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plot_hosp_trends_divid_twographs_map_lastweek.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 hospitalisations data
dat <- read.csv("https://epistat.sciensano.be/Data/COVID19BE_HOSP.csv")
# aggregate new intakes by province and date
dat <- aggregate(NEW_IN ~ DATE + PROVINCE, dat, sum)
# add new intakes for Belgium as a whole
belgium <- aggregate(NEW_IN ~ DATE, dat, sum)
belgium$PROVINCE <- "Belgium"
col_order <- c("DATE", "PROVINCE", "NEW_IN")
belgium <- belgium[, col_order]
dat <- rbind(dat, belgium)
# transform date and provinces
dat$DATE <- as.Date(dat$DATE)
dat$PROVINCE <- factor(dat$PROVINCE,
levels = c(
"Antwerpen",
"BrabantWallon",
"Brussels",
"Hainaut",
"Liège",
"Limburg",
"Luxembourg",
"Namur",
"OostVlaanderen",
"VlaamsBrabant",
"WestVlaanderen",
"Belgium"
),
labels = c(
"Antwerpen",
"Brabant Wallon",
"Brussels",
"Hainaut",
"Liège",
"Limburg",
"Luxembourg",
"Namur",
"Oost-Vlaanderen",
"Vlaams-Brabant",
"West-Vlaanderen",
"Belgique/België"
)
)
# compute NEW_IN by population size
dat <- dat %>%
mutate(population = case_when(
PROVINCE == "Antwerpen" ~ 1857986,
PROVINCE == "Brabant Wallon" ~ 403599,
PROVINCE == "Brussels" ~ 1208542,
PROVINCE == "Hainaut" ~ 1344241,
PROVINCE == "Liège" ~ 1106992,
PROVINCE == "Limburg" ~ 874048,
PROVINCE == "Luxembourg" ~ 284638,
PROVINCE == "Namur" ~ 494325,
PROVINCE == "Oost-Vlaanderen" ~ 1515064,
PROVINCE == "Vlaams-Brabant" ~ 1146175,
PROVINCE == "West-Vlaanderen" ~ 1195796,
PROVINCE == "Belgique/België" ~ 11431406
)) %>%
mutate(NEW_IN_divid = NEW_IN / population * 100000)
# Create plot in dutch/fr
fig_trends <- ggplot(
dat,
aes(x = DATE, y = NEW_IN_divid)
) +
geom_point(
size = 1L,
colour = "steelblue"
) +
labs(x = "", y = "Nombre d'hospitalisations (par 100,000 habitants) / Hospitalisaties (per 100,000 inwoners)") +
theme_minimal() +
facet_wrap(vars(PROVINCE),
scales = "free"
) +
geom_smooth(
se = FALSE,
col = "grey",
method = "gam",
formula = y ~ s(x)
) +
geom_vline(
xintercept = as.Date("2020-05-04"), linetype = "dashed",
color = "darkgrey", size = 0.5
) +
geom_text(aes(x = as.Date("2020-05-04"), label = "1a", y = 9),
colour = "darkgrey", angle = 90, vjust = 1.3
) +
geom_vline(
xintercept = as.Date("2020-05-11"), linetype = "dashed",
color = "darkgrey", size = 0.5
) +
geom_text(aes(x = as.Date("2020-05-11"), label = "1b", y = 9),
colour = "darkgrey", angle = 90, vjust = 1.3
) +
geom_vline(
xintercept = as.Date("2020-05-18"), linetype = "dashed",
color = "darkgrey", size = 0.5
) +
geom_text(aes(x = as.Date("2020-05-18"), label = "2", y = 9),
colour = "darkgrey", angle = 90, vjust = 1.3
) +
annotate("rect",
ymin = -Inf, ymax = Inf,
xmin = as.Date("2020-03-15"), xmax = as.Date("2020-04-01"),
alpha = .2
) +
annotate("rect",
ymin = -Inf, ymax = Inf,
xmin = as.Date("2020-04-01"), xmax = as.Date("2020-05-01"),
alpha = .05
) +
annotate("rect",
ymin = -Inf, ymax = Inf,
xmin = as.Date("2020-05-01"), xmax = as.Date("2020-06-01"),
alpha = .2
) +
annotate("rect",
ymin = -Inf, ymax = Inf,
xmin = as.Date("2020-06-01"), xmax = as.Date("2020-06-05"),
alpha = .05
) +
labs(
title = "Evolution des admissions hospitalières / Evolutie van de hospitalisaties - COVID-19"
) +
scale_y_continuous(breaks = seq(from = 0, to = 10, by = 2), limits = c(0, 10)) +
scale_x_date(labels = date_format("%m-%Y"))
## adjust caption at the end of the trend figure
caption <- grobTree(
textGrob(" * Lignes solides : courbes ajustées aux observations / Volle lijnen : gefitte curves \n * Lignes pointillées : phases de déconfinement 1a, 1b & 2 / Gestippelde lijnen: fases afbouw lockdown maatregelen 1a, 1b & 2",
x = 0, hjust = 0, vjust = 0,
gp = gpar(col = "darkgray", fontsize = 7, lineheight = 1.2)
),
textGrob("Niko Speybroeck (@NikoSpeybroeck), Antoine Soetewey (@statsandr) & Angel Rosas (@arosas_aguirre) \n Data: https://epistat.wiv-isp.be/covid/ ",
x = 1, hjust = 1, vjust = 0,
gp = gpar(col = "black", fontsize = 7.5, lineheight = 1.2)
),
cl = "ann"
)
##### MAPS
### Obtaining Belgium shapefile at province level
library(GADMTools)
library(RColorBrewer)
library(tmap)
## sf structure
map <- gadm_sf_loadCountries(c("BEL"), level = 2, basefile = "./")$sf
map <- map %>%
rename("PROVINCE" = NAME_2)
map$PROVINCE[c(1, 5)] <- c("Brussels", "Vlaams-Brabant")
## agregating data
dat_ag <- dat %>%
group_by(PROVINCE) %>%
summarize(
"new_in" = sum(NEW_IN, na.rm = T),
"new_in2" = sum(NEW_IN[DATE >= (Sys.Date() - 15)], na.rm = T),
"population" = max(population, na.rm = T)
) %>%
mutate(
new_in_divid = new_in / population * 100000,
new_in_divid2 = new_in2 / population * 100000
)
map.data <- left_join(map, dat_ag, by = "PROVINCE")
map.data <- subset(map.data, !PROVINCE %in% "Belgium")
###### MAPS WITH GGPLOT
points <- st_centroid(map.data)
points <- cbind(map.data, st_coordinates(st_centroid(map.data$geometry)))
points <- mutate(points,
num_1 = paste("(", round(new_in_divid, 1), ")"),
num_2 = paste("(", round(new_in_divid2, 1), ")"),
q1 = as.numeric(cut(new_in_divid,
breaks = quantile(new_in_divid, probs = seq(0, 1, by = 0.25), na.rm = TRUE),
include.lowest = TRUE
)),
q2 = as.numeric(cut(new_in_divid2,
breaks = quantile(new_in_divid2, probs = seq(0, 1, by = 0.25), na.rm = TRUE),
include.lowest = TRUE
)),
q1 = as.factor(ifelse(q1 < 4, 1, 2)),
q2 = as.factor(ifelse(q2 < 4, 1, 2))
)
points1 <- subset(points, !PROVINCE %in% "Vlaams-Brabant")
points2 <- subset(points, PROVINCE %in% "Vlaams-Brabant")
period1 <- paste0("Période / periode : 15/03 - ", format(Sys.Date() - 1, format = "%d/%m"), " ")
period2 <- paste0(
"Période / periode : ", format(Sys.Date() - 15, format = "%d/%m"), " - ",
format(Sys.Date() - 1, format = "%d/%m"), " "
)
fig_map1 <- ggplot(map.data) +
geom_sf(aes(fill = new_in_divid)) +
# here you can change the number of blues in the pallete "n" (maximum=9)
scale_fill_gradientn(colors = brewer.pal(n = 9, name = "Blues")) +
geom_text(
data = points1, aes(x = X, y = Y + 0.03, label = PROVINCE, colour = q1), size = 3,
check_overlap = TRUE
) +
scale_colour_manual(values = c("black", "white"), guide = FALSE) +
geom_text(
data = points1, aes(x = X, y = Y - 0.03, label = num_1, colour = q1), size = 3,
check_overlap = TRUE
) +
geom_text(
data = points2, aes(x = X + 0.07, y = Y + 0.09, label = PROVINCE), col = "black", size = 3,
check_overlap = TRUE
) +
geom_text(
data = points2, aes(x = X + 0.07, y = Y + 0.03, label = num_1), col = "black", size = 3,
check_overlap = TRUE
) +
labs(fill = bquote(atop(NA, atop("Admissions hospitalières / \nHospitalisaties (x 100,000 hab./inw.)", bold(.(period1)))))) +
theme_void() +
theme(
# Change legend
legend.position = c(0.2, 0.22),
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")
)
fig_map2 <- ggplot(map.data) +
geom_sf(aes(fill = new_in_divid2)) +
# here you can change the number of blues in the pallete "n" (maximum=9)
scale_fill_gradientn(colors = brewer.pal(n = 9, name = "Blues")) +
geom_text(
data = points1, aes(x = X, y = Y + 0.03, label = PROVINCE, colour = q2), size = 3,
check_overlap = TRUE
) +
scale_colour_manual(values = c("black", "white"), guide = FALSE) +
geom_text(
data = points1, aes(x = X, y = Y - 0.03, label = num_2, colour = q2), size = 3,
check_overlap = TRUE
) +
geom_text(
data = points2, aes(x = X + 0.07, y = Y + 0.09, label = PROVINCE), col = "black", size = 3,
check_overlap = TRUE
) +
geom_text(
data = points2, aes(x = X + 0.07, y = Y + 0.03, label = num_2), col = "black", size = 3,
check_overlap = TRUE
) +
labs(fill = bquote(atop(NA, atop("Admissions hospitalières / \nHospitalisaties (x 100,000 hab./inw.)", bold(.(period2)))))) +
theme_void() +
theme(
# Change legend
legend.position = c(0.2, 0.22),
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_Hospitalisations_COVID-19_0106.png", width = 15 * 360, heigh = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(ggarrange(fig_map1, fig_map2, ncol = 1),
grid.arrange(fig_trends, bottom = caption),
ncol = 2, widths = c(1, 1.5)
)
dev.off()