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Created February 20, 2021 09:45
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Covid-19 Vaccination Program Growth Rates
# ---
# title: "Covid-19 Vaccination Program Growth Rates"
# author: "Christopher Gandrud"
# date: "2021-02-21"
# ---
# For hrbragg installation instructions see:
# https://git.rud.is/hrbrmstr/hrbragg#installation
xfun::pkg_attach2("hrbragg", "tsibble", "tidyverse")
# Load data
covid_data <- read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/vaccinations/vaccinations.csv")
# Summarise data by week ---------
covid_data$year_week <- tsibble::yearweek(covid_data$date)
covid_week <- covid_data %>% group_by(location, year_week) %>%
summarise(doses = sum(daily_vaccinations))
growth <- function(change, old) {
(change / old) * 100
}
covid_week <- covid_week %>% group_by(location) %>%
mutate(diff_doses = difference(doses)) %>%
mutate(lag_doses = lag(doses)) %>%
drop_na(diff_doses) %>%
slice(2:n()) # Drop possibly incomplete first comparison week of data
covid_week$doses_growth <- growth(covid_week$diff_doses,
covid_week$lag_doses)
# Plot selected locations -----------
locations <- c("Germany", "United States", "European Union",
"Spain", "Italy", "Denmark", "Poland",
"Israel", "United Kingdom")
covid_week <- subset(covid_week, location %in% locations)
# drop likely incomplete week
covid_week <- filter(covid_week, year_week != yearweek("2021 W07"))
ggplot(covid_week, aes(year_week, doses_growth, group = location)) +
facet_wrap(.~location) +
geom_line(color = "#f953c6") +
geom_hline(yintercept = 0) +
scale_y_continuous(breaks = c(0, 25, 50, 100, 150)) +
ylab("Week-over-Week Doses (% change)\n") +
xlab("\nCalendar Week") +
ggtitle("How fast are Covid-19 vaccination programs growing?") +
labs(caption = "Note: first data point compares the 2nd to 3rd weeks of program rollout to avoid including incomplete weeks.\nData source: Our World in Data") +
theme_inter(grid = "XY", mode = "dark")
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covid-vaccine-growth

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