The coronavirus package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository.
More details available
here, and a csv format
of the package dataset available
here
A summary dashboard is available here
Source: Centers for Disease Control and Prevention’s Public Health Image Library
Install the CRAN version:
install.packages("coronavirus") Install the Github version (refreshed on a daily bases):
# install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")The package contains a single dataset - coronavirus:
library(coronavirus)
data("coronavirus") This coronavirus dataset has the following fields:
head(coronavirus)
#> Province.State Country.Region Lat Long date cases type
#> 1 Japan 36.0000 138.0000 2020-01-22 2 confirmed
#> 2 South Korea 36.0000 128.0000 2020-01-22 1 confirmed
#> 3 Thailand 15.0000 101.0000 2020-01-22 2 confirmed
#> 4 Anhui Mainland China 31.8257 117.2264 2020-01-22 1 confirmed
#> 5 Beijing Mainland China 40.1824 116.4142 2020-01-22 14 confirmed
#> 6 Chongqing Mainland China 30.0572 107.8740 2020-01-22 6 confirmed
tail(coronavirus)
#> Province.State Country.Region Lat Long date cases type
#> 2577 Shanxi Mainland China 37.5777 112.2922 2020-03-02 3 recovered
#> 2578 Sichuan Mainland China 30.6171 102.7103 2020-03-02 21 recovered
#> 2579 Taiwan Taiwan 23.7000 121.0000 2020-03-02 3 recovered
#> 2580 Xinjiang Mainland China 41.1129 85.2401 2020-03-02 2 recovered
#> 2581 Yunnan Mainland China 24.9740 101.4870 2020-03-02 5 recovered
#> 2582 Zhejiang Mainland China 29.1832 120.0934 2020-03-02 23 recoveredHere is an example of a summary total cases by region and type (top 20):
library(dplyr)
summary_df <- coronavirus %>% group_by(Country.Region, type) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases)
summary_df %>% head(20)
#> # A tibble: 20 x 3
#> # Groups: Country.Region [13]
#> Country.Region type total_cases
#> <chr> <chr> <int>
#> 1 Mainland China confirmed 80026
#> 2 Mainland China recovered 44810
#> 3 South Korea confirmed 4335
#> 4 Mainland China death 2912
#> 5 Italy confirmed 2036
#> 6 Iran confirmed 1501
#> 7 Others confirmed 705
#> 8 Iran recovered 291
#> 9 Japan confirmed 274
#> 10 France confirmed 191
#> 11 Germany confirmed 159
#> 12 Italy recovered 149
#> 13 Spain confirmed 120
#> 14 Singapore confirmed 108
#> 15 US confirmed 105
#> 16 Hong Kong confirmed 100
#> 17 Singapore recovered 78
#> 18 Iran death 66
#> 19 Kuwait confirmed 56
#> 20 Italy death 52Summary of new cases during the past 24 hours by country and type (as of 2020-03-02):
library(tidyr)
coronavirus %>%
filter(date == max(date)) %>%
select(country = Country.Region, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
#> # A tibble: 38 x 4
#> # Groups: country [38]
#> country confirmed death recovered
#> <chr> <int> <int> <int>
#> 1 South Korea 599 11 NA
#> 2 Iran 523 12 116
#> 3 Italy 342 18 66
#> 4 Mainland China 200 42 2692
#> 5 France 61 1 NA
#> 6 Spain 36 NA NA
#> 7 Germany 29 NA NA
#> 8 US 29 5 2
#> 9 Japan 18 NA NA
#> 10 Switzerland 15 NA NA
#> 11 Kuwait 11 NA NA
#> 12 Netherlands 8 NA NA
#> 13 Iraq 7 NA NA
#> 14 San Marino 7 NA NA
#> 15 Belgium 6 NA NA
#> 16 Norway 6 NA NA
#> 17 Austria 4 NA NA
#> 18 Hong Kong 4 NA NA
#> 19 UK 4 NA NA
#> 20 Australia 3 NA NA
#> 21 Canada 3 NA NA
#> 22 Iceland 3 NA NA
#> 23 Lebanon 3 NA NA
#> 24 Algeria 2 NA NA
#> 25 Bahrain 2 NA NA
#> 26 India 2 NA NA
#> 27 Indonesia 2 NA NA
#> 28 Portugal 2 NA NA
#> 29 Singapore 2 NA 6
#> 30 Andorra 1 NA NA
#> 31 Latvia 1 NA NA
#> 32 Morocco 1 NA NA
#> 33 Russia 1 NA NA
#> 34 Saudi Arabia 1 NA NA
#> 35 Senegal 1 NA NA
#> 36 Sweden 1 NA NA
#> 37 Taiwan 1 NA 3
#> 38 Thailand 1 NA 3The raw data pulled and arranged by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from the following resources:
- World Health Organization (WHO): https://www.who.int/
- DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia.
- BNO News:
https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
- National Health Commission of the People’s Republic of China (NHC): http:://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
- China CDC (CCDC):
http:://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
- Hong Kong Department of Health:
https://www.chp.gov.hk/en/features/102465.html
- Macau Government: https://www.ssm.gov.mo/portal/
- Taiwan CDC:
https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
- US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
- Government of Canada:
https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
- Australia Government Department of Health:
https://www.health.gov.au/news/coronavirus-update-at-a-glance
- European Centre for Disease Prevention and Control (ECDC):
https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases