Brazil regional cases and deaths, daily time-series

Extracts regional case and death counts for Brazil. Data available on Github, curated by Wesley Cota: DOI 10.1590/SciELOPreprints.362 Source

get_brazil_regional_cases(geography = "states")

Arguments

geography	Character string specifying geography: "regions": n=5, or "states": n=27, or "municipalities": n=3180. Default: "states".

Examples

get_brazil_regional_cases()
#> # A tibble: 2,022 x 5
#> # Groups:   date, state_code [2,022]
#>    date       state_code state_name     cases deaths
#>    <date>     <chr>      <chr>          <dbl>  <dbl>
#>  1 2020-02-25 SP         São Paulo          1      0
#>  2 2020-02-26 SP         São Paulo          0      0
#>  3 2020-02-27 SP         São Paulo          0      0
#>  4 2020-02-28 SP         São Paulo          1      0
#>  5 2020-02-29 SP         São Paulo          0      0
#>  6 2020-03-01 SP         São Paulo          0      0
#>  7 2020-03-02 SP         São Paulo          0      0
#>  8 2020-03-03 SP         São Paulo          0      0
#>  9 2020-03-04 SP         São Paulo          1      0
#> 10 2020-03-05 RJ         Rio de Janeiro     1      0
#> # … with 2,012 more rows

if (FALSE) {

# Mapping

brazil_latest <- get_brazil_regional_cases(geography = "states") %>%
  dplyr::filter(date == max(date))

brazil_map <- rnaturalearth::ne_states(country = "Brazil", returnclass = "sf") %>%
  dplyr::mutate(state_code = stringr::str_remove_all(iso_3166_2, "BR-"))

brazil_map_data <- dplyr::left_join(brazil_map, brazil_latest,
by = c("state_code" = "state_code")) %>%
  ggplot2::ggplot() +
  ggplot2::geom_sf(ggplot2::aes(fill = cases))

}