Plot Covid-19 Stripes — plot_covid19

Provides a flexible visualization of the country-level Covid-19 spread, plotted by colored daily lines to generate a stripes display. Uses data from the Johns Hopkins University CSSE team (https://github.com/CSSEGISandData/COVID-19) and the World Bank (https://data.worldbank.org).

plot_covid19_stripes(
  data = download_merged_data(cached = TRUE, silent = TRUE),
  type = "deaths",
  min_cases = ifelse(per_capita, ifelse(type == "deaths", 5, 50), ifelse(type ==
    "deaths", 1000, 10000)),
  cumulative = FALSE,
  change_ave = 7,
  per_capita = FALSE,
  population_cutoff = 0,
  diverging_color_scale = FALSE,
  countries = NULL,
  sort_countries = NULL,
  data_date_str = format(lubridate::as_date(data$timestamp[1]), "%B %d, %Y")
)

Arguments

data	The data frame to base the plot on. Should be a merged data frame obtained by download_merged_data and defaults to `download_merged_data(cached = TRUE, silent = TRUE)`.
type	The statistic that you want to plot. Needs to be either "confirmed", "deaths", "recovered" or "active", defined as the difference of "confirmed", "deaths" and "recovered".
min_cases	Only countries that have a maximum of `type` cases higher than `min_cases` during the data period are included in the plot. Uses reasonable defaults depending on `type` and `per_capita`.
cumulative	If `TRUE`, data is being plotted as cumulative (showing the total figures). If `FALSE`, (the default) (averaged) daily changes are plotted instead. See `change_ave` to set the averaging window.
change_ave	Number of days to average over when you plot daily changes.
per_capita	If `TRUE` data is being plotted as per capita measure based on World Bank data. Defaults to `FALSE`.
population_cutoff	Do you want to restrict the plot to countries that exceed a certain population cutoff? Takes a value in millions and defaults to 0. Useful for per capita displays.
diverging_color_scale	Should be set to `TRUE` when a `type` is chosen that can have meaningful negative values (this is the case for "active" cases). Defaults to `FALSE`.
countries	A character vector of ISO3c (ISO 3166-1 alpha-3) codes that you want to include. Note that including many countries can lead to long plotting times and might cause the plot to fail rendering when the display is to small.
sort_countries	By default, countries are sorted alphabetically by ISO3c code. "start" will sort countries by when they first exceeded `min_cases`. "magnitude" will sort by decreasing maximum value for `type`. "countries" requires `countries` to be set and sorts the display in the order of the ISO3c codes provided. Alternatively, you can recode the `iso3c` in your data to a factor in the ordering that you prefer. See examples below.
data_date_str	A date string to include in the annotation of the plot giving the time when the data was pulled. Defaults to the timestamp of the data. Note that you might run into issues with the default when running this in a non-english locale. Consider setting it by hand then.

Value

A ggplot2 oobject.

Examples

plot_covid19_stripes()

merged <- download_merged_data(cached = TRUE, silent = TRUE)
plot_covid19_stripes(merged, per_capita = TRUE, population_cutoff = 10)
#> Population data required. Observations for the following jurisdictions will be dropped as the World Bank is not providing population data for them:  AIA, ATA, BES, COK, ESH, FLK, GGY, JEY, MSR, NIU, PCN, SHN, TKL, TWN, VAT, WLF

plot_covid19_stripes(merged,
  countries = c("ITA", "ESP", "FRA", "DEU", "USA"),
  sort_countries = "countries"
)

sortdf <- dplyr::tibble(
  iso3c = unique(merged$iso3c),
  continent = countrycode::countrycode(iso3c, "iso3c", "continent")
) %>% dplyr::arrange(continent, iso3c)
#> Warning: Some values were not matched unambiguously: ATA
df <- merged
df$iso3c <- factor(merged$iso3c, sortdf$iso3c)
plot_covid19_stripes(df, type = "confirmed")