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
As this an ongoing situation, frequent changes in the data format may occur, please visit the package news to get updates about those changes
Install the CRAN version:
install.packages("coronavirus") Install the Github version (refreshed on a daily bases):
# install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")While the coronavirus CRAN
version is updated
every month or two, the Github (Dev)
version is updated on a
daily bases. The update_dataset function enables to overcome this gap
and keep the installed version with the most recent data available on
the Github version:
library(coronavirus)
update_dataset()Note: must restart the R session to have the updates available
data("coronavirus")This coronavirus dataset has the following fields:
head(coronavirus)
#> Province.State Country.Region Lat Long date cases type
#> 1 Afghanistan 33 65 2020-01-22 0 confirmed
#> 2 Afghanistan 33 65 2020-01-23 0 confirmed
#> 3 Afghanistan 33 65 2020-01-24 0 confirmed
#> 4 Afghanistan 33 65 2020-01-25 0 confirmed
#> 5 Afghanistan 33 65 2020-01-26 0 confirmed
#> 6 Afghanistan 33 65 2020-01-27 0 confirmed
tail(coronavirus)
#> Province.State Country.Region Lat Long date cases type
#> 83883 Zhejiang China 29.1832 120.0934 2020-05-02 0 recovered
#> 83884 Zhejiang China 29.1832 120.0934 2020-05-03 0 recovered
#> 83885 Zhejiang China 29.1832 120.0934 2020-05-04 2 recovered
#> 83886 Zhejiang China 29.1832 120.0934 2020-05-05 0 recovered
#> 83887 Zhejiang China 29.1832 120.0934 2020-05-06 0 recovered
#> 83888 Zhejiang China 29.1832 120.0934 2020-05-07 0 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 [12]
#> Country.Region type total_cases
#> <chr> <chr> <int>
#> 1 US confirmed 1257023
#> 2 Spain confirmed 221447
#> 3 Italy confirmed 215858
#> 4 United Kingdom confirmed 207977
#> 5 US recovered 195036
#> 6 Russia confirmed 177160
#> 7 France confirmed 174918
#> 8 Germany confirmed 169430
#> 9 Germany recovered 141700
#> 10 Brazil confirmed 135773
#> 11 Turkey confirmed 133721
#> 12 Spain recovered 128511
#> 13 Iran confirmed 103135
#> 14 Italy recovered 96276
#> 15 China confirmed 83975
#> 16 Turkey recovered 82984
#> 17 Iran recovered 82744
#> 18 China recovered 78977
#> 19 US death 75662
#> 20 Canada confirmed 66201Summary of new cases during the past 24 hours by country and type (as of 2020-05-07):
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: 187 x 4
#> # Groups: country [187]
#> country confirmed death recovered
#> <chr> <int> <int> <int>
#> 1 US 28420 2231 5126
#> 2 Russia 11231 88 2476
#> 3 Brazil 9162 602 3980
#> 4 United Kingdom 5618 539 36
#> 5 Peru 3709 94 861
#> 6 India 3364 104 1445
#> 7 Mexico 1982 257 0
#> 8 Turkey 1977 57 4782
#> 9 Saudi Arabia 1793 10 1015
#> 10 Chile 1533 4 475
#> 11 Canada 1507 175 1076
#> 12 Iran 1485 68 1157
#> 13 Italy 1401 274 3031
#> 14 Germany 1268 117 1800
#> 15 Spain 1122 213 2509
#> 16 Qatar 918 0 216
#> 17 Belarus 913 4 679
#> 18 Singapore 741 0 78
#> 19 Bangladesh 706 13 507
#> 20 Sweden 705 99 897
#> 21 France 694 178 1112
#> 22 Belgium 639 76 249
#> 23 Pakistan 571 21 0
#> 24 Portugal 533 16 182
#> 25 Ukraine 507 13 299
#> 26 United Arab Emirates 502 8 213
#> 27 Colombia 497 10 152
#> 28 Netherlands 455 85 1
#> 29 South Africa 424 8 0
#> 30 Egypt 393 13 72
#> 31 Romania 392 24 356
#> 32 Nigeria 381 4 67
#> 33 Philippines 339 27 112
#> 34 Indonesia 338 35 64
#> 35 Poland 307 22 207
#> 36 Dominican Republic 288 11 104
#> 37 Kuwait 278 2 162
#> 38 Bahrain 265 0 140
#> 39 Honduras 224 6 22
#> 40 Japan 224 21 422
#> # … with 147 more rowsThe 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