/
contribution.R
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contribution.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)
############
# PROVINCE #
############
# import data
dat <- read.csv("https://epistat.sciensano.be/Data/COVID19BE_CASES_AGESEX.csv")
dat <- select(dat, c("DATE", "PROVINCE", "CASES"))
dat <- transform(dat,
DATE = as.Date(DATE)
)
dat$PROVINCE <- factor(dat$PROVINCE,
levels = c(
"Antwerpen",
"BrabantWallon",
"Brussels",
"Hainaut",
"Liège",
"Limburg",
"Luxembourg",
"Namur",
"OostVlaanderen",
"VlaamsBrabant",
"WestVlaanderen"
),
labels = c(
"Antwerpen",
"Brabant wallon",
"Brussels",
"Hainaut",
"Liège",
"Limburg",
"Luxembourg",
"Namur",
"Oost-Vlaanderen",
"Vlaams-Brabant",
"West-Vlaanderen"
)
)
dat_full <- aggregate(CASES ~ PROVINCE, dat, sum)
max_date <- max(as.Date(dat$DATE), na.rm = TRUE)
dat <- subset(dat, DATE >= max_date - 14)
dat$week <- ifelse(dat$DATE <= max_date - 7,
"week1",
"week2")
belgium <- aggregate(CASES ~ week, dat, sum)
dat_week1 <- subset(dat, week == "week1")
dat_week1 <- aggregate(CASES ~ week + PROVINCE, dat_week1, sum)
dat_week2 <- subset(dat, week == "week2")
dat_week2 <- aggregate(CASES ~ week + PROVINCE, dat_week2, sum)
dat_diff <- data.frame(PROVINCE = dat_week1$PROVINCE,
CASES_diff = dat_week2$CASES - dat_week1$CASES)
dat_diff$contrib_change <- dat_diff$CASES_diff / (belgium[2, 2] - belgium[1, 2])
dat_diff$contrib_level <- dat_week2$CASES / (belgium[2, 2])
dat_final <- dat_diff %>%
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
)) %>%
mutate(CASES = dat_full$CASES) %>%
mutate(incidence = CASES / population) %>%
mutate(contrib_change_divid = contrib_change / population) %>%
mutate(contrib_level_divid = contrib_level / population)
p <- ggplot(dat_final, aes(x = contrib_level, y = contrib_change)) +
geom_point(color = "red") +
geom_text_repel(aes(label = paste0(PROVINCE, " (", round(incidence * 100, digits = 1), "%)"))) +
theme_minimal() +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
labs(
x = "Contribution to level",
y = "Contribution to change"
) +
NULL
p
## adjust caption at the end of the figure
caption <- grobTree(
textGrob(" * Incidence in parentheses \n",
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"
)
# save plot
png(file = "contrib_provinces.png", width = 15 * 360, heigh = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(grid.arrange(p, bottom = caption),
ncol = 1, widths = c(1, 1.5)
)
dev.off()
## plot bis
p <- ggplot(dat_final, aes(x = contrib_level_divid * 10000000, y = contrib_change_divid * 10000000)) +
geom_point(color = "red") +
geom_text_repel(aes(label = paste0(PROVINCE, " (", round(incidence * 100, digits = 1), "%)"))) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
theme_minimal() +
labs(
x = "Contribution to level (per 10,000,000)",
y = "Contribution to change (per 10,000,000)"
) +
NULL
p
# save plot
png(file = "contrib_provinces_bis.png", width = 15 * 360, heigh = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(grid.arrange(p, bottom = caption),
ncol = 1, widths = c(1, 1.5)
)
dev.off()
#############
# AGE GROUP #
#############
# import data
dat <- read.csv("https://epistat.sciensano.be/Data/COVID19BE_CASES_AGESEX.csv")
dat <- select(dat, c("DATE", "AGEGROUP", "CASES"))
dat <- transform(dat,
DATE = as.Date(DATE),
AGEGROUP = factor(AGEGROUP)
)
dat_full <- aggregate(CASES ~ AGEGROUP, dat, sum)
max_date <- max(as.Date(dat$DATE), na.rm = TRUE)
dat <- subset(dat, DATE >= max_date - 14)
dat$week <- ifelse(dat$DATE <= max_date - 7,
"week1",
"week2")
belgium <- aggregate(CASES ~ week, dat, sum)
dat_week1 <- subset(dat, week == "week1")
dat_week1 <- aggregate(CASES ~ week + AGEGROUP, dat_week1, sum)
dat_week2 <- subset(dat, week == "week2")
dat_week2 <- aggregate(CASES ~ week + AGEGROUP, dat_week2, sum)
dat_diff <- data.frame(AGEGROUP = dat_week1$AGEGROUP,
CASES_diff = dat_week2$CASES - dat_week1$CASES)
dat_diff$contrib_change <- dat_diff$CASES_diff / (belgium[2, 2] - belgium[1, 2])
dat_diff$contrib_level <- dat_week2$CASES / (belgium[2, 2])
dat_final <- dat_diff %>%
mutate(population = case_when(
AGEGROUP == "0-9" ~ 1269068,
AGEGROUP == "10-19" ~ 1300254,
AGEGROUP == "20-29" ~ 1407645,
AGEGROUP == "30-39" ~ 1492290,
AGEGROUP == "40-49" ~ 1504539,
AGEGROUP == "50-59" ~ 1590628,
AGEGROUP == "60-69" ~ 1347139,
AGEGROUP == "70-79" ~ 924291,
AGEGROUP == "80-89" ~ 539390,
AGEGROUP == "90+" ~ 117397
)) %>%
mutate(CASES = dat_full$CASES) %>%
mutate(incidence = CASES / population) %>%
mutate(contrib_change_divid = contrib_change / population) %>%
mutate(contrib_level_divid = contrib_level / population)
p <- ggplot(dat_final, aes(x = contrib_level, y = contrib_change)) +
geom_point(color = "red") +
geom_text_repel(aes(label = paste0(AGEGROUP, " years (", round(incidence * 100, digits = 1), "%)"))) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
theme_minimal() +
labs(
x = "Contribution to level",
y = "Contribution to change"
) +
NULL
p
# save plot
png(file = "contrib_age.png", width = 15 * 360, heigh = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(grid.arrange(p, bottom = caption),
ncol = 1, widths = c(1, 1.5)
)
dev.off()
## plot bis
p <- ggplot(dat_final, aes(x = contrib_level_divid * 10000000, y = contrib_change_divid * 10000000)) +
geom_point(color = "red") +
geom_text_repel(aes(label = paste0(AGEGROUP, " years (", round(incidence * 100, digits = 1), "%)"))) +
scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
scale_x_continuous(labels = scales::percent_format(accuracy = 1)) +
theme_minimal() +
labs(
x = "Contribution to level (per 10,000,000)",
y = "Contribution to change (per 10,000,000)"
) +
NULL
p
# save plot
png(file = "contrib_age_bis.png", width = 15 * 360, heigh = 7 * 360, units = "px", pointsize = 7, res = 300)
ggarrange(grid.arrange(p, bottom = caption),
ncol = 1, widths = c(1, 1.5)
)
dev.off()