The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset

Daily summary of the Coronavirus (COVID-19) cases by state/province.

coronavirus

Format

A data frame with 7 variables.

date: Date in YYYY-MM-DD format.
province: Name of province/state, for countries where data is provided split across multiple provinces/states.
country: Name of country/region.
lat: Latitude of center of geographic region, defined as either country or, if available, province.
long: Longitude of center of geographic region, defined as either country or, if available, province.
type: An indicator for the type of cases (confirmed, death, recovered).
cases: Number of cases on given date.
uid: Country code
iso2: Officially assigned country code identifiers with two-letter
iso3: Officially assigned country code identifiers with three-letter
code3: UN country code
combined_key: Country and province (if applicable)
population: Country or province population
continent_name: Continent name
continent_code: Continent code

Source

Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus website.

Details

The dataset contains the daily summary of Coronavirus cases (confirmed, death, and recovered), by state/province.

Examples

data(coronavirus)

require(dplyr)
#> Loading required package: dplyr
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# Get top confirmed cases by state
coronavirus %>%
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total = sum(cases)) %>%
  arrange(-total) %>%
  head(20)
#> # A tibble: 20 × 2
#>    country            total
#>    <chr>              <dbl>
#>  1 US             103802702
#>  2 India           44690738
#>  3 France          39866718
#>  4 Germany         38249060
#>  5 Brazil          37076053
#>  6 Japan           33320438
#>  7 Korea, South    30615522
#>  8 Italy           25603510
#>  9 United Kingdom  24658705
#> 10 Russia          22075858
#> 11 Turkey          17042722
#> 12 Spain           13770429
#> 13 Vietnam         11526994
#> 14 Australia       11399460
#> 15 Argentina       10044957
#> 16 Taiwan*          9970937
#> 17 Netherlands      8712835
#> 18 Iran             7572311
#> 19 Mexico           7483444
#> 20 Indonesia        6738225

# Get the number of recovered cases in China by province
coronavirus %>%
  filter(type == "recovered", country == "China") %>%
  group_by(province) %>%
  summarise(total = sum(cases)) %>%
  arrange(-total)
#> # A tibble: 0 × 2
#> # … with 2 variables: province <chr>, total <dbl>