/
pyramid-plot_facets.R
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pyramid-plot_facets.R
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# clean environment
remove(list = ls())
# required packages
library(ggplot2)
library(ggrepel)
library(zoo)
library(lme4)
library(plyr)
library(dplyr)
library(scales)
library(ggpubr)
library(grid)
library(gridExtra)
library(magrittr)
library(ggpol)
library(reshape2)
library(XML)
library(patchwork)
# import Sciensano hospitalisations data
dat <- read.csv("https://epistat.sciensano.be/Data/COVID19BE_CASES_AGESEX.csv",
fileEncoding = "UTF-8",
stringsAsFactors = FALSE
)
# aggregate new intakes by province and date
dat <- dat %>%
mutate(
DATE = as.Date(DATE),
SEX = factor(SEX, labels = c("Women", "Men")),
AGEGROUP = as.factor(AGEGROUP),
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 = as.factor(PROVINCE2)
) %>%
select(DATE, PROVINCE, AGEGROUP, SEX, CASES)
# add new intakes for Belgium as a whole
belgium <- aggregate(CASES ~ DATE + AGEGROUP + SEX, dat, sum) %>%
mutate(PROVINCE = "Belgium") %>%
select(DATE, PROVINCE, AGEGROUP, SEX, CASES)
##
dat_all <- 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
),
CASES_divid = CASES / population * 100000
)
dat_all <- dat_all[!is.na(dat_all$AGEGROUP), ]
# subset for period and province
start <- as.Date("2020-03-01")
end <- as.Date("2020-05-31")
dat <- subset(dat_all, DATE >= start & DATE <= end & PROVINCE == "Belgium")
lab <- aggregate(CASES ~ AGEGROUP + SEX, dat, sum)
# plot for Belgium
bel_p1 <- ggplot(data = dat) +
geom_bar(aes(AGEGROUP, CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Women")
) +
geom_bar(aes(AGEGROUP, -CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Men")
) +
scale_y_continuous(
breaks = seq(-round_any(max(lab$CASES), 1000, f = ceiling), round_any(max(lab$CASES), 1000, f = ceiling), 2000),
labels = abs(seq(-round_any(max(lab$CASES), 1000, f = ceiling), round_any(max(lab$CASES), 1000, f = ceiling), 2000))
) +
coord_flip() +
theme_minimal() +
labs(
title = "Belgium",
subtitle = paste0(format(start, format = "%d/%m/%Y"), " - ", format(end, format = "%d/%m/%Y")),
x = "Age group",
y = "Number of cases"
) +
theme(
legend.position = c(.95, .15),
legend.justification = c("right", "top"),
legend.box.just = "right",
legend.margin = margin(6, 6, 6, 6),
legend.title = element_blank(),
legend.text = element_text(size = 12),
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 12),
plot.margin = unit(c(5.5, 5.5, 30, 5.5), "points")
)
## plots for provinces
dat <- subset(dat_all, DATE >= start & DATE <= end & PROVINCE != "Belgium")
lab <- aggregate(CASES ~ AGEGROUP + SEX, dat, sum)
pro_p1 <- ggplot(data = dat) +
facet_wrap(vars(PROVINCE),
scales = "fixed",
ncol = 9
) +
geom_bar(aes(AGEGROUP, CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Women")
) +
geom_bar(aes(AGEGROUP, -CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Men")
) +
scale_y_continuous(
breaks = seq(-round_any(max(lab$CASES), 500, f = ceiling), round_any(max(lab$CASES), 500, f = ceiling), 500),
labels = abs(seq(-round_any(max(lab$CASES), 500, f = ceiling), round_any(max(lab$CASES), 500, f = ceiling), 500))
) +
coord_flip() +
theme_minimal() +
labs(
title = "Axe and sex specific COVID-19 cases in Belgium",
subtitle = paste0(format(start, format = "%d/%m/%Y"), " - ", format(end, format = "%d/%m/%Y")),
x = "Age group",
y = "Number of cases"
) +
theme(
legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 12),
plot.margin = unit(c(5.5, 5.5, 0, 5.5), "points")
)
# subset for period and province
start <- as.Date("2020-06-01")
end <- as.Date("2020-08-31")
dat <- subset(dat_all, DATE >= start & DATE <= end & PROVINCE == "Belgium")
lab <- aggregate(CASES ~ AGEGROUP + SEX, dat, sum)
# plot for Belgium
bel_p2 <- ggplot(data = dat) +
geom_bar(aes(AGEGROUP, CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Women")
) +
geom_bar(aes(AGEGROUP, -CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Men")
) +
scale_y_continuous(
breaks = seq(-round_any(max(lab$CASES), 1000, f = ceiling), round_any(max(lab$CASES), 1000, f = ceiling), 500),
labels = abs(seq(-round_any(max(lab$CASES), 1000, f = ceiling), round_any(max(lab$CASES), 1000, f = ceiling), 500))
) +
coord_flip() +
theme_minimal() +
labs(
title = "",
subtitle = paste0(format(start, format = "%d/%m/%Y"), " - ", format(end, format = "%d/%m/%Y")),
x = "",
y = "Number of cases"
) +
theme(
legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 12),
plot.margin = unit(c(5.5, 5.5, 30, 5.5), "points")
)
## plots for provinces
dat <- subset(dat_all, DATE >= start & DATE <= end & PROVINCE != "Belgium")
lab <- aggregate(CASES ~ AGEGROUP + SEX, dat, sum)
pro_p2 <- ggplot(data = dat) +
facet_wrap(vars(PROVINCE),
scales = "fixed",
ncol = 9
) +
geom_bar(aes(AGEGROUP, CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Women")
) +
geom_bar(aes(AGEGROUP, -CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Men")
) +
scale_y_continuous(
breaks = seq(-round_any(max(lab$CASES), 300, f = ceiling), round_any(max(lab$CASES), 300, f = ceiling), 300),
labels = abs(seq(-round_any(max(lab$CASES), 300, f = ceiling), round_any(max(lab$CASES), 300, f = ceiling), 300))
) +
coord_flip() +
theme_minimal() +
labs(
subtitle = paste0(format(start, format = "%d/%m/%Y"), " - ", format(end, format = "%d/%m/%Y")),
x = "Age group",
y = "Number of cases"
) +
theme(
legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 12),
plot.margin = unit(c(5.5, 5.5, 0, 5.5), "points")
)
# subset for period and province
start <- as.Date("2020-09-01")
end <- as.Date(max(dat_all$DATE, na.rm = TRUE))
dat <- subset(dat_all, DATE >= start & DATE <= end & PROVINCE == "Belgium")
lab <- aggregate(CASES ~ AGEGROUP + SEX, dat, sum)
# plot for Belgium
bel_p3 <- ggplot(data = dat) +
geom_bar(aes(AGEGROUP, CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Women")
) +
geom_bar(aes(AGEGROUP, -CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Men")
) +
scale_y_continuous(
breaks = seq(-round_any(max(lab$CASES), 1000, f = ceiling), round_any(max(lab$CASES), 1000, f = ceiling), 1000),
labels = abs(seq(-round_any(max(lab$CASES), 1000, f = ceiling), round_any(max(lab$CASES), 1000, f = ceiling), 1000))
) +
coord_flip() +
theme_minimal() +
labs(
title = "",
subtitle = paste0(format(start, format = "%d/%m/%Y"), " - ", format(end, format = "%d/%m/%Y")),
x = "",
y = "Number of cases"
) +
theme(
legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 12),
plot.margin = unit(c(5.5, 5.5, 30, 5.5), "points")
)
## adjust caption at the end of the figure
caption <- grobTree(
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 = 11, lineheight = 1.2)
),
cl = "ann"
)
## plots for provinces
dat <- subset(dat_all, DATE >= start & DATE <= end & PROVINCE != "Belgium")
lab <- aggregate(CASES ~ AGEGROUP + SEX, dat, sum)
pro_p3 <- ggplot(data = dat) +
facet_wrap(vars(PROVINCE),
scales = "fixed",
ncol = 9
) +
geom_bar(aes(AGEGROUP, CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Women")
) +
geom_bar(aes(AGEGROUP, -CASES, group = SEX, fill = SEX),
stat = "identity",
subset(dat, SEX == "Men")
) +
scale_y_continuous(
breaks = seq(-round_any(max(lab$CASES), 500, f = ceiling), round_any(max(lab$CASES), 500, f = ceiling), 500),
labels = abs(seq(-round_any(max(lab$CASES), 500, f = ceiling), round_any(max(lab$CASES), 500, f = ceiling), 500))
) +
coord_flip() +
theme_minimal() +
labs(
subtitle = paste0(format(start, format = "%d/%m/%Y"), " - ", format(end, format = "%d/%m/%Y")),
x = "Age group",
y = "Number of cases"
) +
theme(
legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 14, face = "bold"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 12),
strip.text = element_text(size = 12),
plot.margin = unit(c(5.5, 5.5, 0, 5.5), "points")
)
# save plot
png(file = "pyramid-plot_facets.png", width = 25.71428 * 360, height = 12 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(ggarrange(pro_p1, pro_p2, pro_p3, ncol = 1),
grid.arrange(bel_p1, bel_p2, bel_p3, bottom = caption, ncol = 3),
ncol = 1, heights = c(2, 1)
)
dev.off()