inst/Updates/LFA 27-32/AtSeaSamplesIndicators27-32Updates.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
p = bio.lobster::load.environment()
require(bio.utilities)
require(PBSmapping)
require(dplyr)
require(ggplot2)
#Choose one
#assessment.year = p$current.assessment.year
assessment.year = p$current.assessment.year-1
figdir = file.path(project.datadirectory("bio.lobster","assessments","Updates","LFA27-32",assessment.year, "contextual"))
dir.create( figdir, recursive = TRUE, showWarnings = FALSE )
lobster.db('atSea.redo')
lobster.db('atSea.clean.redo')
atSea.clean = lobster.db('atSea.clean')
atSea.clean = subset(atSea.clean, SPECIESCODE==2550)
atSea.clean$PORT = ifelse(atSea.clean$PORT==-99,0,atSea.clean$PORT)
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
mls$X = NULL
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(atSea.clean$LFA,atSea.clean$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
names(mls)[1] <- 'YEAR'
ad = merge(ad,mls)
ad = subset(ad,YEAR<2027 & LFA<33)
atsea = atSea.clean
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary.redo')
out = list() #
outN = list()
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
po = ad[i,'LFA']
yo = ad[i,'YEAR']
mm = ad[i,'MLS_MM']
#weighting at sea samples by the proportion of landings
da = atSeaWeightings(atSea = atsea, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']), year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,at.sea.samples=T,mls=mm)
op = weightedCLF(x=da,returnLF=T,at.sea.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- os
#Tc is fractional year of catch used for cohort analysis
if(po == 27) {ll = "LFA27-30"; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(po == 28) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('28',names(DTs))]]; Tc = 0.67}
if(po == 29) {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(po == 30) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(po == '31A') {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(po == '31B') {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(po == 32) {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(!is.null(op)){
vec = mm:250
oo = op$vec[op$vec>mm & op$vec<250]
v0 = hist(oo, breaks=vec,plot=F)
v0$wts = lobLW(v0$mids)
v0$bwts = v0$counts * v0$wts
le = subset(lp,YR == yo)[,2]
if(po ==33) le = subset(lp,substr(YR,6,9) == yo)[,2]
v0$acWt = v0$bwts / sum(v0$bwts) * le
v0$N = v0$acWt / v0$wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = v0$N, Len = v0$mids,LFA = po, Year = yo,MLS=mm)
#newly recruited fraction
outS[[i]] = c(outS[[i]], new.rec = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+100,by=0.5)]))
outS[[i]] = c(outS[[i]], recWt = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]*v0$wts[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]))
#Production
iw = v0$mids>mm
pMature = sum(pMat(lfa=ll,cl=v0$mids[iw]) * v0$N[iw]) /sum(v0$N[iw])
eggProd = sum(pMat(lfa=ll,cl=v0$mids) * Fecundity(lle,v0$mids) * v0$N * as.numeric(outS[[i]]['prop.female']))
outS[[i]] = c(outS[[i]], PropMating = pMature,EggProduction = eggProd)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt =data.frame(dt=dt,brks = as.numeric(names(dt)))
dt$dt = dt$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt = dt[1:length(brks),]
dt$brks = brks
v0$LCA = brks[findInterval(v0$mids,vec=brks)]
LCAN = aggregate(v0$N~v0$LCA,FUN=sum)
LCA = merge(LCAN,dt,by.x='v0$LCA',by.y = 'brks')
k = which(LCA[,2]==0)[1]
LCA = LCA[1:(k-1),]
ca = cohortAnalysis(lens = LCA[,1], N = LCA[,2], dt = LCA[,3]) #annual
LCA$LFA = po
LCA$Year = yo
LCA$MLS = mm
out[[i]] = c(LFA = po, YEAR=yo, MLS=mm, N = sum(v0$N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
out.temp=out
outS.temp=outS
#out=out.temp
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-32.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-32.rdata'))
outS = as.data.frame(do.call(rbind,outS))
#outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-32.rdata'))
#loader
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-32.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-32.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-32.rdata'))
L = unique(out$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'NLandings.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(out,LFA==L[i])
plot(xpp$YEAR,xpp$N,type='p', pch=16, ,xlab='Year',ylab = 'Lobsters Landed (millions)')
rmean = runmed(xpp$N,k=3,endrule='median')
rmean.yr = xpp$YEAR[1:length(xpp$YEAR)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
par(mar=c(4,5,3,5),new=T)
plot(xpp$YEAR,xpp$Land,type='p',lty=3,xaxt='n', yaxt='n',ylab='',xlab='',pch=17)
rmean = runmed(xpp$Land,k=3,endrule='median')
rmean.yr = xpp$YEAR[1:length(xpp$YEAR)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='blue')
axis(side=4,srt=90)
mtext(side=4,'Landings (mt)',line=3,col='blue')
title(paste("LFA",L[i]))
dev.off()
}
L = unique(outS$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'propBerried.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(outS,LFA==L[[i]][1])
plot(xpp$Year,xpp$prop.berried,type='p', pch=16, ,xlab='Year',ylab = 'Proportion Berried')
rmean = runmed(xpp$prop.berried,k=3,endrule='median')
rmean.yr = xpp$Year[1:length(xpp$Year)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
title(paste("LFA",L[[i]][1]))
dev.off()
}
L = unique(outS$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'EggProd.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(outS,LFA==L[[i]][1])
plot(xpp$Year,xpp$EggProduction,type='p', pch=16, ,xlab='Year',ylab = 'Reproductive Potential')
rmean = runmed(xpp$EggProduction,k=3,endrule='median')
rmean.yr = xpp$Year[1:length(xpp$Year)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
title(paste("LFA",L[[i]][1]))
dev.off()
}
L = unique(outS$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'Recruitment.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(outS,LFA==L[[i]][1])
plot(xpp$Year,xpp$new.rec,type='p', pch=16, ,xlab='Year',ylab = 'Proportion New Recruits')
rmean = runmed(xpp$new.rec,k=3,endrule='median')
rmean.yr = xpp$Year[1:length(xpp$Year)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
title(paste("LFA",L[[i]][1]))
dev.off()
}
#Temperature
LFAs=c("31A","31B", "27", "29","30")
for(i in LFAs){
if (i %ni% c("27")){
pdf(paste0(figdir,"/temp/",i,"_",'bottomtemp.pdf'))
loc=paste0(figdir,"/temp/LFA",i,"GloryFsrs.csv")
x=read.csv(loc)
par(mar=c(4,5,3,5))
plot(x$yr,x$Temp,type='b', pch=16, ,xlab='Year',ylab = 'Bottom Temp (C)', main=paste0("LFA",i," Last Week of Season"),
lty=1,lwd=3, ylim=c(3,max(x$fsrstemp[is.finite(x$fsrstemp)])))
lines(x$yr,x$fsrstemp, pch=16,lty=1,lwd=3,col='firebrick2')
points(x$yr,x$fsrstemp, pch=16,lty=1,lwd=3,col='firebrick2')
text(x=2018, y=4, "FSRS", col="firebrick2", pos=4)
text(x=2018, y=3.5, "Modelled", col="black", pos=4)
title(paste0("LFA",i," Last Week of Season"))
dev.off()
}
if(i %in% c("27")){
pdf(paste0(figdir,"/temp/",i,"_",'bottomtemp.pdf'))
loc=paste0(figdir,"/temp/LFA",i,"GloryFsrs.csv")
x=read.csv(loc)
x$area[x$area=="27.North"]="North"
x$area[x$area=="27.Central"]="Central"
x$area[x$area=="27.South"]="South"
names(x)=c("Index", "Year", "Area", "Modeled", "FSRS")
x$Area=factor(x$Area, levels=c("North", "Central", "South"))
print(ggplot(x, aes(x = Year, y = Modeled, color="Modeled" )) +
geom_line() +
facet_wrap(~ Area, ncol = 1) +
ggtitle("LFA 27 Last Week of Season") +
xlab("Year") +
ylab("Bottom Temp (C)") +
labs(color="Data Source")+
theme(strip.text.x = element_text(size = 14))+
geom_line(aes(y = FSRS, color = "FSRS")) )
dev.off()
}
}
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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p = bio.lobster::load.environment()
require(bio.utilities)
require(PBSmapping)
require(dplyr)
require(ggplot2)
#Choose one
#assessment.year = p$current.assessment.year
assessment.year = p$current.assessment.year-1
figdir = file.path(project.datadirectory("bio.lobster","assessments","Updates","LFA27-32",assessment.year, "contextual"))
dir.create( figdir, recursive = TRUE, showWarnings = FALSE )
lobster.db('atSea.redo')
lobster.db('atSea.clean.redo')
atSea.clean = lobster.db('atSea.clean')
atSea.clean = subset(atSea.clean, SPECIESCODE==2550)
atSea.clean$PORT = ifelse(atSea.clean$PORT==-99,0,atSea.clean$PORT)
g = lobster.db('annual.landings')
p = lobster.db('seasonal.landings')
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
mls$X = NULL
load(file.path(project.datadirectory('bio.lobster'),'outputs','deltaTsSimBH.rdata')) #DTs
ad = as.data.frame(unique(cbind(atSea.clean$LFA,atSea.clean$SYEAR)))
ad = ad[order(ad[,1],ad[,2]),]
names(ad) = c('LFA','YEAR')
names(mls)[1] <- 'YEAR'
ad = merge(ad,mls)
ad = subset(ad,YEAR<2027 & LFA<33)
atsea = atSea.clean
cG = lobster.db('community.to.grid.historic')
cH = lobster.db('community.to.grid.contemporary.redo')
out = list() #
outN = list()
outS = list()
for(i in 1:nrow(ad)) {
print(ad[i,])
po = ad[i,'LFA']
yo = ad[i,'YEAR']
mm = ad[i,'MLS_MM']
#weighting at sea samples by the proportion of landings
da = atSeaWeightings(atSea = atsea, comGridHist =subset(cG,LFA==ad[i,'LFA']),comGridCont = subset(cH,LFA==ad[i,'LFA'] & SYEAR==ad[i,'YEAR']), year=ad[i,'YEAR'],lfa=ad[i,'LFA'],females.only=F,at.sea.samples=T,mls=mm)
op = weightedCLF(x=da,returnLF=T,at.sea.samples=T)
os = op
os$vec<-NULL
outS[[i]] <- os
#Tc is fractional year of catch used for cohort analysis
if(po == 27) {ll = "LFA27-30"; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('27N',names(DTs))]]; Tc = 0.67}
if(po == 28) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('28',names(DTs))]]; Tc = 0.67}
if(po == 29) {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('29',names(DTs))]]; Tc = 0.67}
if(po == 30) {ll = 'LFA28,30'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('30',names(DTs))]]; Tc = 0.67}
if(po == '31A') {ll = 'LFA29'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31A',names(DTs))]]; Tc = 0.67}
if(po == '31B') {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('31B',names(DTs))]]; Tc = 0.67}
if(po == 32) {ll = 'LFA32'; lle = 'all areas'; lp = g[,c('YR',names(g)[grep(po,names(g))])]; dt = DTs[[grep('32',names(DTs))]]; Tc = 0.67}
if(!is.null(op)){
vec = mm:250
oo = op$vec[op$vec>mm & op$vec<250]
v0 = hist(oo, breaks=vec,plot=F)
v0$wts = lobLW(v0$mids)
v0$bwts = v0$counts * v0$wts
le = subset(lp,YR == yo)[,2]
if(po ==33) le = subset(lp,substr(YR,6,9) == yo)[,2]
v0$acWt = v0$bwts / sum(v0$bwts) * le
v0$N = v0$acWt / v0$wts # tons / g = #'s in '000000
outN[[i]] = data.frame(N = v0$N, Len = v0$mids,LFA = po, Year = yo,MLS=mm)
#newly recruited fraction
outS[[i]] = c(outS[[i]], new.rec = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+100,by=0.5)]))
outS[[i]] = c(outS[[i]], recWt = sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]*v0$wts[v0$mids %in% seq(mm,mm+11,by=0.5)]) / sum(v0$N[v0$mids %in% seq(mm,mm+11,by=0.5)]))
#Production
iw = v0$mids>mm
pMature = sum(pMat(lfa=ll,cl=v0$mids[iw]) * v0$N[iw]) /sum(v0$N[iw])
eggProd = sum(pMat(lfa=ll,cl=v0$mids) * Fecundity(lle,v0$mids) * v0$N * as.numeric(outS[[i]]['prop.female']))
outS[[i]] = c(outS[[i]], PropMating = pMature,EggProduction = eggProd)
brks = seq(mm,max(as.numeric(names(dt))),by=5)
dt = dt[which.min(abs(brks[1]-as.numeric(names(dt)))):length(dt)]
dt =data.frame(dt=dt,brks = as.numeric(names(dt)))
dt$dt = dt$dt / 365 #* (5 / (as.numeric(names(dt))*0.15))
dt = dt[1:length(brks),]
dt$brks = brks
v0$LCA = brks[findInterval(v0$mids,vec=brks)]
LCAN = aggregate(v0$N~v0$LCA,FUN=sum)
LCA = merge(LCAN,dt,by.x='v0$LCA',by.y = 'brks')
k = which(LCA[,2]==0)[1]
LCA = LCA[1:(k-1),]
ca = cohortAnalysis(lens = LCA[,1], N = LCA[,2], dt = LCA[,3]) #annual
LCA$LFA = po
LCA$Year = yo
LCA$MLS = mm
out[[i]] = c(LFA = po, YEAR=yo, MLS=mm, N = sum(v0$N),Land = le,expl =ca$expl, F = ca$wF,M = ca$M,tF = ca$termF)
}
}
out.temp=out
outS.temp=outS
#out=out.temp
out = as.data.frame(do.call(rbind,out))
out = toNums(out,2:ncol(out))
save(out,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-32.rdata'))
outN = as.data.frame(do.call(rbind,outN))
save(outN,file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-32.rdata'))
outS = as.data.frame(do.call(rbind,outS))
#outS = toNums(outS,2:ncol(outS))
save(outS,file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-32.rdata'))
#loader
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-32.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNatSizeLFA27-32.rdata'))
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-32.rdata'))
L = unique(out$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'NLandings.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(out,LFA==L[i])
plot(xpp$YEAR,xpp$N,type='p', pch=16, ,xlab='Year',ylab = 'Lobsters Landed (millions)')
rmean = runmed(xpp$N,k=3,endrule='median')
rmean.yr = xpp$YEAR[1:length(xpp$YEAR)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
par(mar=c(4,5,3,5),new=T)
plot(xpp$YEAR,xpp$Land,type='p',lty=3,xaxt='n', yaxt='n',ylab='',xlab='',pch=17)
rmean = runmed(xpp$Land,k=3,endrule='median')
rmean.yr = xpp$YEAR[1:length(xpp$YEAR)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='blue')
axis(side=4,srt=90)
mtext(side=4,'Landings (mt)',line=3,col='blue')
title(paste("LFA",L[i]))
dev.off()
}
L = unique(outS$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'propBerried.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(outS,LFA==L[[i]][1])
plot(xpp$Year,xpp$prop.berried,type='p', pch=16, ,xlab='Year',ylab = 'Proportion Berried')
rmean = runmed(xpp$prop.berried,k=3,endrule='median')
rmean.yr = xpp$Year[1:length(xpp$Year)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
title(paste("LFA",L[[i]][1]))
dev.off()
}
L = unique(outS$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'EggProd.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(outS,LFA==L[[i]][1])
plot(xpp$Year,xpp$EggProduction,type='p', pch=16, ,xlab='Year',ylab = 'Reproductive Potential')
rmean = runmed(xpp$EggProduction,k=3,endrule='median')
rmean.yr = xpp$Year[1:length(xpp$Year)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
title(paste("LFA",L[[i]][1]))
dev.off()
}
L = unique(outS$LFA)
for(i in 1:length(L)){
pdf(paste0(figdir,"/",L[i],"_",'Recruitment.pdf'))
par(mar=c(4,5,3,5))
xpp = subset(outS,LFA==L[[i]][1])
plot(xpp$Year,xpp$new.rec,type='p', pch=16, ,xlab='Year',ylab = 'Proportion New Recruits')
rmean = runmed(xpp$new.rec,k=3,endrule='median')
rmean.yr = xpp$Year[1:length(xpp$Year)]
lines(rmean.yr,rmean,lty=1,lwd=3,col='firebrick2')
title(paste("LFA",L[[i]][1]))
dev.off()
}
#Temperature
LFAs=c("31A","31B", "27", "29","30")
for(i in LFAs){
if (i %ni% c("27")){
pdf(paste0(figdir,"/temp/",i,"_",'bottomtemp.pdf'))
loc=paste0(figdir,"/temp/LFA",i,"GloryFsrs.csv")
x=read.csv(loc)
par(mar=c(4,5,3,5))
plot(x$yr,x$Temp,type='b', pch=16, ,xlab='Year',ylab = 'Bottom Temp (C)', main=paste0("LFA",i," Last Week of Season"),
lty=1,lwd=3, ylim=c(3,max(x$fsrstemp[is.finite(x$fsrstemp)])))
lines(x$yr,x$fsrstemp, pch=16,lty=1,lwd=3,col='firebrick2')
points(x$yr,x$fsrstemp, pch=16,lty=1,lwd=3,col='firebrick2')
text(x=2018, y=4, "FSRS", col="firebrick2", pos=4)
text(x=2018, y=3.5, "Modelled", col="black", pos=4)
title(paste0("LFA",i," Last Week of Season"))
dev.off()
}
if(i %in% c("27")){
pdf(paste0(figdir,"/temp/",i,"_",'bottomtemp.pdf'))
loc=paste0(figdir,"/temp/LFA",i,"GloryFsrs.csv")
x=read.csv(loc)
x$area[x$area=="27.North"]="North"
x$area[x$area=="27.Central"]="Central"
x$area[x$area=="27.South"]="South"
names(x)=c("Index", "Year", "Area", "Modeled", "FSRS")
x$Area=factor(x$Area, levels=c("North", "Central", "South"))
print(ggplot(x, aes(x = Year, y = Modeled, color="Modeled" )) +
geom_line() +
facet_wrap(~ Area, ncol = 1) +
ggtitle("LFA 27 Last Week of Season") +
xlab("Year") +
ylab("Bottom Temp (C)") +
labs(color="Data Source")+
theme(strip.text.x = element_text(size = 14))+
geom_line(aes(y = FSRS, color = "FSRS")) )
dev.off()
}
}
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