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paper_plots.R
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paper_plots.R
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library(ggplot2)
library(latex2exp)
library(RColorBrewer)
library(ggpubr)
setwd("~/PosDoc/Coronavirus/LTCF/Code_september/cluster_data/")
iso = "total"
build = "new"
beta = "06"
type_ind = "res"
################ testing AR
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_sev_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
sum(data)/8000/120
type_data = "latent"
type_ind = "res"
n_boot = 1000
scenarios = c("S0","S1","S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
tempo = c()
result = c()
cenario = c()
for(j in 1:length(scenarios)){
print(j)
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = rowMeans(data)
t = seq(1,length(m))/24/5
t = ceiling(t)
media_d = rep(0,max(t))
for(i in 1:length(t)){
media_d[t[i]] = media_d[t[i]]+m[i]
}
tempo = c(tempo,seq(1,max(t)))
result = c(result,media_d)
cenario = c(cenario,rep(scen,length(media_d)))
}
df = data.frame(tempo=tempo*5,media = result*1000/120,cenario)
write.table(df,paste("time_series_",build,"_",type_ind,".dat",sep = ""),row.names = F)
#rm(matrix_r)
type_data = "latent"
type_ind = "hcw"
n_boot = 1000
scenarios = c("S0","S1","S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
tempo = c()
result = c()
cenario = c()
for(j in 1:length(scenarios)){
print(j)
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = rowMeans(data)
t = seq(1,length(m))/24/5
t = ceiling(t)
media_d = rep(0,max(t))
for(i in 1:length(t)){
media_d[t[i]] = media_d[t[i]]+m[i]
}
tempo = c(tempo,seq(1,max(t)))
result = c(result,media_d)
cenario = c(cenario,rep(scen,length(media_d)))
}
df = data.frame(tempo=tempo*5,media = result*1000/120,cenario)
write.table(df,paste("time_series_",build,"_",type_ind,".dat",sep = ""),row.names = F)
#rm(matrix_r)
#####################################################################################################################3
#######################################################################################################################
####################### Plots total number ###########################################################################
####################################################################################################################
library(ggplot2)
library(latex2exp)
library(RColorBrewer)
library(ggpubr)
scenarios = c("S0","S1","S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
n_boot = 1000
type_ind = "res"
type_data = "latent"
result = c()
cenario = c()
lab_ = "a"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/120,cenario)
write.table(df,paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "hosp"
result = c()
cenario = c()
lab_ = "b"
for(j in 1:length(scenarios)){
print(j)
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/120,cenario)
write.table(df,paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "dead"
result = c()
cenario = c()
lab_ = "c"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/120,cenario)
write.table(df,paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_ind = "hcw"
type_data = "latent"
result = c()
cenario = c()
lab_ = "d"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/68,cenario)
write.table(df,paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "hosp"
result = c()
cenario = c()
lab_ = "e"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/68,cenario)
write.table(df,paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "dead"
result = c()
cenario = c()
lab_ = "f"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/68,cenario)
write.table(df,paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
###########################################################################################################################
#################################################### Plot percentage of silent infections
#######################################################################################################
library(ggplot2)
library(latex2exp)
library(RColorBrewer)
library(ggpubr)
setwd("/data/thomas-covid/LTCF/")
scenarios = c("S0","S1","S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
n_boot = 1000
type_ind = "res"
type_data = "latent"
n_boot = 1000
scenarios = c("S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
tempo = c()
result = c()
cenario = c()
for(j in 1:length(scenarios)){
rs = c()
print(j)
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_pre_res.dat",sep=""),h=F,stringsAsFactors = F)
sr = colSums(data)+1
found_r = read.table(paste(folder,"beta_0_",beta,"_iso_pre_res.dat",sep=""),h=F,stringsAsFactors = F)$V1
data = read.table(paste(folder,"beta_0_",beta,"_pre_hcw.dat",sep=""),h=F,stringsAsFactors = F)
sh = colSums(data)+1
found_h = read.table(paste(folder,"beta_0_",beta,"_iso_pre.dat",sep=""),h=F,stringsAsFactors = F)$V1
sh = sh+sr
found_h = found_h+found_r
data = read.table(paste(folder,"beta_0_",beta,"_asymp_res.dat",sep=""),h=F,stringsAsFactors = F)
sr = colSums(data)+1
found_r = read.table(paste(folder,"beta_0_",beta,"_iso_asymp_res.dat",sep=""),h=F,stringsAsFactors = F)$V1
sh = sh+sr
found_h = found_h+found_r
data = read.table(paste(folder,"beta_0_",beta,"_asymp_hcw.dat",sep=""),h=F,stringsAsFactors = F)
sr = colSums(data)+1
found_r = read.table(paste(folder,"beta_0_",beta,"_iso_asymp.dat",sep=""),h=F,stringsAsFactors = F)$V1
sh = sh+sr
found_h = found_h+found_r
for(i in 1:n_boot){
pos = sample(seq(1,length(sr)),length(sr),replace = T)
st = sum(sh[pos])
if(st>0){
rs[i] = sum(found_h[pos])/st
}else
rs[i] = NA
}
result = c(result,rs)
cenario = c(cenario,rep(scen,length(rs)))
}
df = data.frame(proportion = result,scenario=cenario)
write.table(df,paste("proportion_",build,".dat",sep=""),row.names = F)
######################################################################################################33
######################33333 Calculating Reproductive Number
library(ggplot2)
library(latex2exp)
library(RColorBrewer)
library(ggpubr)
scenarios = c("S0","S1","S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
n_boot = 1000
R0_m = c()
R0_sd = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_R0.dat",sep=""),h=F,stringsAsFactors = F)$V1
R0_m[i] = mean(data)
R0_sd[i] = sd(data)
}
df = data.frame(scenarios,R0_m,R0_sd)
write.csv(df,paste("reproduction_number_",build,".csv",sep=""),row.names=F)
plot(df$R0_m)
######################################################################################################
######################################### Average reduction
#########################################################################################################
library(ggplot2)
library(latex2exp)
library(RColorBrewer)
library(ggpubr)
scenarios = c("S1","S2a","S2b","S3a","S3b","S4a","S4b","S5a","S5b","S6a","S6b")
n_boot = 1000
type_data = "latent"
type_ind = "res"
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T,stringsAsFactors = F)
d0 = data$result[data$cenario=="S0"]
l_res = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
d = data$result[data$cenario==scen]
d = -(d-d0)/d0
m =median(d[!is.infinite(d)])
s1 = quantile(d[!is.infinite(d)],0.975,name=F)
s2 = quantile(d[!is.infinite(d)],0.025,name=F)
l_res[i] = paste(round(m,digits=3)," ","(",round(s2,digits=3),",",round(s1,digits=3),")",sep="")
}
type_data = "hosp"
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T,stringsAsFactors = F)
d0 = data$result[data$cenario=="S0"]
h_res = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
d = data$result[data$cenario==scen]
d = -(d-d0)/d0
m =median(d[!is.infinite(d)])
s1 = quantile(d[!is.infinite(d)],0.975,name=F)
s2 = quantile(d[!is.infinite(d)],0.025,name=F)
h_res[i] = paste(round(m,digits=3)," ","(",round(s2,digits=3),",",round(s1,digits=3),")",sep="")
}
type_data = "dead"
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T,stringsAsFactors = F)
d0 = data$result[data$cenario=="S0"]
d_res = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
d = data$result[data$cenario==scen]
d = -(d-d0)/d0
m =mean(d[!is.infinite(d)])
m =median(d[!is.infinite(d)])
s1 = quantile(d[!is.infinite(d)],0.975,name=F)
s2 = quantile(d[!is.infinite(d)],0.025,name=F)
d_res[i] = paste(round(m,digits=3)," ","(",round(s2,digits=3),",",round(s1,digits=3),")",sep="")
}
type_ind="hcw"
type_data = "latent"
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T,stringsAsFactors = F)
d0 = data$result[data$cenario=="S0"]
l_hcw = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
d = data$result[data$cenario==scen]
d = -(d-d0)/d0
m =median(d[!is.infinite(d)])
s1 = quantile(d[!is.infinite(d)],0.975,name=F)
s2 = quantile(d[!is.infinite(d)],0.025,name=F)
l_hcw[i] = paste(round(m,digits=3)," ","(",round(s2,digits=3),",",round(s1,digits=3),")",sep="")
}
type_data = "hosp"
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T,stringsAsFactors = F)
d0 = data$result[data$cenario=="S0"]
h_hcw = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
d = data$result[data$cenario==scen]
d = -(d-d0)/d0
m =median(d[!is.infinite(d)])
s1 = quantile(d[!is.infinite(d)],0.975,name=F)
s2 = quantile(d[!is.infinite(d)],0.025,name=F)
h_hcw[i] = paste(round(m,digits=3)," ","(",round(s2,digits=3),",",round(s1,digits=3),")",sep="")
}
type_data = "dead"
folder = paste("results_",build,"_",iso,"_S0/",sep = "")
data = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T,stringsAsFactors = F)
d0 = data$result[data$cenario=="S0"]
d_hcw = c()
for(i in 1:length(scenarios)){
scen = scenarios[i]
d = data$result[data$cenario==scen]
d = -(d-d0)/d0
m =median(d[!is.infinite(d)])
s1 = quantile(d[!is.infinite(d)],0.975,name=F)
s2 = quantile(d[!is.infinite(d)],0.025,name=F)
d_hcw[i] = paste(round(m,digits=3)," ","(",round(s2,digits=3),",",round(s1,digits=3),")",sep="")
}
df = data.frame(scenarios,l_res,h_res,d_res,l_hcw,h_hcw,d_hcw)
write.csv(df,paste("reduction_",build,".csv",sep=""),row.names=F)
######################################################################################
#### SA
scenarios = c("SA60","SA70","SA80","SA90")
n_boot = 1000
type_ind = "res"
type_data = "latent"
result = c()
cenario = c()
lab_ = "a"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/120,cenario)
write.table(df,paste("average_bootstrap_SA_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "hosp"
result = c()
cenario = c()
lab_ = "b"
for(j in 1:length(scenarios)){
print(j)
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/120,cenario)
write.table(df,paste("average_bootstrap_SA_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "dead"
result = c()
cenario = c()
lab_ = "c"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/120,cenario)
write.table(df,paste("average_bootstrap_SA_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_ind = "hcw"
type_data = "latent"
result = c()
cenario = c()
lab_ = "d"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/68,cenario)
write.table(df,paste("average_bootstrap_SA_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "hosp"
result = c()
cenario = c()
lab_ = "e"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/68,cenario)
write.table(df,paste("average_bootstrap_SA_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
type_data = "dead"
result = c()
cenario = c()
lab_ = "f"
for(j in 1:length(scenarios)){
scen = scenarios[j]
folder = paste("results_",build,"_",iso,"_",scen,"/",sep = "")
data = read.table(paste(folder,"beta_0_",beta,"_",type_data,"_",type_ind,".dat",sep=""),h=F,stringsAsFactors = F)
m = colSums(data)
media = c()
for(i in 1:n_boot){
media[i] = mean(sample(m,1000,replace = T))
}
result = c(result,media)
cenario = c(cenario,rep(scen,length(media)))
}
df = data.frame(result=result*1000/68,cenario)
write.table(df,paste("average_bootstrap_SA_",type_ind,"_",build,"-",type_data,".dat",sep=""),row.names = F)
##################################################################
### Plot time series
build = "old"
df = read.table(paste("time_series_",build,"_res.dat",sep=""),h=T,stringsAsFactors = F)
baseline = "S0"
df1 = df[df$cenario == baseline,]
curves = c("S1","S2a","S2b","S3a","S3b")
cv = c(brewer.pal(5,"Set1"),brewer.pal(7,"Set1")[7])
df2 = df[df$cenario %in% curves,]
lab_ = "a"
mp1=ggplot()+geom_col(data=df1,aes(x=tempo,y=media),color="lightgrey",fill="lightgrey",alpha = 0.6,size = 0)+
geom_line(data=df2,aes(x=tempo,y=media,color = as.factor(cenario)),alpha = 0.9,size = 2)+
scale_x_continuous(name = TeX('Time (days)'),limits = c(0,150))+
scale_y_continuous(name = TeX("Incidence of infections"))+
scale_color_manual(values = cv[1:length(curves)],breaks = curves,labels = curves,name = "Scenarios")+
guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
# panel.border = element_blank(),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25,angle=90,hjust = 0.5),
axis.title.y = element_text(colour="black", size = 25),
axis.title.x = element_text(colour="black", size = 25),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "top",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp1
curves = c("S4a","S4b","S5a","S5b","S6a","S6b")
df2 = df[df$cenario %in% curves,]
lab_ = "b"
mp2=ggplot()+geom_col(data=df1,aes(x=tempo,y=media),color="lightgrey",fill="lightgrey",alpha = 0.6,size = 0)+
geom_line(data=df2,aes(x=tempo,y=media,color = as.factor(cenario)),alpha = 0.9,size = 2)+
scale_x_continuous(name = TeX('Time (days)'),limits = c(0,150))+
scale_y_continuous(name = TeX("Incidence of infections"))+
scale_color_manual(values = cv[1:length(curves)],breaks = curves,labels = curves,name = "Scenarios")+
guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
# panel.border = element_blank(),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25,angle=90,hjust = 0.5),
axis.title.y = element_text(colour="black", size = 25),
axis.title.x = element_text(colour="black", size = 25),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "top",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp2
paste("time_series_",build,"_hcw.dat",sep="")
baseline = "S0"
df1 = df[df$cenario == baseline,]
curves = c("S1","S2a","S2b","S3a","S3b")
df2 = df[df$cenario %in% curves,]
lab_ = "c"
mp3=ggplot()+geom_col(data=df1,aes(x=tempo,y=media),color="lightgrey",fill="lightgrey",alpha = 0.6,size = 0)+
geom_line(data=df2,aes(x=tempo,y=media,color = as.factor(cenario)),alpha = 0.9,size = 2)+
scale_x_continuous(name = TeX('Time (days)'),limits = c(0,150))+
scale_y_continuous(name = TeX("Incidence of infections"))+
scale_color_manual(values =cv[1:length(curves)],breaks = curves,labels = curves,name = "Scenarios")+
guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
# panel.border = element_blank(),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25,angle=90,hjust = 0.5),
axis.title.y = element_text(colour="black", size = 25),
axis.title.x = element_text(colour="black", size = 25),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "top",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp3
curves = c("S4a","S4b","S5a","S5b","S6a","S6b")
df2 = df[df$cenario %in% curves,]
lab_ = "d"
mp4=ggplot()+geom_col(data=df1,aes(x=tempo,y=media),color="lightgrey",fill="lightgrey",alpha = 0.6,size = 0)+
geom_line(data=df2,aes(x=tempo,y=media,color = as.factor(cenario)),alpha = 0.9,size = 2)+
scale_x_continuous(name = TeX('Time (days)'),limits = c(0,150))+
scale_y_continuous(name = TeX("Incidence of infections"))+
scale_color_manual(values = cv[1:length(curves)],breaks = curves,labels = curves,name = "Scenarios")+
guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
# panel.border = element_blank(),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25,angle=90,hjust = 0.5),
axis.title.y = element_text(colour="black", size = 25),
axis.title.x = element_text(colour="black", size = 25),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "top",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp4
legend <- cowplot::get_legend(mp1+guides(colour = guide_legend(override.aes = list(size=10),nrow=2,ncol = 3))+ theme(legend.position = "bottom",legend.text = element_text(size = 40),
legend.title = element_text(size = 40)))
legend2 <- cowplot::get_legend(mp2+guides(nrow=2,colour = guide_legend(override.aes = list(size=10)))+ theme(legend.position = "bottom",legend.text = element_text(size = 40),
legend.title = element_text(size = 40)))
mp = ggarrange(mp1+theme(legend.position = "none"),NULL,mp2+theme(legend.position = "none")+rremove("y.title")+rremove("x.title"),mp3+theme(legend.position = "none"),NULL,mp4+rremove("y.title")+theme(legend.position = "none"),legend,NULL,legend2,ncol = 3,nrow = 3,widths = c(1,0.1,1,1,0.1,1),heights = c(1,1,0.5))
ggsave(paste("time_series_",build,".png",sep=""),mp,device="png",width = 50,height = 50,units = "cm",dpi=300)
########################################################3
########### here the plots for comparison #######
type_ind = "res"
type_data = "latent"
result = c()
cenario = c()
lab_ = "a"
df = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T)
df = data.frame(df)
mp1=ggplot()+
stat_summary(data=df,mapping = aes(x = result, y = cenario),fun = median,geom = "col",alpha = 0.4,color = "red",fill = "red",size = 0)+
geom_boxplot(data=df,aes(x=result,y=cenario),color = "red",fill="red",outlier.shape = NA,alpha = 0.7,size = 2)+
scale_x_continuous(name = TeX('Number of infections'),expand = c(0,0))+
scale_y_discrete(name = TeX("Scenarios"))+
#scale_color_manual(values = brewer.pal(12,"Paired"),breaks = scenarios,labels = scenarios,name = "Scenarios")+
#guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
#panel.border = element_blank(colour = "black"),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25),
axis.title.y = element_text(colour="black", size = 35),
axis.title.x = element_text(colour="black", size = 35),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "none",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp1
type_data = "hosp"
result = c()
cenario = c()
lab_ = "b"
df = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T)
df = data.frame(df)
mp2=ggplot()+
stat_summary(data=df,mapping = aes(x = result, y = cenario),fun = median,geom = "col",alpha = 0.4,color = "blue",fill = "blue",size = 0)+
geom_boxplot(data=df,aes(x=result,y=cenario),color = "blue",fill="blue",outlier.shape = NA,alpha = 0.7,size = 2)+
scale_x_continuous(name = TeX('Number of hospitalizations'),expand = c(0,0))+
scale_y_discrete(name = TeX("Scenarios"))+
#scale_color_manual(values = brewer.pal(12,"Paired"),breaks = scenarios,labels = scenarios,name = "Scenarios")+
#guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
#panel.border = element_blank(colour = "black"),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25),
axis.title.y = element_text(colour="black", size = 35),
axis.title.x = element_text(colour="black", size = 35),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "none",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp2
type_data = "dead"
result = c()
cenario = c()
lab_ = "c"
df = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T)
df = data.frame(df)
mp3=ggplot()+
stat_summary(data=df,mapping = aes(x = result, y = cenario),fun = median,geom = "col",alpha = 0.4,color = "black",fill = "black",size = 0)+
geom_boxplot(data=df,aes(x=result,y=cenario),color = "black",fill="black",outlier.shape = NA,alpha = 0.7,size = 2)+
scale_x_continuous(name = TeX('Number of deaths'),expand = c(0,0))+
scale_y_discrete(name = TeX("Scenarios"))+
#scale_color_manual(values = brewer.pal(12,"Paired"),breaks = scenarios,labels = scenarios,name = "Scenarios")+
#guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
#panel.border = element_blank(colour = "black"),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25),
axis.title.y = element_text(colour="black", size = 35),
axis.title.x = element_text(colour="black", size = 35),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "none",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp3
type_ind = "hcw"
type_data = "latent"
result = c()
cenario = c()
lab_ = "d"
df = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T)
df = data.frame(df)
mp4=ggplot()+
stat_summary(data=df,mapping = aes(x = result, y = cenario),fun = median,geom = "col",alpha = 0.4,color = "red",fill = "red",size = 0)+
geom_boxplot(data=df,aes(x=result,y=cenario),color = "red",fill="red",outlier.shape = NA,alpha = 0.7,size = 2)+
scale_x_continuous(name = TeX('Number of infections'),expand = c(0,0))+
scale_y_discrete(name = TeX("Scenarios"))+
#scale_color_manual(values = brewer.pal(12,"Paired"),breaks = scenarios,labels = scenarios,name = "Scenarios")+
#guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
#panel.border = element_blank(colour = "black"),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25),
axis.title.y = element_text(colour="black", size = 35),
axis.title.x = element_text(colour="black", size = 35),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
legend.position = "none",
plot.tag.position = "topleft",
#axis.line = element_line(size=0.5, colour = "black")
)+labs(tag = paste(lab_,")",sep=""))
mp4
type_data = "hosp"
result = c()
cenario = c()
lab_ = "e"
df = read.table(paste("average_bootstrap_",type_ind,"_",build,"-",type_data,".dat",sep=""),h=T)
df = data.frame(df)
mp5=ggplot()+
stat_summary(data=df,mapping = aes(x = result, y = cenario),fun = median,geom = "col",alpha = 0.4,color = "blue",fill = "blue",size = 0)+
geom_boxplot(data=df,aes(x=result,y=cenario),color = "blue",fill="blue",outlier.shape = NA,alpha = 0.7,size = 2)+
scale_x_continuous(name = TeX('Number of hospitalizations'),expand = c(0,0))+
scale_y_discrete(name = TeX("Scenarios"))+
#scale_color_manual(values = brewer.pal(12,"Paired"),breaks = scenarios,labels = scenarios,name = "Scenarios")+
#guides(color = guide_legend(reverse = FALSE, override.aes = list(size=5), byrow=TRUE,nrow = 2))+
#scale_linetype_manual(values = c(1,4,10),breaks = c("not_testing","test_7","test_14"))+
# scale_fill_manual(values = c("red","blue"),name="Individual",breaks = c("res","hcw"),labels = c("Residents","HCW"))+
theme_bw()+
theme( panel.grid.major = element_line(colour = "#d3d3d3"),
panel.grid.minor = element_blank(),
#panel.border = element_blank(colour = "black"),
panel.background = element_blank(),
#plot.title = element_text(size = 14, family = "Tahoma", face = "bold"),
#text=element_text(family = "Tahoma"),
axis.title = element_text(face="bold"),
axis.text.x = element_text(colour="black", size = 25),
axis.text.y = element_text(colour="black", size = 25),
axis.title.y = element_text(colour="black", size = 35),
axis.title.x = element_text(colour="black", size = 35),
plot.tag = element_text(size = 40),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),