/
plot_hosp_trends_divid_4weeks_limited.R
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plot_hosp_trends_divid_4weeks_limited.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)
library(broom)
# 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$DATE)
dat <- subset(dat, DATE >= max(dat$DATE) - 27)
dat$PROVINCE <- factor(dat$PROVINCE,
levels = c(
"Antwerpen",
"BrabantWallon",
"Brussels",
"Hainaut",
unique(dat$PROVINCE)[5],
"Limburg",
"Luxembourg",
"Namur",
"OostVlaanderen",
"VlaamsBrabant",
"WestVlaanderen",
"Belgium"
),
labels = c(
"Antwerpen",
"Brabant Wallon",
"Brussels",
"Hainaut",
"Liège",
"Limburg",
"Luxembourg",
"Namur",
"Oost-Vlaanderen",
"Vlaams-Brabant",
"West-Vlaanderen",
"Belgique/België"
)
)
# compute NEW_IN by population size
dat <- dat %>%
mutate(population = case_when(
PROVINCE == levels(dat$PROVINCE)[1] ~ 1857986,
PROVINCE == levels(dat$PROVINCE)[2] ~ 403599,
PROVINCE == levels(dat$PROVINCE)[3] ~ 1208542,
PROVINCE == levels(dat$PROVINCE)[4] ~ 1344241,
PROVINCE == levels(dat$PROVINCE)[5] ~ 1106992,
PROVINCE == levels(dat$PROVINCE)[6] ~ 874048,
PROVINCE == levels(dat$PROVINCE)[7] ~ 284638,
PROVINCE == levels(dat$PROVINCE)[8] ~ 494325,
PROVINCE == levels(dat$PROVINCE)[9] ~ 1515064,
PROVINCE == levels(dat$PROVINCE)[10] ~ 1146175,
PROVINCE == levels(dat$PROVINCE)[11] ~ 1195796,
PROVINCE == levels(dat$PROVINCE)[12] ~ 11431406
)) %>%
mutate(NEW_IN_divid = NEW_IN / population * 1000000)
# create df of last n days to previous n days
n <- 7
dat_t7 <- dat %>%
group_by(PROVINCE) %>%
slice((n() - (n - 1)):n())
dat_t14 <- dat %>%
group_by(PROVINCE) %>%
slice((n() - (2 * n - 1)):(n() - n))
dat_t21 <- dat %>%
group_by(PROVINCE) %>%
slice((n() - (3 * n - 1)):(n() - 2 * n))
dat_t28 <- dat %>%
group_by(PROVINCE) %>%
slice((n() - (4 * n - 1)):(n() - 3 * n))
# create means for the periods
dat_7mean <- aggregate(NEW_IN_divid ~ PROVINCE, dat_t7, mean)
dat_14mean <- aggregate(NEW_IN_divid ~ PROVINCE, dat_t14, mean)
dat_21mean <- aggregate(NEW_IN_divid ~ PROVINCE, dat_t21, mean)
dat_28mean <- aggregate(NEW_IN_divid ~ PROVINCE, dat_t28, mean)
# Create plot in dutch/fr
fig_trends <- ggplot(
subset(dat, DATE >= "2020-05-02"), # subset data from May 3
aes(x = DATE, y = NEW_IN_divid)
) +
geom_point(
size = 1L,
colour = "steelblue"
) +
labs(x = "", y = "Nombre d'hospitalisations (par 1,000,000 habitants) / Hospitalisaties (per 1,000,000 inwoners)") +
theme_minimal() +
facet_wrap(vars(PROVINCE),
scales = "free"
) +
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 = 2 * 10),
colour = "darkgrey", hjust = -0.1,
) +
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 = 2 * 10),
colour = "darkgrey", hjust = -0.1,
) +
labs(
title = "Evolution des admissions hospitalières / Evolutie van de hospitalisaties - COVID-19"
) +
scale_y_continuous(breaks = seq(from = 0, to = 10 * 10, by = 1 * 10), limits = c(0, 2 * 10)) +
scale_x_date(labels = date_format("%d-%m")) +
geom_hline(
yintercept = 100 / (11431406 / 1000000),
linetype = "dashed",
color = "red"
) +
geom_hline(
yintercept = 50 / (11431406 / 1000000),
linetype = "dashed",
color = "steelblue"
) +
geom_segment(
data = dat_7mean,
aes(
y = NEW_IN_divid,
yend = NEW_IN_divid,
x = max(dat$DATE) - (n - 1),
xend = max(dat$DATE)
),
color = "darkgrey",
lwd = 1.2
) +
geom_segment(
data = dat_14mean,
aes(
y = NEW_IN_divid,
yend = NEW_IN_divid,
x = max(dat$DATE) - n - (n - 1),
xend = max(dat$DATE) - n
),
color = "darkgrey",
lwd = 1.2
) +
geom_segment(
data = dat_21mean,
aes(
y = NEW_IN_divid,
yend = NEW_IN_divid,
x = max(dat$DATE) - 2 * n - (n - 1),
xend = max(dat$DATE) - 2 * n
),
color = "darkgrey",
lwd = 1.2
) +
geom_segment(
data = dat_28mean,
aes(
y = NEW_IN_divid,
yend = NEW_IN_divid,
x = max(dat$DATE) - 3 * n - (n - 1),
xend = max(dat$DATE) - 3 * n
),
color = "darkgrey",
lwd = 1.2
) +
geom_text(
data = dat_7mean,
mapping = aes(
x = max(dat$DATE) - 1,
y = NEW_IN_divid,
label = format(round(NEW_IN_divid, 1), nsmall = 1),
vjust = -0.5,
hjust = 1
),
color = "darkgrey",
size = 4,
fontface = "bold"
) +
geom_text(
data = dat_14mean,
mapping = aes(
x = max(dat$DATE) - n - 1,
y = NEW_IN_divid,
label = format(round(NEW_IN_divid, 1), nsmall = 1),
vjust = -0.5,
hjust = 1
),
color = "darkgrey",
size = 4,
fontface = "bold"
) +
geom_text(
data = dat_21mean,
mapping = aes(
x = max(dat$DATE) - 2 * n - 1,
y = NEW_IN_divid,
label = format(round(NEW_IN_divid, 1), nsmall = 1),
vjust = -0.5,
hjust = 1
),
color = "darkgrey",
size = 4,
fontface = "bold"
) +
geom_text(
data = dat_28mean,
mapping = aes(
x = max(dat$DATE) - 3 * n - 1,
y = NEW_IN_divid,
label = format(round(NEW_IN_divid, 1), nsmall = 1),
vjust = -0.5,
hjust = 1
),
color = "darkgrey",
size = 4,
fontface = "bold"
)
# fig_trends
## adjust caption at the end of the trend figure
caption <- grobTree(
textGrob(" * Lignes solides : moyennes 7 jours (4 semaines) / Volle lijnen : 7-daags gemiddelde (4 weken) \n * Lignes pointillées : phases de déconfinement / Gestippelde lijnen : fases afbouw lockdown \n * Lignes rouge et bleue : seuils de 100 et 50 cas par jour / Rode en blauwe lijnen : drempels van 100 en 50 gevallen per dag",
x = 0, hjust = 0, vjust = 0,
gp = gpar(col = "darkgray", fontsize = 7, lineheight = 0.8)
),
textGrob("Niko Speybroeck (@NikoSpeybroeck), Antoine Soetewey (@statsandr) & Angel Rosas (@arosas_aguirre) \n Data : https://epistat.wiv-isp.be/covid/ ",
x = 1, hjust = 1, vjust = 0,
gp = gpar(col = "black", fontsize = 7.5, lineheight = 1.2)
),
cl = "ann"
)
##### MAPS
### Obtaining Belgium shapefile at province level
library(GADMTools)
library(RColorBrewer)
library(tmap)
## sf structure
map <- gadm_sf_loadCountries(c("BEL"), level = 2, basefile = "./")$sf
map <- map %>%
rename("PROVINCE" = NAME_2)
map$PROVINCE[c(1, 5)] <- c("Brussels", "Vlaams-Brabant")
## agregating data
dat_ag <- dat %>%
group_by(PROVINCE) %>%
summarize(
"new_in" = mean(NEW_IN[DATE >= (max(dat$DATE) - 29)], na.rm = T),
"new_in2" = mean(NEW_IN[DATE >= (max(dat$DATE) - 6)], na.rm = T),
"population" = max(population, na.rm = T)
) %>%
mutate(
new_in_divid = new_in / population * 1000000,
new_in_divid2 = new_in2 / population * 1000000,
class2 = cut(new_in_divid2,
breaks = c(0, 0.001, 2.5, 9, 100),
include.lowest = TRUE, labels = c("0.0", "] 0.0, 2.5 ]", "] 2.5, 9.0 ]", "> 9.0")
)
)
map.data <- left_join(map, dat_ag, by = "PROVINCE")
map.data <- subset(map.data, !PROVINCE %in% "Belgium")
###### MAPS WITH GGPLOT
points <- st_centroid(map.data)
points <- cbind(map.data, st_coordinates(st_centroid(map.data$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 = 0.2), na.rm = TRUE),
include.lowest = TRUE
)),
q2 = as.numeric(cut(new_in_divid2,
breaks = quantile(new_in_divid2, probs = seq(0, 1, by = 0.2), na.rm = TRUE),
include.lowest = TRUE
)),
q1 = as.factor(ifelse(q1 < 5, 1, 2)),
q2 = as.factor(ifelse(q2 < 5, 1, 2))
)
points1 <- subset(points, !PROVINCE %in% "Vlaams-Brabant")
points2 <- subset(points, PROVINCE %in% "Vlaams-Brabant")
period1 <- paste0(
"Période / periode : ", format(max(dat$DATE) - 30, format = "%d/%m"), " - ",
format(max(dat$DATE), format = "%d/%m"), " "
)
period2 <- paste0(
"Période / periode : ", format(max(dat$DATE) - 6, format = "%d/%m"), " - ",
format(max(dat$DATE), format = "%d/%m"), " "
)
fig_map2 <- ggplot(map.data) +
geom_sf(aes(fill = class2)) +
# here you can change the number of blues in the pallete "n" (maximum=9)
scale_fill_manual(values = c("white", "#ffe5e5", "#ff9999", "#ff0000"), drop = FALSE) +
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", "black"), guide = FALSE) +
geom_text(
data = points1, aes(x = X, y = Y - 0.03, label = num_2, 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_2), col = "black", size = 3,
check_overlap = TRUE
) +
labs(fill = bquote(atop(NA, atop("Admissions hospitalières / \nHospitalisaties (x 1,000,000 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")
)
png(file = "Belgian_Hospitalisations_COVID-19_4weeks_limited.png", width = 15 * 360, heigh = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(fig_map2,
grid.arrange(fig_trends, bottom = caption),
ncol = 2, widths = c(1, 1.5)
)
dev.off()