North Sea SMS Keyrun Supplemental Plots
Sarah Gaichas, Valerio Bartolino, Xochitl Cormon
13 December, 2023
# load the species list (checked that they are the same for the 2020 and 2017 runs)
spp <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/NorthSeaKeyRun_2023/species_names.in", nrows=27)
spp <- gsub("_","",spp$V1)
spp <- data.frame(Species.n=1:length(spp), stkName=spp)Observed Data vs SMS Predicted
Catch
2023 SMS KeyRun
# plot Obs vs Pred catch (here only 2023 run)
dat <- read.table("https://github.com/ices-eg/wg_WGSAM/raw/master/NorthSeaKeyRun_2023/summary_table_raw.out", sep="", header=T , na.strings="", stringsAsFactors=F)
#dat[1:3,]
dat <- read.table("/Users/sarah.gaichas/Documents/0_Data/ICES_WGSAM/2023/summary_table_raw.out", header = TRUE)
dat <- left_join(dat,spp) %>%
mutate(Yield = as.numeric(as.character(SOP)),
Yield.hat = as.numeric(as.character(SOP.hat)),
Year = as.numeric(as.character(Year))) %>%
gather("Source","Yield",9:10)
#postscript(paste(dirFigs,"catch_obsVSpre.ps",sep="/"))
ggplot(dat) +
geom_point(aes(Year,Yield,col=Source)) +
geom_line(aes(Year,Yield,col=Source,lty=Source)) +
facet_wrap(~stkName, scales="free_y")+
xlim(1973,2023)+
theme_tufte() +
theme(legend.position="bottom")
#dev.off()2020 SMS KeyRun
# plot Obs vs Pred catch (here only 2020 run)
dat <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2020/summary_table_raw.out", sep="", header=T , na.strings="", stringsAsFactors=F)
#dat[1:3,]
dat <- left_join(dat,spp) %>%
mutate(Yield = as.numeric(as.character(SOP)),
Yield.hat = as.numeric(as.character(SOP.hat)),
Year = as.numeric(as.character(Year))) %>%
gather("Source","Yield",8:9)
#postscript(paste(dirFigs,"catch_obsVSpre.ps",sep="/"))
ggplot(dat) +
geom_point(aes(Year,Yield,col=Source)) +
geom_line(aes(Year,Yield,col=Source,lty=Source)) +
facet_wrap(~stkName, scales="free_y")+
xlim(1973,2023)+
theme_tufte() +
theme(legend.position="bottom")
#dev.off()2017 SMS KeyRun
# plot Obs vs Pred catch (here only 2020 run)
dat <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/NorthSeaKeyRun_2017/summary_table_raw.out", sep="", header=T , na.strings="", stringsAsFactors=F)
#dat[1:3,]
dat <- left_join(dat,spp) %>%
mutate(Yield = as.numeric(as.character(Yield)),
Yield.hat = as.numeric(as.character(Yield.hat)),
Year = as.numeric(as.character(Year))) %>%
gather("Source","Yield",8:9)
#postscript(paste(dirFigs,"catch_obsVSpre.ps",sep="/"))
ggplot(dat) +
geom_point(aes(Year,Yield,col=Source)) +
geom_line(aes(Year,Yield,col=Source,lty=Source)) +
facet_wrap(~stkName, scales="free_y")+
xlim(1973,2023)+
theme_tufte() +
theme(legend.position="bottom")
#dev.off()Diet proportion 2023
# plot Obs vs Pred stomachs (here only 2020 run)
dat <- read.table("https://github.com/ices-eg/wg_WGSAM/raw/master/NorthSeaKeyRun_2023/summary_stom.out", header=T)
#dat[1:4,]
sppPred <- spp %>% mutate(Predator.no = Species.n) %>% rename(PredName=stkName)
sppPrey <- spp %>% mutate(Prey.no = Species.n) %>% rename(PreyName=stkName)
dat <- dat %>%
left_join(sppPred) %>%
mutate(Species.n = NULL) %>%
left_join(sppPrey) %>%
mutate(PreyName = as.character(PreyName),
PreyName = ifelse(is.na(PreyName), "otherfood", PreyName))
tmp <- dat %>%
group_by(Year,PredName,PreyName) %>%
summarise(stomcon = sum(stomcon,na.rm=T),
stomcon.hat = sum(stomcon.hat,na.rm=T))
# pie plot Obs and Pred
preys <- as.character(unique(tmp$PreyName))
preds <- as.character(unique(tmp$PredName))
#plList <- vector("list",length(preds))
nb.cols <- 14
colPalette <- colorRampPalette(brewer.pal(12, "Paired"))(nb.cols)
names(colPalette) <- as.factor(preys)
sourcelab <- c('observed', 'predicted')
names(sourcelab) <- c('stomcon', 'stomcon.hat')
plist2 = lapply(split(tmp, tmp$PredName), function(d) {
#for(i in 1:length(preds)){
ggplot(d %>% gather("source","stomcon",4:5)) +
geom_bar(aes(x="",stomcon,fill=PreyName), width = 1, stat = "identity", position="fill") +
scale_fill_manual(values=colPalette) +
facet_grid(source~Year,
labeller = labeller(source = sourcelab)) +
#ggtitle(preds[[i]]) +
coord_polar("y") +
ylab("Stomach contents proportion") +
xlab("") +
theme(axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank())
})
# for(i in 1:length(plList)){
# postscript(paste(dirFigs,"/stomachs_fitting_",preds[i],"_v1.ps",sep=""))
# print(plList[[i]])
# dev.off()
# }
# same thing as barplot plot Obs and Pred
#for(i in 1:length(preds)){
plist3 = lapply(split(tmp, tmp$PredName), function(d) {
ggplot(d) +
geom_col(aes(Year-0.2,stomcon,fill=PreyName), width=0.35, stat="identity", position="stack") +
geom_col(aes(Year+0.2,stomcon.hat,fill=PreyName), width=0.35, stat="identity", position="stack") +
scale_fill_manual(values=colPalette) +
#ggtitle(preds[[i]]) +
xlab("Year") +
theme_bw()
})
# for(i in 1:length(plList)){
# postscript(paste(dirFigs,"/stomachs_fitting_",preds[i],"_v2.ps",sep=""))
# print(plList[[i]])
# dev.off()
# }Pie charts
for(i in 1:length(preds)) {
cat(" \n####", preds[i]," \n")
print(plist2[preds[i]])
cat(" \n")
}Cod
$Cod
Fulmar
$Fulmar
GBB.Gull
$GBB.Gull
Gannet
$Gannet
Guillemot
$Guillemot
Haddock
$Haddock
Her.Gull
$Her.Gull
Kittiwake
$Kittiwake
Mackerel
$Mackerel
Puffin
$Puffin
Razorbill
$Razorbill
Saithe
$Saithe
Whiting
$Whiting
Grey.seal
$Grey.seal
H.porpoise
$H.porpoise
N.horse.mac
$N.horse.mac
A.radiata
$A.radiata
G.gurnards
$G.gurnards
W.horse.mac
$W.horse.mac
Hake
$Hake
Bar charts
for(i in 1:length(preds)) {
cat(" \n####", preds[i]," \n")
print(plist3[preds[i]])
cat(" \n")
}Cod
$Cod
Fulmar
$Fulmar
GBB.Gull
$GBB.Gull
Gannet
$Gannet
Guillemot
$Guillemot
Haddock
$Haddock
Her.Gull
$Her.Gull
Kittiwake
$Kittiwake
Mackerel
$Mackerel
Puffin
$Puffin
Razorbill
$Razorbill
Saithe
$Saithe
Whiting
$Whiting
Grey.seal
$Grey.seal
H.porpoise
$H.porpoise
N.horse.mac
$N.horse.mac
A.radiata
$A.radiata
G.gurnards
$G.gurnards
W.horse.mac
$W.horse.mac
Hake
$Hake
Diet proportion 2020
# plot Obs vs Pred stomachs (here only 2020 run)
dat <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2020/summary_stom.out", header=T)
#dat[1:4,]
sppPred <- spp %>% mutate(Predator.no = Species.n) %>% rename(PredName=stkName)
sppPrey <- spp %>% mutate(Prey.no = Species.n) %>% rename(PreyName=stkName)
dat <- dat %>%
left_join(sppPred) %>%
mutate(Species.n = NULL) %>%
left_join(sppPrey) %>%
mutate(PreyName = as.character(PreyName),
PreyName = ifelse(is.na(PreyName), "otherfood", PreyName))
tmp <- dat %>%
group_by(Year,PredName,PreyName) %>%
summarise(stomcon = sum(stomcon,na.rm=T),
stomcon.hat = sum(stomcon.hat,na.rm=T))
# pie plot Obs and Pred
preys <- as.character(unique(tmp$PreyName))
preds <- as.character(unique(tmp$PredName))
#plList <- vector("list",length(preds))
nb.cols <- 14
colPalette <- colorRampPalette(brewer.pal(12, "Paired"))(nb.cols)
names(colPalette) <- as.factor(preys)
sourcelab <- c('observed', 'predicted')
names(sourcelab) <- c('stomcon', 'stomcon.hat')
plist2 = lapply(split(tmp, tmp$PredName), function(d) {
#for(i in 1:length(preds)){
ggplot(d %>% gather("source","stomcon",4:5)) +
geom_bar(aes(x="",stomcon,fill=PreyName), width = 1, stat = "identity", position="fill") +
scale_fill_manual(values=colPalette) +
facet_grid(source~Year,
labeller = labeller(source = sourcelab)) +
#ggtitle(preds[[i]]) +
coord_polar("y") +
ylab("Stomach contents proportion") +
xlab("") +
theme(axis.ticks = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank())
})
# for(i in 1:length(plList)){
# postscript(paste(dirFigs,"/stomachs_fitting_",preds[i],"_v1.ps",sep=""))
# print(plList[[i]])
# dev.off()
# }
# same thing as barplot plot Obs and Pred
#for(i in 1:length(preds)){
plist3 = lapply(split(tmp, tmp$PredName), function(d) {
ggplot(d) +
geom_col(aes(Year-0.2,stomcon,fill=PreyName), width=0.35, stat="identity", position="stack") +
geom_col(aes(Year+0.2,stomcon.hat,fill=PreyName), width=0.35, stat="identity", position="stack") +
scale_fill_manual(values=colPalette) +
#ggtitle(preds[[i]]) +
xlab("Year") +
theme_bw()
})
# for(i in 1:length(plList)){
# postscript(paste(dirFigs,"/stomachs_fitting_",preds[i],"_v2.ps",sep=""))
# print(plList[[i]])
# dev.off()
# }Pie charts
for(i in 1:length(preds)) {
cat(" \n####", preds[i]," \n")
print(plist2[preds[i]])
cat(" \n")
}Cod
$Cod
Fulmar
$Fulmar
GBB.Gull
$GBB.Gull
Gannet
$Gannet
Guillemot
$Guillemot
Haddock
$Haddock
Her.Gull
$Her.Gull
Kittiwake
$Kittiwake
Mackerel
$Mackerel
Puffin
$Puffin
Razorbill
$Razorbill
Saithe
$Saithe
Whiting
$Whiting
Grey.seal
$Grey.seal
H.porpoise
$H.porpoise
N.horse.mac
$N.horse.mac
A.radiata
$A.radiata
G.gurnards
$G.gurnards
W.horse.mac
$W.horse.mac
Hake
$Hake
Bar charts
for(i in 1:length(preds)) {
cat(" \n####", preds[i]," \n")
print(plist3[preds[i]])
cat(" \n")
}Cod
$Cod
Fulmar
$Fulmar
GBB.Gull
$GBB.Gull
Gannet
$Gannet
Guillemot
$Guillemot
Haddock
$Haddock
Her.Gull
$Her.Gull
Kittiwake
$Kittiwake
Mackerel
$Mackerel
Puffin
$Puffin
Razorbill
$Razorbill
Saithe
$Saithe
Whiting
$Whiting
Grey.seal
$Grey.seal
H.porpoise
$H.porpoise
N.horse.mac
$N.horse.mac
A.radiata
$A.radiata
G.gurnards
$G.gurnards
W.horse.mac
$W.horse.mac
Hake
$Hake
SMS output comparisons
Numbers at age in Q1
# plot Num@age from 2017 and 2020 runs
dat17 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/NorthSeaKeyRun_2017/summary.out", sep="", header=T , na.strings="", stringsAsFactors=F)
dat20 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2020/summary.out", sep="", header=T , na.strings="", stringsAsFactors=F)
dat23 <- read.table("https://github.com/ices-eg/wg_WGSAM/raw/master/NorthSeaKeyRun_2023/summary.out", sep="", header=T , na.strings="", stringsAsFactors=F)
dat17 <- left_join(dat17,spp) %>% mutate(run="r17")
dat20 <- left_join(dat20,spp) %>% mutate(run="r20")
dat23 <- left_join(dat23,spp) %>% mutate(run="r23")
dat <- bind_rows(dat17,dat20, dat23)
# select Herring in Q1
#i <- spp$stkName[21] # 21 is herring
#postscript(paste(dirFigs,"/numAtAge_",i,".ps",sep=""))
plist = lapply(split(dat, dat$stkName), function(d) {
ggplot(d %>% filter(Quarter == 1)) +
geom_point(aes(Year,N, col=run)) +
geom_line(aes(Year,N, col=run)) +
facet_wrap(~Age, scale="free_y")+
theme_tufte() +
theme(legend.position="bottom")
})
#dev.off()Cod
plist$Cod
Haddock
plist$Haddock
Herring
plist$Herring
Mackerel
plist$Mackerel
N. Sandeel
plist$'N.sandeel'
Norway pout
plist$'Nor.pout'
Saithe
plist$'Saithe'
S. Sandeel
plist$'S.sandeel'
Sprat
plist$Sprat
Whiting
plist$Whiting
# plot part M2 per predator
dat23 <- read.table("https://github.com/ices-eg/wg_WGSAM/raw/master/NorthSeaKeyRun_2023/Input_Output/Output/WhoEatsWhom/who_eats_whom_level1.csv", sep=",", header=T)
dat20 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2020/who_eats_whom_level1.csv", sep=",", header=T)
#dat20 <- read.csv("C://Users//xocor//Documents//Work//Projects//WGSAM//who_eats_whom_level1.csv", sep=",", header=T, dec=".",stringsAsFactors=FALSE)
#head(dat20)
dat17 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2017/who_eats_whom_level1.csv", sep=",", header=T)
dat17 <- dat17 %>% mutate(run="r17")
dat20 <- dat20 %>% mutate(run="r20")
dat23 <- dat23 %>% mutate(run="r23")
dat2 <- bind_rows(dat17,dat20, dat23)
# aggregate birds, standardize names
dat3 <- dat2 %>%
mutate(Predator = as.character(Predator),
Predator = ifelse(Predator %in% c("Fulmar","Gannet","GBB. Gull","GBB.Gull","Guillemot","Her. Gull","Her.Gull","Kittiwake","Puffin","Razorbill"), "Birds", Predator),
Predator = ifelse(Predator %in% c("A. radiata", "A.radiata"), "A.radiata", Predator),
Predator = ifelse(Predator %in% c("G. gurnards", "G.gurnards"), "G.gurnards", Predator),
Predator = ifelse(Predator %in% c("Grey seal" , "Grey.seal"), "Grey.seal", Predator),
Predator = ifelse(Predator %in% c("H. porpoise", "H.porpoise"), "H.porpoise", Predator),
Predator = ifelse(Predator %in% c("N.horse mac", "N.horse.mac"), "N.horse.mac", Predator),
Predator = ifelse(Predator %in% c("W.horse mac", "W.horse.mac"), "W.horse.mac", Predator),
Prey = ifelse(Prey %in% c("N. sandeel", "N.sandeel"), "N.sandeel", Prey),
Prey = ifelse(Prey %in% c("Nor. pout", "Nor.pout"), "Nor.pout", Prey),
Prey = ifelse(Prey %in% c("S. sandeel", "S.sandeel"), "S.sandeel", Prey)
) %>%
group_by(Year,Quarter,Predator, Predator.age,Prey, Prey.age, run) %>%
summarise(M2 = sum(Part.M2, na.rm=T)) %>%
ungroup()## `summarise()` has grouped output by 'Year', 'Quarter', 'Predator',
## 'Predator.age', 'Prey', 'Prey.age'. You can override using the `.groups`
## argument.# length(unique(dat3$Predator)) #http://medialab.github.io/iwanthue/
col <- c("#000047","#858a00","#ff2b47","#00d3c9", "#0188d2", "#7426d6","#e37b00","#ffa0ee","#930025","#00bd3b","yellow","black","#005144")
names(col) <- as.factor(unique(dat3$Predator))
preycol <- c("#da62e7", "#549700", "#00609a", "#8dd971", "#535622", "lightgray")
names(preycol) <- as.factor(c("N.sandeel","Nor.pout","S.sandeel","Herring","Sprat","Plaice"))
col <- c(col, preycol)Modeled Q1 predator diet
2023 SMS KeyRun
# plot predators model diet (here only 2020 run)
dat23 <- read.table("https://github.com/ices-eg/wg_WGSAM/raw/master/NorthSeaKeyRun_2023/Input_Output/Output/WhoEatsWhom/who_eats_whom_level2.csv", sep=",", header=T)
#dat[1:4,]
# aggregate birds
datQ23 <- dat23 %>%
mutate(Predator = as.character(Predator),
Predator = ifelse(Predator %in% c("Fulmar","Gannet","GBB. Gull","GBB.Gull","Guillemot","Her. Gull","Her.Gull","Kittiwake","Puffin","Razorbill"), "Birds", Predator),
Predator = ifelse(Predator %in% c("A. radiata", "A.radiata"), "A.radiata", Predator),
Predator = ifelse(Predator %in% c("G. gurnards", "G.gurnards"), "G.gurnards", Predator),
Predator = ifelse(Predator %in% c("Grey seal" , "Grey.seal"), "Grey.seal", Predator),
Predator = ifelse(Predator %in% c("H. porpoise", "H.porpoise"), "H.porpoise", Predator),
Predator = ifelse(Predator %in% c("N.horse mac", "N.horse.mac"), "N.horse.mac", Predator),
Predator = ifelse(Predator %in% c("W.horse mac", "W.horse.mac"), "W.horse.mac", Predator),
Prey = ifelse(Prey %in% c("N. sandeel", "N.sandeel"), "N.sandeel", Prey),
Prey = ifelse(Prey %in% c("Nor. pout", "Nor.pout"), "Nor.pout", Prey),
Prey = ifelse(Prey %in% c("S. sandeel", "S.sandeel"), "S.sandeel", Prey)
) %>%
group_by(Year,Quarter,Predator,Prey) %>%
summarise(eatenW = sum(eatenW, na.rm=T))
# All predators diet in Q1
#postscript(paste(dirFigs,"diet_byPredator_Q1.ps",sep="/"))
ggplot(datQ23 %>% filter(Quarter == 1)) +
geom_bar(aes(Year,eatenW,fill=Prey), stat="identity", position="fill") +
facet_wrap(~Predator)+
scale_fill_manual(values=col) +
xlim(1973,2023)+
theme(legend.position="bottom")
#dev.off()2020 SMS KeyRun
# plot predators model diet (here only 2020 run)
dat20 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2020/who_eats_whom_level2.csv", sep=",", header=T)
#dat[1:4,]
# aggregate birds
datQ20 <- dat20 %>%
mutate(Predator = as.character(Predator),
Predator = ifelse(Predator %in% c("Fulmar","Gannet","GBB. Gull","GBB.Gull","Guillemot","Her. Gull","Her.Gull","Kittiwake","Puffin","Razorbill"), "Birds", Predator),
Predator = ifelse(Predator %in% c("A. radiata", "A.radiata"), "A.radiata", Predator),
Predator = ifelse(Predator %in% c("G. gurnards", "G.gurnards"), "G.gurnards", Predator),
Predator = ifelse(Predator %in% c("Grey seal" , "Grey.seal"), "Grey.seal", Predator),
Predator = ifelse(Predator %in% c("H. porpoise", "H.porpoise"), "H.porpoise", Predator),
Predator = ifelse(Predator %in% c("N.horse mac", "N.horse.mac"), "N.horse.mac", Predator),
Predator = ifelse(Predator %in% c("W.horse mac", "W.horse.mac"), "W.horse.mac", Predator),
Prey = ifelse(Prey %in% c("N. sandeel", "N.sandeel"), "N.sandeel", Prey),
Prey = ifelse(Prey %in% c("Nor. pout", "Nor.pout"), "Nor.pout", Prey),
Prey = ifelse(Prey %in% c("S. sandeel", "S.sandeel"), "S.sandeel", Prey)
) %>%
group_by(Year,Quarter,Predator,Prey) %>%
summarise(eatenW = sum(eatenW, na.rm=T))
# All predators diet in Q1
#postscript(paste(dirFigs,"diet_byPredator_Q1.ps",sep="/"))
ggplot(datQ20 %>% filter(Quarter == 1)) +
geom_bar(aes(Year,eatenW,fill=Prey), stat="identity", position="fill") +
facet_wrap(~Predator)+
scale_fill_manual(values=col) +
xlim(1973,2023)+
theme(legend.position="bottom")
#dev.off()Modeled predators contributing to predation by prey
2023 SMS proportion by weight
# ------------------------
# plot predators contributing to predation by prey
preys <- as.character(unique(datQ23$Prey))
plList <- vector("list",length(preys))
#nb.cols <- 14
#colPalette <- colorRampPalette(brewer.pal(12, "Paired"))(nb.cols)
# # by quarter
# for(i in 1:length(plList)){
# plList[[i]] <- ggplot(datQ %>% filter(Prey == preys[i])) +
# geom_bar(aes(Year,eatenW,fill=Predator), stat="identity", position="fill") +
# scale_fill_manual(values=colPalette) +
# facet_wrap(~Quarter) +
# ggtitle(preys[i])
# }
# for(i in 1:length(plList)){
# postscript(paste(dirFigs,"/predators_on_",preys[i],".ps",sep=""))
# print(plList[[i]])
# dev.off()
# }
# by year
datY <- datQ23 %>%
group_by(Year,Predator,Prey) %>%
summarise(eatenW = sum(eatenW, na.rm=T))
#postscript(paste(dirFigs,"predators_on_eachprey.ps",sep="/"))
ggplot(datY) +
geom_bar(aes(Year,eatenW,fill=Predator), stat="identity", position="fill") +
#scale_fill_manual(values=colPalette) +
scale_fill_manual(values=col) +
facet_wrap(~Prey)+
xlim(1973,2023)+
theme(legend.position="bottom")
#dev.off()2023 SMS weight
ggplot(datY) +
geom_bar(aes(Year,eatenW,fill=Predator), stat="identity") +
#scale_fill_manual(values=colPalette) +
scale_fill_manual(values=col) +
facet_wrap(~Prey)+
xlim(1973,2023)+
#ylim(0,2e+06)+
theme(legend.position="bottom")
2020 SMS proportion by weight
# ------------------------
# plot predators contributing to predation by prey
preys <- as.character(unique(datQ20$Prey))
plList <- vector("list",length(preys))
#nb.cols <- 14
#colPalette <- colorRampPalette(brewer.pal(12, "Paired"))(nb.cols)
# # by quarter
# for(i in 1:length(plList)){
# plList[[i]] <- ggplot(datQ %>% filter(Prey == preys[i])) +
# geom_bar(aes(Year,eatenW,fill=Predator), stat="identity", position="fill") +
# scale_fill_manual(values=colPalette) +
# facet_wrap(~Quarter) +
# ggtitle(preys[i])
# }
# for(i in 1:length(plList)){
# postscript(paste(dirFigs,"/predators_on_",preys[i],".ps",sep=""))
# print(plList[[i]])
# dev.off()
# }
# by year
datY <- datQ20 %>%
group_by(Year,Predator,Prey) %>%
summarise(eatenW = sum(eatenW, na.rm=T))
#postscript(paste(dirFigs,"predators_on_eachprey.ps",sep="/"))
ggplot(datY) +
geom_bar(aes(Year,eatenW,fill=Predator), stat="identity", position="fill") +
#scale_fill_manual(values=colPalette) +
scale_fill_manual(values=col) +
facet_wrap(~Prey)+
xlim(1973,2023)+
theme(legend.position="bottom")
#dev.off()2020 SMS weight
ggplot(datY) +
geom_bar(aes(Year,eatenW,fill=Predator), stat="identity") +
#scale_fill_manual(values=colPalette) +
scale_fill_manual(values=col) +
facet_wrap(~Prey)+
xlim(1973,2023)+
#ylim(0,2e+06)+
theme(legend.position="bottom")
Modeled M2 by predator on prey
# plot part M2 per predator
#dat20 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/KeyRunComparisons/NorthSeaSMS2020/who_eats_whom_level1.csv", sep=",", header=T)
#dat20 <- read.csv("C://Users//xocor//Documents//Work//Projects//WGSAM//who_eats_whom_level1.csv", sep=",", header=T, dec=".",stringsAsFactors=FALSE)
#head(dat20)
#dat17 <- read.table("https://raw.githubusercontent.com/ices-eg/wg_WGSAM/master/Input_Output/Output/WhoEatsWhom/who_eats_whom_level1.csv", sep=",", header=T)
#dat17 <- dat17 %>% mutate(run="r17")
#dat20 <- dat20 %>% mutate(run="r20")
#dat2 <- bind_rows(dat17,dat20)
# aggregate birds
#dat3 <- dat2 %>%
# mutate(Predator = as.character(Predator),
# Predator = ifelse(Predator %in% c("Fulmar","Gannet","GBB. Gull","Guillemot","Her. Gull","Kittiwake","Puffin","Razorbill"), "Birds", Predator)) %>%
# group_by(Year,Quarter,Predator, Predator.age,Prey, Prey.age, run) %>%
# summarise(M2 = sum(Part.M2, na.rm=T)) %>%
# ungroup()
# length(unique(dat3$Predator)) #http://medialab.github.io/iwanthue/
#col <- c("#000047","#858a00","#ff2b47","#00d3c9", "#0188d2", "#7426d6","#e37b00","#ffa0ee","#930025","#00bd3b","yellow","black","#005144")
#names(col) <- as.factor(unique(dat3$Predator))
dat3 <- subset(dat3, Year <=2017)
#pdf("C://Users//xocor//Documents//Work//Projects//WGSAM//M2.pdf", pointsize=10)
for (s in c("Herring", "Whiting", "Sprat")){
for(x in c(1:max(dat3[dat3$Prey==s,"Prey.age"]))){
g <- ggplot(dat3 %>% subset(Prey==s&Prey.age==x)) +
geom_bar(aes(Year,M2,fill=Predator), stat="identity", position="fill") +
facet_grid(Quarter~run)+ scale_fill_manual(values=col) + ggtitle(paste(s, "Age", x, sep=" "))
#windows()
cat(" \n####", s, " Age ",x," \n")
print(g)
cat(" \n")
}}Herring Age 1
Herring Age 2
Herring Age 3
Herring Age 4
Herring Age 5
Herring Age 6
Herring Age 7
Herring Age 8
Herring Age 9
Whiting Age 1
Whiting Age 2
Whiting Age 3
Whiting Age 4
Whiting Age 5
Whiting Age 6
Whiting Age 7
Whiting Age 8
Sprat Age 1
Sprat Age 2
Sprat Age 3
#dev.off()