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plot-3-panel.r
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plot-3-panel.r
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library(ggplot2)
library(tidyr)
library(dplyr)
library(rstan)
library(data.table)
library(lubridate)
library(gdata)
library(EnvStats)
library(matrixStats)
library(scales)
library(gridExtra)
library(ggpubr)
library(bayesplot)
library(cowplot)
source("utils/geom-stepribbon.r")
#---------------------------------------------------------------------------
make_three_pannel_plot <- function(){
args <- commandArgs(trailingOnly = TRUE)
filename2 <- args[1]
load(paste0("results/", filename2))
print(sprintf("loading: %s",paste0("results/",filename2)))
data_interventions <- read.csv("data/interventions.csv",
stringsAsFactors = FALSE)
covariates <- data_interventions[1:11, c(1,2,3,4,5,6, 7, 8)]
for(i in 1:11){
print(i)
N <- length(dates[[i]])
country <- countries[[i]]
predicted_cases <- colMeans(prediction[,1:N,i])
predicted_cases_li <- colQuantiles(prediction[,1:N,i], probs=.025)
predicted_cases_ui <- colQuantiles(prediction[,1:N,i], probs=.975)
predicted_cases_li2 <- colQuantiles(prediction[,1:N,i], probs=.25)
predicted_cases_ui2 <- colQuantiles(prediction[,1:N,i], probs=.75)
estimated_deaths <- colMeans(estimated.deaths[,1:N,i])
estimated_deaths_li <- colQuantiles(estimated.deaths[,1:N,i], probs=.025)
estimated_deaths_ui <- colQuantiles(estimated.deaths[,1:N,i], probs=.975)
estimated_deaths_li2 <- colQuantiles(estimated.deaths[,1:N,i], probs=.25)
estimated_deaths_ui2 <- colQuantiles(estimated.deaths[,1:N,i], probs=.75)
rt <- colMeans(out$Rt[,1:N,i])
rt_li <- colQuantiles(out$Rt[,1:N,i],probs=.025)
rt_ui <- colQuantiles(out$Rt[,1:N,i],probs=.975)
rt_li2 <- colQuantiles(out$Rt[,1:N,i],probs=.25)
rt_ui2 <- colQuantiles(out$Rt[,1:N,i],probs=.75)
# delete these 2 lines
covariates_country <- covariates[which(covariates$Country == country), 2:8]
# Remove sport
covariates_country$sport = NULL
covariates_country$travel_restrictions = NULL
covariates_country_long <- gather(covariates_country[], key = "key",
value = "value")
covariates_country_long$x <- rep(NULL, length(covariates_country_long$key))
un_dates <- unique(covariates_country_long$value)
for (k in 1:length(un_dates)){
idxs <- which(covariates_country_long$value == un_dates[k])
max_val <- round(max(rt_ui)) + 0.3
for (j in idxs){
covariates_country_long$x[j] <- max_val
max_val <- max_val - 0.3
}
}
covariates_country_long$value <- as_date(covariates_country_long$value)
covariates_country_long$country <- rep(country,
length(covariates_country_long$value))
data_country <- data.frame("time" = as_date(as.character(dates[[i]])),
"country" = rep(country, length(dates[[i]])),
"reported_cases" = reported_cases[[i]],
"reported_cases_c" = cumsum(reported_cases[[i]]),
"predicted_cases_c" = cumsum(predicted_cases),
"predicted_min_c" = cumsum(predicted_cases_li),
"predicted_max_c" = cumsum(predicted_cases_ui),
"predicted_cases" = predicted_cases,
"predicted_min" = predicted_cases_li,
"predicted_max" = predicted_cases_ui,
"predicted_min2" = predicted_cases_li2,
"predicted_max2" = predicted_cases_ui2,
"deaths" = deaths_by_country[[i]],
"deaths_c" = cumsum(deaths_by_country[[i]]),
"estimated_deaths_c" = cumsum(estimated_deaths),
"death_min_c" = cumsum(estimated_deaths_li),
"death_max_c"= cumsum(estimated_deaths_ui),
"estimated_deaths" = estimated_deaths,
"death_min" = estimated_deaths_li,
"death_max"= estimated_deaths_ui,
"death_min2" = estimated_deaths_li2,
"death_max2"= estimated_deaths_ui2,
"rt" = rt,
"rt_min" = rt_li,
"rt_max" = rt_ui,
"rt_min2" = rt_li2,
"rt_max2" = rt_ui2)
make_plots(data_country = data_country,
covariates_country_long = covariates_country_long,
filename2 = filename2,
country = country)
}
}
#---------------------------------------------------------------------------
make_plots <- function(data_country, covariates_country_long,
filename2, country){
data_cases_95 <- data.frame(data_country$time, data_country$predicted_min,
data_country$predicted_max)
names(data_cases_95) <- c("time", "cases_min", "cases_max")
data_cases_95$key <- rep("nintyfive", length(data_cases_95$time))
data_cases_50 <- data.frame(data_country$time, data_country$predicted_min2,
data_country$predicted_max2)
names(data_cases_50) <- c("time", "cases_min", "cases_max")
data_cases_50$key <- rep("fifty", length(data_cases_50$time))
data_cases <- rbind(data_cases_95, data_cases_50)
levels(data_cases$key) <- c("ninetyfive", "fifty")
p1 <- ggplot(data_country) +
geom_bar(data = data_country, aes(x = time, y = reported_cases),
fill = "coral4", stat='identity', alpha=0.5) +
geom_ribbon(data = data_cases,
aes(x = time, ymin = cases_min, ymax = cases_max, fill = key)) +
xlab("") +
ylab("Daily number of infections") +
scale_x_date(date_breaks = "weeks", labels = date_format("%e %b")) +
scale_fill_manual(name = "", labels = c("50%", "95%"),
values = c(alpha("deepskyblue4", 0.55),
alpha("deepskyblue4", 0.45))) +
theme_pubr() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "None") +
guides(fill=guide_legend(ncol=1))
data_deaths_95 <- data.frame(data_country$time, data_country$death_min,
data_country$death_max)
names(data_deaths_95) <- c("time", "death_min", "death_max")
data_deaths_95$key <- rep("nintyfive", length(data_deaths_95$time))
data_deaths_50 <- data.frame(data_country$time, data_country$death_min2,
data_country$death_max2)
names(data_deaths_50) <- c("time", "death_min", "death_max")
data_deaths_50$key <- rep("fifty", length(data_deaths_50$time))
data_deaths <- rbind(data_deaths_95, data_deaths_50)
levels(data_deaths$key) <- c("ninetyfive", "fifty")
p2 <- ggplot(data_country, aes(x = time)) +
geom_bar(data = data_country, aes(y = deaths, fill = "reported"),
fill = "coral4", stat='identity', alpha=0.5) +
geom_ribbon(
data = data_deaths,
aes(ymin = death_min, ymax = death_max, fill = key)) +
scale_x_date(date_breaks = "weeks", labels = date_format("%e %b")) +
scale_fill_manual(name = "", labels = c("50%", "95%"),
values = c(alpha("deepskyblue4", 0.55),
alpha("deepskyblue4", 0.45))) +
theme_pubr() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "None") +
guides(fill=guide_legend(ncol=1))
plot_labels <- c("Complete lockdown",
"Public events banned",
"School closure",
"Self isolation",
"Social distancing")
# Plotting interventions
data_rt_95 <- data.frame(data_country$time,
data_country$rt_min, data_country$rt_max)
names(data_rt_95) <- c("time", "rt_min", "rt_max")
data_rt_95$key <- rep("nintyfive", length(data_rt_95$time))
data_rt_50 <- data.frame(data_country$time, data_country$rt_min2,
data_country$rt_max2)
names(data_rt_50) <- c("time", "rt_min", "rt_max")
data_rt_50$key <- rep("fifty", length(data_rt_50$time))
data_rt <- rbind(data_rt_95, data_rt_50)
levels(data_rt$key) <- c("ninetyfive", "fifth")
p3 <- ggplot(data_country) +
geom_stepribbon(data = data_rt, aes(x = time, ymin = rt_min, ymax = rt_max,
group = key,
fill = key)) +
geom_hline(yintercept = 1, color = 'black', size = 0.1) +
geom_segment(data = covariates_country_long,
aes(x = value, y = 0, xend = value, yend = max(x)),
linetype = "dashed", colour = "grey", alpha = 0.75) +
geom_point(data = covariates_country_long, aes(x = value,
y = x,
group = key,
shape = key,
col = key), size = 2) +
xlab("") +
ylab(expression(R[t])) +
scale_fill_manual(name = "", labels = c("50%", "95%"),
values = c(alpha("seagreen", 0.75), alpha("seagreen", 0.5))) +
scale_shape_manual(name = "Interventions", labels = plot_labels,
values = c(21, 22, 23, 24, 25, 12)) +
scale_colour_discrete(name = "Interventions", labels = plot_labels) +
scale_x_date(date_breaks = "weeks", labels = date_format("%e %b"),
limits = c(data_country$time[1],
data_country$time[length(data_country$time)])) +
theme_pubr() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(legend.position="right")
p <- plot_grid(p1, p2, p3, ncol = 3, rel_widths = c(1, 1, 2))
save_plot(filename = paste0("figures/", country, "_three_pannel_", filename2, ".pdf"),
p, base_width = 14)
}
#-----------------------------------------------------------------------------------------------
#filename <- "base-joint-1236305.pbs.Rdata"
# make_three_pannel_plot(filename)
make_three_pannel_plot()