In databrew/covid19: COVID-19 App
# Basic knitr options
library(knitr)
opts_chunk$set(comment = NA,
# echo = FALSE,
warning = FALSE,
message = FALSE,
error = TRUE,
cache = FALSE,
fig.width = 8.64,
fig.height = 4.86,
fig.path = 'figures/')
## Load libraries
library(covid19)
library(ggplot2)
library(lubridate)
library(dplyr)
library(ggplot2)
ylog <- TRUE
options(scipen = '999')
library(tidyr)
FINAL CHARTS FOR PUBLICATION
a) All notified cases in Italy and Spain (cumulative and per day). So the line that shows cases per day is well seen, it would be great if we could make use of both axis (with different scales).
b) All notified deaths in Italy and Spain (cumulative and per day). Both axis
c) All notificed ICU deaths in Italy and Spain (cumulative and per day). Both axis
d) Case notification risk (per 100.000) in Italy and Spain (cumulative and per day)
e) COVID-19 mortality risk (per 100,000) in Italy and Spain (cumulative and per day)
f) COVID-19 ICU admission risk (per 100.000) in Italy and Spain (cumulative and per day).
A. All notified cases in Italy and Spain (cumulative and per day). So the line that shows cases per day is well seen, it would be great if we could make use of both axis (with different scales).
cols <- c('black', 'red')
text_size <- 12
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases
pd$value_non_cum <- pd$cases_non_cum
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
aa <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Date',
y = 'Cumulative cases',
title = 'A',
caption = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative cases'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily cases')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top')) +
xlim(as.Date('2020-02-25'),
max(pd$date))
aa
A.ii. All notified cases in Italy and Spain (cumulative and per day). RELATIVE TIME
make_day_zero <- function(x, start_value = 1){
x %>%
arrange(date) %>%
group_by(country) %>%
mutate(day0 = min(date[value >= start_value])) %>%
mutate(date = as.numeric(date - day0))
}
make_day_zero_var <- function(x, start_value = 1, start_var = 'cases'){
x$start_var <- as.numeric(unlist(x[,start_var]))
x <- x %>%
arrange(date) %>%
group_by(country) %>%
mutate(day0 = min(date[start_var >= start_value])) %>%
mutate(date = as.numeric(date - day0))
}
cols <- c('black', 'red')
text_size <- 12
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases
pd$value_non_cum <- pd$cases_non_cum
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
make_day_zero_var(start_value = 1, start_var = 'cases') %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 60
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
aaii <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Days since first case',
y = 'Cumulative cases',
title = 'A',
caption = paste0('Data as of ',max_date)) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative cases'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily cases')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top'))
aaii
B All notified deaths in Italy and Spain (cumulative and per day). Both axis
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths
pd$value_non_cum <- pd$deaths_non_cum
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
# make_day_zero() %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 80
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
bb <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Date',
y = 'Cumulative deaths',
title = 'C',
caption = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative deaths'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily deaths')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top')) +
xlim(as.Date('2020-02-25'),
max(pd$date))
bb
B.ii. All notified deaths in Italy and Spain (cumulative and per day). Both axis
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths
pd$value_non_cum <- pd$deaths_non_cum
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
make_day_zero_var(start_value = 1, start_var = 'cases') %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 80
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
bbii <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Days since first case',
y = 'Cumulative deaths',
title = 'C',
caption = paste0('Data as of ', max_date)) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative deaths'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily deaths')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top'))
bbii
C. ICU in Italy and Spain cumulative and per day
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci
pd$value_non_cum <- pd$uci_non_cum
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
cc <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Date',
y = 'Cumulative ICU admissions',
title = 'E',
caption = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative ICU admissions'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily ICU admissions')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top')) +
xlim(as.Date('2020-02-25'),
max(pd$date))
cc
C.ii. ICU in Italy and Spain cumulative and per day
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci
pd$value_non_cum <- pd$uci_non_cum
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
make_day_zero_var(start_value = 1, start_var = 'cases') %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
ccii <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Days since first case',
y = 'Cumulative ICU admissions',
title = 'E',
caption = paste0('Data as of ', max_date)) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative ICU admissions'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily ICU admissions')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top'))
ccii
D. Case notification risk (per 100.000) in Italy and Spain (cumulative and per day)
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases / pd$pop * 100000
pd$value_non_cum <- pd$cases_non_cum / pd$pop * 100000
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
dd <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Date',
y = 'Cumulative cases per 100,000',
title = 'B',
caption = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative cases per 100,000'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily cases per 100,000')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top')) +
xlim(as.Date('2020-02-25'),
max(pd$date))
dd
D.ii. Case notification risk (per 100.000) in Italy and Spain (cumulative and per day)
make_day_zero_var <- function(x, start_value = 1, start_var = 'cases'){
x$start_var <- as.numeric(unlist(x[,start_var]))
x <- x %>%
arrange(date) %>%
group_by(country) %>%
mutate(day0 = min(date[start_var >= start_value])) %>%
mutate(date = as.numeric(date - day0))
}
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases / pd$pop * 100000
pd$value_non_cum <- pd$cases_non_cum / pd$pop * 100000
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
make_day_zero_var(start_value = 1, start_var = 'cases') %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) %>%
filter(date >= -5)
ddii <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Days since first case',
y = 'Cumulative cases per 100,000',
title = 'B',
caption = paste0('Data as of ',max_date)) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative cases per 100,000'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily cases per 100,000')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top'))
ddii
E Mortality risk
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths / pd$pop * 100000
pd$value_non_cum <- pd$deaths_non_cum / pd$pop * 100000
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 70
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
ee <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Date',
y = 'Cumulative deaths per 100,000',
title = 'D',
caption = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative deaths per 100,000'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily deaths per 100,000')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top')) +
xlim(as.Date('2020-02-25'),
max(pd$date))
ee
E.ii Mortality risk
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths / pd$pop * 100000
pd$value_non_cum <- pd$deaths_non_cum / pd$pop * 100000
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
make_day_zero_var(start_value = 1, start_var = 'cases') %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) %>%
filter(date >= -5)
eeii <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Days since first case',
y = 'Cumulative deaths per 100,000',
title = 'D',
caption = paste0('Data as of ', max_date)) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative deaths per 100,000'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily deaths per 100,000')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top'))
eeii
F ICU admission risk
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci / pd$pop * 100000
pd$value_non_cum <- pd$uci_non_cum / pd$pop * 100000
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative'))
ff <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Date',
y = 'Cumulative ICU admissions per 100,000',
title = 'F',
caption = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative ICU admissions per 100,000'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily ICU admissions per 100,000')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top')) +
xlim(as.Date('2020-02-25'),
max(pd$date))
ff
F.ii ICU admission risk
cols <- c('black', 'red')
pd <- df %>% filter(country %in% c('Spain','Italy'))
max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci / pd$pop * 100000
pd$value_non_cum <- pd$uci_non_cum / pd$pop * 100000
pd <- pd %>% filter(value > 0,
date <= max_date)
pd <- pd %>%
make_day_zero_var(start_value = 1, start_var = 'cases') %>%
group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE),
value_non_cum = sum(value_non_cum, na.rm = TRUE))
# Define transofrmer value
transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40
# gather
pd <- pd %>%
mutate(value_non_cum = value_non_cum / transformer) %>%
gather(key, value, value:value_non_cum) %>%
mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) %>%
filter(date >= -5)
ffii <- ggplot(data = pd,
aes(x = date,
y = value,
color = country,
lty = key)) +
theme_simple() +
geom_line() +
labs(x = 'Days since first case',
y = 'Cumulative ICU admissions per 100,000',
title = 'F',
caption = paste0('Data as of ', max_date)) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date),
key == 'Cumulative ICU admissions per 100,000'),
aes(x = date,
y = value * 1.05,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7,
show.legend = FALSE) +
scale_color_manual(name = '', values = cols) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer,
name = 'Daily ICU admissions per 100,000')) +
scale_linetype_manual(name = '', values = c(1,2)) +
theme(legend.position = 'top',
legend.title = element_blank(),
axis.title = element_text(size = text_size * 0.8),
legend.text = element_text(size = text_size * 0.98,
hjust = 0.5)) +
guides(lty = guide_legend(ncol = 1,
title.position = 'top'),
color = guide_legend(ncol = 1,
title.position = 'top'))
ffii
pdf('6panelforpublication.pdf',
height =12,
width = 12)
small_text_size = 7
Rmisc::multiplot(aa, bb, cc,
dd, ee, ff,
layout = matrix(1:6, nrow = 3, byrow = F))
dev.off()
pdf('6panelforpublication_relativetime.pdf',
height =12,
width = 12)
small_text_size = 7
Rmisc::multiplot(aaii, bbii, ccii,
ddii, eeii, ffii,
layout = matrix(1:6, nrow = 3, byrow = F))
dev.off()
OLD CHARTS
Linear-scale charts
Cumulative cases (absolute)
text_size <- 20
pd <- df %>% filter(country == 'Spain')
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g1 <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line() +
geom_point() +
labs(x = 'Date',
y = 'Cases',
title = 'Spain: Confirmed COVID-19 cases',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date - 3,
y = value,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7)
if(ylog){
g1 <- g1 +
scale_y_log10()
}
g1
Comparison with Italy
text_size <- 20
pd <- df %>% filter(country %in% c('Italy', 'Spain'))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g1x <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
labs(x = 'Date',
y = 'Cases',
title = 'Confirmed COVID-19 cases',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date - 3,
y = value,
color = country,
label = round(value, digits = 1)),
alpha = 0.7) +
scale_color_manual(name = '', values = c('red', 'black'))
if(ylog){
g1x <- g1x + scale_y_log10()
}
g1x
Cumulative deaths (absolute)
text_size <- 20
pd <- df %>% filter(country == 'Spain')
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g2 <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line() +
geom_point() +
labs(x = 'Date',
y = 'Deaths',
title = 'Spain: Confirmed COVID-19 deaths',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date,
y = value - ifelse(ylog, 1000, 300),
label = round(value, digits = 1)),
alpha = 0.7)
if(ylog){
g2 <- g2 + scale_y_log10()
}
g2
Comparison with Italy
text_size <- 20
pd <- df %>% filter(country %in% c('Italy', 'Spain'))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g2x <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
labs(x = 'Date',
y = 'Deaths',
title = 'Confirmed COVID-19 deaths',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date - 2,
y = value,
label = round(value, digits = 1)),
alpha = 0.7) +
scale_color_manual(name = '', values = c('red', 'black'))
if(ylog){
g2x <- g2x + scale_y_log10()
}
g2x
Cumulative cases by population
text_size <- 20
pd <- df %>% filter(country == 'Spain')
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases / pd$pop * 100000
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g3 <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line() +
geom_point() +
labs(x = 'Date',
y = 'Cases (per 100,000)',
title = 'Spain: Confirmed COVID-19 cases per 100,000 population',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date,
y = value - ifelse(ylog, 40, 10),
label = round(value, digits = 1)),
alpha = 0.7)
if(ylog){
g3 <- g3 + scale_y_log10()
}
g3
Comparison with Italy
text_size <- 20
pd <- df %>% filter(country %in% c('Spain', 'Italy'))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$cases / pd$pop * 100000
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g3x <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
labs(x = 'Date',
y = 'Cases (per 100,000)',
title = 'Confirmed COVID-19 cases per 100,000 population',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date +2,
y = value,
label = round(value, digits = 1)),
alpha = 0.7) +
scale_color_manual(name ='',
values = c('red', 'black'))
if(ylog){
g3x <- g3x + scale_y_log10()
}
g3x
Cumulative deaths by population
text_size <- 20
pd <- df %>% filter(country == 'Spain')
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths / pd$pop * 100000
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g4 <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line() +
geom_point() +
labs(x = 'Date',
y = 'Mortality rate (per 100,000)',
title = 'Spain: Confirmed COVID-19 deaths per 100,000 population',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date -1,
y = value,
label = round(value, digits = 1)),
alpha = 0.7)
if(ylog){
g4 <- g4 + scale_y_log10()
}
g4
Comparison with Italy
text_size <- 20
pd <- df %>% filter(country %in% c('Spain', 'Italy'))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$deaths / pd$pop * 100000
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g4x <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
labs(x = 'Date',
y = 'Mortality rate (per 100,000)',
title = 'Confirmed COVID-19 deaths per 100,000 population',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date -1,
y = value,
label = round(value, digits = 1)),
alpha = 0.7) +
scale_color_manual(name ='', values = c('red', 'black') )
if(ylog){
g4x <- g4x + scale_y_log10()
}
g4x
UCI charts
Spain only, UCI, (not pop adjusted)
text_size <- 20
pd <- esp_df %>% mutate(iso = 'ESP') %>% #bind_rows(ita %>% mutate(iso = 'ITA')) %>%
group_by(iso, date) %>%
summarise(uci = sum(uci),
deaths = sum(deaths),
cases = sum(cases))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci # /pd$pop * 100000
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g5 <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line() +
geom_point() +
labs(x = 'Date',
y = 'UCI admissions',
title = 'Spain: COVID-19 UCI admissions',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date - 1,
y = value,
label = scales::comma(round(value, digits = 1))),
alpha = 0.7)
if(ylog){
g5 <- g5 +
scale_y_log10()
}
g5
UCI total admissions (not pop adjusted)
text_size <- 20
pd <- esp_df %>% mutate(iso = 'ESP') %>% bind_rows(ita %>% mutate(iso = 'ITA')) %>%
group_by(iso, date) %>%
summarise(uci = sum(uci),
deaths = sum(deaths),
cases = sum(cases))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci # /pd$pop * 100000
pd <- pd %>% filter(value > 0, !is.na(value))
g5x <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
labs(x = 'Date',
y = 'UCI admissions',
title = 'COVID-19 UCI admissions',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date -1,
y = value,
label = round(value, digits = 1)),
alpha = 0.7) +
scale_color_manual(name ='', values = c('red', 'black') )
if(ylog){
g5x <- g5x + scale_y_log10()
}
g5x
Spain only, UCI, (pop adjusted)
text_size <- 20
pd <- esp_df %>% mutate(iso = 'ESP') %>% #bind_rows(ita %>% mutate(iso = 'ITA')) %>%
group_by(iso, date) %>%
summarise(uci = sum(uci),
deaths = sum(deaths),
cases = sum(cases))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci /pd$pop * 100000
pd <- pd %>% filter(value > 0)
pd <- pd %>% group_by(country, date) %>%
summarise(value = sum(value, na.rm = TRUE))
g6 <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line() +
geom_point() +
labs(x = 'Date',
y = 'UCI admissions per 100,000',
title = 'Spain: COVID-19 UCI admissions per 100,000',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date - 1,
y = value,
label = round(value, digits = 1)),
alpha = 0.7)
if(ylog){
g6 <- g6 +
scale_y_log10()
}
g6
UCI admissions (pop adjusted)
text_size <- 20
pd <- esp_df %>% mutate(iso = 'ESP') %>% bind_rows(ita %>% mutate(iso = 'ITA')) %>%
group_by(iso, date) %>%
summarise(uci = sum(uci),
deaths = sum(deaths),
cases = sum(cases))
pd <- pd %>% left_join(world_pop)
pd$value <- pd$uci / pd$pop * 100000
pd <- pd %>% filter(value > 0, !is.na(value))
g6x <- ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
geom_line(aes(color = country)) +
geom_point(aes(color = country)) +
labs(x = 'Date',
y = 'UCI admissions (per 100,000)',
title = 'COVID-19 UCI admissions per 100,000 population',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8)) +
geom_text(data = pd %>% filter(date == max(date)),
aes(x = date -1,
y = value,
label = round(value, digits = 1)),
alpha = 0.7) +
scale_color_manual(name ='', values = c('red', 'black') )
if(ylog){
g6x <- g6x + scale_y_log10()
}
g6x
New charts
text_size <- 12
pd <- esp_df %>% mutate(iso = 'ESP',
cases_non_cum = cases_non_cum) %>% bind_rows(ita %>% mutate(iso = 'ITA') %>% mutate(cases = cases))
pd <- pd %>% group_by(iso, date) %>%
summarise(uci = sum(uci, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE),
cases = sum(cases, na.rm = TRUE),
uci_non_cum = sum(uci_non_cum, na.rm = TRUE),
deaths_non_cum = sum(deaths_non_cum, na.rm = TRUE),
cases_non_cum = sum(cases_non_cum, na.rm = TRUE)) %>%
ungroup %>%
tidyr::gather(key, value, uci:cases_non_cum)
pd <- pd %>% left_join(world_pop %>% dplyr::select(iso, pop, country))
pd$p <- pd$value / pd$pop * 100000
keys <- tibble(key = c('cases', 'cases_non_cum',
'deaths', 'deaths_non_cum',
'uci', 'uci_non_cum'),
new_key = c('Cases\n(cumulative)',
'Cases\n(daily)',
'Deaths\n(cumulative)',
'Deaths\n(daily)',
'ICU\n(cumulative)',
'ICU\n(daily)'))
pd <- left_join(pd, keys) %>%
dplyr::select(-key) %>%
dplyr::rename(key = new_key)
ggplot(data = pd,
aes(x = date,
y = value)) +
theme_simple() +
facet_grid(key~country, scales = 'free') +
geom_bar(stat = 'identity') +
labs(x = '',
y = '',
title = 'COVID-19: Spain and Italy',
subtitle = paste0('Data as of ', max(pd$date))) +
theme(axis.title = element_text(size = text_size),
axis.text = element_text(size = text_size* 0.8),
strip.text.y = element_text(hjust = 0.5, size = 15),
strip.text.x = element_text(size = 15, hjust = 0.5))
ggsave('~/Desktop/special.pdf')
pdf('~/Desktop/alberto1_LOG.pdf',
height =12,
width = 12)
small_text_size = 9
Rmisc::multiplot(g1 + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g2 + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g5 + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g3 + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g4 + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g6 + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
cols = 2)
dev.off()
pdf('~/Desktop/alberto1_with_italy_LOG.pdf',
height =12,
width = 12)
small_text_size = 9
Rmisc::multiplot(g1x + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g2x + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g5x + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g3x + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g4x + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
g6x + theme(axis.title = element_text(size = small_text_size),
axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)),
cols = 2)
dev.off()
databrew/covid19 documentation built on Aug. 24, 2020, 10:39 a.m.
R Package Documentation
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# Basic knitr options library(knitr) opts_chunk$set(comment = NA, # echo = FALSE, warning = FALSE, message = FALSE, error = TRUE, cache = FALSE, fig.width = 8.64, fig.height = 4.86, fig.path = 'figures/')
## Load libraries library(covid19) library(ggplot2) library(lubridate) library(dplyr) library(ggplot2) ylog <- TRUE options(scipen = '999') library(tidyr)
FINAL CHARTS FOR PUBLICATION
a) All notified cases in Italy and Spain (cumulative and per day). So the line that shows cases per day is well seen, it would be great if we could make use of both axis (with different scales).
b) All notified deaths in Italy and Spain (cumulative and per day). Both axis
c) All notificed ICU deaths in Italy and Spain (cumulative and per day). Both axis
d) Case notification risk (per 100.000) in Italy and Spain (cumulative and per day)
e) COVID-19 mortality risk (per 100,000) in Italy and Spain (cumulative and per day)
f) COVID-19 ICU admission risk (per 100.000) in Italy and Spain (cumulative and per day).
A. All notified cases in Italy and Spain (cumulative and per day). So the line that shows cases per day is well seen, it would be great if we could make use of both axis (with different scales).
cols <- c('black', 'red') text_size <- 12 pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$cases pd$value_non_cum <- pd$cases_non_cum pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) aa <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Date', y = 'Cumulative cases', title = 'A', caption = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative cases'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily cases')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) + xlim(as.Date('2020-02-25'), max(pd$date)) aa
A.ii. All notified cases in Italy and Spain (cumulative and per day). RELATIVE TIME
make_day_zero <- function(x, start_value = 1){ x %>% arrange(date) %>% group_by(country) %>% mutate(day0 = min(date[value >= start_value])) %>% mutate(date = as.numeric(date - day0)) } make_day_zero_var <- function(x, start_value = 1, start_var = 'cases'){ x$start_var <- as.numeric(unlist(x[,start_var])) x <- x %>% arrange(date) %>% group_by(country) %>% mutate(day0 = min(date[start_var >= start_value])) %>% mutate(date = as.numeric(date - day0)) } cols <- c('black', 'red') text_size <- 12 pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$cases pd$value_non_cum <- pd$cases_non_cum pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% make_day_zero_var(start_value = 1, start_var = 'cases') %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 60 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) aaii <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Days since first case', y = 'Cumulative cases', title = 'A', caption = paste0('Data as of ',max_date)) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative cases'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily cases')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) aaii
B All notified deaths in Italy and Spain (cumulative and per day). Both axis
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths pd$value_non_cum <- pd$deaths_non_cum pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% # make_day_zero() %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 80 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) bb <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Date', y = 'Cumulative deaths', title = 'C', caption = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative deaths'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily deaths')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) + xlim(as.Date('2020-02-25'), max(pd$date)) bb
B.ii. All notified deaths in Italy and Spain (cumulative and per day). Both axis
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths pd$value_non_cum <- pd$deaths_non_cum pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% make_day_zero_var(start_value = 1, start_var = 'cases') %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 80 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) bbii <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Days since first case', y = 'Cumulative deaths', title = 'C', caption = paste0('Data as of ', max_date)) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative deaths'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily deaths')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) bbii
C. ICU in Italy and Spain cumulative and per day
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$uci pd$value_non_cum <- pd$uci_non_cum pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) cc <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Date', y = 'Cumulative ICU admissions', title = 'E', caption = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative ICU admissions'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily ICU admissions')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) + xlim(as.Date('2020-02-25'), max(pd$date)) cc
C.ii. ICU in Italy and Spain cumulative and per day
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$uci pd$value_non_cum <- pd$uci_non_cum pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% make_day_zero_var(start_value = 1, start_var = 'cases') %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) ccii <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Days since first case', y = 'Cumulative ICU admissions', title = 'E', caption = paste0('Data as of ', max_date)) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative ICU admissions'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily ICU admissions')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) ccii
D. Case notification risk (per 100.000) in Italy and Spain (cumulative and per day)
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$cases / pd$pop * 100000 pd$value_non_cum <- pd$cases_non_cum / pd$pop * 100000 pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) dd <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Date', y = 'Cumulative cases per 100,000', title = 'B', caption = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative cases per 100,000'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily cases per 100,000')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) + xlim(as.Date('2020-02-25'), max(pd$date)) dd
D.ii. Case notification risk (per 100.000) in Italy and Spain (cumulative and per day)
make_day_zero_var <- function(x, start_value = 1, start_var = 'cases'){ x$start_var <- as.numeric(unlist(x[,start_var])) x <- x %>% arrange(date) %>% group_by(country) %>% mutate(day0 = min(date[start_var >= start_value])) %>% mutate(date = as.numeric(date - day0)) } cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$cases / pd$pop * 100000 pd$value_non_cum <- pd$cases_non_cum / pd$pop * 100000 pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% make_day_zero_var(start_value = 1, start_var = 'cases') %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) %>% filter(date >= -5) ddii <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Days since first case', y = 'Cumulative cases per 100,000', title = 'B', caption = paste0('Data as of ',max_date)) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative cases per 100,000'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily cases per 100,000')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) ddii
E Mortality risk
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths / pd$pop * 100000 pd$value_non_cum <- pd$deaths_non_cum / pd$pop * 100000 pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 70 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) ee <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Date', y = 'Cumulative deaths per 100,000', title = 'D', caption = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative deaths per 100,000'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily deaths per 100,000')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) + xlim(as.Date('2020-02-25'), max(pd$date)) ee
E.ii Mortality risk
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths / pd$pop * 100000 pd$value_non_cum <- pd$deaths_non_cum / pd$pop * 100000 pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% make_day_zero_var(start_value = 1, start_var = 'cases') %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) %>% filter(date >= -5) eeii <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Days since first case', y = 'Cumulative deaths per 100,000', title = 'D', caption = paste0('Data as of ', max_date)) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative deaths per 100,000'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily deaths per 100,000')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) eeii
F ICU admission risk
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$uci / pd$pop * 100000 pd$value_non_cum <- pd$uci_non_cum / pd$pop * 100000 pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) ff <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Date', y = 'Cumulative ICU admissions per 100,000', title = 'F', caption = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative ICU admissions per 100,000'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily ICU admissions per 100,000')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) + xlim(as.Date('2020-02-25'), max(pd$date)) ff
F.ii ICU admission risk
cols <- c('black', 'red') pd <- df %>% filter(country %in% c('Spain','Italy')) max_date <- pd %>% group_by(country) %>% summarise(d = max(date)) %>% summarise(d = min(d)) %>% .$d pd <- pd %>% left_join(world_pop) pd$value <- pd$uci / pd$pop * 100000 pd$value_non_cum <- pd$uci_non_cum / pd$pop * 100000 pd <- pd %>% filter(value > 0, date <= max_date) pd <- pd %>% make_day_zero_var(start_value = 1, start_var = 'cases') %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE), value_non_cum = sum(value_non_cum, na.rm = TRUE)) # Define transofrmer value transformer <- ((max(pd$value) / max(pd$value_non_cum)) )/ 40 # gather pd <- pd %>% mutate(value_non_cum = value_non_cum / transformer) %>% gather(key, value, value:value_non_cum) %>% mutate(key = ifelse(key != 'value', 'Daily', 'Cumulative')) %>% filter(date >= -5) ffii <- ggplot(data = pd, aes(x = date, y = value, color = country, lty = key)) + theme_simple() + geom_line() + labs(x = 'Days since first case', y = 'Cumulative ICU admissions per 100,000', title = 'F', caption = paste0('Data as of ', max_date)) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date), key == 'Cumulative ICU admissions per 100,000'), aes(x = date, y = value * 1.05, label = scales::comma(round(value, digits = 1))), alpha = 0.7, show.legend = FALSE) + scale_color_manual(name = '', values = cols) + scale_y_continuous(sec.axis = sec_axis(trans = ~ . * transformer, name = 'Daily ICU admissions per 100,000')) + scale_linetype_manual(name = '', values = c(1,2)) + theme(legend.position = 'top', legend.title = element_blank(), axis.title = element_text(size = text_size * 0.8), legend.text = element_text(size = text_size * 0.98, hjust = 0.5)) + guides(lty = guide_legend(ncol = 1, title.position = 'top'), color = guide_legend(ncol = 1, title.position = 'top')) ffii
pdf('6panelforpublication.pdf', height =12, width = 12) small_text_size = 7 Rmisc::multiplot(aa, bb, cc, dd, ee, ff, layout = matrix(1:6, nrow = 3, byrow = F)) dev.off()
pdf('6panelforpublication_relativetime.pdf', height =12, width = 12) small_text_size = 7 Rmisc::multiplot(aaii, bbii, ccii, ddii, eeii, ffii, layout = matrix(1:6, nrow = 3, byrow = F)) dev.off()
OLD CHARTS
Linear-scale charts
Cumulative cases (absolute)
text_size <- 20 pd <- df %>% filter(country == 'Spain') pd <- pd %>% left_join(world_pop) pd$value <- pd$cases pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g1 <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line() + geom_point() + labs(x = 'Date', y = 'Cases', title = 'Spain: Confirmed COVID-19 cases', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date - 3, y = value, label = scales::comma(round(value, digits = 1))), alpha = 0.7) if(ylog){ g1 <- g1 + scale_y_log10() } g1
Comparison with Italy
text_size <- 20 pd <- df %>% filter(country %in% c('Italy', 'Spain')) pd <- pd %>% left_join(world_pop) pd$value <- pd$cases pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g1x <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line(aes(color = country)) + geom_point(aes(color = country)) + labs(x = 'Date', y = 'Cases', title = 'Confirmed COVID-19 cases', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date - 3, y = value, color = country, label = round(value, digits = 1)), alpha = 0.7) + scale_color_manual(name = '', values = c('red', 'black')) if(ylog){ g1x <- g1x + scale_y_log10() } g1x
Cumulative deaths (absolute)
text_size <- 20 pd <- df %>% filter(country == 'Spain') pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g2 <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line() + geom_point() + labs(x = 'Date', y = 'Deaths', title = 'Spain: Confirmed COVID-19 deaths', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date, y = value - ifelse(ylog, 1000, 300), label = round(value, digits = 1)), alpha = 0.7) if(ylog){ g2 <- g2 + scale_y_log10() } g2
Comparison with Italy
text_size <- 20 pd <- df %>% filter(country %in% c('Italy', 'Spain')) pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g2x <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line(aes(color = country)) + geom_point(aes(color = country)) + labs(x = 'Date', y = 'Deaths', title = 'Confirmed COVID-19 deaths', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date - 2, y = value, label = round(value, digits = 1)), alpha = 0.7) + scale_color_manual(name = '', values = c('red', 'black')) if(ylog){ g2x <- g2x + scale_y_log10() } g2x
Cumulative cases by population
text_size <- 20 pd <- df %>% filter(country == 'Spain') pd <- pd %>% left_join(world_pop) pd$value <- pd$cases / pd$pop * 100000 pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g3 <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line() + geom_point() + labs(x = 'Date', y = 'Cases (per 100,000)', title = 'Spain: Confirmed COVID-19 cases per 100,000 population', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date, y = value - ifelse(ylog, 40, 10), label = round(value, digits = 1)), alpha = 0.7) if(ylog){ g3 <- g3 + scale_y_log10() } g3
Comparison with Italy
text_size <- 20 pd <- df %>% filter(country %in% c('Spain', 'Italy')) pd <- pd %>% left_join(world_pop) pd$value <- pd$cases / pd$pop * 100000 pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g3x <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line(aes(color = country)) + geom_point(aes(color = country)) + labs(x = 'Date', y = 'Cases (per 100,000)', title = 'Confirmed COVID-19 cases per 100,000 population', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date +2, y = value, label = round(value, digits = 1)), alpha = 0.7) + scale_color_manual(name ='', values = c('red', 'black')) if(ylog){ g3x <- g3x + scale_y_log10() } g3x
Cumulative deaths by population
text_size <- 20 pd <- df %>% filter(country == 'Spain') pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths / pd$pop * 100000 pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g4 <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line() + geom_point() + labs(x = 'Date', y = 'Mortality rate (per 100,000)', title = 'Spain: Confirmed COVID-19 deaths per 100,000 population', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date -1, y = value, label = round(value, digits = 1)), alpha = 0.7) if(ylog){ g4 <- g4 + scale_y_log10() } g4
Comparison with Italy
text_size <- 20 pd <- df %>% filter(country %in% c('Spain', 'Italy')) pd <- pd %>% left_join(world_pop) pd$value <- pd$deaths / pd$pop * 100000 pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g4x <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line(aes(color = country)) + geom_point(aes(color = country)) + labs(x = 'Date', y = 'Mortality rate (per 100,000)', title = 'Confirmed COVID-19 deaths per 100,000 population', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date -1, y = value, label = round(value, digits = 1)), alpha = 0.7) + scale_color_manual(name ='', values = c('red', 'black') ) if(ylog){ g4x <- g4x + scale_y_log10() } g4x
UCI charts
Spain only, UCI, (not pop adjusted)
text_size <- 20 pd <- esp_df %>% mutate(iso = 'ESP') %>% #bind_rows(ita %>% mutate(iso = 'ITA')) %>% group_by(iso, date) %>% summarise(uci = sum(uci), deaths = sum(deaths), cases = sum(cases)) pd <- pd %>% left_join(world_pop) pd$value <- pd$uci # /pd$pop * 100000 pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g5 <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line() + geom_point() + labs(x = 'Date', y = 'UCI admissions', title = 'Spain: COVID-19 UCI admissions', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date - 1, y = value, label = scales::comma(round(value, digits = 1))), alpha = 0.7) if(ylog){ g5 <- g5 + scale_y_log10() } g5
UCI total admissions (not pop adjusted)
text_size <- 20 pd <- esp_df %>% mutate(iso = 'ESP') %>% bind_rows(ita %>% mutate(iso = 'ITA')) %>% group_by(iso, date) %>% summarise(uci = sum(uci), deaths = sum(deaths), cases = sum(cases)) pd <- pd %>% left_join(world_pop) pd$value <- pd$uci # /pd$pop * 100000 pd <- pd %>% filter(value > 0, !is.na(value)) g5x <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line(aes(color = country)) + geom_point(aes(color = country)) + labs(x = 'Date', y = 'UCI admissions', title = 'COVID-19 UCI admissions', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date -1, y = value, label = round(value, digits = 1)), alpha = 0.7) + scale_color_manual(name ='', values = c('red', 'black') ) if(ylog){ g5x <- g5x + scale_y_log10() } g5x
Spain only, UCI, (pop adjusted)
text_size <- 20 pd <- esp_df %>% mutate(iso = 'ESP') %>% #bind_rows(ita %>% mutate(iso = 'ITA')) %>% group_by(iso, date) %>% summarise(uci = sum(uci), deaths = sum(deaths), cases = sum(cases)) pd <- pd %>% left_join(world_pop) pd$value <- pd$uci /pd$pop * 100000 pd <- pd %>% filter(value > 0) pd <- pd %>% group_by(country, date) %>% summarise(value = sum(value, na.rm = TRUE)) g6 <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line() + geom_point() + labs(x = 'Date', y = 'UCI admissions per 100,000', title = 'Spain: COVID-19 UCI admissions per 100,000', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date - 1, y = value, label = round(value, digits = 1)), alpha = 0.7) if(ylog){ g6 <- g6 + scale_y_log10() } g6
UCI admissions (pop adjusted)
text_size <- 20 pd <- esp_df %>% mutate(iso = 'ESP') %>% bind_rows(ita %>% mutate(iso = 'ITA')) %>% group_by(iso, date) %>% summarise(uci = sum(uci), deaths = sum(deaths), cases = sum(cases)) pd <- pd %>% left_join(world_pop) pd$value <- pd$uci / pd$pop * 100000 pd <- pd %>% filter(value > 0, !is.na(value)) g6x <- ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + geom_line(aes(color = country)) + geom_point(aes(color = country)) + labs(x = 'Date', y = 'UCI admissions (per 100,000)', title = 'COVID-19 UCI admissions per 100,000 population', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8)) + geom_text(data = pd %>% filter(date == max(date)), aes(x = date -1, y = value, label = round(value, digits = 1)), alpha = 0.7) + scale_color_manual(name ='', values = c('red', 'black') ) if(ylog){ g6x <- g6x + scale_y_log10() } g6x
New charts
text_size <- 12 pd <- esp_df %>% mutate(iso = 'ESP', cases_non_cum = cases_non_cum) %>% bind_rows(ita %>% mutate(iso = 'ITA') %>% mutate(cases = cases)) pd <- pd %>% group_by(iso, date) %>% summarise(uci = sum(uci, na.rm = TRUE), deaths = sum(deaths, na.rm = TRUE), cases = sum(cases, na.rm = TRUE), uci_non_cum = sum(uci_non_cum, na.rm = TRUE), deaths_non_cum = sum(deaths_non_cum, na.rm = TRUE), cases_non_cum = sum(cases_non_cum, na.rm = TRUE)) %>% ungroup %>% tidyr::gather(key, value, uci:cases_non_cum) pd <- pd %>% left_join(world_pop %>% dplyr::select(iso, pop, country)) pd$p <- pd$value / pd$pop * 100000 keys <- tibble(key = c('cases', 'cases_non_cum', 'deaths', 'deaths_non_cum', 'uci', 'uci_non_cum'), new_key = c('Cases\n(cumulative)', 'Cases\n(daily)', 'Deaths\n(cumulative)', 'Deaths\n(daily)', 'ICU\n(cumulative)', 'ICU\n(daily)')) pd <- left_join(pd, keys) %>% dplyr::select(-key) %>% dplyr::rename(key = new_key) ggplot(data = pd, aes(x = date, y = value)) + theme_simple() + facet_grid(key~country, scales = 'free') + geom_bar(stat = 'identity') + labs(x = '', y = '', title = 'COVID-19: Spain and Italy', subtitle = paste0('Data as of ', max(pd$date))) + theme(axis.title = element_text(size = text_size), axis.text = element_text(size = text_size* 0.8), strip.text.y = element_text(hjust = 0.5, size = 15), strip.text.x = element_text(size = 15, hjust = 0.5)) ggsave('~/Desktop/special.pdf')
pdf('~/Desktop/alberto1_LOG.pdf', height =12, width = 12) small_text_size = 9 Rmisc::multiplot(g1 + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g2 + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g5 + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g3 + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g4 + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g6 + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), cols = 2) dev.off()
pdf('~/Desktop/alberto1_with_italy_LOG.pdf', height =12, width = 12) small_text_size = 9 Rmisc::multiplot(g1x + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g2x + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g5x + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g3x + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g4x + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), g6x + theme(axis.title = element_text(size = small_text_size), axis.text = element_text(size = small_text_size), plot.title = element_text(size = small_text_size * 1.4)), cols = 2) dev.off()
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