8BD", "#FDDAEC", "#F2F2F2", "#B3E2CD", "#FDCDAC", "#CBD5E8", "#F4CAE4", "#E6F5C9", "#FFF2AE", "#F1E2CC", "#CCCCCC", "#E41A1C",
"#377EB8", "#4DAF4A", "#984EA3", "#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999", "#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854", "#FFD92F", "#E5C494", "#B3B3B3", "#8DD3C7", "#FFFFB3", "#BEBADA", "#FB8072", "#80B1D3", "#FDB462", "#B3DE69",
"#FCCDE5", "#D9D9D9", "#BC80BD", "#CCEBC5", "#FFED6F")
load(file=file.path(getwd(), "outputs2020_pel", paste("aggResultPerMetAllyMet6AndRatiosForPel.RData", sep=""))) # aggResultPerMetAlly
met_set_1 <- friendly_met_names(aggResultPerMetAlly)
load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContactAndGNS.RData", sep=""))) # aggResultPerMetAlly
met_set_2 <- friendly_met_names(aggResultPerMetAlly)
names(some_color_seg) <- unique(c(met_set_1, met_set_2))
some_color_seg <- some_color_seg[!is.na(names(some_color_seg))]
friendly_met_names <- function(dd){
# a more friendly naming of metiers
dd$met_desc1 <- NA # init
dd$met_desc2 <- NA # init
dd$met_desc3 <- NA # init
dd[grepl("27.3",dd$LE_MET), "met_desc1"] <- "BS"
dd[grepl("27.4",dd$LE_MET), "met_desc1"] <- "NS"
dd[grepl("OTB",dd$LE_MET), "met_desc2"] <- "dem.trawl for\n"
dd[grepl("PTB",dd$LE_MET), "met_desc2"] <- "paired trawl for\n"
dd[grepl("OTB",dd$LE_MET), "met_desc2"] <- "dem.trawl for\n"
dd[grepl("SDN",dd$LE_MET), "met_desc2"] <- "dem.seine for\n"
dd[grepl("SSC",dd$LE_MET), "met_desc2"] <- "scot.seine for\n"
dd[grepl("PS_",dd$LE_MET), "met_desc2"] <- "purse seine for\n"
dd[grepl("GNS",dd$LE_MET), "met_desc2"] <- "gillnet for\n"
dd[grepl("DRB",dd$LE_MET), "met_desc2"] <- "dredge for\n"
dd[grepl("TBB",dd$LE_MET), "met_desc2"] <- "beam.trawl for\n"
dd[grepl("TM",dd$LE_MET), "met_desc2"] <- "midw.trawl for\n"
dd[grepl("LLS",dd$LE_MET), "met_desc2"] <- "longline for\n"
dd[grepl("LHP",dd$LE_MET), "met_desc2"] <- "handline for\n"
dd[grepl("LLD",dd$LE_MET), "met_desc2"] <- "longline for\n"
dd[grepl("FPN",dd$LE_MET), "met_desc2"] <- "pots for\n"
dd[grepl("OTHER",dd$LE_MET), "met_desc2"] <- "misc."
dd[grepl(">=120_0_0",dd$LE_MET), "met_desc3"] <- "fish (>120mm)"
dd[grepl(">=105_1_120",dd$LE_MET), "met_desc3"] <- "fish (105-120mm)"
dd[grepl(">=105_1_110",dd$LE_MET), "met_desc3"] <- "fish (105-110mm)"
dd[grepl("120-219_0",dd$LE_MET), "met_desc3"] <- "fish (120-219mm)"
dd[grepl("90-104_0",dd$LE_MET), "met_desc3"] <- "fish (90-104mm)"
dd[grepl("70-99_0",dd$LE_MET), "met_desc3"] <- "fish (70-99mm)"
dd[grepl("90-119_0_0",dd$LE_MET), "met_desc3"] <- "fish (90-119mm)"
dd[grepl("100-119_0_0",dd$LE_MET), "met_desc3"] <- "fish (100-119mm)"
dd[grepl("120-219_0",dd$LE_MET), "met_desc3"] <- "fish (120-219mm)"
dd[grepl("<16_0_0",dd$LE_MET), "met_desc3"] <- "forage fish (<16mm)"
dd[grepl("SPF_16-31_0_0",dd$LE_MET), "met_desc3"] <- "pelagics (16-31mm)"
dd[grepl("_PS_SPF_>0_0_0",dd$LE_MET), "met_desc3"] <- "pelagics"
dd[grepl("SPF_32-69_0_0",dd$LE_MET), "met_desc3"] <- "pelagics (32-69mm)"
dd[grepl("DEF_16-31_0_0",dd$LE_MET), "met_desc3"] <- "d/pelagics (16-31mm)"
dd[grepl("CRU_32-69",dd$LE_MET), "met_desc3"] <- "crustaceans (32-69mm)"
dd[grepl("CRU_80-99",dd$LE_MET), "met_desc3"] <- "crustaceans (80-99mm)"
dd[grepl("CRU_>0_0",dd$LE_MET), "met_desc3"] <- "crustaceans"
dd[grepl("CRU_>=120_0_0",dd$LE_MET), "met_desc3"] <- "crustaceans (>120mm)"
dd[grepl("LHP_FIF",dd$LE_MET), "met_desc3"] <- "fish"
dd[grepl("ANA",dd$LE_MET), "met_desc3"] <- "mig. fish"
dd[grepl("CAT",dd$LE_MET), "met_desc3"] <- "catadromus sp"
dd[grepl("MOL",dd$LE_MET), "met_desc3"] <- "molluscs"
dd[grepl("TBB_CRU_16-31",dd$LE_MET), "met_desc3"] <- "shrimp"
dd[grepl("LLS_DEF_0_0_0",dd$LE_MET), "met_desc3"] <- "fish"
dd[grepl("157_0_0",dd$LE_MET), "met_desc3"] <- "fish (>157mm)"
dd[grepl("110-156_0_0",dd$LE_MET), "met_desc3"] <- "fish (110-156mm)"
dd$met_desc2[is.na(dd$met_desc2)] <- ""
dd$met_desc3[is.na(dd$met_desc3)] <- ""
return(paste(dd$met_desc1, dd$met_desc2, dd$met_desc3))
}
#load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndVsizeAndRatiosBottContact.RData", sep=""))) # aggResultPerMetAlly
#load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContact.RData", sep=""))) # aggResultPerMetAlly
load(file=file.path(getwd(), "outputs2020", paste("aggResultPerMetAllyMet6AndRatiosBottContactAndGNS.RData", sep=""))) # aggResultPerMetAlly
head(aggResultPerMetAlly[aggResultPerMetAlly$LE_MET=="SmallMesh_27.3_OTB_CRU_32-69_0_0",])
library(ggplot2)
# compare prices e.g seine vs. trawls for cod
#aggResultPerMetAlly[aggResultPerMetAlly$LE_MET=="LargeMesh_27.3_SDN_DEF_>=120_0_0","LE_MPRICE_COD"]
#[1] 2.766031 3.310001 3.993804 3.396713 2.590793 3.018366 3.013149 3.092763 3.211551 2.927512 3.304162 3.328078 3.332026 3.047696 3.332285
# aggResultPerMetAlly[aggResultPerMetAlly$LE_MET=="LargeMesh_27.3_OTB_DEF_>=105_1_120","LE_MPRICE_COD"]
#[1] 1.156881 1.237244 1.215207 1.128402 1.081045 1.105845 1.171007 1.296540 1.363090 1.592291
### 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$Year==y, ]
dd$met_desc <- friendly_met_names(dd)
# 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("met_desc","LE_MET", a_variable, colnames(VarThisStk))]), id.vars = c("met_desc","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$LE_MET=="SmallMesh_27.3_OTB_CRU_32-69_0_0 (b)",])
if(y==years[1]){
agg <- cbind.data.frame(as.data.frame(long), year=y)
}
else{
agg <- rbind.data.frame(as.data.frame(agg),
cbind.data.frame(long, year=y))
}
}
the_agg <- rbind.data.frame(as.data.frame(the_agg), as.data.frame(agg))
}
the_agg <- the_agg[!grepl("misc.",the_agg$met_desc),] # get rid of OTHER
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a_width <- 6200 ; a_height <- 10500
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$met_desc <- friendly_met_names (dd)
dd$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)
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$LE_MET)
p1_area_bottomfishing_dem_land <- ggplot(the_agg_plot1, aes(x=as.character(year), y=value/a_unit, group=Stock)) +
facet_wrap(. ~ met_desc, scales = "free_y", ncol=1) + theme_minimal() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
geom_area(aes(fill=Stock)) + labs(y = "Landings (tons)", x = "Year") +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1, position="right")) +
xlab("")
#print(p1_bottomfishing_dem_land)
the_agg_plot3 <- as.data.frame(the_agg_plot[grep("(c)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot3$LE_MET <- gsub("\\(c)","", the_agg_plot3$LE_MET)
p3_area_bottomfishing_dem_cpuf <- ggplot(the_agg_plot3, aes(x=as.character(year), y=value/a_unit, group=Stock)) +
facet_wrap(. ~ met_desc, scales = "free_y", ncol=1) + theme_minimal() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
geom_area(aes(fill=Stock)) + labs(y = "CPUF (kg per litre)", x = "Year") +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1, position="right")) +
xlab("")
#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$LE_MET)
p4_area_bottomfishing_dem_vpuf <- ggplot(the_agg_plot4, aes(x=as.character(year), y=value/a_unit, group=Stock)) +
facet_wrap(. ~ met_desc, scales = "free_y", ncol=1) + theme_minimal() +
theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
geom_area(aes(fill=Stock)) + labs(y = "VPUF (euro per litre)", x = "Year") +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1, position="right")) +
xlab("")
#print(p4)
the_agg_plot5 <- as.data.frame(the_agg_plot[grep("(e)",the_agg_plot$LE_MET, fixed=TRUE),])
the_agg_plot5$LE_MET <- gsub("\\(e)","", the_agg_plot5$LE_MET)
p5_area_bottomfishing_dem_fpuc <- ggplot(the_agg_plot5, aes(x=as.character(year), y=value/a_unit, group=Stock)) +
facet_wrap(. ~ met_desc, scales = "free_y", ncol=1) + theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +
geom_area(aes(fill=Stock)) + labs(y = "Litre per kg catch", x = "Year") +
scale_fill_manual(values=some_color_species, name="Species") + guides(fill =guide_legend(ncol=1, position="right")) +
xlab("")
#print(p5)
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)
p6_area_bottomfishing_dem_fpuv <- ggplot(the_agg_plot6, aes(x=as.character(year), y=value/a_unit, group=Stock)) +
facet_wrap(. ~ met_desc, scales = "free_y", ncol=1) + theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) +