inst/Updates/LFA34/2020/LFA34Assessment.r

In LobsterScience/bio.lobster: Lobster Stock Assessment Tools for DFO Maritimes

# LFA 34 Assessment Script
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 require(bio.lobster)
 require(devtools)
 require(lubridate)
 require(bio.utilities)
 require(SpatialHub)
	p = bio.lobster::load.environment()
	la()

	assessment.year = 2020 ########### check the year ############### !!!!!!!!!!!


    p$syr = 1989
    p$yrs = p$syr:assessment.year



	    # define place for figures to go
	    figdir = file.path(project.datadirectory("bio.lobster"),"figures","LFA34Update")

	    p$lfas = "34" # specify lfa
    	p$subareas = c("34") # specify subareas for data summary
	    
	    CPUE.data<-CPUEModelData(p,redo=T)
	 
# Map ################

	#	x11(width=5, height=5)
	#	LobsterMap('34')
	#	text(x=c(-65.2,-65.7,-67.4),y=c(43.4,44.9,43.1),labels=c(33,35,41),col=rgb(0,0,0,0.8),cex=1.5)

	#	savePlot(file.path(figdir,'LFA34map.png'),type='png')


# CPUE ###############
		
		logs=lobster.db("process.logs")
		TempModelling = TempModel( annual.by.area=F)
		CPUE.data<-CPUEModelData(p,redo=T,TempModelling)

		## Commercial CPUE MOdels
		mf1 = formula(logWEIGHT ~ fYEAR + DOS + TEMP + DOS * TEMP)

	#	CPUE.data<- CPUEModelData(p,redo=F)
		t=mean(subset(CPUE.data,DOS==1)$TEMP)

		mdata = subset(CPUE.data,SYEAR%in%p$yrs & LFA==34)

		CPUEModelResults = CPUEmodel(mf1,mdata,t=t,d=1,lfa=34)
		crd = CPUEModelResults$pData[,c("YEAR","mu")]
		cpue1= CPUEModelPlot(CPUEModelResults,TempModelling,
			mdata=mdata,combined=T, lfa = p$lfas,xlim=c(1989,2020.4),ylim=c(0,10.5),
			graphic='R',path=figdir,lab=1,wd=11,ht=8)

	# plot
	x11(width=8,height=5)
	CatchRatePlot(data = crd, lfa = 34, fd=figdir)
	cpueData=read.csv(file.path(figdir,"CatchRateRefs34.csv"))

	#2020 going with raw CPUES
gg = 	aggregate(cbind(WEIGHT_KG, NUM_OF_TRAPS)~LFA+SYEAR, data=subset(logs,LFA==34),FUN=sum)
gg$CPUE = gg$WEIGHT_KG/gg$NUM_OF_TRAPS	
gM = rmed(gg$SYEAR, gg$CPUE)
with(gg,plot(SYEAR,CPUE,xlab='Year',ylab='CPUE', pch=c(rep(16,times=(nrow(gg)-1)),17)))
lines(gM$yr, gM$x, lwd=2, col='salmon')
graphics.off()
cpueData=gg
	savePlot(file.path(figdir,'CPUELFA342020.png'))




# Landings and Effort ############

	 	land = lobster.db('seasonal.landings')


		land$YEAR = as.numeric(substr(land$SYEAR,6,9))
		land$LANDINGS = land$LFA34
		cpueData$YEAR=cpueData$SYEAR
		cpueData$mu=cpueData$CPUE
		fishData = merge(cpueData,land[,c("YEAR","LANDINGS")]) 
		fishData$EFFORT2 = fishData$LANDINGS * 1000 / fishData$mu



	# plot
	x11(width=8,height=5)
	FisheryPlot(fishData[,c("YEAR","LANDINGS","EFFORT2")],lfa = 34,fd=figdir, preliminary=nrow(fishData), units='kt')



# Contextual Indicators #############