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 object

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 object is masked from ‘package:testthat’:
#> 
#>     matches
#> 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.Region) %>%
 summarise(total = sum(cases)) %>%
 arrange(-total) %>%
 head(20)
#> # A tibble: 20 x 2
#>    Country.Region  total
#>    <chr>           <int>
#>  1 US             580619
#>  2 Spain          170099
#>  3 Italy          159516
#>  4 France         137875
#>  5 Germany        130072
#>  6 United Kingdom  89570
#>  7 China           83213
#>  8 Iran            73303
#>  9 Turkey          61049
#> 10 Belgium         30589
#> 11 Netherlands     26710
#> 12 Switzerland     25688
#> 13 Canada          25679
#> 14 Brazil          23430
#> 15 Russia          18328
#> 16 Portugal        16934
#> 17 Austria         14041
#> 18 Israel          11586
#> 19 Sweden          10948
#> 20 Ireland         10647

# Get the number of recovered cases in Mainland China by province
 coronavirus %>%
    filter(type == "recovered", Country.Region == "Mainland China") %>%
    group_by(Province.State) %>%
    summarise(total = sum(cases)) %>%
    arrange(-total)
#> # A tibble: 0 x 2
#> # … with 2 variables: Province.State <chr>, total <int>