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make-table.r
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make-table.r
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library(matrixStats)
args <- commandArgs(trailingOnly = TRUE)
filename <- args[1]
print(sprintf("Running %s",filename))
load(filename)
df_pop= read.csv("data/popt_ifr.csv", stringsAsFactors = FALSE)
df_pop$country[df_pop$country == "United Kingdom"] = "United_Kingdom"
dates_italy <- dates[[which(countries == "Italy")]]
len_dates <- length(dates_italy)
date_till_percentage <- as.character(Sys.Date())
if(date_till_percentage > max(dates[[which(countries == "Italy")]]))
date_till_percentage = max(dates[[which(countries == "Italy")]])
cases <- vector("list", length = length(countries))
total_cases <- vector("list", length = length(countries))
total_cases_ui <- vector("list", length = length(countries))
total_cases_li <- vector("list", length = length(countries))
deaths <- vector("list", length = length(countries))
total_deaths <- vector("list", length = length(countries))
rt <- vector("list", length = length(countries))
fraction_infected <- vector("list", length = length(countries))
fraction_infected_li <- vector("list", length = length(countries))
fraction_infected_ui <- vector("list", length = length(countries))
fraction_obs_infected <- vector("list", length = length(countries))
fraction_total_obs_infected <- vector("list", length = length(countries))
y <- vector("list", length = length(countries))
for(i in 1:length(countries)) {
Country = countries[i]
x = dates[[i]]
N = length(x)
forecast = 7
x = c(x,x[length(x)]+1:forecast)
padding <- len_dates - length(dates[[i]])
y[[i]] = c(rep(0, padding),reported_cases[[i]], rep(NA, forecast))
cases[[i]] = c(rep(0, padding), round(colMeans(prediction[,1:length(x),i])))
total_cases[[i]] = c( round(cumsum(colMeans(prediction[,1:length(x),i]))))
# chk = c(round((colMeans(rowCumsums(prediction[,1:length(x),i])))))
total_cases_li[[i]] = c(
round((colQuantiles(rowCumsums(prediction[,1:length(x),i]),probs=.025))))
total_cases_ui[[i]] = c(
round((colQuantiles(rowCumsums(prediction[,1:length(x),i]),probs=.975))))
deaths[[i]] = c(rep(0, padding), round(colMeans(estimated.deaths[,1:length(x),i])))
total_deaths[[i]] = c(rep(0, padding), round(cumsum(colMeans(estimated.deaths[,1:length(x),i]))))
rt[[i]] = c(rep(NA, padding), colMeans(out$Rt[,1:length(x),i]))
fraction_infected[[i]] = c(rep(0, padding), total_cases[[i]]/ df_pop[df_pop$country==Country,]$popt)
fraction_infected_li[[i]] = c(rep(0, padding),
total_cases_li[[i]]/ df_pop[df_pop$country==Country,]$popt)
fraction_infected_ui[[i]] = c(rep(0, padding),
total_cases_ui[[i]]/ df_pop[df_pop$country==Country,]$popt)
fraction_obs_infected[[i]] = c(rep(0, padding), y[[i]] / cases[[i]])
fraction_total_obs_infected[[i]] = c(rep(0, padding), cumsum(y[[i]]) / cases[[i]])
total_cases[[i]] = c(rep(0, padding),total_cases[[i]])
}
dates_italy = c(dates_italy,dates_italy[length(dates_italy)]+1:forecast)
cases <- do.call(rbind, cases)
cases_df <- as.data.frame(cases)
names(cases_df) <- dates_italy
cases_df$countries <- countries
# write.csv(cases_df, "figures/cases.csv")
total_cases <- do.call(rbind, total_cases)
total_cases_df <- as.data.frame(total_cases)
names(total_cases_df) <- dates_italy
total_cases_df$countries <- countries
# write.csv(total_cases_df, "figures/total_cases.csv")
deaths <- do.call(rbind, deaths)
deaths_df <- as.data.frame(deaths)
names(deaths_df) <- dates_italy
deaths_df$countries <- countries
# write.csv(deaths_df, "figures/deaths.csv")
total_deaths <- do.call(rbind, total_deaths)
total_deaths_df <- as.data.frame(total_deaths)
names(total_deaths_df) <- dates_italy
total_deaths_df$countries <- countries
# write.csv(total_deaths_df, "figures/total_deaths.csv")
rt <- do.call(rbind, rt)
rt_df <- as.data.frame(rt)
names(rt_df) <- dates_italy
rt_df$countries <- countries
# write.csv(rt_df, "figures/rt.csv")
fraction_infected <- do.call(rbind, fraction_infected)
fraction_infected_df <- as.data.frame(fraction_infected)
names(fraction_infected_df) <- dates_italy
fraction_infected_df$countries <- countries
# write.csv(fraction_infected_df, "figures/fraction_infected.csv")
fraction_infected_li <- do.call(rbind, fraction_infected_li)
fraction_infected_li_df <- as.data.frame(fraction_infected_li)
names(fraction_infected_li_df) <- dates_italy
fraction_infected_li_df$countries <- countries
# write.csv(fraction_infected_li_df, "figures/fraction_infected_li.csv")
fraction_infected_ui <- do.call(rbind, fraction_infected_ui)
fraction_infected_ui_df <- as.data.frame(fraction_infected_ui)
names(fraction_infected_ui_df) <- dates_italy
fraction_infected_ui_df$countries <- countries
# write.csv(fraction_infected_ui_df, "figures/fraction_infected_ui.csv")
total_infected = data.frame(countries=countries,mean=fraction_infected[,dates_italy == date_till_percentage],
li=fraction_infected_li[,dates_italy == date_till_percentage],ui=fraction_infected_ui[,dates_italy == date_till_percentage])
total_infected$value = sprintf("%.02f%% [%.02f%%-%.02f%%]",
total_infected$mean*100,total_infected$li*100,total_infected$ui*100)
total_infected[order(total_infected$countries),c("countries","value")]
total_infected <- total_infected[,c("countries","value")]
write.csv(total_infected,paste0("results/total_infected_",date_till_percentage,".csv"),row.names=F)
# Store copy for web output
dir.create("web/data/", showWarnings = FALSE, recursive = TRUE)
write.csv(total_infected,paste0("web/data/total_infected.csv"),row.names=F)
fraction_obs_infected <- do.call(rbind, fraction_obs_infected)
fraction_obs_infected_df <- as.data.frame(fraction_obs_infected)
names(fraction_obs_infected_df) <- dates_italy
fraction_obs_infected_df$countries <- countries
# write.csv(fraction_obs_infected_df, "figures/fraction_obs_infected.csv")
fraction_total_obs_infected <- do.call(rbind, fraction_total_obs_infected)
fraction_total_obs_infected_df <- as.data.frame(fraction_total_obs_infected)
names(fraction_total_obs_infected_df) <- dates_italy
fraction_total_obs_infected_df$countries <- countries
# write.csv(fraction_total_obs_infected_df, "figures/fraction_total_obs_infected.csv")