inst/Updates/LFA 27-32/LFA27-32 Older/Update27-33.2018.R
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
####### Update 27-33 2017:
p = bio.lobster::load.environment()
la()
#lobster.db('logs.redo',p=p)
lobster.db(DS='process.logs.redo', p=p)
logsInSeason<-lobster.db('process.logs')
p$lfas = c("27", "28", "29", "30", "31A", "31B", "32", "33") # specify lfas for data summary
p$subareas = c("27N","27S", "28", "29", "30", "31A", "31B", "32", "33E", "33W") # specify lfas for data summary
TempModelling = TempModel( annual.by.area=F)
CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
CPUE.data<-CPUEModelData(p,redo=F)
cpueSubAreaMan.dat = CPUEplot(subset(CPUE.data,type=="mandatory"),subarea= p$subareas,yrs=1981:2017,graphic='R')
manlogs = cpueSubAreaMan.dat$annual.dat
cpueSubAreaVol.dat = CPUEplot(subset(CPUE.data,type=="voluntary"),subarea= p$subareas,yrs=1981:2017,graphic='R')
vollogs = cpueSubAreaVol.dat$annual.dat
#---------------------------------------------------------------------------Plots for Update
require(lattice)
require(plyr)
require(ggplot2)
#for update
#long.term.mean<-ddply(subset(manlogs,YEAR>2007), .(LFA), summarize, lt.mean=mean(CPUE))
png(file.path(project.figuredirectory('bio.lobster'),'CommercialCPUELFA27332018.png'),width=8,height=10,units='in',res=200)
p1 = ggplot(vollogs,aes(x=as.factor(YEAR),y=CPUE, group=LFA, colour="red")) +
geom_line () +
facet_wrap(~LFA, ncol=2) +
#geom_segment(aes(x='2008', y=lt.mean,xend='2017',yend=lt.mean,group=LFA), colour="cornflowerblue", linetype="dashed", data=long.term.mean) +
geom_line(data=manlogs,colour="black") +
geom_point(data=manlogs,colour="black") +
scale_y_continuous(limits=c(0,2.3), breaks=c(0,1,2)) +
scale_x_discrete(breaks=seq(1980,2020,5))+
theme_bw() +
theme(axis.text.x=element_text(angle=0,hjust=0.5,size=9.0),strip.text=element_text(size=12.0),legend.position="none") +
xlab("Year") +
ylab("CPUE (Kg/Trap Haul)")
p1
dev.off()
# FSRS model plot
FSRSvesday<-FSRSModelData()
#FSRSvesdayComm<-FSRSModelData(trap.type="commercial")
FSRSModelResultsRecruit = list()
FSRSModelResultsShort = list()
FSRSModelResultsLegal = list()
shorts.lst = list()
legals.lst = list()
recruit.lst = list()
for(i in 1:length( p$subareas)){
st = Sys.time()
mdata = subset(FSRSvesday,subarea==p$subareas[i])
FSRSModelResultsShort[[i]]=FSRSmodel(mdata, response="SHORTS",interaction=F,type="bayesian",iter=5000,redo=F,ptraps=1000)
pdata = FSRSModelResultsShort[[i]]$pData
pdata$Area = p$subareas[i]
shorts.lst[[i]] = pdata
#FSRSModelResultsLegal[[i]]=FSRSmodel(mdata, response="LEGALS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
#pdata = FSRSModelResultsLegal[[i]]$pData
#pdata$Area = p$subareas[i]
#legals.lst[[i]] = pdata
#
#FSRSModelResultsRecruit[[i]]=FSRSmodel(mdata, response="RECRUITS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
#pdata = FSRSModelResultsRecruit[[i]]$pData
#pdata$Area = p$subareas[i]
#recruit.lst[[i]] = pdata
print( Sys.time() - st)
}
names(FSRSModelResultsShort) = p$subareas
#names(FSRSModelResultsLegal) = p$subareas
#names(FSRSModelResultsRecruit) = p$subareas
shorts = do.call("rbind",shorts.lst)
#legals = do.call("rbind",legals.lst)
#recruit = do.call("rbind",recruit.lst)
shorts = subset(shorts,YEAR<2018)
library(ggplot2)
#pdf(file.path( project.figuredirectory('bio.lobster'),"FSRSmodelBayesShorts.pdf"),8, 10)
png(file.path(project.figuredirectory('bio.lobster'),"FSRSmodelBayesShorts.png"),width=8,height=10,units='in',res=200)
sp <- ggplot()
sp <- sp + geom_point(data = shorts, aes(y = median, x = YEAR), shape = 16, size = 2)
sp <- sp + xlab("Year") + ylab("Lobsters / Trap")
sp <- sp + theme(text = element_text(size=15)) + theme_bw()
sp <- sp + geom_line(data = shorts, aes(x = YEAR, y = median), colour = "black")
sp <- sp + geom_ribbon(data = shorts, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
sp <- sp + facet_wrap( ~Area, ncol=2,scales = "fixed")
sp
dev.off()
##pdf(file.path( project.figuredirectory('bio.lobster'),"FSRSmodelBayesLegals.pdf"),8, 10)
#png(file.path(project.figuredirectory('bio.lobster'),"FSRSmodelBayesLegals.png"),width=8,height=10,units='in',res=200)
#lp <- ggplot()
#lp <- lp + geom_point(data = legals, aes(y = median, x = YEAR), shape = 16, size = 2)
#lp <- lp + xlab("Year") + ylab("Lobsters / Trap")
#lp <- lp + theme(text = element_text(size=15)) + theme_bw()
#lp <- lp + geom_line(data = legals, aes(x = YEAR, y = median), colour = "black")
#lp <- lp + geom_ribbon(data = legals, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
#lp <- lp + facet_wrap( ~Area, ncol=2,scales = "fixed")
#lp
#dev.off()
##pdf(file.path( project.figuredirectory('bio.lobster'),"FSRSmodelBayesRecruits.pdf"),8, 10)
#png(file.path(project.figuredirectory('bio.lobster'),"FSRSmodelBayesRecruits.png"),width=8,height=10,units='in',res=200)
#rp <- ggplot()
#rp <- rp + geom_point(data = recruit, aes(y = median, x = YEAR), shape = 16, size = 2)
#rp <- rp + xlab("Year") + ylab("Lobsters / Trap")
#rp <- rp + theme(text = element_text(size=15)) + theme_bw()
#rp <- rp + geom_line(data = recruit, aes(x = YEAR, y = median), colour = "black")
#rp <- rp + geom_ribbon(data = recruit, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
#rp <- rp + facet_wrap( ~Area, ncol=2,scales = "fixed")
#rp
dev.off()
# Landings
LandingsUpdate<-lobster.db('annual.landings')
LR = as.data.frame(reshape(LandingsUpdate[,c("YR","LFA27", "LFA28", "LFA29", "LFA30", "LFA31", "LFA32", "LFA33")],
varying=c("LFA27", "LFA28", "LFA29", "LFA30", "LFA31", "LFA32", "LFA33"),v.names='Landings',timevar='LFA',times=c("LFA27", "LFA28", "LFA29", "LFA30", "LFA31", "LFA32", "LFA33"), direction='long'))
attr(LR,'reshapeLong') <- NULL
LR$LFA1 <- bio.utilities::recode(LR$LFA,c("'LFA28'='LFA28.32';'LFA29'='LFA28.32';'LFA30'='LFA28.32'; 'LFA31'='LFA28.32';'LFA32'='LFA28.32'"))
L =aggregate(Landings~LFA1+YR,data=LR,FUN=sum)
# Manon's Plot version - adds Gulf landings portion, USR and LRP. Note that LFA33 USR and LRP are calculated from last data export and differs
# from reference point document
require(ggplot2)
require(gridExtra)
pdf(file.path(project.figuredirectory('bio.lobster'),'Commercial.Landings.LFA27-33.2017.pdf'),width=8,height=10)
L27 = subset(L,LFA1=='LFA27')
#landings data from MComeau
Gulf27 <- data.frame(YR = 2004:2015, LandingsGulf= c(115, 117, 118, 110, 138, 104,146.2,149.3,161.4,208.8,174.3,158.4))
L27 = merge(L27,Gulf27,by='YR',all.x=T)
L27$LandingsGulf[which(is.na(L27$LandingsGulf))] <-0
L27$Landings = L27$Landings - L27$LandingsGulf
L27 = L27[,-4]
Gulf27 <- data.frame(YR = 2004:2015,LFA1='27G', Landings= c(115, 117, 118, 110, 138, 104,146.2,149.3,161.4,208.8,174.3,158.4))
L27 = rbind(L27,Gulf27)
names(L27)[2] <- 'LFA'
MEAN3YR<-c(NA,NA,1027,1106,1202,1217,1308,1268,1284,1241,1119,1017,1027,1145,1275,1174,1135,1037,1013,894,743,691,803,894,940,782,662,
547,535,540,480,369,337,312,337,356,396,651,1053,1695,2279,2590,2423,2230,2234,2260,2095,1900,1725,1763,1686,1906,2006,
2158,2277,2489,2515,2346,2265,2343,2703,2729,3011,3126,3561,4136,4792,5435,6083,7651)
L27.rp<-data.frame(YR= rep(1947:2016,times=2),LFA=rep("LFA27",times=140),RP=c(rep("USR",times=70),rep("LRP",times=70)),
Landings=c(rep(1629,times=70),rep(814,times=70)))
L28.32.rp<-data.frame(YR= rep(1947:2016,times=2),LFA=rep("LFA28.32",times=),RP=c(rep("USR",times=70),rep("LRP",times=70)),
Landings=c(rep(691,times=70),rep(346,times=70)))
L33.rp<-data.frame(YR= rep(1947:2016,times=3),LFA=rep("LFA33",times=210),TYPE=c(rep("USR",times=70),rep("LRP",times=70),rep("3-year Mean",times=70)),
Landings=c(rep(1794,times=70),rep(897,times=70),MEAN3YR))
L27.rp$RP <- factor(L27.rp$RP)
L27.rp$RP <- factor(L27.rp$RP, levels = rev(levels(L27.rp$RP)))
p1<-ggplot(L27, aes(x=YR,y=Landings,fill=factor(LFA))) + geom_bar(stat='identity', width=1.0) +
scale_y_continuous(limits=c(0,5000),breaks=seq(0, 5000, 1000)) + scale_x_continuous(breaks=seq(1947, 2016, 4)) +
scale_fill_manual(values=c("#999999","#000000"),name='Landings',breaks=c("LFA27","27G"), labels=c("LFA 27","LFA 27 Gulf")) +
ggtitle("LFA 27") + theme(axis.text.x=element_text(size=10, colour='black'),panel.border=element_rect(fill=NA,colour='black'),
axis.text.y=element_text(size=10, colour='black'),axis.title.y= element_text(size=15),legend.position=c(0.10,0.69),
legend.title = element_blank()) + xlab('') + ylab('Landings (t)') +
geom_line(data=L27.rp, aes(x=YR,y=Landings,group=RP,linetype=RP, color= RP),size=1.0) +
scale_color_manual(values=c("#0072B2","#009E73")) + scale_linetype_manual(values = c("dashed","solid"))
L28.32 <- subset(L,LFA1=='LFA28.32')
names(L28.32)[1] <- 'LFA'
L28.32.rp$RP <- factor(L28.32.rp$RP)
L28.32.rp$RP <- factor(L28.32.rp$RP, levels = rev(levels(L28.32.rp$RP)))
p2<-ggplot(L28.32,aes(x=YR,y=Landings,fill=(LFA))) + geom_bar(stat='identity', width=1.0) + guides(fill=FALSE) +
scale_y_continuous(limits=c(0,5000),breaks=seq(0, 5000, 1000)) + scale_x_continuous(breaks=seq(1947, 2016, 4)) +
scale_fill_manual(values="#000000") +ggtitle("LFA 28-32") +
theme(axis.text.x=element_text(size=10, colour='black'),panel.background=element_rect(colour='black'),
panel.border=element_rect(fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15),legend.position=c(0.07,0.85),legend.title = element_blank()) +
xlab('') + ylab('Landings (t)') +
geom_line(data=L28.32.rp, aes(x=YR,y=Landings,group=RP,linetype=RP, color= RP),size=1.0) +
scale_color_manual(values=c("#0072B2","#009E73")) + scale_linetype_manual(values = c("dashed","solid"))
LandingsUpdate<-lobster.db('seasonal.landings')
LU = LandingsUpdate[,c('SYEAR','LFA33')]
LU$YR = 1976:2017
L3 = subset(L,LFA1=='LFA33' & YR<1976)
LU = subset(LU,YR<2017,select=c(LFA33,YR))
names(LU) <- c('Landings','YR')
LU$TYPE <- 'Seasonal'
L3 = L3[,c('Landings','YR')]
L3$TYPE = 'Annual'
LU = rbind(L3,LU)
L33.rp$TYPE <- factor(L33.rp$TYPE)
L33.rp$TYPE <- factor(L33.rp$TYPE, levels = rev(levels(L33.rp$TYPE)))
p3<-ggplot(LU,aes(x=YR,y=Landings,colour=TYPE, group=TYPE)) +
geom_bar(data=subset(LU,TYPE=='Annual'),fill='darkgray',colour='darkgray',stat='identity', width=1.0) +
geom_bar(data=subset(LU,TYPE=='Seasonal'),fill='black',colour='black', stat='identity',width=1.0) +
#geom_line(data=LU, aes(x=YR,y=MEAN3YR), color="#D55E00") + geom_point(data=LU, aes(x=YR,y=MEAN3YR),colour="#D55E00") +
scale_y_continuous(limits=c(0,10200),breaks=seq(0, 10200, 1000)) + scale_x_continuous(breaks=seq(1947, 2016, 4)) +
ggtitle("LFA 33") + theme(axis.text.x=element_text(size=10, colour='black'),panel.background=element_rect(colour='black'),
panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15),legend.position=c(0.10,0.80),legend.title = element_blank()) +
xlab('') + ylab('Landings (t)') +
geom_line(data=L33.rp, aes(x=YR,y=Landings,group=TYPE,linetype=TYPE, color= TYPE),size=1.0) +
scale_color_manual(values=c("#0072B2","#009E73","#D55E00")) + scale_linetype_manual(values = c("dashed","solid","dotted"))
grid.arrange(p1,p2,p3,ncol=1)
dev.off()
# for LFA33 presentation
pdf(file.path( project.datadirectory("lobster"),"R","FSRScpueLFA33.pdf"),8, 5)
xyplot(cpue~year|subarea, data=subset(cpue.dat,lfa=='33'), ylab="CPUE (Kg / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,1))
xyplot(pred.s.cpue~SYEAR|subarea, data=subset(FSRScpue.dat,LFA=='33'), ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,4.5))
xyplot(pred.l.cpue~SYEAR|subarea, data=subset(FSRScpue.dat,LFA=='33'), ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,1.2))
dev.off()
subareas<-read.csv(file.path( project.datadirectory("lobster"), "data","LFA2733subarea.csv"))
polyprop33<-merge(subset(subareas,LFA==33),data.frame(LFA=33,subarea=c("33 West","33 East"),col=c(rgb(1,0,0,0.5),rgb(0,0,1,0.5))),all=T)
names(polyprop33)[1:3]<-c("PID","label","SID")
LFAgrid<-read.csv(file.path( project.datadirectory("lobster"), "data","maps","LFAgridPolys.csv"))
pdf(file.path( project.datadirectory("lobster"),"R","LFA33map.pdf"),8, 8)
LobsterMap('33',poly.lst=list(subset(LFAgrid,PID==33),polyprop33),boundaries='none',isobaths=c(seq(50,450,50),seq(500,1000,100)),bathcol=rgb(0,0,1,0.3))
legend('bottomright',c("West","East"),fill=c(rgb(1,0,0,0.5),rgb(0,0,1,0.5)))
dev.off()
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####### Update 27-33 2017:
p = bio.lobster::load.environment()
la()
#lobster.db('logs.redo',p=p)
lobster.db(DS='process.logs.redo', p=p)
logsInSeason<-lobster.db('process.logs')
p$lfas = c("27", "28", "29", "30", "31A", "31B", "32", "33") # specify lfas for data summary
p$subareas = c("27N","27S", "28", "29", "30", "31A", "31B", "32", "33E", "33W") # specify lfas for data summary
TempModelling = TempModel( annual.by.area=F)
CPUE.data<-CPUEModelData(p,redo=T,TempModelling)
CPUE.data<-CPUEModelData(p,redo=F)
cpueSubAreaMan.dat = CPUEplot(subset(CPUE.data,type=="mandatory"),subarea= p$subareas,yrs=1981:2017,graphic='R')
manlogs = cpueSubAreaMan.dat$annual.dat
cpueSubAreaVol.dat = CPUEplot(subset(CPUE.data,type=="voluntary"),subarea= p$subareas,yrs=1981:2017,graphic='R')
vollogs = cpueSubAreaVol.dat$annual.dat
#---------------------------------------------------------------------------Plots for Update
require(lattice)
require(plyr)
require(ggplot2)
#for update
#long.term.mean<-ddply(subset(manlogs,YEAR>2007), .(LFA), summarize, lt.mean=mean(CPUE))
png(file.path(project.figuredirectory('bio.lobster'),'CommercialCPUELFA27332018.png'),width=8,height=10,units='in',res=200)
p1 = ggplot(vollogs,aes(x=as.factor(YEAR),y=CPUE, group=LFA, colour="red")) +
geom_line () +
facet_wrap(~LFA, ncol=2) +
#geom_segment(aes(x='2008', y=lt.mean,xend='2017',yend=lt.mean,group=LFA), colour="cornflowerblue", linetype="dashed", data=long.term.mean) +
geom_line(data=manlogs,colour="black") +
geom_point(data=manlogs,colour="black") +
scale_y_continuous(limits=c(0,2.3), breaks=c(0,1,2)) +
scale_x_discrete(breaks=seq(1980,2020,5))+
theme_bw() +
theme(axis.text.x=element_text(angle=0,hjust=0.5,size=9.0),strip.text=element_text(size=12.0),legend.position="none") +
xlab("Year") +
ylab("CPUE (Kg/Trap Haul)")
p1
dev.off()
# FSRS model plot
FSRSvesday<-FSRSModelData()
#FSRSvesdayComm<-FSRSModelData(trap.type="commercial")
FSRSModelResultsRecruit = list()
FSRSModelResultsShort = list()
FSRSModelResultsLegal = list()
shorts.lst = list()
legals.lst = list()
recruit.lst = list()
for(i in 1:length( p$subareas)){
st = Sys.time()
mdata = subset(FSRSvesday,subarea==p$subareas[i])
FSRSModelResultsShort[[i]]=FSRSmodel(mdata, response="SHORTS",interaction=F,type="bayesian",iter=5000,redo=F,ptraps=1000)
pdata = FSRSModelResultsShort[[i]]$pData
pdata$Area = p$subareas[i]
shorts.lst[[i]] = pdata
#FSRSModelResultsLegal[[i]]=FSRSmodel(mdata, response="LEGALS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
#pdata = FSRSModelResultsLegal[[i]]$pData
#pdata$Area = p$subareas[i]
#legals.lst[[i]] = pdata
#
#FSRSModelResultsRecruit[[i]]=FSRSmodel(mdata, response="RECRUITS",interaction=F,type="bayesian",iter=5000,redo=T,ptraps=1000)
#pdata = FSRSModelResultsRecruit[[i]]$pData
#pdata$Area = p$subareas[i]
#recruit.lst[[i]] = pdata
print( Sys.time() - st)
}
names(FSRSModelResultsShort) = p$subareas
#names(FSRSModelResultsLegal) = p$subareas
#names(FSRSModelResultsRecruit) = p$subareas
shorts = do.call("rbind",shorts.lst)
#legals = do.call("rbind",legals.lst)
#recruit = do.call("rbind",recruit.lst)
shorts = subset(shorts,YEAR<2018)
library(ggplot2)
#pdf(file.path( project.figuredirectory('bio.lobster'),"FSRSmodelBayesShorts.pdf"),8, 10)
png(file.path(project.figuredirectory('bio.lobster'),"FSRSmodelBayesShorts.png"),width=8,height=10,units='in',res=200)
sp <- ggplot()
sp <- sp + geom_point(data = shorts, aes(y = median, x = YEAR), shape = 16, size = 2)
sp <- sp + xlab("Year") + ylab("Lobsters / Trap")
sp <- sp + theme(text = element_text(size=15)) + theme_bw()
sp <- sp + geom_line(data = shorts, aes(x = YEAR, y = median), colour = "black")
sp <- sp + geom_ribbon(data = shorts, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
sp <- sp + facet_wrap( ~Area, ncol=2,scales = "fixed")
sp
dev.off()
##pdf(file.path( project.figuredirectory('bio.lobster'),"FSRSmodelBayesLegals.pdf"),8, 10)
#png(file.path(project.figuredirectory('bio.lobster'),"FSRSmodelBayesLegals.png"),width=8,height=10,units='in',res=200)
#lp <- ggplot()
#lp <- lp + geom_point(data = legals, aes(y = median, x = YEAR), shape = 16, size = 2)
#lp <- lp + xlab("Year") + ylab("Lobsters / Trap")
#lp <- lp + theme(text = element_text(size=15)) + theme_bw()
#lp <- lp + geom_line(data = legals, aes(x = YEAR, y = median), colour = "black")
#lp <- lp + geom_ribbon(data = legals, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
#lp <- lp + facet_wrap( ~Area, ncol=2,scales = "fixed")
#lp
#dev.off()
##pdf(file.path( project.figuredirectory('bio.lobster'),"FSRSmodelBayesRecruits.pdf"),8, 10)
#png(file.path(project.figuredirectory('bio.lobster'),"FSRSmodelBayesRecruits.png"),width=8,height=10,units='in',res=200)
#rp <- ggplot()
#rp <- rp + geom_point(data = recruit, aes(y = median, x = YEAR), shape = 16, size = 2)
#rp <- rp + xlab("Year") + ylab("Lobsters / Trap")
#rp <- rp + theme(text = element_text(size=15)) + theme_bw()
#rp <- rp + geom_line(data = recruit, aes(x = YEAR, y = median), colour = "black")
#rp <- rp + geom_ribbon(data = recruit, aes(x = YEAR, ymax = ub, ymin = lb ), alpha = 0.5)
#rp <- rp + facet_wrap( ~Area, ncol=2,scales = "fixed")
#rp
dev.off()
# Landings
LandingsUpdate<-lobster.db('annual.landings')
LR = as.data.frame(reshape(LandingsUpdate[,c("YR","LFA27", "LFA28", "LFA29", "LFA30", "LFA31", "LFA32", "LFA33")],
varying=c("LFA27", "LFA28", "LFA29", "LFA30", "LFA31", "LFA32", "LFA33"),v.names='Landings',timevar='LFA',times=c("LFA27", "LFA28", "LFA29", "LFA30", "LFA31", "LFA32", "LFA33"), direction='long'))
attr(LR,'reshapeLong') <- NULL
LR$LFA1 <- bio.utilities::recode(LR$LFA,c("'LFA28'='LFA28.32';'LFA29'='LFA28.32';'LFA30'='LFA28.32'; 'LFA31'='LFA28.32';'LFA32'='LFA28.32'"))
L =aggregate(Landings~LFA1+YR,data=LR,FUN=sum)
# Manon's Plot version - adds Gulf landings portion, USR and LRP. Note that LFA33 USR and LRP are calculated from last data export and differs
# from reference point document
require(ggplot2)
require(gridExtra)
pdf(file.path(project.figuredirectory('bio.lobster'),'Commercial.Landings.LFA27-33.2017.pdf'),width=8,height=10)
L27 = subset(L,LFA1=='LFA27')
#landings data from MComeau
Gulf27 <- data.frame(YR = 2004:2015, LandingsGulf= c(115, 117, 118, 110, 138, 104,146.2,149.3,161.4,208.8,174.3,158.4))
L27 = merge(L27,Gulf27,by='YR',all.x=T)
L27$LandingsGulf[which(is.na(L27$LandingsGulf))] <-0
L27$Landings = L27$Landings - L27$LandingsGulf
L27 = L27[,-4]
Gulf27 <- data.frame(YR = 2004:2015,LFA1='27G', Landings= c(115, 117, 118, 110, 138, 104,146.2,149.3,161.4,208.8,174.3,158.4))
L27 = rbind(L27,Gulf27)
names(L27)[2] <- 'LFA'
MEAN3YR<-c(NA,NA,1027,1106,1202,1217,1308,1268,1284,1241,1119,1017,1027,1145,1275,1174,1135,1037,1013,894,743,691,803,894,940,782,662,
547,535,540,480,369,337,312,337,356,396,651,1053,1695,2279,2590,2423,2230,2234,2260,2095,1900,1725,1763,1686,1906,2006,
2158,2277,2489,2515,2346,2265,2343,2703,2729,3011,3126,3561,4136,4792,5435,6083,7651)
L27.rp<-data.frame(YR= rep(1947:2016,times=2),LFA=rep("LFA27",times=140),RP=c(rep("USR",times=70),rep("LRP",times=70)),
Landings=c(rep(1629,times=70),rep(814,times=70)))
L28.32.rp<-data.frame(YR= rep(1947:2016,times=2),LFA=rep("LFA28.32",times=),RP=c(rep("USR",times=70),rep("LRP",times=70)),
Landings=c(rep(691,times=70),rep(346,times=70)))
L33.rp<-data.frame(YR= rep(1947:2016,times=3),LFA=rep("LFA33",times=210),TYPE=c(rep("USR",times=70),rep("LRP",times=70),rep("3-year Mean",times=70)),
Landings=c(rep(1794,times=70),rep(897,times=70),MEAN3YR))
L27.rp$RP <- factor(L27.rp$RP)
L27.rp$RP <- factor(L27.rp$RP, levels = rev(levels(L27.rp$RP)))
p1<-ggplot(L27, aes(x=YR,y=Landings,fill=factor(LFA))) + geom_bar(stat='identity', width=1.0) +
scale_y_continuous(limits=c(0,5000),breaks=seq(0, 5000, 1000)) + scale_x_continuous(breaks=seq(1947, 2016, 4)) +
scale_fill_manual(values=c("#999999","#000000"),name='Landings',breaks=c("LFA27","27G"), labels=c("LFA 27","LFA 27 Gulf")) +
ggtitle("LFA 27") + theme(axis.text.x=element_text(size=10, colour='black'),panel.border=element_rect(fill=NA,colour='black'),
axis.text.y=element_text(size=10, colour='black'),axis.title.y= element_text(size=15),legend.position=c(0.10,0.69),
legend.title = element_blank()) + xlab('') + ylab('Landings (t)') +
geom_line(data=L27.rp, aes(x=YR,y=Landings,group=RP,linetype=RP, color= RP),size=1.0) +
scale_color_manual(values=c("#0072B2","#009E73")) + scale_linetype_manual(values = c("dashed","solid"))
L28.32 <- subset(L,LFA1=='LFA28.32')
names(L28.32)[1] <- 'LFA'
L28.32.rp$RP <- factor(L28.32.rp$RP)
L28.32.rp$RP <- factor(L28.32.rp$RP, levels = rev(levels(L28.32.rp$RP)))
p2<-ggplot(L28.32,aes(x=YR,y=Landings,fill=(LFA))) + geom_bar(stat='identity', width=1.0) + guides(fill=FALSE) +
scale_y_continuous(limits=c(0,5000),breaks=seq(0, 5000, 1000)) + scale_x_continuous(breaks=seq(1947, 2016, 4)) +
scale_fill_manual(values="#000000") +ggtitle("LFA 28-32") +
theme(axis.text.x=element_text(size=10, colour='black'),panel.background=element_rect(colour='black'),
panel.border=element_rect(fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15),legend.position=c(0.07,0.85),legend.title = element_blank()) +
xlab('') + ylab('Landings (t)') +
geom_line(data=L28.32.rp, aes(x=YR,y=Landings,group=RP,linetype=RP, color= RP),size=1.0) +
scale_color_manual(values=c("#0072B2","#009E73")) + scale_linetype_manual(values = c("dashed","solid"))
LandingsUpdate<-lobster.db('seasonal.landings')
LU = LandingsUpdate[,c('SYEAR','LFA33')]
LU$YR = 1976:2017
L3 = subset(L,LFA1=='LFA33' & YR<1976)
LU = subset(LU,YR<2017,select=c(LFA33,YR))
names(LU) <- c('Landings','YR')
LU$TYPE <- 'Seasonal'
L3 = L3[,c('Landings','YR')]
L3$TYPE = 'Annual'
LU = rbind(L3,LU)
L33.rp$TYPE <- factor(L33.rp$TYPE)
L33.rp$TYPE <- factor(L33.rp$TYPE, levels = rev(levels(L33.rp$TYPE)))
p3<-ggplot(LU,aes(x=YR,y=Landings,colour=TYPE, group=TYPE)) +
geom_bar(data=subset(LU,TYPE=='Annual'),fill='darkgray',colour='darkgray',stat='identity', width=1.0) +
geom_bar(data=subset(LU,TYPE=='Seasonal'),fill='black',colour='black', stat='identity',width=1.0) +
#geom_line(data=LU, aes(x=YR,y=MEAN3YR), color="#D55E00") + geom_point(data=LU, aes(x=YR,y=MEAN3YR),colour="#D55E00") +
scale_y_continuous(limits=c(0,10200),breaks=seq(0, 10200, 1000)) + scale_x_continuous(breaks=seq(1947, 2016, 4)) +
ggtitle("LFA 33") + theme(axis.text.x=element_text(size=10, colour='black'),panel.background=element_rect(colour='black'),
panel.border=element_rect(,fill=NA,colour='black'),axis.text.y=element_text(size=10, colour='black'),
axis.title.y= element_text(size=15),legend.position=c(0.10,0.80),legend.title = element_blank()) +
xlab('') + ylab('Landings (t)') +
geom_line(data=L33.rp, aes(x=YR,y=Landings,group=TYPE,linetype=TYPE, color= TYPE),size=1.0) +
scale_color_manual(values=c("#0072B2","#009E73","#D55E00")) + scale_linetype_manual(values = c("dashed","solid","dotted"))
grid.arrange(p1,p2,p3,ncol=1)
dev.off()
# for LFA33 presentation
pdf(file.path( project.datadirectory("lobster"),"R","FSRScpueLFA33.pdf"),8, 5)
xyplot(cpue~year|subarea, data=subset(cpue.dat,lfa=='33'), ylab="CPUE (Kg / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,1))
xyplot(pred.s.cpue~SYEAR|subarea, data=subset(FSRScpue.dat,LFA=='33'), ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,4.5))
xyplot(pred.l.cpue~SYEAR|subarea, data=subset(FSRScpue.dat,LFA=='33'), ylab="CPUE (No. Lobsters / Trap Haul)",xlab= "Year", as.table=T,type='b',ylim=c(0,1.2))
dev.off()
subareas<-read.csv(file.path( project.datadirectory("lobster"), "data","LFA2733subarea.csv"))
polyprop33<-merge(subset(subareas,LFA==33),data.frame(LFA=33,subarea=c("33 West","33 East"),col=c(rgb(1,0,0,0.5),rgb(0,0,1,0.5))),all=T)
names(polyprop33)[1:3]<-c("PID","label","SID")
LFAgrid<-read.csv(file.path( project.datadirectory("lobster"), "data","maps","LFAgridPolys.csv"))
pdf(file.path( project.datadirectory("lobster"),"R","LFA33map.pdf"),8, 8)
LobsterMap('33',poly.lst=list(subset(LFAgrid,PID==33),polyprop33),boundaries='none',isobaths=c(seq(50,450,50),seq(500,1000,100)),bathcol=rgb(0,0,1,0.3))
legend('bottomright',c("West","East"),fill=c(rgb(1,0,0,0.5),rgb(0,0,1,0.5)))
dev.off()
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