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("covid19r/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 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
#> 64569 Zhejiang China 29.1832 120.0934 2020-04-08 2 recovered
#> 64570 Zhejiang China 29.1832 120.0934 2020-04-09 3 recovered
#> 64571 Zhejiang China 29.1832 120.0934 2020-04-10 0 recovered
#> 64572 Zhejiang China 29.1832 120.0934 2020-04-11 1 recovered
#> 64573 Zhejiang China 29.1832 120.0934 2020-04-12 2 recovered
#> 64574 Zhejiang China 29.1832 120.0934 2020-04-13 1 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 US confirmed 580619
#> 2 Spain confirmed 170099
#> 3 Italy confirmed 159516
#> 4 France confirmed 137875
#> 5 Germany confirmed 130072
#> 6 United Kingdom confirmed 89570
#> 7 China confirmed 83213
#> 8 China recovered 78039
#> 9 Iran confirmed 73303
#> 10 Spain recovered 64727
#> 11 Germany recovered 64300
#> 12 Turkey confirmed 61049
#> 13 Iran recovered 45983
#> 14 US recovered 43482
#> 15 Italy recovered 35435
#> 16 Belgium confirmed 30589
#> 17 France recovered 28001
#> 18 Netherlands confirmed 26710
#> 19 Switzerland confirmed 25688
#> 20 Canada confirmed 25679Summary of new cases during the past 24 hours by country and type (as of 2020-04-13):
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: 185 x 4
#> # Groups: country [185]
#> country confirmed death recovered
#> <chr> <int> <int> <int>
#> 1 US 25306 1509 10494
#> 2 United Kingdom 4364 718 -322
#> 3 France 4205 574 532
#> 4 Turkey 4093 98 511
#> 5 Spain 3268 547 2336
#> 6 Italy 3153 566 1224
#> 7 Russia 2558 18 179
#> 8 Peru 2265 23 844
#> 9 Germany 2218 172 4000
#> 10 Iran 1617 111 2089
#> 11 Canada 1381 65 635
#> 12 India 1248 27 101
#> 13 Brazil 1238 105 0
#> 14 Ireland 992 31 0
#> 15 Netherlands 964 86 0
#> 16 Belgium 942 303 244
#> 17 Japan 622 15 22
#> 18 Saudi Arabia 472 6 44
#> 19 Sweden 465 20 0
#> 20 Mexico 442 23 71
#> 21 Israel 441 13 228
#> 22 Serbia 424 5 0
#> 23 United Arab Emirates 398 3 172
#> 24 Singapore 386 1 26
#> 25 Portugal 349 31 0
#> 26 Belarus 341 3 0
#> 27 Romania 333 15 62
#> 28 Ukraine 325 10 8
#> 29 Indonesia 316 26 21
#> 30 Chile 312 2 308
#> 31 Philippines 284 18 45
#> 32 Switzerland 273 32 1000
#> 33 Pakistan 266 2 67
#> 34 Poland 260 13 48
#> 35 Qatar 252 0 59
#> 36 Bahrain 225 0 33
#> 37 Dominican Republic 200 4 21
#> 38 Bangladesh 182 5 3
#> 39 Panama 166 8 6
#> 40 Denmark 144 12 112
#> # … with 145 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