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plot_hosp_trends_divid_twographs_total_2810.R
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plot_hosp_trends_divid_twographs_total_2810.R
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# clean environment
remove(list = ls())
# required packages
library(ggplot2)
library(ggrepel)
library(zoo)
library(lme4)
library(dplyr)
library(scales)
library(ggpubr)
library(grid)
library(gridExtra)
# import Sciensano hospitalizations data
dat <- read.csv("https://epistat.sciensano.be/Data/COVID19BE_HOSP.csv", fileEncoding = "UTF-8", stringsAsFactors = FALSE)
# aggregate new intakes by province and date
dat <- dat %>%
mutate(
DATE = as.Date(DATE),
PROVINCE2 = case_when(
PROVINCE %in% c("BrabantWallon", "VlaamsBrabant", "Brussels") ~ "Brabant",
!PROVINCE %in% c("BrabantWallon", "VlaamsBrabant", "Brussels") ~ PROVINCE
),
PROVINCE2 = case_when(
PROVINCE == "OostVlaanderen" ~ "Oost-Vlaanderen",
PROVINCE == "WestVlaanderen" ~ "West-Vlaanderen",
!PROVINCE %in% c("OostVlaanderen", "WestVlaanderen") ~ PROVINCE2
),
PROVINCE = PROVINCE2
)
dat <- aggregate(TOTAL_IN ~ DATE + PROVINCE, dat, sum)
# add new intakes for Belgium as a whole
belgium <- aggregate(TOTAL_IN ~ DATE, dat, sum) %>%
mutate(PROVINCE = "Belgium") %>%
select(DATE, PROVINCE, TOTAL_IN)
##
dat <- rbind(dat, belgium) %>%
mutate(
population = case_when(
PROVINCE == "Antwerpen" ~ 1857986,
PROVINCE == "Brabant" ~ 403599 + 1208542 + 1146175,
PROVINCE == "Hainaut" ~ 1344241,
PROVINCE == "Liège" ~ 1106992,
PROVINCE == "Limburg" ~ 874048,
PROVINCE == "Luxembourg" ~ 284638,
PROVINCE == "Namur" ~ 494325,
PROVINCE == "Oost-Vlaanderen" ~ 1515064,
PROVINCE == "West-Vlaanderen" ~ 1195796,
PROVINCE == "Belgium" ~ 11431406
),
TOTAL_IN_divid = TOTAL_IN / population * 100000
)
dat$PROVINCE <- relevel(as.factor(dat$PROVINCE), ref = "Belgium")
# choose period
period <- "2020-09-20"
subdat <- subset(dat, DATE >= period)
period2 <- min(dat$DATE) + (max(dat$DATE) - as.Date(period))
subdat2 <- subset(dat, DATE <= period2)
time_diff <- as.Date(period) - min(subdat2$DATE)
subdat2$DATE <- subdat2$DATE + time_diff
break.vec <- c(seq(
from = as.Date(period), to = max(dat$DATE),
by = "2 weeks"
))
# Create plot in English
fig_trends <- ggplot(
subdat,
aes(x = DATE, y = TOTAL_IN_divid)
) +
# geom_vline(
# xintercept = as.Date("2020-07-01"), linetype = "dashed",
# color = "lightgrey", size = 0.5
# ) +
# geom_vline(
# xintercept = as.Date("2020-08-01"), linetype = "dashed",
# color = "lightgrey", size = 0.5
# ) +
# geom_vline(
# xintercept = as.Date("2020-09-01"), linetype = "dashed",
# color = "lightgrey", size = 0.5
# ) +
geom_point(
data = subdat2,
aes(x = DATE, y = TOTAL_IN_divid),
col = "grey",
shape = 1
) +
geom_line(
data = subdat2,
aes(x = DATE, y = TOTAL_IN_divid),
stat = "smooth",
method = "gam",
formula = y ~ s(x),
col = "grey",
alpha = 0.5,
size = 1L,
linetype = 1
) +
geom_point(
colour = "steelblue",
shape = 1
) +
labs(x = "", y = "Number of patients in hospitals (per 100,00 inhabitants)") +
theme_minimal() +
facet_wrap(vars(PROVINCE),
scales = "free",
ncol = 5
) +
geom_smooth(
se = FALSE,
col = "steelblue",
method = "gam",
formula = y ~ s(x)
) +
labs(
title = "Evolution of patients in hospitals in Belgium - COVID-19",
subtitle = paste0(format(as.Date(period), "%B %d"), " to ", format(max(dat$DATE), "%B %d"), " (in blue) vs. ", format(min(dat$DATE), "%B %d"), " to ", format(period2, "%B %d"), " (in gray)")
) +
scale_y_continuous(
breaks = seq(from = 0, to = max(subdat$TOTAL_IN_divid), by = 10),
limits = c(0, max(subdat$TOTAL_IN_divid))
) +
scale_x_date(
labels = date_format("%d/%m"),
# date_breaks = "2 weeks",
breaks = break.vec,
sec.axis = sec_axis(~ . - time_diff,
labels = date_format("%d/%m"),
breaks = break.vec - time_diff
)
) +
theme(
axis.title = element_text(size = 14),
plot.title = element_text(size = 16, face = "bold"),
axis.text = element_text(size = 12),
axis.text.x = element_text(colour = "steelblue"),
axis.text.x.top = element_text(color = "darkgray"),
strip.text = element_text(size = 12),
strip.placement = "outside",
plot.margin = unit(c(5.5, 5.5, 20, 5.5), "points")
)
dat_peak <- data.frame(
PROVINCE = as.factor(levels(dat$PROVINCE)),
h_int = aggregate(y ~ PANEL, data = ggplot_build(fig_trends)[["data"]][[2]], max)$y,
PANEL = 1:nlevels(dat$PROVINCE)
)
fig_trends <- fig_trends +
geom_hline(
data = dat_peak,
aes(yintercept = h_int),
linetype = "dashed",
size = 0.75,
color = "darkgrey",
alpha = 0.75
) +
facet_wrap(~PROVINCE,
scales = "free",
ncol = 5
)
fig_trends
## adjust caption at the end of the trend figure
caption <- grobTree(
textGrob(paste0("* ", format(as.Date(period), "%d/%m"), " to ", format(max(dat$DATE), "%d/%m"), " in blue; ", format(min(dat$DATE), "%d/%m"), " to ", format(period2, "%d/%m"), " in gray\n* Solid lines: curves fitted to observations"),
x = 0, hjust = 0, vjust = 0,
gp = gpar(col = "darkgrey", fontsize = 8, lineheight = 1.2)
),
textGrob("Niko Speybroeck (@NikoSpeybroeck), Antoine Soetewey (@statsandr) & Angel Rosas (@arosas_aguirre) \nData: https://epistat.wiv-isp.be/covid/ ",
x = 1, hjust = 1, vjust = 0,
gp = gpar(col = "black", fontsize = 10, lineheight = 1.2)
),
cl = "ann"
)
##### MAPS
### Obtaining Belgium shapefile at province level
library(GADMTools)
library(RColorBrewer)
library(tmap)
library(sf)
maxi <- max(dat$DATE)
mini <- max(dat$DATE) - 13
divi <- length(mini:maxi)
# agregated data to join with the map
# calculating the daily rate in two periods :
## 1. last week of March- first week of April (14 days)
## 2. Last 14 days reported by Scienciano
dat$PROVINCE <- as.character(dat$PROVINCE)
dat_ag <- filter(dat, PROVINCE != "Belgium") %>%
group_by(PROVINCE) %>%
summarize(
"per1" = sum(TOTAL_IN_divid[DATE >= as.Date("2020-03-25") & DATE <= as.Date("2020-04-07")], na.rm = T) / 14,
"per2" = sum(TOTAL_IN_divid[DATE >= mini & DATE <= maxi], na.rm = T) / divi
)
## sf structure
map <- gadm_sf_loadCountries(c("BEL"), level = 2, basefile = "./")$sf
map <- map %>%
mutate(PROVINCE = case_when(
NAME_2 %in% c("Brabant Wallon", "Vlaams Brabant", "Bruxelles") ~ "Brabant",
!NAME_2 %in% c("Brabant Wallon", "Vlaams Brabant", "Bruxelles") ~ NAME_2
)) %>%
group_by(PROVINCE) %>%
summarise(geometry = st_union(geometry)) %>%
left_join(dat_ag, by = "PROVINCE") %>%
mutate(
class1 = cut(per1,
breaks = c(0, 0.5, 1.0, 1.5, 3, 5, 7) * 10,
include.lowest = TRUE,
labels = c("[ 0, 5 ]", "] 5, 10 ]", "] 10, 15 ]", "] 15, 30 ]", "] 30, 50 ]", " > 50")
),
class2 = cut(per2,
breaks = c(0, 0.5, 1.0, 1.5, 3, 5, 7) * 10,
include.lowest = TRUE,
labels = c("[ 0, 5 ]", "] 5, 10 ]", "] 10, 15 ]", "] 15, 30 ]", "] 30, 50 ]", " > 50")
)
)
###### MAPS WITH GGPLOT
points <- st_centroid(map)
points <- cbind(map, st_coordinates(st_centroid(map$geometry)))
points <- mutate(points,
num_1 = paste("(", format(round(per1, 2), nsmall = 2), ")"),
num_2 = paste("(", format(round(per2, 2), nsmall = 2), ")")
)
period1 <- paste0("Period: 25/03 - 07/04", " ")
period2 <- paste0(
"Period: ", format(mini, format = "%d/%m"), " - ",
format(maxi, format = "%d/%m"), " "
)
library(RColorBrewer)
reds <- brewer.pal(7, "Reds")
blues <- brewer.pal(7, "Blues")
map1 <- ggplot(map) +
geom_sf(aes(fill = class1)) +
scale_fill_manual(values = blues, drop = FALSE) +
geom_text(
data = points, aes(x = X, y = Y + 0.06, label = PROVINCE), col = "black", size = 2.4, nudge_x = -0.07,
check_overlap = TRUE
) +
geom_text(
data = points, aes(x = X, y = Y, label = num_1), col = "black", size = 3, nudge_x = -0.07,
check_overlap = TRUE
) +
labs(fill = bquote(atop(NA, atop("Average number of patients\nin hospitals (x100,000 inh.)", bold(.(period1)))))) +
theme_void() +
theme(
# Change legend
legend.position = c(0.2, 0.2),
legend.key.size = unit(0.9, "line"),
legend.title = element_text(size = 12, color = "black"),
legend.text = element_text(color = "black"),
plot.margin = unit(c(+0.2, 0, +0.5, 3), "cm")
)
map2 <- ggplot(map) +
geom_sf(aes(fill = class2)) +
scale_fill_manual(values = blues, drop = FALSE) +
geom_text(
data = points, aes(x = X, y = Y + 0.06, label = PROVINCE), col = "black", size = 2.4, nudge_x = -0.07,
check_overlap = TRUE
) +
geom_text(
data = points, aes(x = X, y = Y, label = num_2), col = "black", size = 3, nudge_x = -0.07,
check_overlap = TRUE
) +
labs(fill = bquote(atop(NA, atop("Average number of patients\nin hospitals (x100,000 inh.)", bold(.(period2)))))) +
theme_void() +
theme(
# Change legend
legend.position = c(0.2, 0.2),
legend.key.size = unit(0.9, "line"),
legend.title = element_text(size = 12, color = "black"),
legend.text = element_text(color = "black"),
plot.margin = unit(c(+0.2, 0, +0.5, 3), "cm")
)
# save plot
png(file = "Belgian_Hospitalizations_total_2810.png", width = 15 * 360, height = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(ggarrange(map1, map2, ncol = 1),
grid.arrange(fig_trends, bottom = caption),
ncol = 2, widths = c(1, 1.5)
)
dev.off()