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plotFunction.r
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# Plotting methods for the MizerSim class
library(gridBase) # to get grid.newpage
# Biomass through time
#' Plot the biomass of each species through time
#'
#' After running a projection, the biomass of each species can be plotted against time. The biomass is calculated within user defined size limits (see \code{\link{getBiomass}}).
#' This plot is pretty easy to do by hand. It just gets the biomass using the \code{\link{getBiomass}} method and plots using the ggplot2 package. You can then fiddle about with colours and linetypes etc. Just look at the source code for details.
plotBiomass <- function(object, print_it=TRUE, start_time=as.numeric(dimnames(object@n)[[1]][1]), end_time = as.numeric(dimnames(object@n)[[1]][dim(object@n)[1]]), ...){
b <- getBiomass(object, ...)
names(dimnames(b))[names(dimnames(b))=="sp"] <- "Species"
if(start_time >= end_time){
stop("start_time must be less than end_time")
}
b <- b[(as.numeric(dimnames(b)[[1]]) >= start_time) & (as.numeric(dimnames(b)[[1]]) <= end_time),,drop=FALSE]
bm <- melt(b)
# Force Species column to be a character (if numbers used - may be interpreted as integer and hence continuous)
bm$species <- as.character(bm$species)
# Due to log10, need to set a minimum value, seems like a feature in ggplot
min_value <- 1e-300
bm <- bm[bm$value >= min_value,]
p <- ggplot(bm) +
geom_line(aes(x=time,y=value, colour=species, linetype=species)) +
scale_y_continuous(trans="log10", name="Biomass") +
scale_x_continuous(name="Time") +
theme(legend.title=element_text(),panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"),legend.position="none",
legend.justification=c(1,1),legend.key = element_rect(fill = "white"))+
ggtitle(NULL)
if (nrow(object@params@species_params)>12){
p <- ggplot(bm) + geom_line(aes(x=time,y=value, group=species)) + scale_y_continuous(trans="log10", name="Biomass") + scale_x_continuous(name="Time")
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Plot the total yield of each species through time
#'
#' After running a projection, the total yield of each species across all
#' fishing gears can be plotted against time.
#' This plot is pretty easy to do by hand. It just gets the biomass using the \code{\link{getYield}} method and plots using the ggplot2 package. You can then fiddle about with colours and linetypes etc. Just look at the source code for details.
plotYield <- function(object, print_it = TRUE, ...){
y <- getYield(object, ...)
names(dimnames(y))[names(dimnames(y))=="sp"] <- "Species"
ym <- melt(y)
p <- ggplot(ym) + geom_line(aes(x=time,y=value, colour=Species, linetype=Species)) + scale_y_continuous(trans="log10", name="Yield") + scale_x_continuous(name="Time")
if (nrow(object@params@species_params)>12){
p <- ggplot(ym) + geom_line(aes(x=time,y=value, group=Species)) + scale_y_continuous(trans="log10", name="Yield") + scale_x_continuous(name="Time")
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Plot the total yield of each species by gear through time
#'
#' After running a projection, the total yield of each species by
#' fishing gear can be plotted against time.
#' This plot is pretty easy to do by hand. It just gets the biomass using the \code{\link{getYieldGear}} method and plots using the ggplot2 package. You can then fiddle about with colours and linetypes etc. Just look at the source code for details.
plotYieldGear <- function(object, print_it=TRUE, ...){
y <- getYieldGear(object, ...)
names(dimnames(y))[names(dimnames(y))=="sp"] <- "Species"
ym <- melt(y)
p <- ggplot(ym) + geom_line(aes(x=time,y=value, colour=Species, linetype=gear)) + scale_y_continuous(trans="log10", name="Yield") + scale_x_continuous(name="Time")
if (nrow(object@params@species_params)>12){
p <- ggplot(ym) + geom_line(aes(x=time,y=value, group=Species)) + scale_y_continuous(trans="log10", name="Yield") + scale_x_continuous(name="Time")
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Plot the abundance spectra of each species and the background population
#'
#' After running a projection, the spectra of the abundance of each species and the background population can be plotted.
#' The abundance is averaged over the specified time range (a single value for the time range can be used to plot a single time step).
#' The abundance can be in terms of numbers or biomass, depending on the \code{biomass} argument.
plotSpectra <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), min_w =min(object@params@w)/100, biomass = TRUE, print_it = TRUE, ...){
time_elements <- get_time_elements(object,time_range)
spec_n <- apply(object@n[time_elements,,,drop=FALSE],c(2,3), mean)
background_n <- apply(object@n_pp[time_elements,,drop=FALSE],2,mean)
y_axis_name = "Abundance"
if (biomass){
spec_n <- sweep(spec_n,2,object@params@w,"*")
background_n <- background_n * object@params@w_full
y_axis_name = "Biomass"
}
# Make data.frame for plot
plot_dat <- data.frame(value = c(spec_n), Species = dimnames(spec_n)[[1]], w = rep(object@params@w, each=nrow(object@params@species_params)))
plot_dat <- rbind(plot_dat, data.frame(value = c(background_n), Species = "Background", w = object@params@w_full))
# lop off 0s in background and apply min_w
plot_dat <- plot_dat[(plot_dat$value > 0) & (plot_dat$w >= min_w),]
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, colour = Species, linetype=Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = y_axis_name, trans="log10")
if (nrow(object@params@species_params)>12){
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, group = Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = y_axis_name, trans="log10")
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Plot the feeding level of each species by size
#'
#' After running a projection, plot the feeding level of each species by size.
#' The feeding level is averaged over the specified time range (a single value for the time range can be used).
plotFeedingLevel <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), print_it = TRUE, ...){
feed_time <- getFeedingLevel(object=object, time_range=time_range, drop=FALSE, ...)
feed <- apply(feed_time, c(2,3), mean)
plot_dat <- data.frame(value = c(feed), Species = dimnames(feed)[[1]], w = rep(object@params@w, each=nrow(object@params@species_params)))
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, colour = Species, linetype=Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = "Feeding Level", lim=c(0,1))
if (nrow(object@params@species_params)>12){
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, group = Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = "Feeding Level", lim=c(0,1))
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Plot M2 of each species by size
#'
#' After running a projection, plot M2 of each species by size.
#' M2 is averaged over the specified time range (a single value for the time range can be used to plot a single time step).
plotM2 <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), print_it = TRUE, ...){
m2_time <- getM2(object, time_range=time_range, drop=FALSE, ...)
m2 <- apply(m2_time, c(2,3), mean)
plot_dat <- data.frame(value = c(m2), Species = dimnames(m2)[[1]], w = rep(object@params@w, each=nrow(object@params@species_params)))
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, colour = Species, linetype=Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = "M2", lim=c(0,max(plot_dat$value)))
if (nrow(object@params@species_params)>12){
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, group = Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = "M2", lim=c(0,max(plot_dat$value)))
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Plot total fishing mortality of each species by size
#'
#' After running a projection, plot the total fishing mortality of each species by size.
#' The total fishing mortality is averaged over the specified time range (a single value for the time range can be used to plot a single time step).
plotFMort <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), print_it = TRUE, ...){
f_time <- getFMort(object, time_range=time_range, drop=FALSE, ...)
f <- apply(f_time, c(2,3), mean)
plot_dat <- data.frame(value = c(f), Species = dimnames(f)[[1]], w = rep(object@params@w, each=nrow(object@params@species_params)))
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, colour = Species, linetype=Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = "Total fishing mortality", lim=c(0,max(plot_dat$value)))
if (nrow(object@params@species_params)>12){
p <- ggplot(plot_dat) + geom_line(aes(x=w, y = value, group = Species)) + scale_x_continuous(name = "Size", trans="log10") + scale_y_continuous(name = "Total fishing mortality", lim=c(0,max(plot_dat$value)))
}
if (print_it)
print(p)
return(p)
}
#---------------------------------------------------------------------------------------------------------------------
#' Summary plot for \code{MizerSim} objects
#'
#' After running a projection, produces 5 plots in the same window:
#' feeding level, abundance spectra, predation mortality and fishing
#' mortality of each species by size; and biomass of each species through
#' time.
#' This method just uses the other plotting methods and puts them
#' all in one window.
plotSummary <- function(x, ...){
p1 <- plotFeedingLevel(x,print_it = FALSE,...)
p2 <- plotSpectra(x,print_it = FALSE,...)
p3 <- plotBiomass(x,print_it = FALSE,...)
p4 <- plotM2(x,print_it = FALSE,...)
p5 <- plotFMort(x,print_it = FALSE,...)
grid.newpage()
glayout <- grid.layout(3,2) # widths and heights arguments
vp <- viewport(layout = glayout)
pushViewport(vp)
vplayout <- function(x,y)
viewport(layout.pos.row=x, layout.pos.col = y)
print(p1+ theme(legend.position="none"), vp = vplayout(1,1))
print(p3+ theme(legend.position="none"), vp = vplayout(1,2))
print(p4+ theme(legend.position="none"), vp = vplayout(2,1))
print(p5+ theme(legend.position="none"), vp = vplayout(2,2))
print(p2+ theme(legend.position="right", legend.key.size=unit(0.1,"cm")), vp = vplayout(3,1:2))
}
# My plots --------------------------------------------------------------------------------------------------------
# plot biomass
plotDynamics <- function(object, phenotype = TRUE, bloodline = NULL, light = FALSE, print_it = T){
cbPalette <- c("#999999","#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") #9 colors for colorblind
biomass <- getBiomass(object) # n * w * dw and sum by species
SpIdx = NULL # getting rid of the species that went extinct at the beginning
for (i in unique(object@params@species_params$species))
if (sum(biomass[,i]) != 0 & dim(object@params@species_params[object@params@species_params$species == i,])[1] != 1)
SpIdx = c(SpIdx,i)
biomassTot = NULL
biomassTemp = biomass
colnames(biomassTemp) = object@params@species_params$species
for (i in SpIdx)
{
biomassSp = biomassTemp[,which(colnames(biomassTemp) == i)]
biomassSp = apply(biomassSp,1,sum)
biomassTot = cbind(biomassTot,biomassSp)
}
colnames(biomassTot) = SpIdx
# biomassTot is the biomass of species
# biomass is the biomass of phenotypes
plotBiom <- function(x,light)
{
Biom <- melt(x) # melt for ggplot
names(Biom) = list("time","sp","value")
# Due to log10, need to set a minimum value, seems like a feature in ggplot
min_value <- 1e-300
Biom <- Biom[Biom$value >= min_value,]
# take the first digit of the species column and put it in a new column
Biom$bloodline = sapply(Biom[,2], function(x) as.numeric(unlist(strsplit(as.character(x), "")))[1])
if(light) Biom= Biom[seq(1,dim(Biom)[1],2),] # if the data is too heavy and it takes to much time
return(Biom)
}
BiomPhen = NULL
if (phenotype) BiomPhen <- plotBiom(x = biomass,light =light)
BiomSp <- plotBiom(biomassTot,light)
# colourCount = length(unique(Biom$sp))
# getPalette = colorRampPalette(brewer.pal(9, "Set1"))# increase the number of colors used
if (is.null(bloodline))
{
p <-
ggplot(BiomSp) +
geom_line(aes(x = time, y = value, colour = as.factor(bloodline), group = sp), size = 1.2) +
geom_line(data = BiomPhen, aes(x = time, y = value, colour = as.factor(bloodline), group = sp), alpha = 0.2) +
scale_y_log10(name = "Biomass in g.m^-3", limits = c(1e-11, NA), breaks = c(1 %o% 10^(-11:-1))) +
scale_x_continuous(name = "Time in years") +
#geom_hline(yintercept = 1e-11) +
#geom_text(aes(0,1e-11,label = "Extinction threshold", vjust = 1, hjust = -0.15), size = 2.5) +
labs(color='Species') +
theme(legend.key = element_rect(fill = "white"))+
theme_bw()+
scale_colour_manual(values=cbPalette)+ # colorblind
#scale_color_grey(name = "Species")+ # grey
ggtitle("Community biomass")
}
else
{
Sbm= Sbm[Sbm$bloodline == bloodline,]
p <-
ggplot(Sbm) +
geom_line(aes(x = time, y = value, colour = as.factor(sp), group = sp)) +#, linetype = sp)) +
scale_y_log10(name = "Biomass in g.m^-3", limits = c(1e-11, NA), breaks = c(1 %o% 10^(-11:-1))) +
scale_x_continuous(name = "Time in years") +
labs(color='Species') +
theme(legend.key = element_rect(fill = "white"))+
theme_bw()+
scale_color_grey()+
ggtitle("Community biomass")
}
if(print_it) print(p)
return(p)
}
plotDynamicsMulti <- function(folder)
{
cbPalette <- c("#999999","#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") #9 colors for colorblind
biom <-function(object,phenotype=T)
{
biomass <- getBiomass(object) # n * w * dw and sum by species
SpIdx = NULL # getting rid of the species that went extinct at the beginning
for (i in unique(object@params@species_params$species))
if (sum(biomass[,i]) != 0 & dim(object@params@species_params[object@params@species_params$species == i,])[1] != 1)
SpIdx = c(SpIdx,i)
biomassTot = NULL
biomassTemp = biomass
colnames(biomassTemp) = object@params@species_params$species
for (i in SpIdx)
{
biomassSp = biomassTemp[,which(colnames(biomassTemp) == i)]
biomassSp = apply(biomassSp,1,sum)
biomassTot = cbind(biomassTot,biomassSp)
}
colnames(biomassTot) = SpIdx
# biomassTot is the biomass of species
# biomass is the biomass of phenotypes
plotB <- function(x)
{
Biom <- melt(x) # melt for ggplot
names(Biom) = list("time","sp","value")
# Due to log10, need to set a minimum value, seems like a feature in ggplot
min_value <- 1e-300
Biom <- Biom[Biom$value >= min_value,]
# take the first digit of the species column and put it in a new column
Biom$bloodline = sapply(Biom[,2], function(x) as.numeric(unlist(strsplit(as.character(x), "")))[1])
return(Biom)
}
BiomPhen = NULL
if (phenotype) BiomPhen <- plotB(x = biomass)
BiomSp <- plotB(biomassTot)
return(list(BiomSp,BiomPhen))
}
#NO FISHERIES PART
path_to_sim = paste(folder,"/normal",sep="")
bigSim <- bunchLoad(folder = path_to_sim)
# dirContent <- dir(path_to_sim)
# bigSim = list()
# for(i in 1:length(dirContent))
# {
# if (file.exists(paste(path_to_sim,"/",dirContent[i],"/run.Rdata",sep = "")))
# {
# sim <- get(load(paste(path_to_sim,"/",dirContent[i],"/run.Rdata",sep="")))
# } else {
# sim <- get(load(paste(path_to_sim,"/",dirContent[i],"/defaultRun.Rdata",sep="")))
# sim = superOpt(sim)
# }
# bigSim[[i]] = sim
# rm(sim)
# }
sim <- superStich(bigSim)
#top left corner biomass of 1 stochastic run without fisheries
plot_dat <- biom(bigSim[[1]])
p1 <- ggplot(plot_dat[[1]]) +
geom_line(aes(x = time, y = value, colour = as.factor(bloodline), group = sp), size = 1.2) +
geom_line(data = plot_dat[[2]], aes(x = time, y = value, colour = as.factor(bloodline), group = sp), alpha = 0.2) +
scale_y_log10(name = expression(paste("Biomass in g.m"^"-3")), limits = c(1e-7, NA), breaks = c(1 %o% 10^(-11:-1))) +
scale_x_continuous(name = NULL,labels = NULL) +
#scale_x_continuous(name = "Time in years") +
labs(color='Species') +
theme(legend.title=element_blank(),panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"), legend.position="none",
legend.justification=c(1,1),legend.key = element_rect(fill = "white"))+ #none remove legend
scale_colour_manual(values=cbPalette)+ # colorblind
ggtitle("a)")
# bottom left corner, biomass of averaged run without fisheries
plot_dat <- biom(sim,phenotype = F)
p2 <- ggplot(plot_dat[[1]]) +
geom_line(aes(x = time, y = value, colour = as.factor(bloodline), group = sp), size = 1.2) +
scale_y_log10(name = expression(paste("Biomass in g.m"^"-3")), limits = c(1e-05, NA), breaks = c(1 %o% 10^(-5:-1))) +
scale_x_continuous(name = "Time in years") +
labs(color='Species') +
theme(legend.title=element_blank(),panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"), legend.position="none",
legend.justification=c(1,1),legend.key = element_rect(fill = "white"))+ #none remove legend
scale_colour_manual(values=cbPalette)+ # colorblind
ggtitle("c)")
#FISHERIES PART
path_to_sim = paste(folder,"/fisheries",sep="")
bigSim <- bunchLoad(folder = path_to_sim)
# dirContent <- dir(path_to_sim)
# bigSim = list()
# for(i in 1:length(dirContent))
# {
# if (file.exists(paste(path_to_sim,"/",dirContent[i],"/run.Rdata",sep = "")))
# {
# sim <- get(load(paste(path_to_sim,"/",dirContent[i],"/run.Rdata",sep="")))
# } else {
# sim <- get(load(paste(path_to_sim,"/",dirContent[i],"/defaultRun.Rdata",sep="")))
# sim = superOpt(sim)
# }
# bigSim[[i]] = sim
# rm(sim)
# }
sim <- superStich(bigSim)
#top right corner biomass of 1 stochastic run with fisheries
plot_dat <- biom(bigSim[[1]])
p3 <- ggplot(plot_dat[[1]]) +
geom_line(aes(x = time, y = value, colour = as.factor(bloodline), group = sp), size = 1.2) +
geom_line(data = plot_dat[[2]], aes(x = time, y = value, colour = as.factor(bloodline), group = sp), alpha = 0.2) +
scale_y_log10(name = NULL, limits = c(1e-7, NA), labels = NULL, breaks = c(1 %o% 10^(-11:-1))) +
scale_x_continuous(name = NULL,labels = NULL) +
labs(color='Species') +
theme(legend.title=element_blank(),panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"), legend.position="none",
legend.justification=c(1,1),legend.key = element_rect(fill = "white"))+ #none remove legend
scale_colour_manual(values=cbPalette)+ # colorblind
ggtitle("b)")
# bottom left corner, biomass of averaged run without fisheries
plot_dat <- biom(sim,phenotype = F)
p4 <- ggplot(plot_dat[[1]]) +
geom_line(aes(x = time, y = value, colour = as.factor(bloodline), group = sp), size = 1.2) +
scale_y_log10(name = NULL, limits = c(1e-5, NA), labels = NULL, breaks = c(1 %o% 10^(-11:-1))) +
scale_x_continuous(name = "Time in years") +
labs(color='Species') +
theme(legend.title=element_blank(),panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"), legend.position="none",
legend.justification=c(1,1),legend.key = element_rect(fill = "white"))+ #none remove legend
scale_colour_manual(values=cbPalette)+ # colorblind
ggtitle("d)")
#multiplot
path_to_png = paste(folder,"/popDynamics.png",sep="")
png(filename=path_to_png,width = 20, height = 20, units = "cm",res = 600)
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow=2, ncol=2,
widths = unit(c(10,10), "cm"),
heights = unit(c(15,5), "cm"))))
print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1))
print(p2, vp = viewport(layout.pos.row = 2, layout.pos.col = 1))
print(p3, vp = viewport(layout.pos.row = 1, layout.pos.col = 2))
print(p4, vp = viewport(layout.pos.row = 2, layout.pos.col = 2))
dev.off()
}
# plot size spectrum
plotSS <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), min_w =min(object@params@w)/100, biomass = TRUE, print_it = TRUE, species = TRUE, ...){
cbPalette <- c("#999999","#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") #9 colors for colorblind
min_w = 0.001
time_elements <- get_time_elements(object,time_range)
spec_n <- apply(object@n[time_elements,,,drop=FALSE],c(2,3), mean)
background_n <- apply(object@n_pp[time_elements,,drop=FALSE],2,mean)
y_axis_name = "Abundance"
if (biomass){
spec_n <- sweep(spec_n,2,object@params@w,"*")
background_n <- background_n * object@params@w_full
y_axis_name = "Biomass"
}
# Make data.frame for plot
plot_datSP <- data.frame(value = c(spec_n), Species = dimnames(spec_n)[[1]], w = rep(object@params@w, each=nrow(object@params@species_params)), bloodline = object@params@species_params$species)
plot_datPkt <- data.frame(value = c(background_n), Species = "Background", w = object@params@w_full)
if (species)
{
dimnames(spec_n)$species = object@params@species_params$species
SpIdx = unique(object@params@species_params$species)
spec_sp = matrix(data = NA, ncol = dim(spec_n)[2], nrow = length(SpIdx), dimnames = list(as.character(SpIdx),dimnames(spec_n)$size))
names(dimnames(spec_sp))=list("species","size")
for (i in 1:dim(spec_sp)[1])
{
temp = spec_n # save to manip
temp[which(rownames(spec_n) != i), ] = 0 # make everything but the targeted species to go 0 to have correct normalisation
temp = apply(temp, 2, sum)
spec_sp[i, ] = temp
}
plot_datSP <- data.frame(value = c(spec_sp), Species = dimnames(spec_sp)[[1]], w = rep(object@params@w, each=length(SpIdx)))
}
# lop off 0s in background and apply min_w
plot_datSP <- plot_datSP[(plot_datSP$value > 0) & (plot_datSP$w >= min_w),]
plot_datPkt <- plot_datPkt[(plot_datPkt$value > 0) & (plot_datPkt$w >= min_w),]
#getPalette = colorRampPalette(brewer.pal(9, "Set1"))# increase the number of colors used
if (species)
{
p <- ggplot(plot_datSP) +
geom_line(aes(x=w, y = value, colour = as.factor(Species), group = Species)) +
geom_line(data = plot_datPkt, aes(x = w, y = value, linetype = Species), size = 1.5) +
scale_x_log10(name = "Size in g", breaks = c(1 %o% 10^(-6:5)))+
scale_y_log10(name = "Abundance density in individuals.m^-3", limits = c(1e-8,1e4)) +
theme(panel.background = element_blank(),legend.key = element_rect(fill = "white"))+
theme_bw()+
scale_colour_manual(values=cbPalette, name = "Species")+ # colorblind
#scale_color_grey(name = "Species")+ # grey
ggtitle("Size spectrum")
}
else
{
p <- ggplot(plot_datSP) +
geom_line(aes(x=w, y = value, colour = as.factor(bloodline), group = Species)) +
geom_line(data = plot_datPkt, aes(x = w, y = value, colour = Species), size = 1.5) +
scale_x_log10(name = "Size in g", breaks = c(1 %o% 10^(-6:5)))+
scale_y_log10(name = "Abundance density in individuals.m^-3", limits = c(1e-35,1e4)) +
theme(panel.background = element_blank(),legend.key = element_rect(fill = "white"))+
theme_bw()+
scale_colour_manual(values=cbPalette)+ # colorblind
#scale_color_grey(name = "Species")+ # grey
ggtitle("Size spectrum")
}
if (print_it)
print(p)
return(p)
}
# fisheries mortality
plotFM <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), print_it = TRUE, ...){
cbPalette <- c("#999999","#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") #9 colors for colorblind
f_time <- getFMort(object, time_range=time_range, drop=FALSE, ...)
f <- apply(f_time, c(2,3), mean)
plot_dat <- data.frame(value = c(f), Species = dimnames(f)[[1]], w = rep(object@params@w, each=nrow(object@params@species_params)))
# take the first digit of the species column and put it in a new column
plot_dat$bloodline = sapply(plot_dat$Species, function(x) as.numeric(unlist(strsplit(as.character(x), "")))[1])
p <- ggplot(plot_dat) +
geom_line(aes(x=w, y = value, group = bloodline,color = as.factor(bloodline))) +
scale_x_continuous(name = "Size", trans="log10", breaks = c(1 %o% 10^(-1:5)), limits = c(1e-2,1e5)) +
scale_y_continuous(name = "Total fishing mortality", lim=c(0,max(plot_dat$value))) +
theme(legend.title=element_text(),panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"), legend.position="none",
legend.justification=c(1,1),legend.key = element_rect(fill = "white"))+ #none remove legend
scale_colour_manual(values=cbPalette)+ # colorblind
ggtitle(NULL)
if (print_it)
print(p)
return(p)
}
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix specifying the layout. If present, 'cols' is ignored.
#
# If the layout is something like matrix(c(1,2,3,3), nrow=2, byrow=TRUE),
# then plot 1 will go in the upper left, 2 will go in the upper right, and
# 3 will go all the way across the bottom.
#
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel
# ncol: Number of columns of plots
# nrow: Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots==1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
# plot feeding / satiation level
plotFood <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), species = T, throughTime = T, start = 1000, every = 1000, print_it = T, ...){
cbPalette <- c("#999999","#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") #9 colors for colorblind
if (throughTime)
{
time_range = seq(start,max(as.numeric(dimnames(object@n)$time)),every)
time_range = c(time_range,max(as.numeric(dimnames(object@n)$time))) # so it counts the last time step which is probably not even
time_range = unique(time_range)
feeding = array(data = NA, dim = c(length(unique(object@params@species_params$species)),100,length(time_range)),
dimnames = list(as.character(unique(object@params@species_params$species)),object@params@w,time_range))
Critfeeding = matrix(data=NA, nrow = length(time_range), ncol= 100, dimnames = list(time_range,object@params@w))
for (i in time_range)
{
feed_time <- getFeedingLevel(object=object, time_range=i, drop=FALSE)#, ...) # get the feeding time
feed <- apply(feed_time, c(2,3), mean) # average on the time frame
Cfeed_time <- getCFeedingLevel(object=object, time_range=i, drop=FALSE)#, ...) # get the critical feeding level
Critfeed <- apply(Cfeed_time, c(2,3), mean) # average on the time frame
Critfeed <- Critfeed[1,] # all rows the same
dimnames(feed)$sp = object@params@species_params$species
SpIdx = unique(object@params@species_params$species) # get the species names
feed_sp = matrix(data = NA, ncol = dim(feed)[2], nrow = length(SpIdx), dimnames = list(as.character(SpIdx),dimnames(feed)$w)) # prepare the new object
names(dimnames(feed_sp))=list("species","size")
for (j in 1:dim(feed_sp)[1])
{
temp = feed # save to manip
temp[which(rownames(feed) != j), ] = 0 # keep the ecotypes from the species only
temp = apply(temp, 2, sum)
temp = temp / length(which(rownames(feed)==j)) # do the mean (in 2 steps)
feed_sp[j, ] = temp
}
feeding[,,which(dimnames(feeding)[[3]] == i)] = feed_sp
Critfeeding[which(dimnames(Critfeeding)[[1]] == i),] = Critfeed
}
a <- c(object@params@species_params$w_inf[1:9]) # to get vline of different col, need to create a data frame
vlines <- data.frame(xint = a,grp = c(1:9))
plot_dat = melt(feeding)
colnames(plot_dat) = c("species","size","time","value")
plot_crit = melt(Critfeeding)
colnames(plot_crit) = c("time","size","value")
p <- ggplot(plot_dat) +
geom_line(aes(x=size, y = value, colour = as.factor(species))) +
geom_line(data = plot_crit, aes(x = size, y = value), linetype = "dashed") +
scale_x_log10(name = "Size") +
scale_y_continuous(name = "Feeding Level", lim=c(0,1))+
geom_vline(data = vlines,aes(xintercept = xint,colour = as.factor(grp)), linetype = "dashed") +
facet_grid(time ~ .)+
scale_colour_manual(values=cbPalette, name = "Species")+ # colorblind
theme(panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"))+
ggtitle("Feeding level through time")
if(print_it) print(p)
return(p)
}
feed_time <- getFeedingLevel(object=object, time_range=time_range, drop=FALSE, ...) # get the feeding time
feed <- apply(feed_time, c(2,3), mean) # average on the time frame
if (species) # if I want to display species instead of ecotypes
{
dimnames(feed)$sp = object@params@species_params$species
SpIdx = unique(object@params@species_params$species) # get the species names
feed_sp = matrix(data = NA, ncol = dim(feed)[2], nrow = length(SpIdx), dimnames = list(as.character(SpIdx),dimnames(feed)$w)) # prepare the new object
names(dimnames(feed_sp))=list("species","size")
for (i in 1:dim(feed_sp)[1])
{
temp = feed # save to manip
temp[which(rownames(feed) != i), ] = 0 # keep the ecotypes from the species only
temp = apply(temp, 2, sum)
temp = temp / length(which(rownames(feed)==i)) # do the mean (in 2 steps)
feed_sp[i, ] = temp
}
feed = feed_sp
}
plot_dat <- data.frame(value = c(feed), Species = dimnames(feed)[[1]], w = rep(object@params@w, each=length(dimnames(feed)[[1]])))
name = paste("Feeding level at time",time_range,sep=" ")
p <- ggplot(plot_dat) +
geom_line(aes(x=w, y = value, colour = Species)) +
scale_x_continuous(name = "Size", trans="log10") +
scale_y_continuous(name = "Feeding Level", lim=c(0,1))+
ggtitle(name)
if(print_it) print(p)
return(p)
}
# plot of the number of ecotype per time step
PlotNoSp <- function(object, print_it = T){
if (is.list(object))
{
avgList = list()
for (x in 1:length(object))
{
sim = object[[x]]
SumPar = cbind(sim@params@species_params$pop,sim@params@species_params$extinct) # weird things happen without the as.numeric
colnames(SumPar) = c("Apparition","Extinction")
rownames(SumPar) = sim@params@species_params$ecotype
SumPar = SumPar[order(SumPar[,1],decreasing=FALSE),]
SumPar = as.data.frame(SumPar) # little dataframe with species apparition and extinction
for (i in 1:dim(SumPar)[1]) if (SumPar$Extinction[i] == 0) SumPar$Extinction[i] = sim@params@species_params$timeMax[1] # the not extinct ones get the end sim as extinction value
avg = matrix(data = 0, nrow = sim@params@species_params$timeMax[1], ncol =4)
avg[,1] = seq(1:sim@params@species_params$timeMax[1])*0.1
ApCount = length(which(SumPar$Apparition == 0)) # number of starting species
ExCount = 0
SumParEx = SumPar[order(SumPar$Extinction,decreasing=F),] #need this order for extinction in loop
for (i in 1:sim@params@species_params$timeMax[1]) # for each time step
{
for (j in 1:dim(SumPar)[1])
if (SumPar$Apparition[j] <= i & SumPar$Extinction[j] >= i) # how many phenotypes are alive right now?
avg[i,2] = avg[i,2] + 1
if (i %in% SumPar$Apparition) ApCount = ApCount + 1 # how many in total
avg[i,3] = ApCount
if (i %in% SumParEx$Extinction) ExCount = ExCount + 1 #hoe many extinct?
avg[i,4] = ExCount
}
avg = as.data.frame(avg)
dimnames(avg)[[2]] = list("time", "number","apparition","extinction") # dataframe of the number of species at each time step
avg = avg[seq(1,dim(avg)[1],10),]
avgList[[x]] = avg
}
# here I have a list of the demographic data of each sim
# now I need to do stats on them
#DemoMean = do.call(mean, )
#ans1 = aaply(laply(avgList, as.matrix), c(2, 3), mean)
avgMatrix <- abind(avgList, along=3) # convert the list in an array
avgMean = apply(avgMatrix, c(1,2), mean) # do the stat manip
avgSd = apply(avgMatrix, c(1,2), sd)
# I have to check if the sd function works properly, but not right now
# f = apply((sweep(yo1,2,avgMean,"-"))^2,2,sum)/length(avgList) # this is the variance at each time step
# g = sqrt(f) # this is the standard population deviation at each time step
# yo1 = avgList[[1]]
# yo2 = avgList[[2]]
stat = data.frame(avgMean,avgSd[,-1])# prepare my data
colnames(stat) = c("time","Mno","Mpop","Mext","Sno","Spop","Sext")
# first build the standard deviation curve only
g1 <- ggplot(stat)+
geom_smooth(aes(x = time, y = (Mno-Sno))) +
geom_smooth(aes(x=time, y = (Mno+Sno))) +
geom_smooth(aes(x=time, y = (Mext-Sext)))+
geom_smooth(aes(x=time, y = (Mext+Sext)))
gg1 <- ggplot_build(g1)
dfRibbon <- data.frame(xNo = gg1$data[[1]]$x, yminNo = gg1$data[[1]]$y, ymaxNo = gg1$data[[2]]$y, #and extract the smooth data for the ribbon
xExt = gg1$data[[3]]$x, yminExt = gg1$data[[3]]$y, ymaxExt = gg1$data[[4]]$y)
p <- ggplot(stat) +
stat_smooth(aes(x = time, y = Mno, color = "green")) +
#geom_smooth(aes(x = time, y = (Mno-Sno))) +
#geom_smooth(aes(x=time, y = (Mno+Sno)))+
geom_ribbon(data = dfRibbon, aes(x = xNo, ymin = yminNo, ymax = ymaxNo),
fill = "grey", alpha = 0.4)+
geom_smooth(aes(x=time, y = Mext, color = "blue"))+
#geom_smooth(aes(x=time, y = (Mext-Sext)))+
#geom_smooth(aes(x=time, y = (Mext+Sext)))+
geom_ribbon(data = dfRibbon, aes(x = xExt, ymin = yminExt, ymax = ymaxExt),
fill = "grey", alpha = 0.4)+
# geom_smooth(data = yo1 ,aes(x=time, y = number))+
# geom_smooth(data = yo2 ,aes(x=time, y = number))+
scale_x_continuous(name = "Time (yr)") +
scale_y_continuous(name = "Number of phenotypes")+
scale_colour_discrete(labels = c("Extinct","Alive"))+
theme(legend.title=element_blank(),
legend.position=c(0.19,0.95),
legend.justification=c(1,1),
legend.key = element_rect(fill = "white"),
panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"))+
guides(color=guide_legend(override.aes=list(fill=NA)))+
ggtitle("Variation of phenotype's number throughout the simulation")
if (print_it) print(p)
return(p)
}
SumPar = cbind(object@params@species_params$pop,object@params@species_params$extinct) # weird things happen without the as.numeric
colnames(SumPar) = c("Apparition","Extinction")
rownames(SumPar) = object@params@species_params$ecotype
SumPar = SumPar[order(SumPar[,1],decreasing=FALSE),]
SumPar = as.data.frame(SumPar) # little dataframe with species apparition and extinction
for (i in 1:dim(SumPar)[1]) if (SumPar$Extinction[i] == 0) SumPar$Extinction[i] = object@params@species_params$timeMax[1] # the not extinct ones get the end sim as extinction value
avg = matrix(data = 0, nrow = object@params@species_params$timeMax[1], ncol =4)
avg[,1] = seq(1:object@params@species_params$timeMax[1])*0.1
ApCount = length(which(SumPar$Apparition == 0)) # number of starting species
ExCount = 0
SumParEx = SumPar[order(SumPar$Extinction,decreasing=F),] #need this order for extinction in loop
for (i in 1:object@params@species_params$timeMax[1]) # for each time step
{
for (j in 1:dim(SumPar)[1])
if (SumPar$Apparition[j] <= i & SumPar$Extinction[j] >= i) # how many phenotypes are alive right now?
avg[i,2] = avg[i,2] + 1
if (i %in% SumPar$Apparition) ApCount = ApCount + 1 # how many in total
avg[i,3] = ApCount
if (i %in% SumParEx$Extinction) ExCount = ExCount + 1 #hoe many extinct?
avg[i,4] = ExCount
}
avg = as.data.frame(avg)
dimnames(avg)[[2]] = list("time", "number","apparition","extinction") # dataframe of the number of species at each time step
p <- ggplot(avg) +
geom_smooth(aes(x = time, y = number, color = "green")) +
geom_smooth(aes(x=time, y = apparition, color = "blue"))+
geom_smooth(aes(x=time, y = extinction, color = "red"))+
scale_x_continuous(name = "Time (yr)") +
scale_y_continuous(name = "Number of phenotypes")+
scale_colour_discrete(labels = c("Total","Alive","Extinct"))+
theme(legend.title=element_blank(),
legend.position=c(0.19,0.95),
legend.justification=c(1,1),
legend.key = element_rect(fill = "white"),
panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"))+
guides(color=guide_legend(override.aes=list(fill=NA)))+
ggtitle("Variation of phenotype's number throughout the simulation")
if (print_it) print(p)
return(p)
}
# my plot of the dead
#' Plot M2 of each species by size
plotUdead <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)),species = T, throughTime = T, print_it = TRUE, ...){
cbPalette <- c("#999999","#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") #9 colors for colorblind
a <- c(object@params@species_params$w_inf[1:9]) # to get vline of different col, need to create a data frame
vlines <- data.frame(xint = a,grp = c(1:9))
if (throughTime)
{
time_range = seq(500,max(as.numeric(dimnames(object@n)$time)),500)
time_range = c(time_range,max(as.numeric(dimnames(object@n)$time))) # so it counts the last time step which is probably not even
time_range = unique(time_range)
mortality = matrix(data = NA, nrow = length(time_range), ncol = 100, dimnames = list(time_range,object@params@w))
for (i in time_range)
{
m2_time <- getM2(object, time_range=i, drop=FALSE, ...)
m2 <- apply(m2_time, c(2,3), mean)
mortality[which(rownames(mortality)==i),] <- m2[1,] #all rows are the same
}
plot_dat = melt(mortality)
colnames(plot_dat) = list("time","size","value")
p <- ggplot(plot_dat)+
geom_line(aes(x=size, y=value, color = as.factor(time)))+
scale_x_log10(name = "Prey size in g") +
scale_y_continuous(name = "Predation mortality in g.m^-3") +
#geom_vline(data = vlines,aes(xintercept = xint,colour = as.factor(grp)), linetype = "dashed") +
geom_vline(xintercept = c(object@params@species_params$w_inf[1:9]), linetype = "dashed")+
scale_colour_manual(values=cbPalette, name = "Time in years")+ # colorblind
theme(panel.background = element_rect(fill = "white", color = "black"),
panel.grid.minor = element_line(colour = "grey92"))+
ggtitle("Predation mortality through time")
if (print_it)
print(p)
return(p)
}
m2_time <- getM2(object, time_range=time_range, drop=FALSE, ...)
m2 <- apply(m2_time, c(2,3), mean)
if (species) # if I want to display species instead of ecotypes
{
dimnames(m2)$prey = object@params@species_params$species
SpIdx = unique(object@params@species_params$species) # get the species names
m2_sp = matrix(data = NA, ncol = dim(m2)[2], nrow = length(SpIdx), dimnames = list(as.character(SpIdx),dimnames(m2)$w_prey)) # prepare the new object
names(dimnames(m2_sp))=list("prey","w_prey")
for (i in 1:dim(m2_sp)[1])
{
temp = m2 # save to manip
temp[which(rownames(m2) != i), ] = 0 # keep the ecotypes from the species only
temp = apply(temp, 2, sum)
temp = temp / length(which(rownames(m2)==i)) # do the mean (in 2 steps)
m2_sp[i, ] = temp
}
m2 = m2_sp
}
name = paste("Predation Mortality at time",time_range,sep=" ")
plot_dat <- data.frame(value = c(m2), Species = dimnames(m2)[[1]], w = rep(object@params@w, each=length(dimnames(m2)[[1]])))
p <- ggplot(plot_dat) +
geom_line(aes(x=w, y = value, colour = Species)) +
scale_x_continuous(name = "Size", trans="log10") +
scale_y_continuous(name = "M2", lim=c(0,max(plot_dat$value))) +
ggtitle(name)
if (print_it)
print(p)
return(p)
}
plotTotMort <- function(directory, predMort = NULL, time_range = NULL )
{
# let's assume that object is a list of different sims
object <- bunchLoad(folder = paste(directory,"/normal",sep=""))
plotList <- list()
for (i in 1:length(object))
{
sim <- object[[i]]
if (is.null(time_range)) time_range = max(as.numeric(dimnames(sim@n)$time))
# predation mortality
if (is.null(predMort))
{
m2_time <- getM2(sim, time_range=time_range, drop=FALSE)#, ...)
m2 <- apply(m2_time, c(2,3), mean)
} else {
m2 <- matrix(data = predMort[i,], nrow = length(sim@params@species_params$ecotype) , ncol = dim(predMort)[2],byrow = T, dimnames = list(sim@params@species_params$ecotype,sim@params@w) )
names(dimnames(m2)) = c("prey","w_prey")
}
# fisheries mortality
f_time <- getFMort(sim, time_range=time_range, drop=FALSE) #, ...)
f <- apply(f_time, c(2,3), mean)
# background mortality
bm <- sim@params@species_params$z0
# and let's assume I'm doing only single species runs for now
m2S <- m2[1,]
fS <- f[1,]
bmS <- rep(bm[1],length(m2S))
dead_dat <- data.frame(m2S,fS,bmS,sim@params@w,row.names = NULL)
colnames(dead_dat) <- c("predation","fisheries","background","size")
p <- ggplot(dead_dat) +
geom_line(aes(x = size, y = predation, color = "predation")) +
geom_line(aes(x = size, y = fisheries, color = "fisheries")) +
geom_line(aes(x = size, y = background, color = "background")) +
scale_y_continuous(name = "mortality") +
scale_x_continuous(name = "size", trans = "log10")
temp <- ggplot_build(p)
temp$data[[1]]$group = i
plotList[[i]] <- temp
}
dat = do.call(rbind,lapply(plotList,function(x) x$data[[1]]))
p <- ggplot(dat) +
geom_line(aes(x=x,y=y,color=as.factor(group)))+
scale_x_continuous(name="size in log") +
scale_y_continuous(name = "mortality in g", limits = c(0,1)) +
scale_colour_grey(name = "Eta")+
ggtitle("Predation mortality")
print(p)
}
# plot growth at time t
plotGrowth <- function(object, time_range = max(as.numeric(dimnames(object@n)$time)), species = T, print_it = T,...){
time_elements <- get_time_elements(object,time_range)
growth_time <- aaply(which(time_elements), 1, function(x){
# Necessary as we only want single time step but may only have 1 species which makes using drop impossible
n <- array(object@n[x,,],dim=dim(object@n)[2:3])
dimnames(n) <- dimnames(object@n)[2:3]
growth <- getEGrowth(object@params, n=n, n_pp = object@n_pp[x,])
return(growth)})
#growth <- apply(growth_time, c(2,3), mean) # use this when I will have time_range on more than one time
growth = growth_time