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script_analysis.R
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script_analysis.R
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library(here)
library(ggplot2)
library(RColorBrewer)
library(plsgenomics)
library(animation)
library(tidyverse)
library(stringr)
library(plyr)
library(dplyr)
#####Scenario 1#####
#Creation of the mean dispersal data table over time
files_names <- list.files()
nb_files <- length(files_names)
data_names <- vector("list",length=nb_files)
for (i in 1 : nb_files){
data_names[i] <- strsplit(files_names[i], split=".txt")
}
dim=50
ntime=(length(read.table(files_names[1],h=F)[,1]))/dim
nb_replicat=20
nstock=10
calcul_disp_moy = function(data,ntime,dim){
dispmoy=c()
for (l in 1:ntime){
vecmat=as.vector(as.matrix(data[((l-1)*dim+1):(((l-1)*dim+1)+(dim-1)),]))
vecmoy=c()
for (m in 1 : length(vecmat)){
if (vecmat[m]!=0){
if(vecmat[m]!=-1){
vecmoy=c(vecmoy,vecmat[m])
}}
}
dispmoy=c(dispmoy,mean(vecmoy))
}
dispmoy
}
dispmoytot=c()
for (i in 1:length(data_names)){
dispmoytot=c(dispmoytot,calcul_disp_moy(read.table(files_names[i]),ntime,dim))
print((i*100)/length(data_names))
}
time=as.numeric(c(rep(c(1,(1:(ntime-1))*nstock),length(files_names))))
datadispmoy=data.frame(dispmoy=as.numeric(dispmoytot),fragmentation=rep(0,length(files_names)*ntime),aggregation=rep(0,length(files_names)*ntime),replicat=rep(0,length(files_names)*ntime),time=as.numeric(time))
for (q in 1:length(files_names)){
dfrag=str_locate(files_names[q],"frag")
ffrag=str_locate(files_names[q],"_paggr")
daggr=str_locate(files_names[q],"_paggr")
faggr=str_locate(files_names[q],"_rep")
drep=str_locate(files_names[q],"_rep")
frep=str_locate(files_names[q],".txt")
datadispmoy[(q*ntime-ntime+1):(q*ntime),2]=rep(as.numeric(str_sub(files_names[q],(dfrag[1,2]+1),(ffrag[1,1]-1))),ntime)
datadispmoy[(q*ntime-ntime+1):(q*ntime),3]=rep(as.numeric(str_sub(files_names[q],(daggr[1,2]+1),(faggr[1,1]-1))),ntime)
datadispmoy[(q*ntime-ntime+1):(q*ntime),4]=rep(str_sub(files_names[q],(drep[1,2]+1),(frep[1,1]-1)),ntime)
}
#Summary of the mean dispersal for each combination of parameters
data_summary <- function(data, varname, groupnames){
require(plyr)
summary_func <- function(x, col){
c(mean = mean(x[[col]], na.rm=TRUE),
sd = sd(x[[col]], na.rm=TRUE))
}
data_sum<-ddply(data, groupnames, .fun=summary_func,
varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
dispmoysumm=data_summary(datadispmoy, varname="dispmoy", groupnames=c("fragmentation","aggregation","time"))
write.table(x = dispmoysumm, "dispmoysumm.txt")
#Occurrence of each dispersal strategy over time
calcul_occ_disp = function(data, ntime, dim, pasdetemps){
maxdisp=max(data)
time=c(1:ntime)[c(1:ntime)%%pasdetemps==0]
occdisp=c(rep(0,length(time)*maxdisp))
temps=c()
dispersion=c()
for (l in time){
for (i in 1:maxdisp){
occdisp[maxdisp*((l/pasdetemps)-1)+i]=sum(data[((l-1)*dim+1):(((l-1)*dim+1)+(dim-1)),]==i)
temps=c(temps,l)
dispersion=c(dispersion,i)
}}
table_occ_disp=cbind(temps,dispersion,occdisp)
table_occ_disp
}
pmut = c(0,1,10,100)
pfrag = c(1,10,100)
paggr = c(0,10,20,40,60,80)
repli = c(1:40)
dim = 50
pasdetemps = 10
data_occ=c()
for (i in 1:length(pmut)){
for (j in 1:length(pfrag)){
for (k in 1:length(paggr)){
conditions = paste0("pmut",pmut[i],"pfrag",pfrag[j],"_paggr",paggr[k],"_rep")
for (g in 1:length(repli)){
ntime=length(read.table(paste0(conditions,repli[g],".txt"))[,1])/50
data_occ_trans=data.frame(calcul_occ_disp(read.table(paste0(conditions,repli[g],".txt")),ntime,dim,pasdetemps))
data_occ_trans2=cbind(data_occ_trans,replicats=rep(g,length(data_occ_trans[,1])), pmut=rep(pmut[i],length(data_occ_trans[,1]))
,pfragr=rep(pfrag[j],length(data_occ_trans[,1])),paggr=rep(paggr[k],length(data_occ_trans[,1])))
data_occ=rbind(data_occ,data_occ_trans2)
}}}}
write.table(data_occ, "data_occ.txt")
#####Scenario 2#####
dim=50
nb_replicat=40
nstock=20
files_names <- list.files()
nb_files <- length(files_names)
data_names <- vector("list",length=nb_files)
for (i in 1 : nb_files){
data_names[i] <- strsplit(files_names[i], split=".txt")
}
#Remove of STOP
for (j in 1:length(data_names)){
if (str_detect(files_names[j],"STOP")==TRUE){
nom=files_names[j]
dSTOP=str_locate(nom,"STOP")
fSTOP=str_locate(nom,".txt")
newname=paste0(str_sub(nom,start = 1, end = dSTOP[1]-2),".txt")
file.rename(nom,newname)
}
}
files_names <- list.files()
nb_files <- length(files_names)
data_names <- vector("list",length=nb_files)
for (i in 1 : nb_files){
data_names[i] <- strsplit(files_names[i], split=".txt")
}
#Creation of the mean dispersal data table over time
calcul_disp_moy = function(data,ntime,dim){
dispmoy=c()
for (l in 1:ntime){
vecmat=as.vector(as.matrix(data[((l-1)*dim+1):(((l-1)*dim+1)+(dim-1)),]))
vecmoy=c()
for (m in 1 : length(vecmat)){
if (vecmat[m]!=0){
if(vecmat[m]!=-1){
vecmoy=c(vecmoy,vecmat[m])
}}
}
dispmoy=c(dispmoy,mean(vecmoy))
}
dispmoy
}
dispmoytot=c()
time=c()
ntimetot=c()
for (i in 1:length(data_names)){
ntime=(length(read.table(files_names[i],h=F)[,1]))/dim
ntimetot=c(ntimetot,ntime)
dispmoytot=c(dispmoytot,calcul_disp_moy(read.table(files_names[i]),ntime,dim))
time=c(time,c((1:(ntime))*nstock))
print((i*100)/length(data_names))
}
datadispmoy=data.frame(dispmoy=as.numeric(dispmoytot),pmut=rep(0,length(dispmoytot)),pfrag=rep(0,length(dispmoytot)),aggregation=rep(0,length(dispmoytot)),replicat=rep(0,length(dispmoytot)),time=as.numeric(time))
for (q in 1:length(files_names)){
dpmut=str_locate(files_names[q],"pmut")
fpmut=str_locate(files_names[q],"pfrag")
dpfrag=str_locate(files_names[q],"pfrag")
fpfrag=str_locate(files_names[q],"_paggr")
daggr=str_locate(files_names[q],"_paggr")
faggr=str_locate(files_names[q],"_rep")
drep=str_locate(files_names[q],"_rep")
frep=str_locate(files_names[q],".txt")
datadispmoy[(sum(ntimetot[1:q])-ntimetot[q]+1):(sum(ntimetot[1:q])),2]=rep(as.numeric(str_sub(files_names[q],(dpmut[1,2]+1),(fpmut[1,1]-1))),ntimetot[q])
datadispmoy[(sum(ntimetot[1:q])-ntimetot[q]+1):(sum(ntimetot[1:q])),3]=rep(as.numeric(str_sub(files_names[q],(dpfrag[1,2]+1),(fpfrag[1,1]-1))),ntimetot[q])
datadispmoy[(sum(ntimetot[1:q])-ntimetot[q]+1):(sum(ntimetot[1:q])),4]=rep(as.numeric(str_sub(files_names[q],(daggr[1,2]+1),(faggr[1,1]-1))),ntimetot[q])
datadispmoy[(sum(ntimetot[1:q])-ntimetot[q]+1):(sum(ntimetot[1:q])),5]=rep(str_sub(files_names[q],(drep[1,2]+1),(frep[1,1]-1)),ntimetot[q])
}
#Number of individuals over time
calcul_nb_ind = function(data,ntime,dim){
nb_ind=c()
for (l in 1:ntime){
vecmat=as.vector(as.matrix(data[((l-1)*dim+1):(((l-1)*dim+1)+(dim-1)),]))
vecmatind=vecmat[vecmat != 0 & vecmat != -1]
nb_ind=c(nb_ind,length(vecmatind))
}
nb_ind
}
nb_ind_tot=c()
time=c()
ntimetot=c()
for (i in 1:length(data_names)){
ntime=(length(read.table(files_names[i],h=F)[,1]))/dim
ntimetot=c(ntimetot,ntime)
nb_ind_tot=c(nb_ind_tot,calcul_nb_ind(read.table(files_names[i]),ntime,dim))
time=c(time,c((1:(ntime))*nstock))
print((i*100)/length(data_names))
}
datatot=cbind(datadispmoy,nb_ind=nb_ind_tot)
#Number of empty patch over time
calcul_nb_vide = function(data,ntime,dim){
nb_vide=c()
for (l in 1:ntime){
vecmat=as.vector(as.matrix(data[((l-1)*dim+1):(((l-1)*dim+1)+(dim-1)),]))
vecmatvide=vecmat[vecmat == 0]
nb_vide=c(nb_vide,length(vecmatvide))
}
nb_vide
}
nb_vide_tot=c()
time=c()
ntimetot=c()
for (i in 1:length(data_names)){
ntime=(length(read.table(files_names[i],h=F)[,1]))/dim
ntimetot=c(ntimetot,ntime)
nb_vide_tot=c(nb_vide_tot,calcul_nb_vide(read.table(files_names[i]),ntime,dim))
time=c(time,c((1:(ntime))*nstock))
print((i*100)/length(data_names))
}
datatot=cbind(datatot,nb_vide=nb_vide_tot)
poccupation=datatot$nb_ind/(datatot$nb_ind+datatot$nb_vide)
datatot=cbind(datatot,poccupation=poccupation)
#Time and percentage of fragmentation at extinction
levfrag=levels(as.factor(datatot$pfrag))
levmut=levels(as.factor(datatot$pmut))
levaggr=levels(as.factor(datatot$aggregation))
levrep=levels(as.factor(datatot$replicat))
tempsext=c()
pfrag=c()
pmut=c()
aggregation=c()
replicat=c()
fragext=c()
for ( i in 1:length(levfrag)){
for ( j in 1:length(levmut)){
for (k in 1:length(levaggr)){
for (l in 1:length(levrep)){
tempsext=c(tempsext,max(datatot$time[datatot$pmut==levmut[j] & datatot$pfrag==levfrag[i] & datatot$aggregation ==levaggr[k] & datatot$replicat==levrep[l]]))
tempsexti=max(datatot$time[datatot$pmut==levmut[j] & datatot$pfrag==levfrag[i] & datatot$aggregation ==levaggr[k] & datatot$replicat==levrep[l]])
nb_frag=2500-(datatot$nb_vide[datatot$pmut==levmut[j] & datatot$pfrag==levfrag[i] & datatot$aggregation ==levaggr[k] & datatot$replicat==levrep[l] & datatot$time==tempsexti] + datatot$nb_ind[datatot$pmut==levmut[j] & datatot$pfrag==levfrag[i] & datatot$aggregation ==levaggr[k] & datatot$replicat==levrep[l] & datatot$time==tempsexti] )
fragext = c(fragext,((nb_frag*100)/2500))
pfrag=c(pfrag,levfrag[i])
pmut=c(pmut,levmut[j])
aggregation=c(aggregation,levaggr[k])
replicat=c(replicat,levrep[l])
}
}
}
}
datatempsext=data.frame(pfrag,pmut,aggregation,replicat,tempsext,fragext)
#Summary each combination of parameters
data_summary <- function(data, varname, groupnames){
require(plyr)
summary_func <- function(x, col){
c(mean = mean(x[[col]], na.rm=TRUE),
sd = sd(x[[col]], na.rm=TRUE))
}
data_sum<-ddply(data, groupnames, .fun=summary_func,
varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
dispmoysumm=data_summary(datatot, varname="dispmoy", groupnames=c("pmut","pfrag","aggregation","time"))
nbindsumm=data_summary(datatot, varname="nb_ind", groupnames=c("pmut","pfrag","aggregation","time"))
poccupasumm=data_summary(datatot, varname="poccupation", groupnames=c("pmut","pfrag","aggregation","time"))
tempsextsumm=data_summary(datatempsext, varname="tempsext", groupnames=c("pmut","pfrag","aggregation"))
fragextsumm=data_summary(datatempsext, varname="fragext", groupnames=c("pmut","pfrag","aggregation"))
write.table(dispmoysumm, "dispmoysumm.txt")
write.table(nbindsumm, "nbindsumm.txt")
write.table(poccupasumm, "poccupasumm.txt")
write.table(tempsextsumm, "tempsextsumm.txt")
write.table(fragextsumm, "fragextsumm.txt")