inst/Projects/Vic Map and Figs/1.EstimatingTrapHaulsFromSlipsandSplit2Grids_vh.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
#
require(devtools)
require(bio.lobster)
require(bio.utilities)
require(sf)
load_all('C:/Users/Cooka/Documents/git/bio.utilities')
la()
wd = ('E:/Nova Scotia/Lobster Job/Stock Assessment/LFA 41/LFA 41 Evaluation')
setwd(wd)
#updated jan 31 2024
layerDir=file.path(project.datadirectory("bio.lobster"), "data","maps")
r<-readRDS(file.path( layerDir,"GridPolysSF.rds"))
r = st_as_sf(r)
a = lobster.db('process.logs')
a = subset(a,SYEAR>2004 & SYEAR<2024)
b = lobster.db('seasonal.landings')
b = subset(b,!is.na(SYEAR))
b$SYEAR = 1976:2024
b$LFA38B <- NULL
b = subset(b,SYEAR>2004 & SYEAR<2024)
b = reshape(b,idvar='SYEAR', varying=list(2:6),direction='long')
b$LFA=rep(c(33,34,35,36,38),each=19)
b$time <- NULL
names(b)[1:2]=c('YR','SlipLand')
d = lobster.db('annual.landings')
d = subset(d,YR>2004 & YR<2024, select=c(YR,LFA27,LFA28,LFA29,LFA30,LFA31A,LFA31B,LFA32))
d = reshape(d,idvar='YR', varying=list(2:8),direction='long')
d$LFA=rep(c(27,28,29,30,'31A','31B',32),each=19)
d$time <- NULL
names(d)[1:2]=c('YR','SlipLand')
bd = rbind(d,b)
bup = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SYEAR+LFA,data=a,FUN=sum)
bup$CPUE = bup$WEIGHT_KG/bup$NUM_OF_TRAPS
bAll = merge(bd,bup,by.x=c('YR','LFA'),by.y=c('SYEAR','LFA'))
sL= split(a,f=list(a$LFA, a$SYEAR))
sL = rm.from.list(sL)
cpue.lst<-list()
cpue.ann = list()
for(i in 1:length(sL)){
tmp<-sL[[i]]
tmp = tmp[,c('DATE_FISHED','WEIGHT_KG','NUM_OF_TRAPS')]
names(tmp)<-c('time','catch','effort')
tmp$date<-as.Date(tmp$time)
first.day<-min(tmp$date)
tmp$time<-julian(tmp$date,origin=first.day-1)
g<-biasCorrCPUE(tmp,by.time = F)
cpue.lst[[i]] <- c(lfa=unique(sL[[i]]$LFA),yr = unique(sL[[i]]$SYEAR),g)
}
cc =as.data.frame(do.call(rbind,cpue.lst))
cAll = merge(bAll,cc,by.x=c('LFA','YR'),by.y=c('lfa','yr'))
cAll$NTRAPs = cAll$SlipLand*1000/as.numeric(cAll$unBCPUE)
cAll$NTRAPSU = cAll$SlipLand*1000/as.numeric(cAll$l95)
cAll$NTRAPSL = cAll$SlipLand*1000/as.numeric(cAll$u95)
###########################################
#part the effort to grids
partEffort = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(NUM_OF_TRAPS~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(NUM_OF_TRAPS~GRID_NUM+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BTTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPs
pTH$BlTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSL
pTH$BuTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSU
partEffort[[i]] = pTH
}
partEffort = do.call(rbind, partEffort)
#pe = merge(partEffort,r,by.x=c('GRID_NUM','LFA'),by.y=c('GRID_NO','LFA'))
saveRDS(partEffort,'DiscretizedData/TrapHaulsWithinGrid.rds')
#############################################
# PartitionLandings to Grids
partLandings = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(WEIGHT_KG~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(WEIGHT_KG~GRID_NUM+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BL = pTH$WEIGHT_KG / (tTH$WEIGHT_KG )* (tC$SlipLand*1000)
partLandings[[i]] = pTH
}
partLandings = do.call(rbind, partLandings)
saveRDS(partLandings,'DiscretizedData/LandingsWithinGrid.rds')
###################################################
##Licenses By Grid and Week
g = lobster.db('process.logs')
g = subset(g,SYEAR>2004 & SYEAR<2024)
gg = aggregate(SD_LOG_ID~LFA+GRID_NUM+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gg,'DiscretizedData/SDLOGSWithinGrid.rds')
#############merge
##Licenses By Grid and Week
g = lobster.db('process.logs')
g = subset(g,SYEAR>2004 & SYEAR<2024)
gKL = aggregate(LICENCE_ID~LFA+GRID_NUM+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gKL,'DiscretizedData/LicencesWithinCommunity.rds')
#############merge
Tot = merge(merge(merge(partEffort,partLandings),gg),gKL)
Tot = subset(Tot,select=c(SYEAR,LFA,GRID_NUM,BTTH,BL,SD_LOG_ID,LICENCE_ID))
names(Tot)= c('FishingYear','LFA','Grid','TrapHauls','Landings','Trips','NLics')
Tot$PrivacyScreen = ifelse(Tot$NLics>4,1,0)
# we lose 149
saveRDS(Tot,'DiscretizedData/PrivacyScreened_TrapHauls_Landings_Trips_Gridand.rds')
##############################################################################################
#by Port#
partEffortCC = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(NUM_OF_TRAPS~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(NUM_OF_TRAPS~COMMUNITY_CODE+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BTTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPs
pTH$BlTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSL
pTH$BuTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSU
partEffortCC[[i]] = pTH
}
partEffortCC = do.call(rbind, partEffortCC)
saveRDS(partEffortCC,'DiscretizedData/TrapHaulsWithinCommunityCode.rds')
partLandingsCC = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(WEIGHT_KG~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(WEIGHT_KG~COMMUNITY_CODE+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BL = pTH$WEIGHT_KG / (tTH$WEIGHT_KG )* (tC$SlipLand*1000)
partLandingsCC[[i]] = pTH
}
partLandingsCC = do.call(rbind, partLandingsCC)
saveRDS(partLandingsCC,'DiscretizedData/LandingsWithinCommunity.rds')
###################################################
##Trips By Grid and Week
g = lobster.db('process.logs.unfiltered')
g = subset(g,SYEAR>2004 & SYEAR<2023)
gg = aggregate(SD_LOG_ID~LFA+COMMUNITY_CODE+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gg,'DiscretizedData/SDLOGSWithinCommunity.rds')
###################################################
##Licenses By Grid and Week
g = lobster.db('process.logs')
g = subset(g,SYEAR>2004 & SYEAR<2023)
gKL = aggregate(LICENCE_ID~LFA+COMMUNITY_CODE+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gKL,'DiscretizedData/LicencesWithinCommunity.rds')
#############merge
TotCC = merge(merge(merge(partEffortCC,partLandingsCC),gg),gKL)
TotCC = subset(TotCC,select=c(SYEAR,LFA,COMMUNITY_CODE,BTTH,BL,SD_LOG_ID,LICENCE_ID))
names(TotCC)= c('FishingYear','LFA','Community','TrapHauls','Landings','Trips','NLics')
TotCC$PrivacyScreen = ifelse(TotCC$NLics>4,1,0)
saveRDS(TotCC,'DiscretizedData/PrivacyScreened_TrapHauls_Landings_Trips_Comunity.rds')
TotCC = readRDS('DiscretizedData/PrivacyScreened_TrapHauls_Landings_Trips_Comunity.rds')
#maps with community code
pl = lobster.db('port_location')
pl = subset(pl, !is.na(CENTLAT) & CENTLAT>40)
pl = st_as_sf(pl,coords = c('CENTLON','CENTLAT'),crs=4326)
ggplot()+geom_sf(data=pl)
###oct 6 2023 this wont work for 35
uu = unique(TotCC$Community)
uni = uu[which(uu %ni% pl$PORT_CODE )]
subset(pl,PORT_CODE %in% uni)
###
PortLandings = merge(TotCC,pl,by.x=c('Community','LFA'),by.y=c('PORT_CODE',"LFA"))
PL = st_as_sf(PortLandings)
ok = ggLobsterMap('custom',xlim=c(-60.8,-59.6),ylim=c(45.55,47.1),return.object = T,colourLFA = F,fill.colours = 'white')
br = c(0,as.numeric(with(subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==1),round(quantile(NLics,seq(0,1,length.out=6))))))
labs = c(paste(br[-length(br)],'-',br[-1],sep=''),paste(br[length(br)],'+',sep=''))
mp<- brewer.pal(7,"YlOrRd")
PL$breaks = cut(PL$NLics, breaks=br, labels=labs, include.lowest=T,right=T)
ok1 = ok+geom_sf(data=subset(PL,FishingYear>2018 & LFA==27), aes(color = breaks),size=2)+
scale_color_manual(values=mp) +
facet_wrap(~FishingYear)+
guides(size=F)
br = as.numeric(with(subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==1),quantile(Landings,seq(0,1,length.out=6))))
PL
ok2 = ok+geom_sf(data=subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==1), aes(color = Landings),size=2)+
scale_fill_brewer(palette='Greens',breaks=br) +
geom_sf(data=subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==0), color = 'pink',size=2)+
facet_wrap(~FishingYear)+
guides(size=F)
width_inch <- 8.5 * 0.8 # 80% of the paper width
height_inch <- 11 * 0.8 # 80% of the paper height
# Save the plot with the specified dimensions
ggsave("my_plot.png", plot = ok1, width = width_inch, height = height_inch, units = "in")
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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#
require(devtools)
require(bio.lobster)
require(bio.utilities)
require(sf)
load_all('C:/Users/Cooka/Documents/git/bio.utilities')
la()
wd = ('E:/Nova Scotia/Lobster Job/Stock Assessment/LFA 41/LFA 41 Evaluation')
setwd(wd)
#updated jan 31 2024
layerDir=file.path(project.datadirectory("bio.lobster"), "data","maps")
r<-readRDS(file.path( layerDir,"GridPolysSF.rds"))
r = st_as_sf(r)
a = lobster.db('process.logs')
a = subset(a,SYEAR>2004 & SYEAR<2024)
b = lobster.db('seasonal.landings')
b = subset(b,!is.na(SYEAR))
b$SYEAR = 1976:2024
b$LFA38B <- NULL
b = subset(b,SYEAR>2004 & SYEAR<2024)
b = reshape(b,idvar='SYEAR', varying=list(2:6),direction='long')
b$LFA=rep(c(33,34,35,36,38),each=19)
b$time <- NULL
names(b)[1:2]=c('YR','SlipLand')
d = lobster.db('annual.landings')
d = subset(d,YR>2004 & YR<2024, select=c(YR,LFA27,LFA28,LFA29,LFA30,LFA31A,LFA31B,LFA32))
d = reshape(d,idvar='YR', varying=list(2:8),direction='long')
d$LFA=rep(c(27,28,29,30,'31A','31B',32),each=19)
d$time <- NULL
names(d)[1:2]=c('YR','SlipLand')
bd = rbind(d,b)
bup = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SYEAR+LFA,data=a,FUN=sum)
bup$CPUE = bup$WEIGHT_KG/bup$NUM_OF_TRAPS
bAll = merge(bd,bup,by.x=c('YR','LFA'),by.y=c('SYEAR','LFA'))
sL= split(a,f=list(a$LFA, a$SYEAR))
sL = rm.from.list(sL)
cpue.lst<-list()
cpue.ann = list()
for(i in 1:length(sL)){
tmp<-sL[[i]]
tmp = tmp[,c('DATE_FISHED','WEIGHT_KG','NUM_OF_TRAPS')]
names(tmp)<-c('time','catch','effort')
tmp$date<-as.Date(tmp$time)
first.day<-min(tmp$date)
tmp$time<-julian(tmp$date,origin=first.day-1)
g<-biasCorrCPUE(tmp,by.time = F)
cpue.lst[[i]] <- c(lfa=unique(sL[[i]]$LFA),yr = unique(sL[[i]]$SYEAR),g)
}
cc =as.data.frame(do.call(rbind,cpue.lst))
cAll = merge(bAll,cc,by.x=c('LFA','YR'),by.y=c('lfa','yr'))
cAll$NTRAPs = cAll$SlipLand*1000/as.numeric(cAll$unBCPUE)
cAll$NTRAPSU = cAll$SlipLand*1000/as.numeric(cAll$l95)
cAll$NTRAPSL = cAll$SlipLand*1000/as.numeric(cAll$u95)
###########################################
#part the effort to grids
partEffort = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(NUM_OF_TRAPS~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(NUM_OF_TRAPS~GRID_NUM+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BTTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPs
pTH$BlTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSL
pTH$BuTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSU
partEffort[[i]] = pTH
}
partEffort = do.call(rbind, partEffort)
#pe = merge(partEffort,r,by.x=c('GRID_NUM','LFA'),by.y=c('GRID_NO','LFA'))
saveRDS(partEffort,'DiscretizedData/TrapHaulsWithinGrid.rds')
#############################################
# PartitionLandings to Grids
partLandings = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(WEIGHT_KG~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(WEIGHT_KG~GRID_NUM+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BL = pTH$WEIGHT_KG / (tTH$WEIGHT_KG )* (tC$SlipLand*1000)
partLandings[[i]] = pTH
}
partLandings = do.call(rbind, partLandings)
saveRDS(partLandings,'DiscretizedData/LandingsWithinGrid.rds')
###################################################
##Licenses By Grid and Week
g = lobster.db('process.logs')
g = subset(g,SYEAR>2004 & SYEAR<2024)
gg = aggregate(SD_LOG_ID~LFA+GRID_NUM+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gg,'DiscretizedData/SDLOGSWithinGrid.rds')
#############merge
##Licenses By Grid and Week
g = lobster.db('process.logs')
g = subset(g,SYEAR>2004 & SYEAR<2024)
gKL = aggregate(LICENCE_ID~LFA+GRID_NUM+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gKL,'DiscretizedData/LicencesWithinCommunity.rds')
#############merge
Tot = merge(merge(merge(partEffort,partLandings),gg),gKL)
Tot = subset(Tot,select=c(SYEAR,LFA,GRID_NUM,BTTH,BL,SD_LOG_ID,LICENCE_ID))
names(Tot)= c('FishingYear','LFA','Grid','TrapHauls','Landings','Trips','NLics')
Tot$PrivacyScreen = ifelse(Tot$NLics>4,1,0)
# we lose 149
saveRDS(Tot,'DiscretizedData/PrivacyScreened_TrapHauls_Landings_Trips_Gridand.rds')
##############################################################################################
#by Port#
partEffortCC = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(NUM_OF_TRAPS~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(NUM_OF_TRAPS~COMMUNITY_CODE+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BTTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPs
pTH$BlTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSL
pTH$BuTH = pTH$NUM_OF_TRAPS / tTH$NUM_OF_TRAPS * tC$NTRAPSU
partEffortCC[[i]] = pTH
}
partEffortCC = do.call(rbind, partEffortCC)
saveRDS(partEffortCC,'DiscretizedData/TrapHaulsWithinCommunityCode.rds')
partLandingsCC = list()
for(i in 1:length(sL)){
tmp = sL[[i]]
tTH = aggregate(WEIGHT_KG~LFA,data=tmp,FUN=sum)
tC = subset(cAll, LFA==unique(tmp$LFA) & YR == unique(tmp$SYEAR))
pTH = aggregate(WEIGHT_KG~COMMUNITY_CODE+LFA+SYEAR,data=tmp,FUN=sum)
pTH$BL = pTH$WEIGHT_KG / (tTH$WEIGHT_KG )* (tC$SlipLand*1000)
partLandingsCC[[i]] = pTH
}
partLandingsCC = do.call(rbind, partLandingsCC)
saveRDS(partLandingsCC,'DiscretizedData/LandingsWithinCommunity.rds')
###################################################
##Trips By Grid and Week
g = lobster.db('process.logs.unfiltered')
g = subset(g,SYEAR>2004 & SYEAR<2023)
gg = aggregate(SD_LOG_ID~LFA+COMMUNITY_CODE+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gg,'DiscretizedData/SDLOGSWithinCommunity.rds')
###################################################
##Licenses By Grid and Week
g = lobster.db('process.logs')
g = subset(g,SYEAR>2004 & SYEAR<2023)
gKL = aggregate(LICENCE_ID~LFA+COMMUNITY_CODE+SYEAR,data = g,FUN=function(x) length(unique(x)))
saveRDS(gKL,'DiscretizedData/LicencesWithinCommunity.rds')
#############merge
TotCC = merge(merge(merge(partEffortCC,partLandingsCC),gg),gKL)
TotCC = subset(TotCC,select=c(SYEAR,LFA,COMMUNITY_CODE,BTTH,BL,SD_LOG_ID,LICENCE_ID))
names(TotCC)= c('FishingYear','LFA','Community','TrapHauls','Landings','Trips','NLics')
TotCC$PrivacyScreen = ifelse(TotCC$NLics>4,1,0)
saveRDS(TotCC,'DiscretizedData/PrivacyScreened_TrapHauls_Landings_Trips_Comunity.rds')
TotCC = readRDS('DiscretizedData/PrivacyScreened_TrapHauls_Landings_Trips_Comunity.rds')
#maps with community code
pl = lobster.db('port_location')
pl = subset(pl, !is.na(CENTLAT) & CENTLAT>40)
pl = st_as_sf(pl,coords = c('CENTLON','CENTLAT'),crs=4326)
ggplot()+geom_sf(data=pl)
###oct 6 2023 this wont work for 35
uu = unique(TotCC$Community)
uni = uu[which(uu %ni% pl$PORT_CODE )]
subset(pl,PORT_CODE %in% uni)
###
PortLandings = merge(TotCC,pl,by.x=c('Community','LFA'),by.y=c('PORT_CODE',"LFA"))
PL = st_as_sf(PortLandings)
ok = ggLobsterMap('custom',xlim=c(-60.8,-59.6),ylim=c(45.55,47.1),return.object = T,colourLFA = F,fill.colours = 'white')
br = c(0,as.numeric(with(subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==1),round(quantile(NLics,seq(0,1,length.out=6))))))
labs = c(paste(br[-length(br)],'-',br[-1],sep=''),paste(br[length(br)],'+',sep=''))
mp<- brewer.pal(7,"YlOrRd")
PL$breaks = cut(PL$NLics, breaks=br, labels=labs, include.lowest=T,right=T)
ok1 = ok+geom_sf(data=subset(PL,FishingYear>2018 & LFA==27), aes(color = breaks),size=2)+
scale_color_manual(values=mp) +
facet_wrap(~FishingYear)+
guides(size=F)
br = as.numeric(with(subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==1),quantile(Landings,seq(0,1,length.out=6))))
PL
ok2 = ok+geom_sf(data=subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==1), aes(color = Landings),size=2)+
scale_fill_brewer(palette='Greens',breaks=br) +
geom_sf(data=subset(PL,FishingYear>2018 & LFA==27 & PrivacyScreen==0), color = 'pink',size=2)+
facet_wrap(~FishingYear)+
guides(size=F)
width_inch <- 8.5 * 0.8 # 80% of the paper width
height_inch <- 11 * 0.8 # 80% of the paper height
# Save the plot with the specified dimensions
ggsave("my_plot.png", plot = ok1, width = width_inch, height = height_inch, units = "in")
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