Code comparing covid cases and deaths in Denmark

world_in_data_epidemic.R

# Load the tidyverse
library(tidyverse)

# Load the data from Our World in Data
csv_url <- "https://covid.ourworldindata.org/data/owid-covid-data.csv"

data <- read_csv(csv_url)

# Filter the data to only get Denmark and data after feb 27
pd <- data %>%
  filter(location == "Denmark") %>%
  select(date, new_cases_smoothed_per_million, new_deaths_smoothed_per_million) %>%
  filter(date >= "2020-02-27")

# Create a table with dates of the recent restrictions
restrictions <- tribble(~date, ~y, ~text,
                        as.Date("2020-07-07"), 2.5, "Forsamling: 100",
                        as.Date("2020-08-14"),   5, "Lukketid udvides",
                        as.Date("2020-08-22"), 7.5, "Masker i offentlig transport",
                        as.Date("2020-09-09"), 10, "Restriktioner natteliv København/Odense",
                        as.Date("2020-09-18"), 12.5, "Restriktioner restauranter mv.",
                        as.Date("2020-09-26"), 15, "Restriktioner private fester",
                        as.Date("2020-10-29"), 17.5, "Masker alle off. steder, forsamling 10 mv.") %>%
  inner_join(pd)

# Create helper variables to help with the plotting
n = 10
ymax = max(pd$new_cases_smoothed_per_million, na.rm = T) / n
ymax = ceiling(ymax / 5) * 5

ymin = max(pd$new_deaths_smoothed_per_million, na.rm = T)
ymin = (ceiling(ymin / 1) * 1) * -1

mindate <- min(pd$date)

# Build the plot
ggplot(pd) +
  geom_ribbon(aes(date, ymin = 0, ymax = new_cases_smoothed_per_million / n), fill = "darkgreen") +
  geom_ribbon(aes(date, ymin = 0, ymax = -new_deaths_smoothed_per_million), fill = "red") +
  theme_minimal() +
  scale_y_continuous(breaks = c(seq(ymin, 0, 1), seq(0, ymax, 5)), 
                     labels = c(seq(ymin, 0, 1) * -1, seq(0, ymax, 5)*n), 
                     limits = c(ymin, ymax)) +
  geom_text(data = restrictions, aes(x = date - 2, y = y, label = text), hjust = 1, color = "#808080") +
  geom_segment(data = restrictions, aes(x = date, xend = date, y = y, yend = (new_cases_smoothed_per_million / n) + .5),
               arrow = arrow(length = unit(0.02, "npc")),
               color = "#808080") +
  geom_segment(data = restrictions, aes(x = date, xend = date, 
                                        y = -new_deaths_smoothed_per_million, 
                                        yend = (new_cases_smoothed_per_million / n)),
               color = "white", linetype = "dotted") +
  geom_point(data = restrictions, aes(x = date, y = y), color = "#808080") +
  annotate(geom = "text", x = mindate, y = 3.5, label = "Nye syge", hjust = 0, color = "darkgreen") +
  annotate(geom = "text", x = mindate, y = -2, label = "Nye døde", hjust = 0, color = "red") +
  scale_x_date(date_breaks = "1 month", date_labels = "%d/%m") + 
  theme(panel.grid.minor = element_blank()) +
  labs(y = "Antal per million (obs: forskel i trin)", x = "",
       caption = "Lavet af Mikkel Freltoft Krogsholm, Data fra Our World in Data.")

# Save the plot
ggsave(filename = "comparisons.png", width = 15, height = 7.5)