inst/Frameworks/LFA2733Framework/6.Indicators.r
In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes
#6.Indicators
require(bio.lobster)
require(bio.utilities)
require(PBSmapping)
la()
#mid seseason indicators - median and max size; new recruits
#LOADS of DATA handling....sorry :(
p = lobster.db('seasonal.landings')
g = lobster.db('annual.landings')
g = g[-nrow(g),]
p = p[-nrow(p),]
p = rename.df(p,'SYEAR','YR')
p$YR = as.numeric(substr(p$YR,6,9))
p = subset(p,YR>=1980)
g = subset(g,YR>=1980)
g = reshape(g,idvar='YR',varying=list(2:ncol(g)),direction='long',times=names(g)[2:ncol(g)])
g$LFA = substr(g$time,4,8)
ad = c(27,28,29,30,'31A','31B',32)
g = subset(g,LFA %in% ad, select=c(YR,LFA27,LFA))
g = rename.df(g,'LFA27','Landings')
p = p[,c('YR','LFA33')]
p$LFA = '33'
p = rename.df(p,'LFA33','Landings')
pp = rbind(g,p)
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
aS = out
aS = rename.df(aS,'YEAR','YR')
df = c('LFA','YR','N','expl')
aS = aS[,df]
names(aS)[3:ncol(aS)] = paste('AtSea',names(aS)[3:ncol(aS)],sep="." )
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-33.rdata')) #at Sea
aSS = outS
aSS = rename.df(aSS,'Year','YR')
aSS = rename.df(aSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "prop.female" , "PropMating" ,"EggProduction" )
aSS = aSS[,df]
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SubsetWksSummaryatSeaIndicatorsDataLFA27-33.rdata')) #at Sea
aSSS = outS
aSSS = rename.df(aSSS,'Year','YR')
aSSS = rename.df(aSSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" ,"new.rec" ,"prop.berried")
aSSS = aSSS[,df]
aSS = merge(aSS,aSSS,all=T)
names(aSS)[3:ncol(aSS)] = paste('AtSea',names(aSS)[3:ncol(aSS)],sep="." )
aS = merge(aSS,aS,by=c('YR','LFA'),all=T)
#FSRS Commercial
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNumbersLanded33.rdata'))
fS = out
fS = rename.df(fS,'YEAR','YR')
df = c('LFA','YR','N','expl')
fS = fS[,df]
names(fS)[3:ncol(fS)] = paste('FSRSComm',names(fS)[3:ncol(fS)],sep="." )
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsCommercialSamplesLanded33.rdata'))
fSS = outS
fSS = rename.df(fSS,'Year','YR')
fSS = rename.df(fSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" , "prop.female" , "prop.berried" , "new.rec" )
fSS = fSS[,df]
names(fSS)[3:ncol(fSS)] = paste('FSRSComm',names(fSS)[3:ncol(fSS)],sep="." )
fS = merge(fSS,fS,by=c('YR','LFA'),all=T)
#Port Samples
load(file.path(project.datadirectory('bio.lobster'),'outputs','portNumbersLanded27-33.rdata'))
pS = out
pS = rename.df(pS,'YEAR','YR')
df = c('LFA','YR','N','expl')
pS = pS[,df]
names(pS)[3:ncol(pS)] = paste('Port',names(pS)[3:ncol(pS)],sep="." )
load(file.path(project.datadirectory('bio.lobster'),'outputs','portSummaryLFA27-33.rdata'))
pSS = outS
pSS = rename.df(pSS,'Year','YR')
pSS = rename.df(pSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "prop.female" , 'PropMating','EggProduction')
pSS = pSS[,df]
load(file.path(project.datadirectory('bio.lobster'),'outputs','subsetweeksportSummaryLFA27-33.rdata'))
pSSS = outS
pSSS = rename.df(pSSS,'Year','YR')
pSSS = rename.df(pSSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" ,"new.rec" )
pSSS = pSSS[,df]
pSS = merge(pSS,pSSS,all=T)
names(pSS)[3:ncol(pSS)] = paste('Port',names(pSS)[3:ncol(pSS)],sep="." )
pS = merge(pSS,pS,by=c('YR','LFA'),all=T)
#Fsrs recruitment
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsrecruitmentSamplesLanded27-33.rdata'))
fR = outS
fR = rename.df(fR,'Year','YR')
fR = rename.df(fR,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" , "prop.female" , "prop.berried" )
fR = fR[,df]
names(fR)[3:ncol(fR)] = paste('FSRSRec',names(fR)[3:ncol(fR)],sep="." )
#Merge data sets
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
mls = rename.df(mls,'Year','YR')
mls = mls[,c('YR','LFA','MLS_MM')]
mls = subset(mls, LFA %in% c(27,28,29,30,'31A','31B',32,33))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','EstimatedRecruitBiomassLFA27-33.rdata'))
aa = aa[,c('YEAR','LFA','RecB')]
aa = rename.df(aa,c("YEAR",'RecB'),c('YR','RecruitmentBiomass'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR.rdata'))
oi$LFA[which(oi$LFA=='LFA 27 Combined')] <- "27"
oi$LFA[which(oi$LFA=='LFA 33 Combined')] <- "33"
oi$LFA[which(oi$LFA=='LFA 30')] <- "30"
oi$LFA[which(oi$LFA=='LFA 29')] <- "29"
oi$LFA[which(oi$LFA=='LFA 31A')] <- "31A"
oi$LFA[which(oi$LFA=='LFA 31B')] <- "31B"
oi$LFA[which(oi$LFA=='LFA 32')] <- "32"
oi = rename.df(oi,'Yr','YR')
oi = oi[grep('LFA',oi$LFA,invert=T),]
oi = subset(oi,ERfl>0,select=c(YR,LFA,ERfm))
oi = rename.df(oi,'ERfm','CCIR.Exploitation')
allS = Reduce(function(...) merge(...,all=T),list(aS,fS,pS,fR,pp,aa,oi))
#FSRS temperature
load(file.path(project.datadirectory('bio.lobster'),'Temperature Data','tempIndicators.rdata'))
tempData = rename.df(tempData,c('area','year','t'),c('LFA','YR','Temperature'))
tempData$t.sd = NULL
allS = merge(allS,tempData,all=T)
#CPUE raw data
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','rawcpueIndicators27-33B.rdata'))
hq = rename.df(cpueData2,'YEAR','YR')
hq$DATE = hq$CATCH = hq$EFFORT <- NULL
allS = merge(allS,hq,all=T)
#FSRS models
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','fsrsModelIndicatorsJan2018.rdata'))
lF = rename.df(legalsLFA,c('YEAR','Area','mu'),c('YR','LFA','FSRS.Legal.CPUE'))
lF = lF[,c('YR','LFA','FSRS.Legal.CPUE')]
rF = rename.df(recruitLFA,c('YEAR','Area','mu'),c('YR','LFA','FSRS.Recruit.CPUE'))
rF = rF[,c('YR','LFA','FSRS.Recruit.CPUE')]
sF = rename.df(shortsLFA,c('YEAR','Area','mu'),c('YR','LFA','FSRS.Shorts.CPUE'))
sF = sF[,c('YR','LFA','FSRS.Shorts.CPUE')]
allS = Reduce(function(...) merge(...,all=T),list(allS,lF,rF,sF))
#
allS = subset(allS,YR<2016)
save(allS, file=file.path(project.datadirectory('bio.lobster'),'outputs','CompiledIndicatorsLFA27-33.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','CompiledIndicatorsLFA27-33.rdata'))
old = c('YR','LFA','AtSea.prop.female','AtSea.PropMating','AtSea.EggProduction','AtSea.median','AtSea.max','AtSea.new.rec','AtSea.prop.berried','AtSea.N','AtSea.expl','FSRSComm.median','FSRSComm.max','FSRSComm.prop.female','FSRSComm.prop.berried','FSRSComm.new.rec','FSRSComm.N','FSRSComm.expl','Port.prop.female','Port.PropMating','Port.EggProduction','Port.median','Port.max','Port.new.rec','Port.N','Port.expl','FSRSRec.median','FSRSRec.max','FSRSRec.prop.female','FSRSRec.prop.berried','Landings','RecruitmentBiomass','CCIR.Exploitation','Temperature')
new = c('YR','LFA','At.Sea.SexRatio','At.Sea.Prop.Mature','At.Sea.Reprod.Pot','At.Sea.Median.CL','At.Sea.Max.CL','At.Sea.Prop.NewRec','At.Sea.Prop.Berried','At.Sea.Landed.Abund.','At.Sea.CA.Exploit','FSRS.Comm.Median.CL','FSRS.Comm.Max.CL','FSRS.Comm.SexRatio','FSRS.Comm.Prop.Berried','FSRS.Comm.Prop.NewRec','FSRS.Comm.Landed.Abund','FSRS.Comm.CA.Exploit','Port.SexRatio','Port.Prop.Mature','Port.Reprod.Pot','Port.Median.CL','Port.Max.CL','Port.Prop.NewRec','Port.Landed.Abund','Port.CA.Exploit','FSRS.Rec.Median.CL','FSRS.Rec.Max.CL','FSRS.Rec.SexRatio','FSRS.Rec.Prop.Berried','Landings.Wt','BiomassRecruits','ExploitationCCIR','Temperature')
allS = rename.df(allS,old,new)
#id the variables for log trans
re = c()
re=c(re,grep('Reprod',names(allS)))
#re=c(re,grep('Abund',names(allS)))
re=c(re,grep('Land',names(allS)))
re=c(re,grep('Bio',names(allS)))
allS[,re] <- log(allS[,re]+1)
#loop through LFAs
lfa = unique(allS$LFA)
for(i in lfa) {
dd = subset(allS,LFA==i)
t0 = 1980
t1 = 2015
rownames(dd) = t0:t1
dd$LFA = dd$YR = NULL
iu = apply(dd,2,function(x) sum(is.na(x)*1)/length(x)<1-5/35)
dd = dd[,names(iu)[iu]] # removing all indicators with less than 5 values
iu=c()
iu = c(iu,which(names(dd)=='FSRS.Rec.Max.CL'))
iu = c(iu,which(names(dd)=='FSRS.Rec.Median.CL'))
iu = c(iu,grep('Sex',names(dd)))
if(i==33) {
iu = c(iu,which(names(dd)=='FSRS.Comm.MaxCL'))
iu = c(iu,which(names(dd)=='FSRS.Comm.MedianCL'))
}
dd = dd[,-iu]
fname = file.path(project.figuredirectory('bio.lobster'),'indicators')
dir.create(fname)
Y = pcaAnalyseData(dd, t0, t1,fname=fname,OFN = paste('OrdinationLFA',i,sep=""),addscores=T)
}
LobsterScience/bio.lobster documentation built on Feb. 14, 2025, 3:28 p.m.
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#6.Indicators
require(bio.lobster)
require(bio.utilities)
require(PBSmapping)
la()
#mid seseason indicators - median and max size; new recruits
#LOADS of DATA handling....sorry :(
p = lobster.db('seasonal.landings')
g = lobster.db('annual.landings')
g = g[-nrow(g),]
p = p[-nrow(p),]
p = rename.df(p,'SYEAR','YR')
p$YR = as.numeric(substr(p$YR,6,9))
p = subset(p,YR>=1980)
g = subset(g,YR>=1980)
g = reshape(g,idvar='YR',varying=list(2:ncol(g)),direction='long',times=names(g)[2:ncol(g)])
g$LFA = substr(g$time,4,8)
ad = c(27,28,29,30,'31A','31B',32)
g = subset(g,LFA %in% ad, select=c(YR,LFA27,LFA))
g = rename.df(g,'LFA27','Landings')
p = p[,c('YR','LFA33')]
p$LFA = '33'
p = rename.df(p,'LFA33','Landings')
pp = rbind(g,p)
load(file.path(project.datadirectory('bio.lobster'),'outputs','atSeaIndicatorsNumbersLandedLFA27-33.rdata'))
aS = out
aS = rename.df(aS,'YEAR','YR')
df = c('LFA','YR','N','expl')
aS = aS[,df]
names(aS)[3:ncol(aS)] = paste('AtSea',names(aS)[3:ncol(aS)],sep="." )
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryatSeaIndicatorsDataLFA27-33.rdata')) #at Sea
aSS = outS
aSS = rename.df(aSS,'Year','YR')
aSS = rename.df(aSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "prop.female" , "PropMating" ,"EggProduction" )
aSS = aSS[,df]
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SubsetWksSummaryatSeaIndicatorsDataLFA27-33.rdata')) #at Sea
aSSS = outS
aSSS = rename.df(aSSS,'Year','YR')
aSSS = rename.df(aSSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" ,"new.rec" ,"prop.berried")
aSSS = aSSS[,df]
aSS = merge(aSS,aSSS,all=T)
names(aSS)[3:ncol(aSS)] = paste('AtSea',names(aSS)[3:ncol(aSS)],sep="." )
aS = merge(aSS,aS,by=c('YR','LFA'),all=T)
#FSRS Commercial
load(file.path(project.datadirectory('bio.lobster'),'outputs','fsrsNumbersLanded33.rdata'))
fS = out
fS = rename.df(fS,'YEAR','YR')
df = c('LFA','YR','N','expl')
fS = fS[,df]
names(fS)[3:ncol(fS)] = paste('FSRSComm',names(fS)[3:ncol(fS)],sep="." )
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsCommercialSamplesLanded33.rdata'))
fSS = outS
fSS = rename.df(fSS,'Year','YR')
fSS = rename.df(fSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" , "prop.female" , "prop.berried" , "new.rec" )
fSS = fSS[,df]
names(fSS)[3:ncol(fSS)] = paste('FSRSComm',names(fSS)[3:ncol(fSS)],sep="." )
fS = merge(fSS,fS,by=c('YR','LFA'),all=T)
#Port Samples
load(file.path(project.datadirectory('bio.lobster'),'outputs','portNumbersLanded27-33.rdata'))
pS = out
pS = rename.df(pS,'YEAR','YR')
df = c('LFA','YR','N','expl')
pS = pS[,df]
names(pS)[3:ncol(pS)] = paste('Port',names(pS)[3:ncol(pS)],sep="." )
load(file.path(project.datadirectory('bio.lobster'),'outputs','portSummaryLFA27-33.rdata'))
pSS = outS
pSS = rename.df(pSS,'Year','YR')
pSS = rename.df(pSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "prop.female" , 'PropMating','EggProduction')
pSS = pSS[,df]
load(file.path(project.datadirectory('bio.lobster'),'outputs','subsetweeksportSummaryLFA27-33.rdata'))
pSSS = outS
pSSS = rename.df(pSSS,'Year','YR')
pSSS = rename.df(pSSS,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" ,"new.rec" )
pSSS = pSSS[,df]
pSS = merge(pSS,pSSS,all=T)
names(pSS)[3:ncol(pSS)] = paste('Port',names(pSS)[3:ncol(pSS)],sep="." )
pS = merge(pSS,pS,by=c('YR','LFA'),all=T)
#Fsrs recruitment
load(file = file.path(project.datadirectory('bio.lobster'),'outputs','SummaryfsrsrecruitmentSamplesLanded27-33.rdata'))
fR = outS
fR = rename.df(fR,'Year','YR')
fR = rename.df(fR,c('YEAR','quants.50%','quants.97.5%'),c('YR','median','max'))
df = c( "LFA", "YR", "median", "max" , "prop.female" , "prop.berried" )
fR = fR[,df]
names(fR)[3:ncol(fR)] = paste('FSRSRec',names(fR)[3:ncol(fR)],sep="." )
#Merge data sets
mls = read.csv(file=file.path(project.datadirectory('bio.lobster'),'data','inputs','MLS.Changes.all.LFA.csv'))
mls = rename.df(mls,'Year','YR')
mls = mls[,c('YR','LFA','MLS_MM')]
mls = subset(mls, LFA %in% c(27,28,29,30,'31A','31B',32,33))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','EstimatedRecruitBiomassLFA27-33.rdata'))
aa = aa[,c('YEAR','LFA','RecB')]
aa = rename.df(aa,c("YEAR",'RecB'),c('YR','RecruitmentBiomass'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','ccir','summary','compiledExploitationCCIR.rdata'))
oi$LFA[which(oi$LFA=='LFA 27 Combined')] <- "27"
oi$LFA[which(oi$LFA=='LFA 33 Combined')] <- "33"
oi$LFA[which(oi$LFA=='LFA 30')] <- "30"
oi$LFA[which(oi$LFA=='LFA 29')] <- "29"
oi$LFA[which(oi$LFA=='LFA 31A')] <- "31A"
oi$LFA[which(oi$LFA=='LFA 31B')] <- "31B"
oi$LFA[which(oi$LFA=='LFA 32')] <- "32"
oi = rename.df(oi,'Yr','YR')
oi = oi[grep('LFA',oi$LFA,invert=T),]
oi = subset(oi,ERfl>0,select=c(YR,LFA,ERfm))
oi = rename.df(oi,'ERfm','CCIR.Exploitation')
allS = Reduce(function(...) merge(...,all=T),list(aS,fS,pS,fR,pp,aa,oi))
#FSRS temperature
load(file.path(project.datadirectory('bio.lobster'),'Temperature Data','tempIndicators.rdata'))
tempData = rename.df(tempData,c('area','year','t'),c('LFA','YR','Temperature'))
tempData$t.sd = NULL
allS = merge(allS,tempData,all=T)
#CPUE raw data
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','rawcpueIndicators27-33B.rdata'))
hq = rename.df(cpueData2,'YEAR','YR')
hq$DATE = hq$CATCH = hq$EFFORT <- NULL
allS = merge(allS,hq,all=T)
#FSRS models
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','fsrsModelIndicatorsJan2018.rdata'))
lF = rename.df(legalsLFA,c('YEAR','Area','mu'),c('YR','LFA','FSRS.Legal.CPUE'))
lF = lF[,c('YR','LFA','FSRS.Legal.CPUE')]
rF = rename.df(recruitLFA,c('YEAR','Area','mu'),c('YR','LFA','FSRS.Recruit.CPUE'))
rF = rF[,c('YR','LFA','FSRS.Recruit.CPUE')]
sF = rename.df(shortsLFA,c('YEAR','Area','mu'),c('YR','LFA','FSRS.Shorts.CPUE'))
sF = sF[,c('YR','LFA','FSRS.Shorts.CPUE')]
allS = Reduce(function(...) merge(...,all=T),list(allS,lF,rF,sF))
#
allS = subset(allS,YR<2016)
save(allS, file=file.path(project.datadirectory('bio.lobster'),'outputs','CompiledIndicatorsLFA27-33.rdata'))
load(file=file.path(project.datadirectory('bio.lobster'),'outputs','CompiledIndicatorsLFA27-33.rdata'))
old = c('YR','LFA','AtSea.prop.female','AtSea.PropMating','AtSea.EggProduction','AtSea.median','AtSea.max','AtSea.new.rec','AtSea.prop.berried','AtSea.N','AtSea.expl','FSRSComm.median','FSRSComm.max','FSRSComm.prop.female','FSRSComm.prop.berried','FSRSComm.new.rec','FSRSComm.N','FSRSComm.expl','Port.prop.female','Port.PropMating','Port.EggProduction','Port.median','Port.max','Port.new.rec','Port.N','Port.expl','FSRSRec.median','FSRSRec.max','FSRSRec.prop.female','FSRSRec.prop.berried','Landings','RecruitmentBiomass','CCIR.Exploitation','Temperature')
new = c('YR','LFA','At.Sea.SexRatio','At.Sea.Prop.Mature','At.Sea.Reprod.Pot','At.Sea.Median.CL','At.Sea.Max.CL','At.Sea.Prop.NewRec','At.Sea.Prop.Berried','At.Sea.Landed.Abund.','At.Sea.CA.Exploit','FSRS.Comm.Median.CL','FSRS.Comm.Max.CL','FSRS.Comm.SexRatio','FSRS.Comm.Prop.Berried','FSRS.Comm.Prop.NewRec','FSRS.Comm.Landed.Abund','FSRS.Comm.CA.Exploit','Port.SexRatio','Port.Prop.Mature','Port.Reprod.Pot','Port.Median.CL','Port.Max.CL','Port.Prop.NewRec','Port.Landed.Abund','Port.CA.Exploit','FSRS.Rec.Median.CL','FSRS.Rec.Max.CL','FSRS.Rec.SexRatio','FSRS.Rec.Prop.Berried','Landings.Wt','BiomassRecruits','ExploitationCCIR','Temperature')
allS = rename.df(allS,old,new)
#id the variables for log trans
re = c()
re=c(re,grep('Reprod',names(allS)))
#re=c(re,grep('Abund',names(allS)))
re=c(re,grep('Land',names(allS)))
re=c(re,grep('Bio',names(allS)))
allS[,re] <- log(allS[,re]+1)
#loop through LFAs
lfa = unique(allS$LFA)
for(i in lfa) {
dd = subset(allS,LFA==i)
t0 = 1980
t1 = 2015
rownames(dd) = t0:t1
dd$LFA = dd$YR = NULL
iu = apply(dd,2,function(x) sum(is.na(x)*1)/length(x)<1-5/35)
dd = dd[,names(iu)[iu]] # removing all indicators with less than 5 values
iu=c()
iu = c(iu,which(names(dd)=='FSRS.Rec.Max.CL'))
iu = c(iu,which(names(dd)=='FSRS.Rec.Median.CL'))
iu = c(iu,grep('Sex',names(dd)))
if(i==33) {
iu = c(iu,which(names(dd)=='FSRS.Comm.MaxCL'))
iu = c(iu,which(names(dd)=='FSRS.Comm.MedianCL'))
}
dd = dd[,-iu]
fname = file.path(project.figuredirectory('bio.lobster'),'indicators')
dir.create(fname)
Y = pcaAnalyseData(dd, t0, t1,fname=fname,OFN = paste('OrdinationLFA',i,sep=""),addscores=T)
}
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