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GenerateTables.r
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GenerateTables.r
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setwd (file.path("D:","FBA","BENTHIS_2020")) # adapt to your need
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#years <- 2012:2019
years <- 2005:2019
if(FALSE){
AllAggResultsBot <- NULL
for (y in years){
# bottom contacting gears dem and pel
#load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForBottContact_", y, ".RData", sep="")))
load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBottContact_", y, ".RData", sep="")))
aggResult$totkg <- apply(aggResult[,grepl("LE_KG_", colnames(aggResult))], 1, sum)
aggResult$toteuros <- apply(aggResult[,grepl("LE_EURO_", colnames(aggResult))], 1, sum)
AllAggResultsBot <- rbind.data.frame(
AllAggResultsBot,
cbind.data.frame(Year=y, aggResult)
)
} # end y
#save(AllAggResultsBot, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForBotAlly_",years[1],"-",years[length(years)],".RData", sep="")))
save(AllAggResultsBot, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBotAlly_",years[1],"-",years[length(years)],".RData", sep="")))
##---
AllAggResultsPel <- NULL
for (y in years){
# pelagic gears
#load(file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForPel_", y, ".RData", sep="")))
load(file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndRatiosForPel_", y, ".RData", sep="")))
aggResult$totkg <- apply(aggResult[,grepl("LE_KG_", colnames(aggResult))], 1, sum)
aggResult$toteuros <- apply(aggResult[,grepl("LE_EURO_", colnames(aggResult))], 1, sum)
AllAggResultsPel <- rbind.data.frame(
AllAggResultsPel,
cbind.data.frame(Year=y, aggResult)
)
} # end y
#save(AllAggResultsPel, file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForPelAlly",years[1],"-",years[length(years)],".RData", sep="")))
save(AllAggResultsPel, file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndRatiosForPelAlly",years[1],"-",years[length(years)],".RData", sep="")))
} # end FALSE
library(doBy)
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# general tables and plot
#load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForBotAlly_",years[1],"-",years[length(years)],".RData", sep="")))
load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBotAlly_",years[1],"-",years[length(years)],".RData", sep="")))
# BOTTOM CONTACTING GEARS
# Fuel use per metier over the period
dd <- round(tapply(AllAggResultsBot$LE_KG_LITRE_FUEL, list(AllAggResultsBot$LE_MET, AllAggResultsBot$Year ), sum))/1e6 # millions litre fuel
dd <- cbind.data.frame(dd, AllMLitres=apply(dd, 1, mean, na.rm=TRUE))
dd <- cbind.data.frame(dd, AllMLitres_cv=apply(dd, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd2 <- round(tapply(AllAggResultsBot$totkg, list(AllAggResultsBot$LE_MET, AllAggResultsBot$Year ), sum))/1e6 # '000 tons
dd2 <- cbind.data.frame(dd2, AllkTons=apply(dd2, 1, mean, na.rm=TRUE))
dd2 <- cbind.data.frame(dd2, AllkTons_cv=apply(dd2, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd3 <- round(tapply(AllAggResultsBot$toteuros, list(AllAggResultsBot$LE_MET, AllAggResultsBot$Year ), sum)/1e6,2) # millions
dd3 <- cbind.data.frame(dd3, AllMEuros=apply(dd3, 1, mean, na.rm=TRUE))
dd3 <- cbind.data.frame(dd3, AllMEuros_cv=apply(dd3, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd4 <- round(tapply(AllAggResultsBot$effort_mins/60, list(AllAggResultsBot$LE_MET, AllAggResultsBot$Year ), sum)/1e3,2) # '000 hours
dd4 <- cbind.data.frame(dd4, AllkHours =apply(dd4, 1, mean, na.rm=TRUE))
dd4 <- cbind.data.frame(dd4, AllkHours_cv=apply(dd4, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
all_indic <- cbind.data.frame( AllkHours =dd4[,"AllkHours"], AllkHours_cv=dd4[,"AllkHours_cv"],
AllMLitres=dd[,"AllMLitres"], AllMLitres_cv=dd[,"AllMLitres_cv"],
AllkTons=dd2[,"AllkTons"] , AllmTons_cv=dd2[,"AllkTons_cv"],
AllMEuros=dd3[,"AllMEuros"], AllMEuros_cv=dd3[,"AllMEuros_cv"])
rownames(all_indic) <- rownames(dd)
library(doBy)
all_indic <- orderBy(~ - AllMLitres, all_indic)
#=> supplementary data
top5 <- rownames(all_indic[1:5,])
write.table(round(all_indic[1:5,],2), "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
top7 <- rownames(all_indic[1:7,])
write.table(round(all_indic[1:7,],2), "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
# a total
apply(all_indic[,-grep("_cv",names(all_indic))], 2, sum, na.rm=TRUE)
apply(all_indic[,grep("_cv",names(all_indic))], 2, mean, na.rm=TRUE)
dd$LE_MET <- rownames(dd)
library(data.table)
long <- melt(setDT(dd[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long$Var <- "MLitres"
dd2$LE_MET <- rownames(dd2)
library(data.table)
long2 <- melt(setDT(dd2[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long2$Var <- "mTons"
dd3$LE_MET <- rownames(dd3)
library(data.table)
long3 <- melt(setDT(dd3[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long3$Var <- "MEuros"
a_data <- rbind.data.frame(long, long2, long3)
a_data <- as.data.frame(a_data)
a_data_to_plot <- a_data[a_data$LE_MET %in% top5,]
a_data_to_plot$LE_MET <- factor(a_data_to_plot$LE_MET)
a_data_to_plot$LE_MET <- with(a_data_to_plot, reorder(LE_MET,value, median)) # reorder
# plot
a_width <- 5000 ; a_height <- 2000
namefile <- paste0("boxplot_top5_metiers_bottom_contacting_gears",years[1], years[length(years)],".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(ggplot2)
p <- ggplot(a_data_to_plot, aes(x=LE_MET, y=value, fill=Var)) + coord_flip() + labs(title="",x="Top 5 Fleet-segments", y = "Value") +
geom_boxplot(outlier.colour="black", outlier.shape="", outlier.size=2, notch=FALSE) + scale_fill_brewer(palette="RdBu") + theme_minimal() # + ylim(c(0,75))
print(p)
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
# PELAGIC GEARS
#load(file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForPelAlly",years[1],"-",years[length(years)],".RData", sep="")))
load(file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndRatiosForPelAlly",years[1],"-",years[length(years)],".RData", sep="")))
dd <- round(tapply(AllAggResultsPel$LE_KG_LITRE_FUEL, list(AllAggResultsPel$LE_MET, AllAggResultsPel$Year ), sum))/1e6 # millions litre fuel
dd <- cbind.data.frame(dd, AllMLitres=apply(dd, 1, mean, na.rm=TRUE))
dd <- cbind.data.frame(dd, AllMLitres_cv=apply(dd, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd2 <- round(tapply(AllAggResultsPel$totkg, list(AllAggResultsPel$LE_MET, AllAggResultsPel$Year ), sum))/1e6 # '000 tons
dd2 <- cbind.data.frame(dd2, AllkTons=apply(dd2, 1, mean, na.rm=TRUE))
dd2 <- cbind.data.frame(dd2, AllkTons_cv=apply(dd2, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd3 <- round(tapply(AllAggResultsPel$toteuros, list(AllAggResultsPel$LE_MET, AllAggResultsPel$Year ), sum)/1e6,2) # millions
dd3 <- cbind.data.frame(dd3, AllMEuros=apply(dd3, 1, mean, na.rm=TRUE))
dd3 <- cbind.data.frame(dd3, AllMEuros_cv=apply(dd3, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd4 <- round(tapply(AllAggResultsPel$effort_mins/60, list(AllAggResultsPel$LE_MET, AllAggResultsPel$Year ), sum)/1e3,2) # '000 hours
dd4 <- cbind.data.frame(dd4, AllkHours =apply(dd4, 1, mean, na.rm=TRUE))
dd4 <- cbind.data.frame(dd4, AllkHours_cv=apply(dd4, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
all_indic <- cbind.data.frame( AllkHours =dd4[,"AllkHours"], AllkHours_cv=dd4[,"AllkHours_cv"],
AllMLitres=dd[,"AllMLitres"], AllMLitres_cv=dd[,"AllMLitres_cv"],
AllkTons=dd2[,"AllkTons"] , AllmTons_cv=dd2[,"AllkTons_cv"],
AllMEuros=dd3[,"AllMEuros"], AllMEuros_cv=dd3[,"AllMEuros_cv"])
rownames(all_indic) <- rownames(dd)
library(doBy)
all_indic <- orderBy(~ - AllMLitres, all_indic)
#=> supplementary data
top7 <- rownames(all_indic[1:7,])
write.table(round(all_indic[1:7,],2), "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
# a total
apply(all_indic[,-grep("_cv",names(all_indic))], 2, sum, na.rm=TRUE)
apply(all_indic[,grep("_cv",names(all_indic))], 2, mean, na.rm=TRUE)
dd$LE_MET <- rownames(dd)
library(data.table)
long <- melt(setDT(dd[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long$Var <- "MLitres"
dd2$LE_MET <- rownames(dd2)
library(data.table)
long2 <- melt(setDT(dd2[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long2$Var <- "mTons"
dd3$LE_MET <- rownames(dd3)
library(data.table)
long3 <- melt(setDT(dd3[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long3$Var <- "MEuros"
a_data <- rbind.data.frame(long, long2, long3)
a_data <- as.data.frame(a_data)
a_data_to_plot <- a_data[a_data$LE_MET %in% top7,]
a_data_to_plot$LE_MET <- factor(a_data_to_plot$LE_MET)
a_data_to_plot$LE_MET <- with(a_data_to_plot, reorder(LE_MET,value, median)) # reorder
# plot
a_width <- 5000 ; a_height <- 2000
library(ggplot2)
namefile <- paste0("boxplot_top5_metiers_pelagic_gears",years[1], years[length(years)],".tif")
tiff(filename=file.path(getwd(), "outputs2020_pel", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
p <- ggplot(a_data_to_plot, aes(x=LE_MET, y=value, fill=Var)) + coord_flip() + labs(title="",x="Top 5 Fleet-segments", y = "Value") +
geom_boxplot(outlier.colour="black", outlier.shape="", outlier.size=2, notch=FALSE) + ylim(c(0,200)) + scale_fill_brewer(palette="RdBu") + theme_minimal()
print(p)
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
## SMALL VESSELS (0-12m)
load(file=file.path(getwd(), "outputs2020_lgbkonly", paste("AggregatedEflaloWithSmallVids",years[1],"-",years[length(years)],".RData", sep="")))
#eflalo
# Fuel use per metier over the period
dd <- round(tapply(eflalo$LE_KG_LITRE_FUEL, list(eflalo$LE_MET, eflalo$Year ), sum))/1e6 # millions litre fuel
dd <- cbind.data.frame(dd, AllMLitres=apply(dd, 1, mean, na.rm=TRUE))
dd <- cbind.data.frame(dd, AllMLitres_cv=apply(dd, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd2 <- round(tapply(eflalo$LE_KG_SPECS, list(eflalo$LE_MET, eflalo$Year ), sum))/1e6 # '000 tons
dd2 <- cbind.data.frame(dd2, AllkTons=apply(dd2, 1, mean, na.rm=TRUE))
dd2 <- cbind.data.frame(dd2, AllkTons_cv=apply(dd2, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd3 <- round(tapply(eflalo$LE_EURO_SPECS, list(eflalo$LE_MET, eflalo$Year ), sum)/1e6,2) # millions
dd3 <- cbind.data.frame(dd3, AllMEuros=apply(dd3, 1, mean, na.rm=TRUE))
dd3 <- cbind.data.frame(dd3, AllMEuros_cv=apply(dd3, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
dd4 <- round(tapply(eflalo$LE_EFF, list(eflalo$LE_MET, eflalo$Year ), sum)/1e3,2) # thousands hours
dd4 <- cbind.data.frame(dd4, AllkHours=apply(dd4, 1, mean, na.rm=TRUE))
dd4 <- cbind.data.frame(dd4, AllkHours_cv=apply(dd4, 1, FUN=function(x) sqrt(var(x, na.rm=TRUE))/mean(x, na.rm=TRUE)))
all_indic <- cbind.data.frame( AllkHours =dd4[,"AllkHours"], AllkHours_cv=dd4[,"AllkHours_cv"],
AllMLitres=dd[,"AllMLitres"], AllMLitres_cv=dd[,"AllMLitres_cv"],
AllkTons=dd2[,"AllkTons"] , AllmTons_cv=dd2[,"AllkTons_cv"],
AllMEuros=dd3[,"AllMEuros"], AllMEuros_cv=dd3[,"AllMEuros_cv"])
rownames(all_indic) <- rownames(dd)
library(doBy)
all_indic <- orderBy(~ - AllMLitres, all_indic)
#=> supplementary data
top5 <- rownames(all_indic[1:5,])
write.table(round(all_indic[1:5,],2), "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
top7 <- rownames(all_indic[1:7,])
write.table(round(all_indic[1:7,],2), "clipboard", sep="\t", row.names=TRUE, col.names=TRUE)
# a total
apply(all_indic[,-grep("_cv",names(all_indic))], 2, sum)
apply(all_indic[,grep("_cv",names(all_indic))], 2, mean)
dd$LE_MET <- rownames(dd)
library(data.table)
long <- melt(setDT(dd[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long$Var <- "MLitres"
dd2$LE_MET <- rownames(dd2)
library(data.table)
long2 <- melt(setDT(dd2[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long2$Var <- "mTons"
dd3$LE_MET <- rownames(dd3)
library(data.table)
long3 <- melt(setDT(dd3[, c("LE_MET", as.character(years))]), id.vars = c("LE_MET"), variable.name = "Year")
long3$Var <- "MEuros"
a_data <- rbind.data.frame(long, long2, long3)
a_data <- as.data.frame(a_data)
a_data_to_plot <- a_data[a_data$LE_MET %in% top5,]
a_data_to_plot$LE_MET <- factor(a_data_to_plot$LE_MET)
a_data_to_plot$LE_MET <- with(a_data_to_plot, reorder(LE_MET,value, median)) # reorder
# plot
a_width <- 5000 ; a_height <- 2000
namefile <- paste0("boxplot_top5_metiers_small_vessels",years[1], years[length(years)],".tif")
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
p <- ggplot(a_data_to_plot, aes(x=LE_MET, y=value, fill=Var)) + coord_flip() + labs(title="",x="Top 5 Fleet-segments", y = "Value") +
geom_boxplot(outlier.colour="black", outlier.shape="", outlier.size=2, notch=FALSE) + ylim(c(0,15)) + scale_fill_brewer(palette="RdBu") + theme_minimal()
print(p)
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#prefix <- "LE_VPUF_" ; a_comment <- "VPUF"
prefix <- "LE_CPUF_" ; a_comment <- "CPUF"
# assign area coding to retrieve stock from species
# general tables and plot
# BOTTOM CONTACTING GEARS
#load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForBotAlly_",years[1],"-",years[length(years)],".RData", sep="")))
load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatiosForBotAlly_",years[1],"-",years[length(years)],".RData", sep="")))
x <- AllAggResultsBot
lon <- "CELL_LONG" ; lat <- "CELL_LATI"
rm(AllAggResultsBot)
gc()
# code F_SUBAREA (time consuming code...)
# Convert all points first to SpatialPoints first
library(rgdal)
library(raster)
# search in Baltic and North Sea # do not use "27.3.a" "27.3.d","27.3.b, c" cause overruling finer division...
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", "27.5","27.6", "27.7", "27.8", "27.14") & fao_areas$F_LEVEL!="MAJOR",] # caution with the MAJOR overidding the over()
fao_areas <- fao_areas[fao_areas$F_CODE %in% c("27.8.c","27.8.b","27.8.e","27.8.d","27.8.a","27.7.j","27.7.h","27.7.f","27.7.g","27.7.k","27.4.c","27.7.c","27.4.b","27.6.b",
"27.5.b","27.5.a","27.14.b","27.2.a","27.2.b","27.14.a","27.7.e","27.7.d","27.6.a","27.7.b","27.7.a","27.4.a","27.8.e.1",
"27.8.d.1","27.8.d.2","27.7.j.1","27.7.k.1","27.7.k.2","27.7.j.2","27.7.c.1","27.7.c.2","27.3.d.24","27.3.b.23","27.3.d.26","27.6.b.1",
"27.6.b.2","27.5.b.1","27.5.a.1", "27.14.b.1","27.5.a.2","27.14.b.2","27.2.a.1","27.2.b.1","27.2.b.2","27.8.e.2","27.5.b.2","27.2.a.2","27.3.c.22","27.3.d.27","27.3.d.28","27.3.d.29",
"27.3.d.32","27.3.d.30","27.3.d.31","27.3.d.25","27.3.a.21","27.3.a.20","27.5.b.1.a","27.5.b.1.b"),]
an <- function(x) as.numeric(as.character(x))
coords <- SpatialPoints(cbind(lon=an(x[, lon]), lat=an(x[, lat])))
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)
x$F_CODE <- idx[,"F_CODE"]
gc()
# check
plot(fao_areas[fao_areas$F_CODE %in% c("27.3.d.24","27.3.b.23"),])
points( x[is.na(x$F_CODE), c(lon, lat)], col=3, pch="+")
#=> pblm with discrete pos and coastline...so brute search below to fix:
for(multi in seq(1,3,by=0.2)) {
for(a_sign1 in c(-1,1)) {
for(a_sign2 in c(-1,1)) {
dx <- 0.2
dy <- 0.2
print(length(which(is.na(x$F_CODE))))
coords <- SpatialPoints(cbind(lon=an(x[ which(is.na(x$F_CODE)), lon])+dx*multi*a_sign1, lat=an(x[ which(is.na(x$F_CODE)),lat])+dy*multi*a_sign2))
projection(coords) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") # a guess!
idx <- over(coords, fao_areas)
x[which(is.na(x$F_CODE)),]$F_CODE <- idx[,"F_CODE"]
length(which(is.na(x$F_CODE)))
}}}
x <- x[!is.na(x$F_CODE),] # remove very few records
save(x, file=file.path("C:", "ToTrash", "x.RData"))
gc()
# convert to long
x$ID <- paste0(x$VE_REF, x$Year, x$LE_ID)
library(data.table)
long <- melt(setDT(x[, c("grID", "LE_MET", "F_CODE", "Year", colnames(x)[grepl(prefix, colnames(x))])]),
id.vars = c("grID", "LE_MET", "F_CODE", "Year"), variable.name = "Var")
long <- long[!(is.na(long$value) | long$value==0),] # remove cells where the value is at 0 to avoid an average bias
long <- long[,c('LE_MET','F_CODE', 'Year','Var','value')]
#long$Species <- sapply(strsplit(as.character(long$Var), split="_"), function (x) x[3]) # out of memory. replace by:
long$Species <- gsub("LE_CPUF_","", as.character(long$Var))
x<- long
gc()
# find out areas to Retrieve Stocks from Species
x$code_area <- as.character(x$F_CODE)
x[(x$code_area %in% c('27.4.a', '27.4.b', '27.4.c', '27.4')), 'code_area'] <- 'nsea'
x[(x$code_area %in% c('27.3.a', '27.3.a.21','27.3.a.20')), 'code_area'] <- 'kask'
x[(x$code_area %in% c('27.3.c.22', '27.3.d.24', '27.3.b, c','27.3.d','27.3.d.27', '27.3.b.23')), 'code_area'] <- '2224'
x[(x$code_area %in% c('27.3.d.25', '27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2', '27.3.d.28_NK','27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), 'code_area'] <- '2532'
x[!(x$code_area %in% c('nsea', 'kask', '2224', '2532')), 'code_area'] <- 'oth'
x$Stock <- paste0(x$Species, ".", x$code_area)
# correct names for special cases (those across management areas)
x[ x$Species %in% c("COD") & (x$F_CODE %in% c('27.4.a', '27.4.b', '27.4.c', '27.4')), "Stock"] <- 'COD.nsea'
x[ x$Species %in% c("COD")& (x$F_CODE %in% c('27.3.a')), "Stock"] <- 'COD.kat'
#head(x[x$Species=="COD.kat",])
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.a', '27.3.a.20', '27.3.c.22')), "Stock"] <- 'HER.3a22'
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.d.28.1')), "Stock"] <- 'HER.281'
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.d.30','27.3.d.31')), "Species"] <- 'HER.3031'
x[ x$Species %in% c("PLE") & (x$F_CODE %in% c('27.3.a.21','27.3.a.20', '27.3.c.22', '27.3.b.23')), "Stock"] <- 'PLE.2123'
x[ x$Species %in% c("PLE") & (x$F_CODE %in% c('27.3.d.24', '27.3.d.25', '27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2', '27.3.d.28_NK','27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), "Stock"] <- 'PLE.2432'
x[ x$Stock %in% c("BLL.2224", "BLL.2532"), "Stock"] <- 'BLL.2232'
x[ x$Stock %in% c("TUR.2224", "TUR.2532"), "Stock"] <- 'TUR.2232'
x[ x$Stock %in% c("DAB.2224", "DAB.2532"), "Stock"] <- 'DAB.2232'
x[ x$Stock %in% c("SPR.2224", "SPR.2532"), "Stock"] <- 'SPR.2232'
x[ x$Stock %in% c("WHG.2224", "WHG.2532"), "Stock"] <- 'WHG.2232'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.c.22')), "Stock"] <- 'FLE.2223'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.24', '27.3.d.25')), "Stock"] <- 'FLE.2425'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2')), "Stock"] <- 'FLE.2628'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), "Stock"] <- 'FLE.2732'
x[ x$Species %in% c("SOL") & (x$F_CODE %in% c('27.3.a.20','27.3.c.22','27.3.d.24')), "Stock"] <- 'SOL.2024'
gc()
# export
x <- cbind.data.frame(datatype="dem", x)
#save(x, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatios",a_comment,"ForBotAllyAndStocks",years[1],"-",years[length(years)],".RData", sep="")))
#save(x, file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatios",a_comment,"ForBotAllyAndStocks",years[1],"-",years[length(years)],".RData", sep="")))
load(file=file.path(getwd(), "outputs2020", paste("AggregatedSweptAreaPlusMet6AndRatios",a_comment,"ForBotAllyAndStocks",years[1],"-",years[length(years)],".RData", sep="")))
# e.g. a table...
xx <- tapply(x$value, list(x$Stock, x$Year), mean, na.rm=TRUE)
# or a plot
xx <- cbind.data.frame(xx, Stock=rownames(xx))
long <- melt(setDT(xx[, c("Stock", as.character(years))]), id.vars = c("Stock"), variable.name = "Year")
long <- as.data.frame(long[complete.cases(long),])
long$Species <- sapply(strsplit(as.character(long$Stock), split="\\."), function(x) x[1])
long$Region <- sapply(strsplit(as.character(long$Stock), split="\\."), function(x) x[2])
a_long <- long[long$Species %in% c("COD","PLE","HKE","NEP", "CSH", "MUS", "NOP", "SAN"),] # caution: filtered out to ease the reading!
a_long <- a_long[!a_long$Stock %in% c("HKE.2532","HKE.kask", "CSH.kask", "NOP.kask", "NOP.kask"),] # caution: filtered out to ease the reading!
a_long <- a_long[!a_long$Region %in% c("oth"),] # caution: filtered out to ease the reading!
namefile <- paste0("ts_",a_comment,"_per_stock_for_dem_gridcells",years[1],"-",years[length(years)],".tif")
a_width <- 7000; a_height=3500
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(ggplot2)
# do al list of plot to avoid using facet_wrap(~Region, scales="free_y")
ggList <- lapply(split(a_long, a_long$Region), function(i) {
ggplot(i, aes(x=Year, y=value, group=Stock, color=Stock)) + labs(title="",x="Year", y = a_comment) +
geom_line(size=2) + scale_color_brewer(palette="RdBu") + theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) # + ylim(c(0,10))
} )
# plot as grid in 1 columns
cowplot::plot_grid(plotlist = ggList, ncol = 3,
align = 'v', labels = levels(long$Region))
dev.off()
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
##!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!##
#prefix <- "LE_VPUF_" ; a_comment <- "VPUF"
prefix <- "LE_CPUF_" ; a_comment <- "CPUF"
# assign area coding to retrieve stock from species
# PELAGIC GEARS
#load(file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatiosForPelAlly",years[1],"-",years[length(years)],".RData", sep="")))
load(file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndRatiosForPelAlly",years[1],"-",years[length(years)],".RData", sep="")))
x <- AllAggResultsPel
lon <- "CELL_LONG" ; lat <- "CELL_LATI"
# code F_SUBAREA (time consuming code...)
# Convert all points first to SpatialPoints first
library(rgdal)
library(raster)
# search in Baltic and North Sea # do not use "27.3.a" "27.3.d","27.3.b, c" cause overruling finer division...
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", "27.5","27.6", "27.7", "27.8", "27.14") & fao_areas$F_LEVEL!="MAJOR",] # caution with the MAJOR overidding the over()
fao_areas <- fao_areas[fao_areas$F_CODE %in% c("27.8.c","27.8.b","27.8.e","27.8.d","27.8.a","27.7.j","27.7.h","27.7.f","27.7.g","27.7.k","27.4.c","27.7.c","27.4.b","27.6.b",
"27.5.b","27.5.a","27.14.b","27.2.a","27.2.b","27.14.a","27.7.e","27.7.d","27.6.a","27.7.b","27.7.a","27.4.a","27.8.e.1",
"27.8.d.1","27.8.d.2","27.7.j.1","27.7.k.1","27.7.k.2","27.7.j.2","27.7.c.1","27.7.c.2","27.3.d.24","27.3.b.23","27.3.d.26","27.6.b.1",
"27.6.b.2","27.5.b.1","27.5.a.1", "27.14.b.1","27.5.a.2","27.14.b.2","27.2.a.1","27.2.b.1","27.2.b.2","27.8.e.2","27.5.b.2","27.2.a.2","27.3.c.22","27.3.d.27","27.3.d.28","27.3.d.29",
"27.3.d.32","27.3.d.30","27.3.d.31","27.3.d.25","27.3.a.21","27.3.a.20","27.5.b.1.a","27.5.b.1.b"),]
an <- function(x) as.numeric(as.character(x))
coords <- SpatialPoints(cbind(lon=an(x[, lon]), lat=an(x[, lat])))
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)
x$F_CODE <- idx[,"F_CODE"]
# check
plot(fao_areas[fao_areas$F_CODE %in% c("27.3.d.24","27.3.b.23"),])
points( x[is.na(x$F_CODE), c(lon, lat)], col=3, pch="+")
#=> pblm with discret pos and coastline...so brute search below to fix:
for(multi in seq(1,3,by=0.2)) {
for(a_sign1 in c(-1,1)) {
for(a_sign2 in c(-1,1)) {
dx <- 0.2
dy <- 0.2
print(length(which(is.na(x$F_CODE))))
coords <- SpatialPoints(cbind(lon=an(x[ which(is.na(x$F_CODE)), lon])+dx*multi*a_sign1, lat=an(x[ which(is.na(x$F_CODE)),lat])+dy*multi*a_sign2))
projection(coords) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") # a guess!
idx <- over(coords, fao_areas)
x[which(is.na(x$F_CODE)),]$F_CODE <- idx[,"F_CODE"]
length(which(is.na(x$F_CODE)))
}}}
x <- x[!is.na(x$F_CODE),] # remove very few records
# convert to long
x$ID <- paste0(x$VE_REF, x$Year, x$LE_ID)
library(data.table)
long <- melt(setDT(x[, c("grID", "LE_MET", "F_CODE", "Year", colnames(x)[grepl(prefix, colnames(x))])]),
id.vars = c("grID", "LE_MET", "F_CODE", "Year"), variable.name = "Var")
long <- long[!(is.na(long$value) | long$value==0),] # remove cells where the value is at 0 to avoid an average bias
long$Species <- sapply(strsplit(as.character(long$Var), split="_"), function (x) x[3])
x<- long
# find out areas to Retrieve Stocks from Species
x$code_area <- as.character(x$F_CODE)
x[(x$code_area %in% c('27.4.a', '27.4.b', '27.4.c', '27.4')), 'code_area'] <- 'nsea'
x[(x$code_area %in% c('27.3.a', '27.3.a.21','27.3.a.20')), 'code_area'] <- 'kask'
x[(x$code_area %in% c('27.3.c.22', '27.3.d.24', '27.3.b, c','27.3.d','27.3.d.27', '27.3.b.23')), 'code_area'] <- '2224'
x[(x$code_area %in% c('27.3.d.25', '27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2', '27.3.d.28_NK','27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), 'code_area'] <- '2532'
x[!(x$code_area %in% c('nsea', 'kask', '2224', '2532')), 'code_area'] <- 'oth'
x$Stock <- paste0(x$Species, ".", x$code_area)
# correct names for special cases (those across management areas)
x[ x$Species %in% c("COD") & (x$F_CODE %in% c('27.4.a', '27.4.b', '27.4.c', '27.4')), "Stock"] <- 'COD.nsea'
x[ x$Species %in% c("COD")& (x$F_CODE %in% c('27.3.a')), "Stock"] <- 'COD.kat'
#head(x[x$Species=="COD.kat",])
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.a', '27.3.a.20', '27.3.c.22')), "Stock"] <- 'HER.3a22'
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.d.28.1')), "Stock"] <- 'HER.281'
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.d.30','27.3.d.31')), "Species"] <- 'HER.3031'
x[ x$Species %in% c("PLE") & (x$F_CODE %in% c('27.3.a.21','27.3.a.20', '27.3.c.22', '27.3.b.23')), "Stock"] <- 'PLE.2123'
x[ x$Species %in% c("PLE") & (x$F_CODE %in% c('27.3.d.24', '27.3.d.25', '27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2', '27.3.d.28_NK','27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), "Stock"] <- 'PLE.2432'
x[ x$Stock %in% c("BLL.2224", "BLL.2532"), "Stock"] <- 'BLL.2232'
x[ x$Stock %in% c("TUR.2224", "TUR.2532"), "Stock"] <- 'TUR.2232'
x[ x$Stock %in% c("DAB.2224", "DAB.2532"), "Stock"] <- 'DAB.2232'
x[ x$Stock %in% c("SPR.2224", "SPR.2532"), "Stock"] <- 'SPR.2232'
x[ x$Stock %in% c("WHG.2224", "WHG.2532"), "Stock"] <- 'WHG.2232'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.c.22')), "Stock"] <- 'FLE.2223'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.24', '27.3.d.25')), "Stock"] <- 'FLE.2425'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2')), "Stock"] <- 'FLE.2628'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), "Stock"] <- 'FLE.2732'
x[ x$Species %in% c("SOL") & (x$F_CODE %in% c('27.3.a.20','27.3.c.22','27.3.d.24')), "Stock"] <- 'SOL.2024'
# export
x <- cbind.data.frame(datatype="pel", x)
# save(x, file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndVsizeAndRatios",a_comment,"ForPelAllyAndStocks",years[1],"-",years[length(years)],".RData", sep="")))
save(x, file=file.path(getwd(), "outputs2020_pel", paste("AggregatedSweptAreaPlusMet6AndRatios",a_comment,"ForPelAllyAndStocks",years[1],"-",years[length(years)],".RData", sep="")))
# e.g. a table...
xx <- tapply(x$value, list(x$Stock, x$Year), mean, na.rm=TRUE)
# or a plot
xx <- cbind.data.frame(xx, Stock=rownames(xx))
long <- melt(setDT(xx[, c("Stock", as.character(years))]), id.vars = c("Stock"), variable.name = "Year")
long <- as.data.frame(long[complete.cases(long),])
long$Species <- sapply(strsplit(as.character(long$Stock), split="\\."), function(x) x[1])
long$Region <- sapply(strsplit(as.character(long$Stock), split="\\."), function(x) x[2])
a_long <- long[!long$Species %in% c("COD","POK", "HKE", "HOM", "MON","PRA", "FLE", "PLE", "ELE", "NEP", "LEM", "WIT", "HAD", "TUR", "DAB", "SOL"),] # caution: filtered out to ease the reading!
namefile <- paste0("ts_",a_comment,"_per_stock_for_pel_gridcells",years[1],"-",years[length(years)],".tif")
a_width <- 9000; a_height=6500
tiff(filename=file.path(getwd(), "outputs2020_pel", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggplot2)
# do al list of plot to avoid using facet_wrap(~Region, scales="free_y")
ggList <- lapply(split(a_long, a_long$Region), function(i) {
ggplot(i, aes(x=Year, y=value, group=Stock, color=Stock)) + labs(title="",x="Year", y = a_comment) +
geom_line(size=2) + scale_color_brewer(palette="RdBu") + theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) # + ylim(c(0,10))
} )
# plot as grid in 1 columns
cowplot::plot_grid(plotlist = ggList, ncol = 3,
align = 'v', labels = levels(long$Region))
dev.off()
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#prefix <- "LE_VPUF_" ; a_comment <- "VPUF"
prefix <- "LE_CPUF_" ; a_comment <- "CPUF"
# assign area coding to retrieve stock from species
load(file=file.path(getwd(), "outputs2020_lgbkonly", paste("AggregatedEflaloWithSmallVids",years[1],"-",years[length(years)],".RData", sep="")))
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
x <- eflalo
# code F_SUBAREA (time consuming code...)
# Convert all points first to SpatialPoints first
library(rgdal)
library(raster)
# search in Baltic and North Sea # do not use "27.3.a" "27.3.d","27.3.b, c" cause overruling finer division...
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", "27.5","27.6", "27.7", "27.8", "27.14") & fao_areas$F_LEVEL!="MAJOR",] # caution with the MAJOR overidding the over()
fao_areas <- fao_areas[fao_areas$F_CODE %in% c("27.8.c","27.8.b","27.8.e","27.8.d","27.8.a","27.7.j","27.7.h","27.7.f","27.7.g","27.7.k","27.4.c","27.7.c","27.4.b","27.6.b",
"27.5.b","27.5.a","27.14.b","27.2.a","27.2.b","27.14.a","27.7.e","27.7.d","27.6.a","27.7.b","27.7.a","27.4.a","27.8.e.1",
"27.8.d.1","27.8.d.2","27.7.j.1","27.7.k.1","27.7.k.2","27.7.j.2","27.7.c.1","27.7.c.2","27.3.d.24","27.3.b.23","27.3.d.26","27.6.b.1",
"27.6.b.2","27.5.b.1","27.5.a.1", "27.14.b.1","27.5.a.2","27.14.b.2","27.2.a.1","27.2.b.1","27.2.b.2","27.8.e.2","27.5.b.2","27.2.a.2","27.3.c.22","27.3.d.27","27.3.d.28","27.3.d.29",
"27.3.d.32","27.3.d.30","27.3.d.31","27.3.d.25","27.3.a.21","27.3.a.20","27.5.b.1.a","27.5.b.1.b"),]
an <- function(x) as.numeric(as.character(x))
coords <- SpatialPoints(cbind(SI_LONG=an(x[, "SI_LONG"]), SI_LATI=an(x[, "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)
x$F_CODE <- idx[,"F_CODE"]
# check
plot(fao_areas[fao_areas$F_CODE %in% c("27.3.d.24","27.3.b.23"),])
points( x[is.na(x$F_CODE), c("SI_LONG","SI_LATI")], col=3, pch="+")
#=> pblm with discret pos and coastline...so brute search below to fix:
for(multi in seq(1,3,by=0.2)) {
for(a_sign1 in c(-1,1)) {
for(a_sign2 in c(-1,1)) {
dx <- 0.2
dy <- 0.2
print(length(which(is.na(x$F_CODE))))
coords <- SpatialPoints(cbind(SI_LONG=an(x[ which(is.na(x$F_CODE)), "SI_LONG"])+dx*multi*a_sign1, SI_LATI=an(x[ which(is.na(x$F_CODE)), "SI_LATI"])+dy*multi*a_sign2))
projection(coords) <- CRS("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0") # a guess!
idx <- over(coords, fao_areas)
x[which(is.na(x$F_CODE)),]$F_CODE <- idx[,"F_CODE"]
length(which(is.na(x$F_CODE)))
}}}
x <- x[!is.na(x$F_CODE),] # remove very few records
# convert to long
x$ID <- paste0(x$VE_REF, x$Year, x$LE_ID)
library(data.table)
long <- melt(setDT(x[, c("ID", "VE_REF", "FT_DDATIM", "VE_LEN", "VE_KW", "VE_TON", "LE_MET", "F_CODE", "Year", colnames(x)[grepl(prefix, colnames(x))])]),
id.vars = c("ID", "VE_REF","FT_DDATIM", "VE_LEN", "VE_KW", "VE_TON", "LE_MET", "F_CODE", "Year"), variable.name = "Var")
long$Species <- sapply(strsplit(as.character(long$Var), split="_"), function (x) x[3])
x<- long
# find out areas to Retrieve Stocks from Species
x$code_area <- as.character(x$F_CODE)
x[(x$code_area %in% c('27.4.a', '27.4.b', '27.4.c', '27.4')), 'code_area'] <- 'nsea'
x[(x$code_area %in% c('27.3.a', '27.3.a.21','27.3.a.20')), 'code_area'] <- 'kask'
x[(x$code_area %in% c('27.3.c.22', '27.3.d.24', '27.3.b, c','27.3.d', '27.3.b.23')), 'code_area'] <- '2224'
x[(x$code_area %in% c('27.3.d.25', '27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2', '27.3.d.28_NK','27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), 'code_area'] <- '2532'
x$Stock <- paste0(x$Species, ".", x$code_area)
# correct names for special cases (those across management areas)
x[ x$Species %in% c("COD") & (x$F_CODE %in% c('27.4.a', '27.4.b', '27.4.c', '27.4')), "Stock"] <- 'COD.nsea'
x[ x$Species %in% c("COD")& (x$F_CODE %in% c('27.3.a')), "Stock"] <- 'COD.kat'
#head(x[x$Species=="COD.kat",])
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.a', '27.3.a.20', '27.3.c.22')), "Stock"] <- 'HER.3a22'
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.d.28.1')), "Stock"] <- 'HER.281'
x[ x$Species %in% c("HER") & (x$F_CODE %in% c('27.3.d.30','27.3.d.31')), "Species"] <- 'HER.3031'
x[ x$Species %in% c("PLE") & (x$F_CODE %in% c('27.3.a.21','27.3.a.20', '27.3.c.22', '27.3.b.23')), "Stock"] <- 'PLE.2123'
x[ x$Species %in% c("PLE") & (x$F_CODE %in% c('27.3.d.24', '27.3.d.25', '27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2', '27.3.d.28_NK','27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), "Stock"] <- 'PLE.2432'
x[ x$Stock %in% c("BLL.2224", "BLL.2532"), "Stock"] <- 'BLL.2232'
x[ x$Stock %in% c("TUR.2224", "TUR.2532"), "Stock"] <- 'TUR.2232'
x[ x$Stock %in% c("DAB.2224", "DAB.2532"), "Stock"] <- 'DAB.2232'
x[ x$Stock %in% c("SPR.2224", "SPR.2532"), "Stock"] <- 'SPR.2232'
x[ x$Stock %in% c("WHG.2224", "WHG.2532"), "Stock"] <- 'WHG.2232'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.c.22')), "Stock"] <- 'FLE.2223'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.24', '27.3.d.25')), "Stock"] <- 'FLE.2425'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.26', '27.3.d.28', '27.3.d.28.1', '27.3.d.28.2')), "Stock"] <- 'FLE.2628'
x[ x$Species %in% c("FLE") & (x$F_CODE %in% c('27.3.d.29', '27.3.d.32', '27.3.d.30', '27.3.d.31')), "Stock"] <- 'FLE.2732'
x[ x$Species %in% c("SOL") & (x$F_CODE %in% c('27.3.a.20','27.3.c.22','27.3.d.24')), "Stock"] <- 'SOL.2024'
# export
x <- cbind.data.frame(datatype="lgbkonly", x)
save(x, file=file.path(getwd(), "outputs2020_lgbkonly", paste("AggregatedEflaloWith",a_comment,"SmallVidsAndStocks",years[1],"-",years[length(years)],".RData", sep="")))
# e.g. a table...
xx <- tapply(x$value, list(x$Stock, x$Year), mean, na.rm=TRUE)
# or a plot
xx <- cbind.data.frame(xx, Stock=rownames(xx))
long <- melt(setDT(xx[, c("Stock", as.character(years))]), id.vars = c("Stock"), variable.name = "Year")
long <- as.data.frame(long[complete.cases(long),])
long$Species <- sapply(strsplit(as.character(long$Stock), split="\\."), function(x) x[1])
long$Region <- sapply(strsplit(as.character(long$Stock), split="\\."), function(x) x[2])
a_long <- long[long$Species!="ELE",] # caution: filtered out to ease the reading!
namefile <- paste0("ts_",a_comment,"_per_stock_for_lgbkonly_vids",years[1],"-",years[length(years)],".tif")
a_width <- 9000; a_height=6500
tiff(filename=file.path(getwd(), "outputs2020_lgbkonly", "output_plots", namefile), width = a_width, height = a_height,
units = "px", pointsize = 12, res=600, compression = c("lzw"))
library(ggplot2)
# do al list of plot to avoid using facet_wrap(~Region, scales="free_y")
ggList <- lapply(split(a_long, a_long$Region), function(i) {
ggplot(i, aes(x=Year, y=value, group=Stock, color=Stock)) + labs(title="",x="Year", y = a_comment) +
geom_line(size=2) + scale_color_brewer(palette="RdBu") + theme_minimal() + theme(axis.text.x=element_text(angle=90,hjust=1,vjust=0.5)) # + ylim(c(0,10))
} )
# plot as grid in 1 columns
cowplot::plot_grid(plotlist = ggList, ncol = 3,
align = 'v', labels = levels(long$Region))
dev.off()
#.....ready to cross with e.g. STECF CFP monitoring data
# see next R routine.