inst/IP/bycatchCSAS/3.DescriptiveBycatch.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
require(bio.lobster)
require(bio.utilities)
require(RODBC)
require(lubridate)
require(devtools)
options(stringAsFactors=F)
la()
wd = ('C:\\Users\\Cooka\\OneDrive - DFO-MPO\\BycatchLobster')
setwd(wd)
bycatch.db('SWLSS.redo')
bycatch.db('GCIFA.redo')
bycatch.db('CBFHA.redo')
####Trips to Targets
b = bycatch.db('logbook.merge',wd=wd)
gt = t = bycatch.db('targets',wd=wd)
t = subset(t,LFA%in%c(27,'31A','31B',33:35))
tt = aggregate(cbind(Period1,Period2,Period3)~LFA+GridGrouping,data=t,FUN=min)
bA = aggregate(SD_LOG_ID~Period+GridGroup+LFA+SYEAR,data=b,FUN=function(x) length(unique(x)))
tR = as.data.frame(reshape(tt,varying=3:5,direction = 'long',sep=""))
names(tR) = c('LFA','GridGroup','Period','Target','Nt')
tR$Nt = NULL
bA = merge(bA,tR,all.x=T)
sss = readRDS('results/BumpedUpEffortByGridGroup.rds')
sss= subset(sss,select=c(Period,GridGroup,LFA,SYEAR,BTTH))
bA = merge(bA,sss)
names(bA)[c(5,7)] = c('LogsReported','BumpTH')
write.csv(bA,file=file.path(wd,'results','LogbooksAgg and Targets.csv'))
#logbooks
bA = read.csv(file=file.path(wd,'results','LogbooksAgg and Targets.csv'))
bA$X = NULL
#SWLSS sea sampling
CDa = bycatch.db(DS='SWLSS')
STraps = aggregate(P~SYEAR+GridGroup+LFA+Period+target,data=CDa,FUN=sum)
ATrips = aggregate(TRIP~SYEAR+LFA+Period+target+GridGroup,data=CDa,FUN=function(x) length(unique(x)))
STTraps =merge(STraps,ATrips,all=T)
names(STTraps)[6:7] = c('NTrapsSampledSWLSS','NTripsSampledSWLSS')
write.csv(STTraps,file=file.path('results','SWLSSTrips2Targets.csv'))
STTraps = merge(STTraps,bA,all=T)
write.csv(STTraps,file=file.path('results','SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps = read.csv(file=file.path('results','SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps$x = NULL
#adding IN ISBD
gg = bycatch.db('ISDB')
## Trips that dont match with grids or something
gr = subset(gg,is.na(target))
gr = as.data.frame(unique(cbind(gr$TRIPNO,gr$LFA,gr$GRIDNO)))
se = connect.command(con,'select * from lobster.issets_mv')
names(gr) = c('TRIP_ID','LFA','Grid')
ss = as.data.frame(unique(cbind(se$TRIP_ID,se$TRIP)))
names(ss) = c('TRIP_ID','TRIP')
merge(gr,ss)
###Moving on to summary of observer data update later after we get more complete info
UTraps = aggregate(UID~SYEAR+LFA+GridGroup+Period+target,data=gg,FUN=function(x) length(unique(x)))
UTrips = aggregate(TRIPNO~SYEAR+LFA+GridGroup+Period+target,data=gg,FUN=function(x) length(unique(x)))
ggg = merge(UTrips,UTraps,all=T)
names(ggg)[c(1,5:7)] = c('SYEAR','Target','ObserverTrips','ObserverTraps')
STTraps = merge(STTraps,ggg,all=T)
STTraps$X=STTraps$target = NULL
write.csv(STTraps,file=file.path('results','SWLSSandObsTrips2TargetsTrapsandTrips.csv'))
STTraps = read.csv(file=file.path('results','SWLSSandObsTrips2TargetsTrapsandTrips.csv'))
#adding in CBFHA
gg = bycatch.db('CBFHA')
STraps = aggregate(P~SYEAR+GridGroup+LFA+Period+target,data=gg,FUN=sum)
ATrips = aggregate(TRIP~SYEAR+LFA+Period+target+GridGroup,data=gg,FUN=function(x) length(unique(x)))
CBTraps =merge(STraps,ATrips,all=T)
names(CBTraps)[6:7] = c('NTrapsSampledCBFHA','NTripsSampledCBFHA')
CBTraps$target=NULL
write.csv(CBTraps,file=file.path('results','CBFHATrips2Targets.csv'))
STTraps = merge(STTraps,CBTraps,all=T)
write.csv(STTraps,file=file.path('results','CBFHA_OBS_SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps = read.csv(file=file.path('results','CBFHA_OBS_SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps$X.1 = STTraps$X = NULL
#adding in GCIFA
gg = bycatch.db('GCIFA')
STraps = aggregate(P~SYEAR+GridGroup+LFA+Period+target,data=gg,FUN=sum)
ATrips = aggregate(TRIP~SYEAR+LFA+Period+target+GridGroup,data=gg,FUN=function(x) length(unique(x)))
CBTraps =merge(STraps,ATrips,all=T)
CBTraps$target = NULL
names(CBTraps)[5:6] = c('NTrapsSampledGCIFA','NTripsSampledGCIFA')
write.csv(CBTraps,file=file.path('results','GCIFATrips2Targets.csv'))
STTraps = merge(STTraps,CBTraps,all=T)
write.csv(STTraps,file=file.path('results','GCIFA_CBFHA_OBS_SWLSSTrips2TargetsTrapsandTrips.csv'))
####Comparison of Lobster Trap catches to Reported on Logs
b=bycatch.db('logbook.merge')
#ISDB
#ISDB
ao = bycatch.db('ISDB.reshape')
aCo = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=mean)
aCs = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster,data=ao,FUN=function(x) length(unique(x)))
aCo = merge(aCo, aCC)
aCo = merge(aCo, aCs)
aCo$mean =aCo$LegalWt
aCo$var =aCo$LegalWtvar
aCo$n = aCo$UID
#clustered mean
o = list()
ui = unique(aCo$TRIP)
for(i in 1:length(ui)){
j = which(aCo$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCo$TRIP[j]),LFA=unique(aCo$LFA[j]),clusterStatistics(aCo[j,]))
}
aCo = as.data.frame(do.call(rbind,o))
aCo = toNums(aCo,1:6)
ms = read.csv('data/ISDB_Trip_id to sd_log_idFinal.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(trip_id,SD_LOG_ID_1))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+trip_id+LFA+SYEAR,data=bLo,FUN=sum)
SBUMP0 = merge(aCo,bLoL,by.x='TRIP',by.y='trip_id')
SBUMP0$TotLegalNTraps = SBUMP0$mean * SBUMP0$NUM_OF_TRAPS
#SWLSS
aoS = bycatch.db('SWLSS')
aCoS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=mean)
aCsS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=var,na.rm=T)
aCsS = rename.df(aCsS,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCS = aggregate(UID~TRIP+LFA+Cluster,data=aoS,FUN=function(x) length(unique(x)))
aCoS = merge(aCoS, aCCS)
aCoS = merge(aCoS, aCsS)
aCoS$mean =aCoS$LegalWt
aCoS$var =aCoS$LegalWtvar
aCoS$n = aCoS$UID
o = list()
ui = unique(aCoS$TRIP)
for(i in 1:length(ui)){
j = which(aCoS$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCoS$TRIP[j]),LFA=unique(aCoS$LFA[j]),clusterStatistics(aCoS[j,]))
}
aCoS = as.data.frame(do.call(rbind,o))
aCoS = toNums(aCoS,2:6)
aacc = as.data.frame(rbind(aCo,aCoS))
write.csv(aacc,file='results/TRIPCPUE33-35.csv')
ms = read.csv('data/SWLSSTripmatch.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(TRIP,SD_LOG_ID_1))
bLoS = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoLS = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLoS,FUN=sum)
SBUMPS = merge(aCoS,bLoLS,by.x='TRIP',by.y='TRIP')
SBUMPS$TotLegalNTraps = SBUMPS$mean * SBUMPS$NUM_OF_TRAPS
SBUMP0 = subset(SBUMP0,mean>0)
SBUMPS = subset(SBUMPS,mean>0)
#FOR ALL DATA COMBINED
SB = rbind(SBUMPS,SBUMP0)
require(MASS)
require(SimDesign)
par(mfrow=c(1,2))
with(SBUMPS,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='SWLSS'))
abline(a=0,b=1)
LMSW = with(SBUMPS,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RLMSW = with(SBUMPS,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
with(SBUMP0,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='Observer'))
abline(a=0,b=1)
LMO = (with(SBUMP0,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMO = (with(SBUMP0,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
with(SB,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='All'))
abline(a=0,b=1)
LMA = (with(SB,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMA = (with(SB,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
#RMSE
summary(LMO)$sigma
summary(RLMO)$sigma
summary(LMSW)$sigma
summary(RLMSW)$sigma
summary(LMA)$sigma
summary(RLMA)$sigma
biasSWLSS = SimDesign::bias(SBUMPS$TotLegalNTraps,SBUMPS$WEIGHT_KG,type='relative')
biasOBs = SimDesign::bias( SBUMP0$TotLegalNTraps ,SBUMP0$WEIGHT_KG ,type='relative') # sum(SBUMP0$TotLegalNTraps - SBUMP0$WEIGHT_KG)/nrow(SBUMP0) under
biasC = SimDesign::bias( SB$TotLegalNTraps ,SB$WEIGHT_KG ,type='relative')
#For Each LFA and YEAR
SBS = split(SB, f=list(SB$LFA.x,SB$SYEAR))
SBS = rm.from.list(SBS)
ooo = list()
for(i in 1:length(SBS)){
O = SBS[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
#RMSE
summary(L)$sigma
summary(RL)$sigma
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
ooo[[i]] = c(unique(O$LFA.x),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
ooo = as.data.frame(do.call(rbind,ooo))
###GCIFA and CBFHA
gg = bycatch.db('GCIFA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gga=ggo
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#off record matching...skip
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
#################
### CBFHA
gg = bycatch.db('CBFHA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gggg = as.data.frame(rbind(gga,ggo))
write.csv(gggg,file='results/CBFHAGCIFACPUE.csv')
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
##########################################################
b = bycatch.db('logbook.merge',wd=wd)
bW = aggregate(NUM_OF_TRAPS~WOS+SYEAR+LFA,data=b,FUN=sum)
aO = bycatch.db('ISDB.reshape.redo')
aS = bycatch.db(DS='SWLSS')
nO = names(aO)
nS = names(aS)
nO = grep('P.',nO,invert=T)
nS = grep('P.',nS,invert=T)
aO = aO[,c(nO,12)]
aS = aS[,c(nS,427)]
aS$SETNO = aS$mn = NULL
aA = rbind(aO,aS)
aA$GPY = paste(aA$SYEAR,aA$LFA,aA$GridGroup,aA$Period,sep="-")
io = unique(aA$GPY)
aA$GP = paste(aA$LFA,aA$GridGroup,sep="-")
write.csv(aA,file=file.path('results','CompliedDataForModelling.csv'))
aa = read.csv(file=file.path('results','CompliedDataForModelling.csv'))
#Comparing Temporal sampling with Fishing (by LFA)
b = bycatch.db('logbook.merge',wd=wd)
bW = aggregate(NUM_OF_TRAPS~WOS+SYEAR+LFA,data=b,FUN=sum)
aO = bycatch.db('ISDB.reshape')
aS = bycatch.db(DS='SWLSS')
ii = c(33,34,35)
jj = 2019:2021
par(mfrow=c(3,3))
for(i in ii){
for(j in jj){
bss = subset(bW,LFA==i & SYEAR==j)
bss$CE = cumsum(bss$NUM_OF_TRAPS)/sum(bss$NUM_OF_TRAPS)
plot(bss$WOS,bss$CE,type='l',lwd=2,xlab='WOS',ylab='Cumulative Effort',main=paste(i,j,sep="-"))
aOs = subset(aO,LFA==i & SYEAR==j)
if(nrow(aOs)>0){
print(c(i,j))
aOs = aggregate(UID~WOS,data=aOs,FUN=function(x) length(unique(x)))
aOs$CE = cumsum(aOs$UID)/sum(aOs$UID)
lines(aOs$WOS,aOs$CE,type='l',lwd=2,col='red',lty=2)
}
aSs = subset(aS,LFA==i & SYEAR==j)
if(nrow(aSs)>0){
print(c(i,j))
aSs = aggregate(UID~WOS,data=aSs,FUN=function(x) length(unique(x)))
aSs$CE = cumsum(aSs$UID)/sum(aSs$UID)
lines(aSs$WOS,aSs$CE,type='l',lwd=2,col='green',lty=3)
}
}
}
#by year, by lfa by grid group
ii = c(33,34,35)
jj = 2019:2021
pdf('Figures/SamplingbyArea.pdf')
for(i in ii){
for(j in jj){
bss = subset(b,LFA==i & SYEAR==j)
gg = c(na.omit(unique(bss$GridGroup)))
for(g in gg){
bsst = subset(bss,GridGroup==g)
bsst = aggregate(NUM_OF_TRAPS~WOS, data=bsst,FUN=sum)
bsst$CE = cumsum(bsst$NUM_OF_TRAPS)/sum(bsst$NUM_OF_TRAPS)
plot(bsst$WOS,bsst$CE,type='l',lwd=2,xlab='WOS',ylab='Cumulative Effort',main=paste(i,j,g,sep="-"))
aOs = subset(aO,LFA==i & SYEAR==j & GridGroup==g)
if(nrow(aOs)>0){
print(c(i,j))
aOs = aggregate(UID~WOS,data=aOs,FUN=function(x) length(unique(x)))
aOs$CE = cumsum(aOs$UID)/sum(aOs$UID)
lines(aOs$WOS,aOs$CE,type='l',lwd=2,col='red',lty=2)
}
aSs = subset(aS,LFA==i & SYEAR==j& GridGroup==g)
if(nrow(aSs)>0){
print(c(i,j))
aSs = aggregate(UID~WOS,data=aSs,FUN=function(x) length(unique(x)))
aSs$CE = cumsum(aSs$UID)/sum(aSs$UID)
lines(aSs$WOS,aSs$CE,type='l',lwd=2,col='green',lty=3)
}
}
}
}
graphics.off()
#by y
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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require(bio.lobster)
require(bio.utilities)
require(RODBC)
require(lubridate)
require(devtools)
options(stringAsFactors=F)
la()
wd = ('C:\\Users\\Cooka\\OneDrive - DFO-MPO\\BycatchLobster')
setwd(wd)
bycatch.db('SWLSS.redo')
bycatch.db('GCIFA.redo')
bycatch.db('CBFHA.redo')
####Trips to Targets
b = bycatch.db('logbook.merge',wd=wd)
gt = t = bycatch.db('targets',wd=wd)
t = subset(t,LFA%in%c(27,'31A','31B',33:35))
tt = aggregate(cbind(Period1,Period2,Period3)~LFA+GridGrouping,data=t,FUN=min)
bA = aggregate(SD_LOG_ID~Period+GridGroup+LFA+SYEAR,data=b,FUN=function(x) length(unique(x)))
tR = as.data.frame(reshape(tt,varying=3:5,direction = 'long',sep=""))
names(tR) = c('LFA','GridGroup','Period','Target','Nt')
tR$Nt = NULL
bA = merge(bA,tR,all.x=T)
sss = readRDS('results/BumpedUpEffortByGridGroup.rds')
sss= subset(sss,select=c(Period,GridGroup,LFA,SYEAR,BTTH))
bA = merge(bA,sss)
names(bA)[c(5,7)] = c('LogsReported','BumpTH')
write.csv(bA,file=file.path(wd,'results','LogbooksAgg and Targets.csv'))
#logbooks
bA = read.csv(file=file.path(wd,'results','LogbooksAgg and Targets.csv'))
bA$X = NULL
#SWLSS sea sampling
CDa = bycatch.db(DS='SWLSS')
STraps = aggregate(P~SYEAR+GridGroup+LFA+Period+target,data=CDa,FUN=sum)
ATrips = aggregate(TRIP~SYEAR+LFA+Period+target+GridGroup,data=CDa,FUN=function(x) length(unique(x)))
STTraps =merge(STraps,ATrips,all=T)
names(STTraps)[6:7] = c('NTrapsSampledSWLSS','NTripsSampledSWLSS')
write.csv(STTraps,file=file.path('results','SWLSSTrips2Targets.csv'))
STTraps = merge(STTraps,bA,all=T)
write.csv(STTraps,file=file.path('results','SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps = read.csv(file=file.path('results','SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps$x = NULL
#adding IN ISBD
gg = bycatch.db('ISDB')
## Trips that dont match with grids or something
gr = subset(gg,is.na(target))
gr = as.data.frame(unique(cbind(gr$TRIPNO,gr$LFA,gr$GRIDNO)))
se = connect.command(con,'select * from lobster.issets_mv')
names(gr) = c('TRIP_ID','LFA','Grid')
ss = as.data.frame(unique(cbind(se$TRIP_ID,se$TRIP)))
names(ss) = c('TRIP_ID','TRIP')
merge(gr,ss)
###Moving on to summary of observer data update later after we get more complete info
UTraps = aggregate(UID~SYEAR+LFA+GridGroup+Period+target,data=gg,FUN=function(x) length(unique(x)))
UTrips = aggregate(TRIPNO~SYEAR+LFA+GridGroup+Period+target,data=gg,FUN=function(x) length(unique(x)))
ggg = merge(UTrips,UTraps,all=T)
names(ggg)[c(1,5:7)] = c('SYEAR','Target','ObserverTrips','ObserverTraps')
STTraps = merge(STTraps,ggg,all=T)
STTraps$X=STTraps$target = NULL
write.csv(STTraps,file=file.path('results','SWLSSandObsTrips2TargetsTrapsandTrips.csv'))
STTraps = read.csv(file=file.path('results','SWLSSandObsTrips2TargetsTrapsandTrips.csv'))
#adding in CBFHA
gg = bycatch.db('CBFHA')
STraps = aggregate(P~SYEAR+GridGroup+LFA+Period+target,data=gg,FUN=sum)
ATrips = aggregate(TRIP~SYEAR+LFA+Period+target+GridGroup,data=gg,FUN=function(x) length(unique(x)))
CBTraps =merge(STraps,ATrips,all=T)
names(CBTraps)[6:7] = c('NTrapsSampledCBFHA','NTripsSampledCBFHA')
CBTraps$target=NULL
write.csv(CBTraps,file=file.path('results','CBFHATrips2Targets.csv'))
STTraps = merge(STTraps,CBTraps,all=T)
write.csv(STTraps,file=file.path('results','CBFHA_OBS_SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps = read.csv(file=file.path('results','CBFHA_OBS_SWLSSTrips2TargetsTrapsandTrips.csv'))
STTraps$X.1 = STTraps$X = NULL
#adding in GCIFA
gg = bycatch.db('GCIFA')
STraps = aggregate(P~SYEAR+GridGroup+LFA+Period+target,data=gg,FUN=sum)
ATrips = aggregate(TRIP~SYEAR+LFA+Period+target+GridGroup,data=gg,FUN=function(x) length(unique(x)))
CBTraps =merge(STraps,ATrips,all=T)
CBTraps$target = NULL
names(CBTraps)[5:6] = c('NTrapsSampledGCIFA','NTripsSampledGCIFA')
write.csv(CBTraps,file=file.path('results','GCIFATrips2Targets.csv'))
STTraps = merge(STTraps,CBTraps,all=T)
write.csv(STTraps,file=file.path('results','GCIFA_CBFHA_OBS_SWLSSTrips2TargetsTrapsandTrips.csv'))
####Comparison of Lobster Trap catches to Reported on Logs
b=bycatch.db('logbook.merge')
#ISDB
#ISDB
ao = bycatch.db('ISDB.reshape')
aCo = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=mean)
aCs = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=ao,FUN=var,na.rm=T)
aCs = rename.df(aCs,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCC = aggregate(UID~TRIP+LFA+Cluster,data=ao,FUN=function(x) length(unique(x)))
aCo = merge(aCo, aCC)
aCo = merge(aCo, aCs)
aCo$mean =aCo$LegalWt
aCo$var =aCo$LegalWtvar
aCo$n = aCo$UID
#clustered mean
o = list()
ui = unique(aCo$TRIP)
for(i in 1:length(ui)){
j = which(aCo$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCo$TRIP[j]),LFA=unique(aCo$LFA[j]),clusterStatistics(aCo[j,]))
}
aCo = as.data.frame(do.call(rbind,o))
aCo = toNums(aCo,1:6)
ms = read.csv('data/ISDB_Trip_id to sd_log_idFinal.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(trip_id,SD_LOG_ID_1))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+trip_id+LFA+SYEAR,data=bLo,FUN=sum)
SBUMP0 = merge(aCo,bLoL,by.x='TRIP',by.y='trip_id')
SBUMP0$TotLegalNTraps = SBUMP0$mean * SBUMP0$NUM_OF_TRAPS
#SWLSS
aoS = bycatch.db('SWLSS')
aCoS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=mean)
aCsS = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=aoS,FUN=var,na.rm=T)
aCsS = rename.df(aCsS,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCS = aggregate(UID~TRIP+LFA+Cluster,data=aoS,FUN=function(x) length(unique(x)))
aCoS = merge(aCoS, aCCS)
aCoS = merge(aCoS, aCsS)
aCoS$mean =aCoS$LegalWt
aCoS$var =aCoS$LegalWtvar
aCoS$n = aCoS$UID
o = list()
ui = unique(aCoS$TRIP)
for(i in 1:length(ui)){
j = which(aCoS$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCoS$TRIP[j]),LFA=unique(aCoS$LFA[j]),clusterStatistics(aCoS[j,]))
}
aCoS = as.data.frame(do.call(rbind,o))
aCoS = toNums(aCoS,2:6)
aacc = as.data.frame(rbind(aCo,aCoS))
write.csv(aacc,file='results/TRIPCPUE33-35.csv')
ms = read.csv('data/SWLSSTripmatch.csv')
ms = subset(ms,!is.na(SD_LOG_ID_1),select=c(TRIP,SD_LOG_ID_1))
bLoS = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_1')
bLoLS = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLoS,FUN=sum)
SBUMPS = merge(aCoS,bLoLS,by.x='TRIP',by.y='TRIP')
SBUMPS$TotLegalNTraps = SBUMPS$mean * SBUMPS$NUM_OF_TRAPS
SBUMP0 = subset(SBUMP0,mean>0)
SBUMPS = subset(SBUMPS,mean>0)
#FOR ALL DATA COMBINED
SB = rbind(SBUMPS,SBUMP0)
require(MASS)
require(SimDesign)
par(mfrow=c(1,2))
with(SBUMPS,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='SWLSS'))
abline(a=0,b=1)
LMSW = with(SBUMPS,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RLMSW = with(SBUMPS,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
with(SBUMP0,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='Observer'))
abline(a=0,b=1)
LMO = (with(SBUMP0,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMO = (with(SBUMP0,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
with(SB,plot(WEIGHT_KG,TotLegalNTraps,ylab='Extrapolated landings from Sea Samples',main='All'))
abline(a=0,b=1)
LMA = (with(SB,lm((TotLegalNTraps)~(WEIGHT_KG)-1)))
RLMA = (with(SB,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)))
#RMSE
summary(LMO)$sigma
summary(RLMO)$sigma
summary(LMSW)$sigma
summary(RLMSW)$sigma
summary(LMA)$sigma
summary(RLMA)$sigma
biasSWLSS = SimDesign::bias(SBUMPS$TotLegalNTraps,SBUMPS$WEIGHT_KG,type='relative')
biasOBs = SimDesign::bias( SBUMP0$TotLegalNTraps ,SBUMP0$WEIGHT_KG ,type='relative') # sum(SBUMP0$TotLegalNTraps - SBUMP0$WEIGHT_KG)/nrow(SBUMP0) under
biasC = SimDesign::bias( SB$TotLegalNTraps ,SB$WEIGHT_KG ,type='relative')
#For Each LFA and YEAR
SBS = split(SB, f=list(SB$LFA.x,SB$SYEAR))
SBS = rm.from.list(SBS)
ooo = list()
for(i in 1:length(SBS)){
O = SBS[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
#RMSE
summary(L)$sigma
summary(RL)$sigma
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
ooo[[i]] = c(unique(O$LFA.x),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
ooo = as.data.frame(do.call(rbind,ooo))
###GCIFA and CBFHA
gg = bycatch.db('GCIFA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gga=ggo
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#off record matching...skip
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
#################
### CBFHA
gg = bycatch.db('CBFHA')
aCg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=mean)
aCgg = aggregate(cbind(Lobster,Cod,Cusk,Jonah,Legal,Berried, Empty,LegalWt)~TRIP+LFA+Cluster,data=gg,FUN=var,na.rm=T)
aCg = rename.df(aCg,c('Lobster','Cod','Cusk','Jonah','Legal','Berried', 'Empty','LegalWt'),c('Lobstervar','Codvar','Cuskvar','Jonahvar','Legalvar','Berriedvar', 'Emptyvar','LegalWtvar'))
aCCg = aggregate(UID~TRIP+LFA+Cluster,data=gg,FUN=function(x) length(unique(x)))
aCog = merge(aCg, aCCg)
aCog = merge(aCog, aCCg)
aCog$mean =aCog$LegalWt
aCog$var =aCog$LegalWtvar
aCog$n = aCog$UID
o = list()
ui = unique(aCog$TRIP)
for(i in 1:length(ui)){
j = which(aCog$TRIP == ui[i])
o[[i]] =c(TRIP=unique(aCog$TRIP[j]),LFA=unique(aCog$LFA[j]),clusterStatistics(aCog[j,]))
}
aCog = as.data.frame(do.call(rbind,o))
ggo = toNums(aCog,3:6)
gggg = as.data.frame(rbind(gga,ggo))
write.csv(gggg,file='results/CBFHAGCIFACPUE.csv')
ms = read.csv('data/GCIFACBFHATripMatch.csv')
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DB[which(is.na(ms$SD_LOG_ID_DO))]
#ms$SD_LOG_ID_DO[which(is.na(ms$SD_LOG_ID_DO))] = ms$SD_LOG_ID_DA[which(is.na(ms$SD_LOG_ID_DO))]
ms = subset(ms,select=c(TRIP,SD_LOG_ID_DO))
bLo = merge(b,ms,by.x='SD_LOG_ID', by.y='SD_LOG_ID_DO')
bLoL = aggregate(cbind(WEIGHT_KG,NUM_OF_TRAPS)~SD_LOG_ID+TRIP+LFA+SYEAR,data=bLo,FUN=sum)
bu = merge(ggo,bLoL)
bu$TotLegalNTraps = bu$mean * bu$NUM_OF_TRAPS
GC = split(bu,f=list(bu$LFA,bu$SYEAR))
og = list()
for(i in 1:length(GC)){
O = GC[[i]]
L = with(O,lm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
RL = with(O,rlm((TotLegalNTraps)~(WEIGHT_KG)-1)) #treating the SWLSS data as 'truth'
biasS = SimDesign::bias(O$TotLegalNTraps,O$WEIGHT_KG,type='relative')
og[[i]] = c(unique(O$LFA),unique(O$SYEAR),nrow(O),biasS,coef(L),summary(L)$sigma,coef(RL),summary(RL)$sigma)
}
og = as.data.frame(do.call(rbind,og))
##########################################################
b = bycatch.db('logbook.merge',wd=wd)
bW = aggregate(NUM_OF_TRAPS~WOS+SYEAR+LFA,data=b,FUN=sum)
aO = bycatch.db('ISDB.reshape.redo')
aS = bycatch.db(DS='SWLSS')
nO = names(aO)
nS = names(aS)
nO = grep('P.',nO,invert=T)
nS = grep('P.',nS,invert=T)
aO = aO[,c(nO,12)]
aS = aS[,c(nS,427)]
aS$SETNO = aS$mn = NULL
aA = rbind(aO,aS)
aA$GPY = paste(aA$SYEAR,aA$LFA,aA$GridGroup,aA$Period,sep="-")
io = unique(aA$GPY)
aA$GP = paste(aA$LFA,aA$GridGroup,sep="-")
write.csv(aA,file=file.path('results','CompliedDataForModelling.csv'))
aa = read.csv(file=file.path('results','CompliedDataForModelling.csv'))
#Comparing Temporal sampling with Fishing (by LFA)
b = bycatch.db('logbook.merge',wd=wd)
bW = aggregate(NUM_OF_TRAPS~WOS+SYEAR+LFA,data=b,FUN=sum)
aO = bycatch.db('ISDB.reshape')
aS = bycatch.db(DS='SWLSS')
ii = c(33,34,35)
jj = 2019:2021
par(mfrow=c(3,3))
for(i in ii){
for(j in jj){
bss = subset(bW,LFA==i & SYEAR==j)
bss$CE = cumsum(bss$NUM_OF_TRAPS)/sum(bss$NUM_OF_TRAPS)
plot(bss$WOS,bss$CE,type='l',lwd=2,xlab='WOS',ylab='Cumulative Effort',main=paste(i,j,sep="-"))
aOs = subset(aO,LFA==i & SYEAR==j)
if(nrow(aOs)>0){
print(c(i,j))
aOs = aggregate(UID~WOS,data=aOs,FUN=function(x) length(unique(x)))
aOs$CE = cumsum(aOs$UID)/sum(aOs$UID)
lines(aOs$WOS,aOs$CE,type='l',lwd=2,col='red',lty=2)
}
aSs = subset(aS,LFA==i & SYEAR==j)
if(nrow(aSs)>0){
print(c(i,j))
aSs = aggregate(UID~WOS,data=aSs,FUN=function(x) length(unique(x)))
aSs$CE = cumsum(aSs$UID)/sum(aSs$UID)
lines(aSs$WOS,aSs$CE,type='l',lwd=2,col='green',lty=3)
}
}
}
#by year, by lfa by grid group
ii = c(33,34,35)
jj = 2019:2021
pdf('Figures/SamplingbyArea.pdf')
for(i in ii){
for(j in jj){
bss = subset(b,LFA==i & SYEAR==j)
gg = c(na.omit(unique(bss$GridGroup)))
for(g in gg){
bsst = subset(bss,GridGroup==g)
bsst = aggregate(NUM_OF_TRAPS~WOS, data=bsst,FUN=sum)
bsst$CE = cumsum(bsst$NUM_OF_TRAPS)/sum(bsst$NUM_OF_TRAPS)
plot(bsst$WOS,bsst$CE,type='l',lwd=2,xlab='WOS',ylab='Cumulative Effort',main=paste(i,j,g,sep="-"))
aOs = subset(aO,LFA==i & SYEAR==j & GridGroup==g)
if(nrow(aOs)>0){
print(c(i,j))
aOs = aggregate(UID~WOS,data=aOs,FUN=function(x) length(unique(x)))
aOs$CE = cumsum(aOs$UID)/sum(aOs$UID)
lines(aOs$WOS,aOs$CE,type='l',lwd=2,col='red',lty=2)
}
aSs = subset(aS,LFA==i & SYEAR==j& GridGroup==g)
if(nrow(aSs)>0){
print(c(i,j))
aSs = aggregate(UID~WOS,data=aSs,FUN=function(x) length(unique(x)))
aSs$CE = cumsum(aSs$UID)/sum(aSs$UID)
lines(aSs$WOS,aSs$CE,type='l',lwd=2,col='green',lty=3)
}
}
}
}
graphics.off()
#by y
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