#############################Work with dive data############################################# ################################################################################################ model.dive=model_data_with_dive_2017 names(model.dive)=c("date","deploy","trap","avg.lob.size","dive.set","amb.lob","amb.gc","amb.rc","lob.catch","gc.catch","soak.time","lob.rate","eelgrass") model.dive par(mfrow=c(1,1)) hist(model.dive$lob.catch) hist(model.dive$amb.lob) ############## model.dive$soak.time<-(model.dive$soak.time)/60 model.dive ##########Dive data################################# ############################## # Poisson models ############################## library(lme4) model7<-glmer(lob.catch ~ trap * amb.lob + (1|deploy), data = model.dive, family="poisson") model8<-glmer(lob.catch ~ trap + amb.lob + (1|deploy), data = model.dive, family="poisson") model9<-glmer(lob.catch ~ trap + (1|deploy), data = model.dive, family="poisson") ####remove trap b/c not sig #### model10<-glmer(lob.catch ~ amb.lob + (1|deploy), data = model.dive, family="poisson") ####look at dispersion parameter#####check for overdispersion > 1.5##### model.dive E1 <- resid(model9, type = "pearson") #M1 is just the poisson GLM N <- nrow(model.dive) p <- length(coef(model9)) Dispersion <- sum(E1^2) / (N - p) Dispersion #Dispersion >1.5 is a problem, may require NB GLM modelG<-lmer(avg.lob.size ~ trap * amb.lob + (1|deploy), data=model.dive) modelH<-lmer(avg.lob.size ~ trap + amb.lob + (1|deploy), data=model.dive) modelI<-lmer(avg.lob.size ~ trap + (1|deploy), data=model.dive) AIC(model7,model8, model9,model10) AIC(modelG,modelH,modelI) # Check residuals for model8 res8<-resid(model8) fit8<-fitted(model8) plot(fit8,res8,main="Residuals VS Fitted Values plot") qqnorm(res8,main="Normal scores for residuals of total catch") qqline(res8) lag.plot(res8,do.lines=FALSE,diag=FALSE,main="Lag residuals of total catch") # Check residuals for modelI resI<-resid(modelI) fitI<-fitted(modelI) plot(fitI,resI,main="Residuals VS Fitted Values plot") qqnorm(resI,main="Normal scores for residuals of total catch") qqline(resI) lag.plot(resI,do.lines=FALSE,diag=FALSE,main="Lag residuals of total catch") # Check p-values for model8 and modelI summary(model10) Anova(model10,type="III") summary(modelI) Anova(modelI,type="III") ######plot model 10 now #### ######for predict function with poisson MUST use type="response" !!!! png(filename="Figure 11.png", width=2500, height=1500, res=200) library(ggplot2) p <- ggplot(model.dive, aes(x = amb.lob, y = lob.catch)) + geom_point(size=3) + geom_line(aes(y = predict(model10,type="response")),size=1) print(p) model.dive$fit <- predict(model10,type=c("response")) ggplot(model.dive,aes(amb.lob, lob.catch)) + geom_line(aes(y=fit), size=0.8) + geom_point(alpha = 1,size=5,colour="dodgerblue") + theme_bw()+ theme(panel.background=element_blank(), panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(2)))+ labs(x=expression(Ambient~lobster~density~per~200~m^{"2"}), y=expression(Lobster~catch~per~deployment)) dev.off() ###plot actual against predicted## plot(predict(model10),model.dive$lob.catch, xlab="predicted",ylab="actual") abline(a=0,b=1) library(car) library(lme4) require(multcomp) #############compare all traps against one another ################### trap.comp.dive=glht(model8,mcp(trap="Tukey")) summary(trap.comp.dive) ########################## # Negative Binomial Models ############################ model10<-glmer.nb(lob.catch ~ trap * amb.lob + (1|deploy), data = model.dive) model11<-glmer.nb(lob.catch ~ trap + amb.lob + (1|deploy), data = model.dive) model12<-glmer.nb(lob.catch ~ trap + (1|deploy), data = model.dive) # AIC Comparison of models AIC(model7, model8, model9, model10, model11, model12) # Check P - Values summary(model6) Anova(model6,type="III") # Check residuals for model 36 res6<-resid(model6) fit6<-fitted(model6) plot(fit6,res6,main="Residuals VS Fitted Values plot") qqnorm(res6,main="Normal scores for residuals of total catch") qqline(res6) lag.plot(res6,do.lines=FALSE,diag=FALSE,main="Lag residuals of total catch") #######calculate R2 for dive model ##### use Nakagawa protocol for GLMMs # Install latest version from CRAN install.packages("piecewiseSEM") # Install dev version from GitHub library(devtools) install_github("jslefche/piecewiseSEM") library(piecewiseSEM) library(lme4) sem.model.fits(model10) library(MuMIn) r.squaredGLMM(model10) ###marginal R2 associated with fixed effects, conditional R2 is fixed effects and random effects #####try method outlined in paper### mod0<-glmer(lob.catch ~ 1 + (1|deploy),data=model.dive,family="poisson") mod1<-glmer(lob.catch ~ amb.lob +(1|deploy),data=model.dive,family="poisson") VarF <- var(as.vector(fixef(mod1) %*% t(mod1@pp$X))) # VarCorr() extracts variance components attr(VarCorr(lmer.model),'sc')^2 # extracts the residual variance, VarCorr()$plot extract the variance of the # plot effect VarF/(VarF + VarCorr(mod1)$plot[1] + attr(VarCorr(mod1), "sc")^2 + log(1 + 1/exp(as.numeric(fixef(mod0))))) # compute the conditionnal R-square (VarF + VarCorr(mod1)$plot[1])/(VarF + VarCorr(mod1)$plot[1] + (attr(VarCorr(mod1), "sc")^2) + log(1 + 1/exp(as.numeric(fixef(mod0))))) # computing the percent of explained variance for the plot slope 1 - (VarCorr(mod1)$plot[1]^2/VarCorr(mod0)$plot[1]^2) 1 - (var(residuals(mod1))/var(residuals(mod0))) ################ Model data ############### ##### trap 4 and 5 (No camera) lumped together lump.data=model_data_2017 names(lump.data)=c("date","deploy","trap","avg.lob.size","camera","dive.set","amb.lob","amb.gc","amb.rc","lob.catch","gc.catch","soak.time","lob.rate","eelgrass") lump.data ########make a new trap column with 4 and 5 coded as the same treatment###### code all as 4 library(plyr) library(dplyr) lump.data=mutate(lump.data,lump.trap=trap) lump.data lump.data$lump.trap=revalue(lump.data$lump.trap,c("5"="4")) lump.data$lump.trap ####relevel so trap 3 is first#### and on the left in graphs## lump.data$lump.trap <- relevel(lump.data$lump.trap, 3)#####change so that compares to trap 3 not trap 1 levels(lump.data$lump.trap) library(ggplot2) library(beanplot) library(Hmisc) library(lme4) library(car) par(mfrow=c(1,1)) ####make model for lumped size### ####lmer assumes gaussian distribution where GLMER assumes non-gaussian distribution ###when look at hist(size) looks relatively normal hist(lump.data$avg.lob.size) modelA<-lmer(avg.lob.size ~ lump.trap * soak.time + (1|deploy), data=lump.data) modelB<-lmer(avg.lob.size ~ lump.trap + soak.time + (1|deploy), data=lump.data) modelC<-lmer(avg.lob.size ~ lump.trap + (1|deploy), data=lump.data) AIC(modelA,modelB,modelC) res3<-resid(modelC) fit3<-fitted(modelC) plot(fitC,resC,main="Residuals VS Fitted Values plot") qqnorm(resC,main="Normal scores for residuals of avg.lob.size") qqline(resC) lag.plot(resC,do.lines=FALSE,diag=FALSE,main="Lag residuals of avg.lob.size") summary(modelC) Anova(modelC,type="III") ####boxplot for all traps##### lump.data library(ggplot2) fill=c("azure2","chartreuse4","darkred","grey50") catch.plot=ggplot(lump.data,aes(lump.data$lump.trap,lump.data$lob.catch))+geom_boxplot(fill=fill,show.legend=FALSE)+ geom_jitter(width=0.5,height=0.5,size=1.0)+ theme(panel.background=element_blank(), panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(2)))+ labs(y = "Number of lobster caught per trap", x = "")+ scale_x_discrete(labels=c("Unstocked","GC","RC","No Cam-Unstocked")) ######replace 1-4 with trap types ##### catch.plot ####boxplot of avg. size per trap library(ggplot2) fill=c("azure2","chartreuse4","darkred","grey50") size.plot=ggplot(avg.size,aes(avg.size$lump.trap,avg.size$size.mean))+geom_boxplot(fill=fill,show.legend=FALSE)+ geom_jitter(width=0.5,height=0.5,size=1.0)+ theme(panel.background=element_blank(), panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(2)))+ labs(y = "Average lobster size (mm)", x = "Pre-stocking condition")+ scale_x_discrete(labels=c("Unstocked","GC","RC","No Cam-Unstocked")) ######replace 1-4 with trap types ##### size.plot=size.plot+geom_hline(yintercept=82.5,linetype="dashed",colour="red",size=1.2) size.plot ##plot catch and size graphs one on top of other (A/B) png(filename="Figure 4.png", width=1700, height=2250, res=200) library(gridExtra) grid.arrange(catch.plot,size.plot,ncol=1,nrow=2) dev.off() ####dotplot: plot points with coefficients from model with confidence interval (1.96*S.E) ##ie. visualization of regression coeffecients### ######make a dot plot ##### library(lattice) dotplot(lump.trap ~ lob.catch, data = lump.data) library(ggplot2) dot.plot=ggplot(lump.data, aes(x = lob.catch, y = lump.trap,col=lump.data$lump.trap))+ geom_point(show.legend=FALSE,size=8,) + theme(axis.line=element_line(size=0.7,colour="black"),text=element_text(size=18),panel.background=element_blank(),panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=14, colour="black"), axis.text.y = element_text(size=14, colour="black"), axis.text = element_text(size=14, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(1.0)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(1.0)))+ labs(y = "Trap Type", x = "Lobster Catch")+ scale_y_discrete(labels=c("Control","GC","RC","No Cam")) dot.plot+scale_colour_manual(values=c("dodgerblue4","chartreuse4","darkred","grey50")) #######now plot camera vs. no camera######## cam.nocam=subset(lump.data,!(lump.trap%in%c("1","2"))) cam.nocam fill=c("azure2","grey50") ggplot(cam.nocam,aes(cam.nocam$lump.trap,cam.nocam$lob.catch))+geom_boxplot(fill=fill,show.legend=FALSE)+ geom_jitter(height=0.5,width=0.5)+ theme(panel.background=element_blank(), panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(2)))+ labs(y = "Lobster Catch", x = "Trap Type")+ scale_x_discrete(labels=c("Camera","No Camera")) ####plot soaks for lumped traps### ####check levels so know what order to assign legend points## lump.data$lump.trap ###plot lump.data$soak.time png(filename="Figure 5.png", width=2500, height=1500, res=300) library(ggplot2) ggplot(lump.data,aes(lump.data$soak.time,lump.data$lob.catch))+scale_colour_manual(labels=c("Unstocked","GC","RC","No Cam-Unstocked"),name="",values=c("dodgerblue4","chartreuse4","darkred","grey50"))+geom_point(aes(colour=lump.data$lump.trap),size=5, show.legend = TRUE)+ theme(axis.line=element_line(size=0.7,colour="black"),text=element_text(size=18),panel.background=element_blank(),panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(1.2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(1.2)))+ labs(y = "Lobster catch per deployment", x = "Soak time (hours)") dev.off() #######plug this into the model#### hist(lump.data$lob.catch) lump.data$soak.time<-(lump.data$soak.time)/60 model1<-glmer(lob.catch ~ lump.trap * soak.time + (1|deploy), data = lump.data, family="poisson") model2<-glmer(lob.catch ~ lump.trap + soak.time + (1|deploy), data = lump.data, family="poisson") model3<-glmer(lob.catch ~ lump.trap + (1|deploy), data = lump.data, family="poisson") ####no need for nb b/c NOT overdistributed (tested below) model4<-glmer.nb(lob.catch ~ lump.trap * soak.time + (1|deploy), data = lump.data) model5<-glmer.nb(lob.catch ~ lump.trap + soak.time + (1|deploy), data = lump.data) model6<-glmer.nb(lob.catch ~ lump.trap + (1|deploy), data = lump.data) AIC(model1,model2,model3) AIC(model4,model5,model6) lrtest(model3,model6) par(mar=c(5.1,4.1,4.1,2.1)) #####look at overdispersion### E1 <- resid(model10, type = "pearson") #M1 is just the poisson GLM N <- nrow(lump.data) p <- length(coef(model10)) Dispersion <- sum(E1^2) / (N - p) Dispersion #Dispersion >1.5 is a problem, may require NB GLM ##Dispersion = 1.1 therefore no need for nb glm ####dotplot: plot points with coefficients from model with confidence interval (1.96*S.E) ##ie. visualization of regression coeffecients### library(coda) library(reshape) install.packages('coefplot2', repos='http://www.math.mcmaster.ca/bolker/R', type='source') library(coefplot2) longnames <- c("GC", "RC", "No Cam") coefplot2(model3, varnames = longnames, main = "Regression Estimates") rsummary(model3) res6<-resid(model6) fit6<-fitted(model6) plot(fit6,res6,main="Residuals VS Fitted Values plot") qqnorm(res6,main="Normal scores for residuals of total catch") qqline(res6) lag.plot(res6,do.lines=FALSE,diag=FALSE,main="Lag residuals of total catch") res3<-resid(model3) fit3<-fitted(model3) plot(fit3,res3,main="Residuals VS Fitted Values plot") qqnorm(res3,main="Normal scores for residuals of total catch") qqline(res3) lag.plot(res3,do.lines=FALSE,diag=FALSE,main="Lag residuals of total catch") lrtest(model3,model6) AIC(model3,model6) # Check P - Values summary(model3) Anova(model6,type="III") ######### Now make plot combining video and dive data ####### ############################################################################################# ######plot with JUST dive deploys (ie. deployments were there is a corresponding dive)#### ie. one bar not 2 ######MEGA PLOT#### 2 stacked bars plots(#S/F attempts) for dive and all and point for mean lob density with S.E bars library(ggplot2) library(plyr) library(dplyr) library(doBy) video.S.F=video_data_lob_success_v_fail_ names(video.S.F)=c("date","deploy","trap","day.time","elapse.time","vid.count","species","behaviour","direction","time.behaviour") video.S.F ###relevel so trap 3 is on far left video.S.F$trap=relevel(video.S.F$trap,3) video.S.F$trap video.S.F=subset(video.S.F,!(behaviour%in%c("start","end","exit"))) video.S.F$behaviour=factor(video.S.F$behaviour) all.table=table(video.S.F$behaviour,video.S.F$trap) all.table barplot(all.table,col=c("dodgerblue4","darkred"),xlab="Trap Type",ylab="Total Number of Attempts",names.arg=c("Control","GC","RC"),add=TRUE,space=c(3)) ####now create table for only dive deploys##remove deploy 2 b/c no GC video video.S.F.dive=subset(video.S.F,!(deploy%in%c("1","2","5","6","7","11","15"))) ######sooo key to remove other deploys from later tables and graphs, apply factor ()############# video.S.F.dive$deploy=factor(video.S.F.dive$deploy) video.S.F.dive$deploy dive.table=table(video.S.F.dive$behaviour,video.S.F.dive$trap) dive.table png(filename="Figure 12.png", width=1800, height=1300, res=200) par(oma=c(0,5,0,3),mar=c(5.1,1,4.1,7.1)) barplot(dive.table,col=c("dodgerblue","firebrick"),xlab="Pre-stocking condition",ylab="Total number of attempts",names.arg=c("Unstocked","GC","RC"),cex.names=1.2,space=c(1,1,1),cex.lab=1.5,cex.axis=1.2) legend("topright", inset=.05, title="Attempt result", c('Success','Failure'), fill=c("dodgerblue","firebrick")) mtext(side=2,line=3,"Total number of lobster attempts",cex=1.5) par(new=T) trap <- c(2,6,10) sd <- c(12.57, 12.80, 8.97) mean <- c(23, 21.18, 21.73) nicci <- as.data.frame(cbind(trap, sd, mean)) nicci ###par(mar(bottom,left,top,right)) par(oma=c(0,7,0,7),mar=c(5,0,4,1)) plot(nicci$trap, nicci$mean, ylim=range(c(0, nicci$mean+nicci$sd)), pch=19,xlim=c(0,10),axes=FALSE,xlab="",xaxt="n",ylab="",cex=1.5) #no x-axis ticks arrows(nicci$trap, mean-nicci$sd, nicci$trap, nicci$mean+nicci$sd, length=0.05, angle=90, code=3,lwd=2) axis(side=4,cex.axis=1.2) mtext(expression(paste( plain("Mean ambient density per 200m")^ plain("2"), plain(" +/- SD") )), side=4, line = 3, cex=1.5)#####add superscript on m^2 ######add GC to trap### model.dive library(Rmisc) avg.amb.GC <- summarySE(model.dive, measurevar="amb.gc", groupvars=c("trap")) avg.amb.GC trap.GC <- c(2.2,6.2,10.2) sd.GC <- c(0.3, 2.41,0) mean.GC <- c(0.091,0.727 ,0) GC <- as.data.frame(cbind(trap.GC, sd.GC, mean.GC)) GC par(new=T) par(oma=c(0,7,0,7),mar=c(5,0,4,1)) plot(GC$trap.GC, GC$mean.GC, ylim=range(c(0, nicci$mean+nicci$sd)), pch=17,col="darkgreen",xlim=c(0,10),axes=FALSE,xlab="",xaxt="n",ylab="",cex=2) #no x-axis ticks arrows(GC$trap.GC, mean.GC-GC$sd.GC, GC$trap.GC, GC$mean.GC+GC$sd.GC, length=0.05, angle=90, code=3,lwd=2,col="darkgreen") dev.off() ############################################################################################################### #####do prop.test for dive data### dive.table.prop=table(video.S.F.dive$trap,video.S.F.dive$behaviour) dive.table.prop prop.test(dive.table.prop,correct=FALSE) ####mean ambient lobster y axis from zero par(oma=c(0,7,0,7),mar=c(5,0,4,1)) plot(nicci$trap, nicci$mean, ylim=range(c(0, nicci$mean+nicci$sd)), pch=19,xlim=c(0,10),axes=FALSE,xlab="",xaxt="n",ylab="",cex=1.5) #no x-axis ticks arrows(nicci$trap, mean-nicci$sd, nicci$trap, nicci$mean+nicci$sd, length=0.05, angle=90, code=3,lwd=2) axis(side=4,cex.axis=1.2) mtext(side = 4, line = 3, "Mean Ambient Lobster +/- SD",cex=1.5) ############################################################################################################################################################################ ############################################################################################################################################################################ my.map=mapping_data ###make a map### library('ggmap') library(ggplot2) #map of NL# NL.map=get_map(location=c(lon=-55.79, lat=+48.87),zoom=6, maptype='terrain', color='bw') NL.map=ggmap(NL.map)+scale_x_continuous(limits = c(-60, -52), expand = c(0, 0)) + scale_y_continuous(limits = c(46, 52), expand = c(0, 0))+ labs(x = "Longitude", y = "Latitude")+ theme(axis.text=element_text(size=12), axis.title=element_text(size=13), text=element_text(size=15)) NL.map #Use Google Maps to pull out the base map for your location - (Ex. Base map of LHE/LBE) - lon & lat values are the center point of the map map <- get_map(location=c(lon=-54.86, lat=+47.59), zoom=1, maptype='terrain', color='bw') #Plotting positions & density heatmap #Create New Small Data frame for the required variables (From my larger data frame called "B2") positions <- data.frame(Lon = my.map$Lon, Lat = my.map$Lat,trap = my.map$`Trap #`) # Plot GPS location points as well as density LHE.map<-ggmap(map,legend="right")+ geom_point(data = positions,aes(x = positions$Lon, y = positions$Lat), size=3, colour="black")+ labs(x = "Longitude", y = "Latitude")+ theme(legend.position="right")+ theme(axis.text=element_text(size=12), axis.title=element_text(size=14), text=element_text(size=15)) LHE.map ############################################################################################################################################################################ ############################################################################################################################################################################ ####look at failed attempts v. successful attempts according to trap type### video.success=video_data_lob_success_v_fail_ names(video.success)=c("date","deploy","trap","day.time","elapse.time","vid.count","species","behaviour","direction","time.behaviour") video.success video.success$trap all.table=table(video.success$trap,video.success$behaviour) all.table large.table=subset(video.success,!(direction%in%c("T","B","K","P"))) ##T=Top,B=Bottom,K=Kitchen, P=Parlour (all between slats) large.table=table(large.table$trap,large.table$behaviour) large.table ###make stacked barplot for Success v. fail for each trap ###subset data so only have S and F in behaviour column## attempts.trap=subset(video.success,!(behaviour%in%c("start","end","exit"))) ######sooo key to remove start,end,exit from later tables and graphs, apply factor ()############# attempts.trap$behaviour=factor(attempts.trap$behaviour) library(ggplot2) library(dplyr) library(plyr) library(doBy) ######test to see whether proportions are the same across groups (all lobster)###### ####making bargraph for total attempts for each trap per deploy (all lob) ##### ###************DEPLOY 2 IS REMOVED B/C NO VID FOR TRAP 1 THEREFORE MISLEADING***** table(attempts.trap$trap,attempts.trap$behaviour) deploy.table=select(attempts.trap,trap,deploy,behaviour) deploy.table.all=table(deploy.table$behaviour,deploy.table$trap) deploy.table.all deploy.table$behaviour=factor(deploy.table$behaviour) ####make a table to remove start,end,exit from table above#### my.table=matrix(c(479,557,1133,578,562,492),ncol=3,byrow=TRUE) rownames(my.table)=c("S_attempt","F_attempt") colnames(my.table)=c("GC","RC","Control") my.table=as.table(my.table) my.table par(mfrow=c(1,1)) barplot(my.table,col=c("dodgerblue4","darkred"),xlab="Trap Type",ylab="Total Number of Attempts") summary(my.table) #####prop test requires 2 rows so will need to rearrange prop.test(my.table,correct=FALSE) ####output=2.2e-16--> reject the hypothesis of equal probability### attempts.trap.success=subset(attempts.trap,!(behaviour%in%c("F_attempt"))) attempts.trap.success ###one way ANOVA trap type and time of behaviour (S_attempt)## fit=aov(attempts.trap.success$time.behaviour~attempts.trap.success$trap) model=lm(attempts.trap.success$time.behaviour~attempts.trap.success$trap) anova(model) res=resid(model) fit=fitted(model) ####plot resid vs fit for homogeniety## cones are bad, want even vert. distribution### plot(fit,res,xlab="fits",ylab="Residuals",main="resid vs. fit success time according to trap type") lag.plot(res,diag=FALSE) qqnorm(res) qqline(res) ###departures from line suggests violations of normality hist(res) tapply(attempts.trap.success$time.behaviour,attempts.trap.success$trap,mean) tapply(attempts.trap.success$time.behaviour,attempts.trap.success$trap,sd) ####code for all in one matrix### fit=aov(attempts.trap.success$time.behaviour~attempts.trap.success$trap) layout(matrix(c(1,2,3,4),2,2)) # optional layout plot(fit) #diagnostics plots summary(fit) ####test for normality# shapiro.test(attempts.trap.success$time.behaviour) ####test for homogeniety## bartlett.test(attempts.trap.success$time.behaviour~attempts.trap.success$trap) ####need to run nonparametric test because violate assumptions of normality####also handle outliers better than parametric tests ###parametric (one way anova) might still be appropriate given sample size (large) ie. robust to the normality assumption ###parametric vs. nonparametric does mean or median better represent centre of distribution## library(FSA) Summarize(time.behaviour~trap,data=attempts.trap.success) ####plot histogram for each trap to see if distribution is similar#####try kruskal-wallis test assumes similar shapes and equal variences library(lattice) histogram(~time.behaviour|trap,data=attempts.trap.success,layout=c(1,3)) kruskal.test(attempts.trap.success$time.behaviour,attempts.trap.success$trap) ###test is significant so post-hoc analysis can be performed to see which levels differ ## pairwise.wilcox.test(attempts.trap.success$time.behaviour,attempts.trap.success$trap,p.adjust.method="none") ################################################################################################################################################################ # First prepare the data##################################################################################################################################### ###make stacked barplot for Success v. fail for each trap ###subset data so only have S and F in behaviour column## video.success=video_data_lob_success_v_fail_ names(video.success)=c("date","deploy","trap","day.time","elapse.time","vid.count","species","behaviour","direction","time.behaviour") video.success attempts.trap=subset(video.success,!(behaviour%in%c("start","end","exit"))) attempts.trap attempts.trap$trap=relevel(attempts.trap$trap,3) ######sooo key to remove start,end,exit from later tables and graphs, apply factor ()############# attempts.trap$behaviour=factor(attempts.trap$behaviour) ##############have to load plyr NOT dplyr otherwise will get error message#### library(plyr) attempts.trap=plyr::count(attempts.trap, c("behaviour", "trap")) attempts.trap # Determine proportions library(doBy) attempts.trap=merge(summaryBy(freq ~ trap, data = attempts.trap, FUN = sum), attempts.trap, by = c("trap")) attempts.trap=mutate(attempts.trap, PROP = (freq/freq.sum)*100) sum(attempts.trap$freq) # Change the plotting order to SUCC, fail attempts.trap= transform(attempts.trap, attempts.trap.ord = factor( behaviour, levels=c('S_attempt','F_attempt'), ordered =TRUE)) attempts.trap.ord=attempts.trap[order(attempts.trap$attempts.trap.ord),] ####now plot stacked barplot for prop, attempts v. fail#### plot2 <- ggplot(attempts.trap.ord, aes(x=trap, y=PROP, fill=attempts.trap.ord))+ geom_bar(stat="identity", colour="black") + scale_x_discrete(labels=c('3'='Control','1'='GC','2'='RC'))+ theme(panel.grid.major = element_line(size=0.5, color="grey"), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_rect(fill = 'white', colour = 'black'), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(size=rel(1.1)), #make axis text big axis.title.y = element_text(size=rel(1.1)) )+ labs(x = "Trap Type", y = "Proportion of all entry attempts (%)")+ scale_fill_manual(values = c("dodgerblue4", "darkred"),labels=c("Successful","Failed"))+ guides(fill=guide_legend(title="Attempt type")) plot2 ############################################################################################################################################################################ #################do again with only large lob############################################## ###subset data so only have S and F in behaviour column## attempts.trap=subset(video.success,!(behaviour%in%c("start","end","exit"))) attempts.trap ###subset again for only large lob ie. direction = E only attempts.trap.large=subset(attempts.trap,!(direction%in%c("T","B","K","P"))) attempts.trap.large$behaviour=factor(attempts.trap.large$behaviour) ######test to see whether proportions are the same across groups (large lobster)###### my.table.large=table(attempts.trap.large$behaviour,attempts.trap.large$trap) my.table.large par(mfrow=c(1,1)) barplot(my.table.large,beside=TRUE,col=c("dodgerblue4","darkred"),legend=rownames(my.table.large),names.arg=c("GC","RC","Control"),xlab="Trap Type",ylab="Total Number of Attempts") ####make a table to remove start,end,exit from table above####or make factor my.table.large=matrix(c(106,515,105,487,139,311),ncol=2,byrow=TRUE) colnames(my.table.large)=c("S_attempt","F_attempt") rownames(my.table.large)=c("1","2","3") my.table.large=as.table(my.table.large) my.table.large prop.test(my.table.large,correct=FALSE) ####output=1.486e-8--> reject the hypothesis of equal probability### #####barplot for all attempts## large (E) only large.revalue=revalue(attempts.trap.large$behaviour,c("S_attempt"="attempt","F_attempt"="attempt")) large.revalue ###subset again for only large lob ie. direction = E only attempts.trap.large=subset(attempts.trap,!(direction%in%c("T","B","K","P"))) attempts.trap.large$behaviour=factor(attempts.trap.large$behaviour) attempts.trap.large$trap=relevel(attempts.trap.large$trap,3) attempts.trap.large$trap # First prepare the data library(doBy) attempts.trap.large=plyr::count(attempts.trap.large, c("behaviour", "trap")) # Determine proportions attempts.trap.large=merge(summaryBy(freq ~ trap, data = attempts.trap.large, FUN = sum), attempts.trap.large, by = c("trap")) attempts.trap.large=mutate(attempts.trap.large, PROP = (freq/freq.sum)*100) sum(attempts.trap.large$freq) attempts.trap.large # Change the plotting order to SUCC, fail attempts.trap.large= transform(attempts.trap.large, attempts.trap.large.ord = factor( behaviour, levels=c('S_attempt','F_attempt'), ordered =TRUE)) attempts.trap.large.ord=attempts.trap.large[order(attempts.trap.large$attempts.trap.large.ord),] ###### plot library(ggplot2) plot3 <- ggplot(attempts.trap.large.ord, aes(x=trap, y=PROP, fill=attempts.trap.large.ord))+ geom_bar(stat="identity", colour="black") + scale_x_discrete(labels=c('1'='GC','2'='RC','3'='Control'))+ theme(panel.grid.major = element_line(size=0.5, color="grey"), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_rect(fill = 'white', colour = 'black'), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(size=rel(1.1)), #make axis text big axis.title.y = element_text(size=rel(1.1)) )+ labs(x = "Trap Type", y = "Proportion of all entry attempts (%)")+ scale_fill_manual(values = c("dodgerblue4", "darkred"),labels=c("Successful","Failed"))+ guides(fill=guide_legend(title="Attempt type")) plot3 #################do again with only small lob############################################## ###subset data so only have S and F in behaviour column## attempts.trap=subset(video.success,!(behaviour%in%c("start","end","exit"))) attempts.trap ###subset again for only small lob ie. direction = NOT E attempts.trap.small=subset(attempts.trap,!(direction%in%c("E"))) attempts.trap.small$trap=relevel(attempts.trap.small$trap,3) ######test to see whether proportions are the same across groups (small lobster)#################################### table(attempts.trap.small$trap,attempts.trap.small$behaviour) ####make a table to remove start,end,exit from table above#### my.table.small=matrix(c(373,65,454,75,994,182),ncol=2,byrow=TRUE) colnames(my.table.small)=c("S_attempt","F_attempt") rownames(my.table.small)=c("1","2","3") my.table.small=as.table(my.table.small) my.table.small prop.test(my.table.small,correct=FALSE) ####output=0.7801--> accept the hypothesis of equal probability############################################################ # First prepare the data attempts.trap.small=count(attempts.trap.small, c("behaviour", "trap")) # Determine proportions attempts.trap.small=merge(summaryBy(freq ~ trap, data = attempts.trap.small, FUN = sum), attempts.trap.small, by = c("trap")) attempts.trap.small=mutate(attempts.trap.small, PROP = (freq/freq.sum)*100) sum(attempts.trap.small$freq) # Change the plotting order to SUCC, fail attempts.trap.small= transform(attempts.trap.small, attempts.trap.small.ord = factor( behaviour, levels=c('S_attempt','F_attempt'), ordered =TRUE)) attempts.trap.small.ord=attempts.trap.small[order(attempts.trap.small$attempts.trap.small.ord),] ####now plot stacked barplot for prop, attempts v. fail#### library(ggplot2) plot4 <- ggplot(attempts.trap.small.ord, aes(x=trap, y=PROP, fill=attempts.trap.small.ord))+ geom_bar(stat="identity", colour="black",width=0.7) + scale_x_discrete(labels=c('1'='GC','2'='RC','3'='Control'))+ theme(panel.grid.major = element_line(size=0.5, color="grey"), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_rect(fill = 'white', colour = 'black'), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(size=rel(1.1)), #make axis text big axis.title.y = element_text(size=rel(1.1)) )+ labs(x = "Trap Type", y = "Proportion of all entry attempts (%)")+ scale_fill_manual(values = c("dodgerblue4", "darkred"),labels=c("Success","Failure"))+ guides(fill=guide_legend(title="Attempt result")) plot4 #####plot all prop graphs in one figure##### ie. plot 2,3,4 library(ggplot2) plot2 <- ggplot(attempts.trap.ord, aes(x=trap, y=PROP, fill=attempts.trap.ord))+ geom_bar(stat="identity", colour="black") + scale_x_discrete(labels=c('3'='Unstocked','1'='GC','2'='RC'))+ theme(panel.grid.major = element_line(size=0.2, color="grey"), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_rect(fill = 'white', colour = 'black'), axis.text.x = element_text(size=25, colour="black"), axis.text.y = element_text(size=25, colour="black"), axis.text = element_text(size=25, colour="black"), axis.title.x = element_text(size=rel(1.2)), #make axis text big axis.title.y = element_text(size=rel(1.2)) )+ labs(x = "", y = "Proportion of all entry attempts (%)")+ scale_fill_manual(values = c("dodgerblue4", "darkred"))+ guides(fill=FALSE) plot2 plot3 <- ggplot(attempts.trap.large.ord, aes(x=trap, y=PROP, fill=attempts.trap.large.ord))+ geom_bar(stat="identity", colour="black") + scale_x_discrete(labels=c('1'='GC','2'='RC','3'='Unstocked'))+ theme(panel.grid.major = element_line(size=0.5, color="grey"), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_rect(fill = 'white', colour = 'black'), axis.text.x = element_text(size=25, colour="black"), axis.text.y = element_text(size=25, colour="black"), axis.text = element_text(size=25, colour="black"), axis.title.x = element_text(size=rel(1.2)), #make axis text big axis.title.y = element_text(size=rel(1.2)) )+ labs(x = "Pre-stocking Condition", y = "")+ scale_fill_manual(values = c("dodgerblue4", "darkred"))+ guides(fill=FALSE) plot3 plot4 <- ggplot(attempts.trap.small.ord, aes(x=trap, y=PROP, fill=attempts.trap.small.ord))+ geom_bar(stat="identity", colour="black") + scale_x_discrete(labels=c('1'='GC','2'='RC','3'='Unstocked'))+ theme(panel.grid.major = element_line(size=0.5, color="grey"), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_rect(fill = 'white', colour = 'black'), axis.text.x = element_text(size=25, colour="black"), axis.text.y = element_text(size=25, colour="black"), axis.text = element_text(size=25, colour="black"), axis.title.x = element_text(size=rel(1.2)), #make axis text big axis.title.y = element_text(size=rel(1.2)) )+ labs(x = "", y = "")+ scale_fill_manual(values = c("dodgerblue4", "darkred"))+ guides(fill=FALSE) plot4 library(ggplot2) library(Hmisc) library(gridExtra) grid.arrange(plot2,plot3,plot4,ncol=3,nrow=1) png(filename="Figure 8.png", width=4000, height=1800, res=200) library(cowplot) theme_set(theme_cowplot(font_size=13)) plot_grid(plot2,plot3,plot4,labels=c('A','B','C'),align='h',ncol=3) dev.off() ##############now work with success v. exit in video over elapsed time##### video.data=trial_video_data_S_attempts_only names(video.data)=c("date","trap.code","deploy","trap","day.time","elapse.time","vid.count","species","behaviour","final.catch","direction","time.behaviour") video.data #####plot boxplot of S_attempt times across trap type ONLY for entrance!!! ##### #####subset data according to successes only ##### library(dplyr) success.data=subset(video.data,!(behaviour%in%c("exit","start","end"))) success.data$behaviour ###re-level so trap 3 on the left on a graph success.data$trap=relevel(success.data$trap,3) success.data$trap ###subset again for entrance only success.data.E=subset(success.data,!(direction%in%c("T","B","K","P"))) ####convert time.behaviour to minutes instead of seconds## success.data.E.min=mutate(success.data.E,time.min=time.behaviour/60) success.data.E.min$time.min ####make a model to test difference in time according to trap## hist(success.data.E.min$time.behaviour) model.success=glmer(time.behaviour ~ trap + (1|deploy),data=success.data.E.min,family="poisson") model.success.nb=glmer.nb(time.behaviour ~ trap + (1|deploy),data=success.data.E.min) #####look at distribution ### E1 <- resid(model.success, type = "pearson") #M1 is just the poisson GLM N <- nrow(success.data.E.min) p <- length(coef(model.success)) Dispersion <- sum(E1^2) / (N - p) Dispersion #Dispersion >1.5 is a problem, may require NB GLM ####poisson is wayyy overdispersed (235.2558) ##so stick with nb AIC(model.success,model.success.nb) resnb<-resid(model.success.nb) fitnb<-fitted(model.success.nb) plot(fitnb,resnb,main="Residuals VS Fitted Values plot") qqnorm(resnb,main="Normal scores for residuals of entry time") qqline(resnb) lag.plot(resnb,do.lines=FALSE,diag=FALSE,main="Lag residuals of entry time") # Check p-values #relevel but watch if generate graphs from re-leveled data as will change x axis!! success.data.E.min$trap <- relevel(success.data.E.min$trap, 3)#####change so that compares to trap 3 not trap 1 levels(success.data.E.min$trap) summary(model.success.nb) Anova(model.success.nb,type="III") ###no sig difference in time ####### ################################################################################## ###plot boxplot for time of behaviour (success) vs. trap type for E only! ##trap 3 will be on left, restrict jitter to only x axis NOT y ###put on log scale so can better see spread ##add minor ticks on y axis! png(filename="Figure 9.png", width=2500, height=1500, res=200) success.data.E.min library(ggplot2) par(mfrow=c(1,1)) fill=c("azure2","chartreuse4","darkred") y=ggplot(success.data.E.min,aes(success.data.E.min$trap,success.data.E.min$time.min))+geom_boxplot(fill=fill,show.legend=FALSE)+ geom_jitter(width=0.5,height=0.5)+ theme(panel.background=element_blank(), panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(2)))+ labs(y = "Duration of entries (minutes)", x = "Pre-stocking condition")+ scale_x_discrete(labels=c("Unstocked","GC","RC")) y+scale_y_log10() dev.off() ######continue to explore lobster accumulation in traps #####does not like period so need duration######## library(lubridate) hms("0:05:57") video.data$elapse.time=hms(video.data$elapse.time) video.data$elapse.time video.data$elapse.time<-as.duration(video.data$elapse.time) video.data$elapse.time ###plot all size and cumul in R #### library(dplyr) library(plyr) all.lob=mutate(video.data,entry.lob=behaviour) all.lob$entry.lob=revalue(all.lob$entry.lob,c("start"="0","end"="0","S_attempt"="1","exit"="-1")) all.lob all.lob$entry.lob=as.numeric(levels(all.lob$entry.lob))[all.lob$entry.lob]####makes 0,-1,1 numeric all.lob=ddply(all.lob,"trap.code",transform,cumsum=cumsum(entry.lob)) all.lob ######final catch vs. video final catch ### all.lob$diff.catch=all.lob$final.catch-all.lob$cumsum all.lob$diff.catch Finals=subset(all.lob, diff.catch!="NA") plot(Finals$trap,Finals$diff.catch) ###descriptive stats for each trap for diff.catch### library(data.table) dt <- data.table(Finals) dt dt[,list(mean=mean(diff.catch),sd=sd(diff.catch),max=max(diff.catch),min=min(diff.catch)),by=trap] #####plot boxplot with trap type on y axis#### png(filename="Figure 10.png", width=2500, height=1500, res=200) library(ggplot2) fill=c("chartreuse4","darkred","azure2") ggplot(Finals,aes(Finals$trap,Finals$diff.catch))+geom_boxplot(fill=fill,show.legend=FALSE)+ coord_flip()+ geom_point(position = position_jitter(w = 0.5, h = 0))+ scale_y_continuous(breaks=c(-6,-4,-2,0,2,4,6,8))+ theme(panel.background=element_blank(), panel.border=element_blank(), axis.line.x=element_line(colour="black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=18, colour="black"), axis.text.y = element_text(size=18, colour="black"), axis.text = element_text(size=18, colour="black"), axis.title.x = element_text(margin=margin(20,0,0,0), size=rel(2)), #make axis text big axis.title.y = element_text(margin=margin(0,20,0,0), size=rel(2)))+ labs(y = "Difference in final catch and final video", x = "Pre-stocking condition")+ scale_x_discrete(labels=c("GC","RC","Unstocked")) dev.off() ######plot final catch and video catch in paired boxplot####### library(reshape2) Finals.pair=select(Finals,final.catch,cumsum,trap) Finals.pair molten=melt(Finals.pair,id=c("trap")) molten molten$variable=revalue(molten$variable,c("cumsum"="video.catch")) molten #####plot with flipped axes library(ggplot2) ggplot(data = molten, aes(x = trap, y = value, fill = variable)) + geom_boxplot(aes(fill = variable), show.legend = TRUE) + theme(strip.background = element_rect(fill = NA), axis.line=element_line(size=0.7, color="black"), text=element_text(size=26), panel.background = element_blank(), panel.border = element_blank(), legend.key = element_blank(), axis.line.x = element_line(colour = "black"), axis.line.y = element_line(colour = "black"), axis.text.x = element_text(size=24, colour="black"), axis.text.y = element_text(size=24, colour="black"), axis.text = element_text(size=24, colour="black"), axis.title.x = element_text(size=rel(1.2)), #make axis text big axis.title.y = element_text(size=rel(1.2)))+ labs(y = "Lobster Catch",x="Trap Type")+ scale_fill_manual(values = c("dodgerblue4","dodgerblue2"), guide = guide_legend(title = NULL))+ scale_x_discrete(labels=c("GC","RC","Control")) ############ plot lobster accumulation plots for large lob only AND ALL lobster png(filename="Figure 6.png", width=2500, height=2000, res=200) ###now plot according to deployment (large.lob) ## par(mfrow=c(4,4)) par(mar=c(0,0,0,0),oma=c(5,5,1,1)) xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels="") vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"1",cex=2) legend(500,13,c("Unstocked","GC","RC"),lty=c(1,1,1),lwd=c(2.5,2.5,2.5),col=c("dodgerblue4","chartreuse4","darkred"),cex=1.7) #####add labels to outside margin##### (Bottom,Left,Top,Right) mtext("Elapsed soak time (hours)",1,3,outer=TRUE,cex=1.5) mtext("Number of lobsters inside the trap",2,3, outer=TRUE,cex=1.5) ##deploy 1 video.data.d1t1=subset(all.lob,deploy=="1"&trap=="1") lines(video.data.d1t1$elapse.time, video.data.d1t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d1t2=subset(all.lob,deploy=="1"&trap=="2") lines(video.data.d1t2$elapse.time, video.data.d1t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d1t3=subset(all.lob,deploy=="1"&trap=="3") lines(video.data.d1t3$elapse.time, video.data.d1t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 2 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels="") vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"2",cex=2) video.data.d2t1=subset(all.lob,deploy=="2"&trap=="1") lines(video.data.d2t1$elapse.time, video.data.d2t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d2t2=subset(all.lob,deploy=="2"&trap=="2") lines(video.data.d2t2$elapse.time, video.data.d2t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d2t3=subset(all.lob,deploy=="2"&trap=="3") lines(video.data.d2t3$elapse.time, video.data.d2t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 3 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels="") vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"3",cex=2) video.data.d3t1=subset(all.lob,deploy=="3"&trap=="1") lines(video.data.d3t1$elapse.time, video.data.d3t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d3t2=subset(all.lob,deploy=="3"&trap=="2") lines(video.data.d3t2$elapse.time, video.data.d3t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d3t3=subset(all.lob,deploy=="3"&trap=="3") lines(video.data.d3t3$elapse.time, video.data.d3t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 4 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels="") vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"4",cex=2) video.data.d4t1=subset(all.lob,deploy=="4"&trap=="1") lines(video.data.d4t1$elapse.time, video.data.d4t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d4t2=subset(all.lob,deploy=="4"&trap=="2") lines(video.data.d4t2$elapse.time, video.data.d4t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d4t3=subset(all.lob,deploy=="4"&trap=="3") lines(video.data.d4t3$elapse.time, video.data.d4t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 5######lump deploy 5-6 (LHE) xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels="") vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(45000,12,"5/6",cex=2) video.data.d5t1=subset(all.lob,deploy=="5"&trap=="1") lines(video.data.d5t1$elapse.time, video.data.d5t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d5t2=subset(all.lob,deploy=="5"&trap=="2") lines(video.data.d5t2$elapse.time, video.data.d5t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d5t3=subset(all.lob,deploy=="5"&trap=="3") lines(video.data.d5t3$elapse.time, video.data.d5t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") video.data.d6t1=subset(all.lob,deploy=="6"&trap=="1") lines(video.data.d6t1$elapse.time, video.data.d6t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d6t2=subset(all.lob,deploy=="6"&trap=="2") lines(video.data.d6t2$elapse.time, video.data.d6t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d6t3=subset(all.lob,deploy=="6"&trap=="3") lines(video.data.d6t3$elapse.time, video.data.d6t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 7 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels="") vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"7",cex=2) video.data.d7t1=subset(all.lob,deploy=="7"&trap=="1") lines(video.data.d7t1$elapse.time, video.data.d7t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d7t2=subset(all.lob,deploy=="7"&trap=="2") lines(video.data.d7t2$elapse.time, video.data.d7t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d7t3=subset(all.lob,deploy=="7"&trap=="3") lines(video.data.d7t3$elapse.time, video.data.d7t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 8 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"8",cex=2) video.data.d8t1=subset(all.lob,deploy=="8"&trap=="1") lines(video.data.d8t1$elapse.time, video.data.d8t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d8t2=subset(all.lob,deploy=="8"&trap=="2") lines(video.data.d8t2$elapse.time, video.data.d8t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d8t3=subset(all.lob,deploy=="8"&trap=="3") lines(video.data.d8t3$elapse.time, video.data.d8t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 9 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"9",cex=2) video.data.d9t1=subset(all.lob,deploy=="9"&trap=="1") lines(video.data.d9t1$elapse.time, video.data.d9t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d9t2=subset(all.lob,deploy=="9"&trap=="2") lines(video.data.d9t2$elapse.time, video.data.d9t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d9t3=subset(all.lob,deploy=="9"&trap=="3") lines(video.data.d9t3$elapse.time, video.data.d9t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 10 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"10",cex=2) video.data.d10t1=subset(all.lob,deploy=="10"&trap=="1") lines(video.data.d10t1$elapse.time, video.data.d10t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d10t2=subset(all.lob,deploy=="10"&trap=="2") lines(video.data.d10t2$elapse.time, video.data.d10t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d10t3=subset(all.lob,deploy=="10"&trap=="3") lines(video.data.d10t3$elapse.time, video.data.d10t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 11 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"11",cex=2) video.data.d11t1=subset(all.lob,deploy=="11"&trap=="1") lines(video.data.d11t1$elapse.time, video.data.d11t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d11t2=subset(all.lob,deploy=="11"&trap=="2") lines(video.data.d11t2$elapse.time, video.data.d11t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d11t3=subset(all.lob,deploy=="11"&trap=="3") lines(video.data.d11t3$elapse.time, video.data.d11t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 12 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"12",cex=2) video.data.d12t1=subset(all.lob,deploy=="12"&trap=="1") lines(video.data.d12t1$elapse.time, video.data.d12t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d12t2=subset(all.lob,deploy=="12"&trap=="2") lines(video.data.d12t2$elapse.time, video.data.d12t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d12t3=subset(all.lob,deploy=="12"&trap=="3") lines(video.data.d12t3$elapse.time, video.data.d12t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 13 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"13",cex=2) video.data.d13t1=subset(all.lob,deploy=="13"&trap=="1") lines(video.data.d13t1$elapse.time, video.data.d13t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d13t2=subset(all.lob,deploy=="13"&trap=="2") lines(video.data.d13t2$elapse.time, video.data.d13t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d13t3=subset(all.lob,deploy=="13"&trap=="3") lines(video.data.d13t3$elapse.time, video.data.d13t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 14 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"14",cex=2) video.data.d14t1=subset(all.lob,deploy=="14"&trap=="1") lines(video.data.d14t1$elapse.time, video.data.d14t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d14t2=subset(all.lob,deploy=="14"&trap=="2") lines(video.data.d14t2$elapse.time, video.data.d14t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d14t3=subset(all.lob,deploy=="14"&trap=="3") lines(video.data.d14t3$elapse.time, video.data.d14t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 15 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"15",cex=2) video.data.d15t1=subset(all.lob,deploy=="15"&trap=="1") lines(video.data.d15t1$elapse.time, video.data.d15t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d15t2=subset(all.lob,deploy=="15"&trap=="2") lines(video.data.d15t2$elapse.time, video.data.d15t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d15t3=subset(all.lob,deploy=="15"&trap=="3") lines(video.data.d15t3$elapse.time, video.data.d15t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 16 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"16",cex=2) video.data.d16t1=subset(all.lob,deploy=="16"&trap=="1") lines(video.data.d16t1$elapse.time, video.data.d16t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d16t2=subset(all.lob,deploy=="16"&trap=="2") lines(video.data.d16t2$elapse.time, video.data.d16t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d16t3=subset(all.lob,deploy=="16"&trap=="3") lines(video.data.d16t3$elapse.time, video.data.d16t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 17 xrange4=range(0:46800) yrange4=range(all.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n",cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,12,"17",cex=2) video.data.d17t1=subset(all.lob,deploy=="17"&trap=="1") lines(video.data.d17t1$elapse.time, video.data.d17t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d17t2=subset(all.lob,deploy=="17"&trap=="2") lines(video.data.d17t2$elapse.time, video.data.d17t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d17t3=subset(all.lob,deploy=="17"&trap=="3") lines(video.data.d17t3$elapse.time, video.data.d17t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") dev.off() ########################################################################################################################################################## ########################################################################################################################################################## png(filename="Figure 7.png", width=2500, height=2000, res=200) par(mfrow=c(4,4)) par(mar=c(0,0,0,0),oma=c(5,5,1,1)) xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"1",cex=2) legend(500,28,c("Unstocked","GC","RC"),lty=c(1,1,1),lwd=c(2.5,2.5,2.5),col=c("dodgerblue4","chartreuse4","darkred"),cex=1.7) #####add labels to outside margin##### (Bottom,Left,Top,Right) mtext("Elapsed soak time (hours)",1,3,outer=TRUE,cex=1.5) mtext("Number of lobsters inside the trap",2,3, outer=TRUE,cex=1.5) ##deploy 1 video.data.d1t1=subset(large.lob,deploy=="1"&trap=="1") lines(video.data.d1t1$elapse.time, video.data.d1t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d1t2=subset(large.lob,deploy=="1"&trap=="2") lines(video.data.d1t2$elapse.time, video.data.d1t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d1t3=subset(large.lob,deploy=="1"&trap=="3") lines(video.data.d1t3$elapse.time, video.data.d1t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 2 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"2",cex=2) video.data.d2t1=subset(large.lob,deploy=="2"&trap=="1") lines(video.data.d2t1$elapse.time, video.data.d2t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d2t2=subset(large.lob,deploy=="2"&trap=="2") lines(video.data.d2t2$elapse.time, video.data.d2t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d2t3=subset(large.lob,deploy=="2"&trap=="3") lines(video.data.d2t3$elapse.time, video.data.d2t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 3 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"3",cex=2) video.data.d3t1=subset(large.lob,deploy=="3"&trap=="1") lines(video.data.d3t1$elapse.time, video.data.d3t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d3t2=subset(large.lob,deploy=="3"&trap=="2") lines(video.data.d3t2$elapse.time, video.data.d3t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d3t3=subset(large.lob,deploy=="3"&trap=="3") lines(video.data.d3t3$elapse.time, video.data.d3t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 4 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"4",cex=2) video.data.d4t1=subset(large.lob,deploy=="4"&trap=="1") lines(video.data.d4t1$elapse.time, video.data.d4t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d4t2=subset(large.lob,deploy=="4"&trap=="2") lines(video.data.d4t2$elapse.time, video.data.d4t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d4t3=subset(large.lob,deploy=="4"&trap=="3") lines(video.data.d4t3$elapse.time, video.data.d4t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 5 and deploy 6 ######### xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(45000,25,"5/6",cex=2) video.data.d5t1=subset(large.lob,deploy=="5"&trap=="1") lines(video.data.d5t1$elapse.time, video.data.d5t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d5t2=subset(large.lob,deploy=="5"&trap=="2") lines(video.data.d5t2$elapse.time, video.data.d5t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d5t3=subset(large.lob,deploy=="5"&trap=="3") lines(video.data.d5t3$elapse.time, video.data.d5t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") video.data.d6t1=subset(large.lob,deploy=="6"&trap=="1") lines(video.data.d6t1$elapse.time, video.data.d6t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d6t2=subset(large.lob,deploy=="6"&trap=="2") lines(video.data.d6t2$elapse.time, video.data.d6t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d6t3=subset(large.lob,deploy=="6"&trap=="3") lines(video.data.d6t3$elapse.time, video.data.d6t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 7 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"7",cex=2) video.data.d7t1=subset(large.lob,deploy=="7"&trap=="1") lines(video.data.d7t1$elapse.time, video.data.d7t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d7t2=subset(large.lob,deploy=="7"&trap=="2") lines(video.data.d7t2$elapse.time, video.data.d7t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d7t3=subset(large.lob,deploy=="7"&trap=="3") lines(video.data.d7t3$elapse.time, video.data.d7t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 8 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"8",cex=2) video.data.d8t1=subset(large.lob,deploy=="8"&trap=="1") lines(video.data.d8t1$elapse.time, video.data.d8t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d8t2=subset(large.lob,deploy=="8"&trap=="2") lines(video.data.d8t2$elapse.time, video.data.d8t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d8t3=subset(large.lob,deploy=="8"&trap=="3") lines(video.data.d8t3$elapse.time, video.data.d8t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 9 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"9",cex=2) video.data.d9t1=subset(large.lob,deploy=="9"&trap=="1") lines(video.data.d9t1$elapse.time, video.data.d9t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d9t2=subset(large.lob,deploy=="9"&trap=="2") lines(video.data.d9t2$elapse.time, video.data.d9t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d9t3=subset(large.lob,deploy=="9"&trap=="3") lines(video.data.d9t3$elapse.time, video.data.d9t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 10 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"10",cex=2) video.data.d10t1=subset(large.lob,deploy=="10"&trap=="1") lines(video.data.d10t1$elapse.time, video.data.d10t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d10t2=subset(large.lob,deploy=="10"&trap=="2") lines(video.data.d10t2$elapse.time, video.data.d10t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d10t3=subset(large.lob,deploy=="10"&trap=="3") lines(video.data.d10t3$elapse.time, video.data.d10t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 11 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"11",cex=2) video.data.d11t1=subset(large.lob,deploy=="11"&trap=="1") lines(video.data.d11t1$elapse.time, video.data.d11t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d11t2=subset(large.lob,deploy=="11"&trap=="2") lines(video.data.d11t2$elapse.time, video.data.d11t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d11t3=subset(large.lob,deploy=="11"&trap=="3") lines(video.data.d11t3$elapse.time, video.data.d11t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 12 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"12",cex=2) video.data.d12t1=subset(large.lob,deploy=="12"&trap=="1") lines(video.data.d12t1$elapse.time, video.data.d12t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d12t2=subset(large.lob,deploy=="12"&trap=="2") lines(video.data.d12t2$elapse.time, video.data.d12t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d12t3=subset(large.lob,deploy=="12"&trap=="3") lines(video.data.d12t3$elapse.time, video.data.d12t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 13 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels='') vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"13",cex=2) video.data.d13t1=subset(large.lob,deploy=="13"&trap=="1") lines(video.data.d13t1$elapse.time, video.data.d13t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d13t2=subset(large.lob,deploy=="13"&trap=="2") lines(video.data.d13t2$elapse.time, video.data.d13t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d13t3=subset(large.lob,deploy=="13"&trap=="3") lines(video.data.d13t3$elapse.time, video.data.d13t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 14 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',cex.axis=1.3) axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"14",cex=2) video.data.d14t1=subset(large.lob,deploy=="14"&trap=="1") lines(video.data.d14t1$elapse.time, video.data.d14t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d14t2=subset(large.lob,deploy=="14"&trap=="2") lines(video.data.d14t2$elapse.time, video.data.d14t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d14t3=subset(large.lob,deploy=="14"&trap=="3") lines(video.data.d14t3$elapse.time, video.data.d14t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 15 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"15",cex=2) video.data.d15t1=subset(large.lob,deploy=="15"&trap=="1") lines(video.data.d15t1$elapse.time, video.data.d15t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d15t2=subset(large.lob,deploy=="15"&trap=="2") lines(video.data.d15t2$elapse.time, video.data.d15t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d15t3=subset(large.lob,deploy=="15"&trap=="3") lines(video.data.d15t3$elapse.time, video.data.d15t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 16 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"16",cex=2) video.data.d16t1=subset(large.lob,deploy=="16"&trap=="1") lines(video.data.d16t1$elapse.time, video.data.d16t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d16t2=subset(large.lob,deploy=="16"&trap=="2") lines(video.data.d16t2$elapse.time, video.data.d16t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d16t3=subset(large.lob,deploy=="16"&trap=="3") lines(video.data.d16t3$elapse.time, video.data.d16t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 17 xrange4=range(0:46800) yrange4=range(large.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n',yaxt="n") axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13),cex.axis=1.3) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) text(46000,25,"17",cex=2) video.data.d17t1=subset(large.lob,deploy=="17"&trap=="1") lines(video.data.d17t1$elapse.time, video.data.d17t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d17t2=subset(large.lob,deploy=="17"&trap=="2") lines(video.data.d17t2$elapse.time, video.data.d17t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d17t3=subset(large.lob,deploy=="17"&trap=="3") lines(video.data.d17t3$elapse.time, video.data.d17t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") dev.off() ############################################################################################################################################################################ ############################################################################################################################################################################ ###want to seperate small from large and do cumul counts## library(dplyr) library(plyr) small.lob=subset(video.data,!(direction%in%c("E"))) small.lob small.lob=mutate(small.lob,entry.lob=behaviour) small.lob$entry.lob=revalue(small.lob$entry.lob,c("start"="0","end"="0","S_attempt"="1","exit"="-1")) small.lob small.lob$entry.lob=as.numeric(levels(small.lob$entry.lob))[small.lob$entry.lob]####makes 0,-1,1 numeric small.lob=ddply(small.lob,"trap.code",transform,cumsum=cumsum(entry.lob)) small.lob ####plot small lob only according to deployment##### par(mfrow=c(4,4)) par(mar=c(3,3,0,0),oma=c(5,5,1,1)) xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) #####add labels to outside margin##### (Bottom,Left,Top,Right) mtext("Elapsed soak time (hours)",1,3,outer=TRUE,cex=1.5) mtext("Number of lobsters inside the trap",2,3, outer=TRUE,cex=1.5) ##deploy 1 video.data.d1t1=subset(small.lob,deploy=="1"&trap=="1") lines(video.data.d1t1$elapse.time, video.data.d1t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d1t2=subset(small.lob,deploy=="1"&trap=="2") lines(video.data.d1t2$elapse.time, video.data.d1t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d1t3=subset(small.lob,deploy=="1"&trap=="3") lines(video.data.d1t3$elapse.time, video.data.d1t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 2 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d2t1=subset(small.lob,deploy=="2"&trap=="1") lines(video.data.d2t1$elapse.time, video.data.d2t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d2t2=subset(small.lob,deploy=="2"&trap=="2") lines(video.data.d2t2$elapse.time, video.data.d2t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d2t3=subset(small.lob,deploy=="2"&trap=="3") lines(video.data.d2t3$elapse.time, video.data.d2t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 3 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d3t1=subset(small.lob,deploy=="3"&trap=="1") lines(video.data.d3t1$elapse.time, video.data.d3t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d3t2=subset(small.lob,deploy=="3"&trap=="2") lines(video.data.d3t2$elapse.time, video.data.d3t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d3t3=subset(small.lob,deploy=="3"&trap=="3") lines(video.data.d3t3$elapse.time, video.data.d3t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 4 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d4t1=subset(small.lob,deploy=="4"&trap=="1") lines(video.data.d4t1$elapse.time, video.data.d4t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d4t2=subset(small.lob,deploy=="4"&trap=="2") lines(video.data.d4t2$elapse.time, video.data.d4t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d4t3=subset(small.lob,deploy=="4"&trap=="3") lines(video.data.d4t3$elapse.time, video.data.d4t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 5######lump deploy 5-6 (LHE) xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d5t1=subset(small.lob,deploy=="5"&trap=="1") lines(video.data.d5t1$elapse.time, video.data.d5t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d5t2=subset(small.lob,deploy=="5"&trap=="2") lines(video.data.d5t2$elapse.time, video.data.d5t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d5t3=subset(small.lob,deploy=="5"&trap=="3") lines(video.data.d5t3$elapse.time, video.data.d5t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") video.data.d6t1=subset(small.lob,deploy=="6"&trap=="1") lines(video.data.d6t1$elapse.time, video.data.d6t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d6t2=subset(small.lob,deploy=="6"&trap=="2") lines(video.data.d6t2$elapse.time, video.data.d6t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d6t3=subset(small.lob,deploy=="6"&trap=="3") lines(video.data.d6t3$elapse.time, video.data.d6t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 7 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d7t1=subset(small.lob,deploy=="7"&trap=="1") lines(video.data.d7t1$elapse.time, video.data.d7t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d7t2=subset(small.lob,deploy=="7"&trap=="2") lines(video.data.d7t2$elapse.time, video.data.d7t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d7t3=subset(small.lob,deploy=="7"&trap=="3") lines(video.data.d7t3$elapse.time, video.data.d7t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 8 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d8t1=subset(small.lob,deploy=="8"&trap=="1") lines(video.data.d8t1$elapse.time, video.data.d8t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d8t2=subset(small.lob,deploy=="8"&trap=="2") lines(video.data.d8t2$elapse.time, video.data.d8t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d8t3=subset(small.lob,deploy=="8"&trap=="3") lines(video.data.d8t3$elapse.time, video.data.d8t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 9 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d9t1=subset(small.lob,deploy=="9"&trap=="1") lines(video.data.d9t1$elapse.time, video.data.d9t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d9t2=subset(small.lob,deploy=="9"&trap=="2") lines(video.data.d9t2$elapse.time, video.data.d9t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d9t3=subset(small.lob,deploy=="9"&trap=="3") lines(video.data.d9t3$elapse.time, video.data.d9t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 10 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d10t1=subset(small.lob,deploy=="10"&trap=="1") lines(video.data.d10t1$elapse.time, video.data.d10t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d10t2=subset(small.lob,deploy=="10"&trap=="2") lines(video.data.d10t2$elapse.time, video.data.d10t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d10t3=subset(small.lob,deploy=="10"&trap=="3") lines(video.data.d10t3$elapse.time, video.data.d10t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 11 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d11t1=subset(small.lob,deploy=="11"&trap=="1") lines(video.data.d11t1$elapse.time, video.data.d11t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d11t2=subset(small.lob,deploy=="11"&trap=="2") lines(video.data.d11t2$elapse.time, video.data.d11t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d11t3=subset(small.lob,deploy=="11"&trap=="3") lines(video.data.d11t3$elapse.time, video.data.d11t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 12 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d12t1=subset(small.lob,deploy=="12"&trap=="1") lines(video.data.d12t1$elapse.time, video.data.d12t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d12t2=subset(small.lob,deploy=="12"&trap=="2") lines(video.data.d12t2$elapse.time, video.data.d12t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d12t3=subset(small.lob,deploy=="12"&trap=="3") lines(video.data.d12t3$elapse.time, video.data.d12t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 13 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d13t1=subset(small.lob,deploy=="13"&trap=="1") lines(video.data.d13t1$elapse.time, video.data.d13t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d13t2=subset(small.lob,deploy=="13"&trap=="2") lines(video.data.d13t2$elapse.time, video.data.d13t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d13t3=subset(small.lob,deploy=="13"&trap=="3") lines(video.data.d13t3$elapse.time, video.data.d13t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 14 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d14t1=subset(small.lob,deploy=="14"&trap=="1") lines(video.data.d14t1$elapse.time, video.data.d14t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d14t2=subset(small.lob,deploy=="14"&trap=="2") lines(video.data.d14t2$elapse.time, video.data.d14t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d14t3=subset(small.lob,deploy=="14"&trap=="3") lines(video.data.d14t3$elapse.time, video.data.d14t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 15 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d15t1=subset(small.lob,deploy=="15"&trap=="1") lines(video.data.d15t1$elapse.time, video.data.d15t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d15t2=subset(small.lob,deploy=="15"&trap=="2") lines(video.data.d15t2$elapse.time, video.data.d15t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d15t3=subset(small.lob,deploy=="15"&trap=="3") lines(video.data.d15t3$elapse.time, video.data.d15t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 16 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d16t1=subset(small.lob,deploy=="16"&trap=="1") lines(video.data.d16t1$elapse.time, video.data.d16t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d16t2=subset(small.lob,deploy=="16"&trap=="2") lines(video.data.d16t2$elapse.time, video.data.d16t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d16t3=subset(small.lob,deploy=="16"&trap=="3") lines(video.data.d16t3$elapse.time, video.data.d16t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4") ###deploy 17 xrange4=range(0:46800) yrange4=range(small.lob$cumsum) plot(xrange4,yrange4,type="n",xlab="",ylab="",xaxt='n') axis(1,at=c(0,3600, 7200, 10800, 14400, 18000, 21600, 25200, 28800, 32400, 36000,39600,43200,46800),labels=c(0:13)) vertjitter=sample(c(-.2, -.1, 0, 0.1, 0.2), size=1) video.data.d17t1=subset(small.lob,deploy=="17"&trap=="1") lines(video.data.d17t1$elapse.time, video.data.d17t1$cumsum+vertjitter, type="l", lwd=2.5, col="chartreuse4") video.data.d17t2=subset(small.lob,deploy=="17"&trap=="2") lines(video.data.d17t2$elapse.time, video.data.d17t2$cumsum+vertjitter, type="l", lwd=2.5, col="darkred") video.data.d17t3=subset(small.lob,deploy=="17"&trap=="3") lines(video.data.d17t3$elapse.time, video.data.d17t3$cumsum+vertjitter, type="l", lwd=2.5, col="dodgerblue4")