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 Republic of 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
#> 3622 Shanghai Mainland China 31.2020 121.4491 2020-03-10 4 recovered
#> 3623 Shanxi Mainland China 37.5777 112.2922 2020-03-10 4 recovered
#> 3624 Sichuan Mainland China 30.6171 102.7103 2020-03-10 12 recovered
#> 3625 Taiwan Taipei and environs 23.7000 121.0000 2020-03-10 2 recovered
#> 3626 Tianjin Mainland China 39.3054 117.3230 2020-03-10 1 recovered
#> 3627 Zhejiang Mainland China 29.1832 120.0934 2020-03-10 15 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 [15]
#> Country.Region type total_cases
#> <chr> <chr> <int>
#> 1 Mainland China confirmed 80757
#> 2 Mainland China recovered 60106
#> 3 Italy confirmed 10149
#> 4 Iran (Islamic Republic of) confirmed 8042
#> 5 Republic of Korea confirmed 7513
#> 6 Mainland China death 3136
#> 7 Iran (Islamic Republic of) recovered 2731
#> 8 France confirmed 1784
#> 9 Spain confirmed 1695
#> 10 Germany confirmed 1457
#> 11 US confirmed 777
#> 12 Italy recovered 724
#> 13 Others confirmed 696
#> 14 Italy death 631
#> 15 Japan confirmed 581
#> 16 Switzerland confirmed 491
#> 17 Norway confirmed 400
#> 18 Netherlands confirmed 382
#> 19 UK confirmed 382
#> 20 Sweden confirmed 355Summary of new cases during the past 24 hours by country and type (as of 2020-03-10):
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: 81 x 4
#> # Groups: country [81]
#> country confirmed recovered death
#> <chr> <int> <int> <int>
#> 1 Italy 977 NA 168
#> 2 Iran (Islamic Republic of) 881 337 54
#> 3 Spain 622 NA 7
#> 4 France 575 NA 14
#> 5 Germany 281 NA NA
#> 6 Norway 195 NA NA
#> 7 US 193 NA 6
#> 8 Denmark 172 NA NA
#> 9 Switzerland 117 NA 1
#> 10 Sweden 107 NA NA
#> 11 Japan 70 25 NA
#> 12 Netherlands 61 NA 1
#> 13 UK 61 NA 2
#> 14 Austria 51 2 NA
#> 15 Republic of Korea 35 129 1
#> 16 United Arab Emirates 29 5 NA
#> 17 Belgium 28 NA NA
#> 18 Mainland China 22 1371 16
#> 19 Israel 19 2 NA
#> 20 Australia 16 NA NA
#> 21 Greece 16 NA NA
#> 22 Bahrain 15 8 NA
#> 23 San Marino 15 NA 1
#> 24 Slovenia 15 NA NA
#> 25 India 13 1 NA
#> 26 Ireland 13 NA NA
#> 27 Philippines 13 1 NA
#> 28 Malaysia 12 NA NA
#> 29 Iceland 11 1 NA
#> 30 Iraq 11 NA 1
#> 31 Portugal 11 NA NA
#> 32 Czech Republic 10 NA NA
#> 33 Finland 10 NA NA
#> 34 Pakistan 10 NA NA
#> 35 Romania 10 NA NA
#> 36 Singapore 10 NA NA
#> 37 Lebanon 9 NA 1
#> 38 Albania 8 NA NA
#> 39 Indonesia 8 2 NA
#> 40 Brazil 6 NA NA
#> 41 Poland 6 NA NA
#> 42 Qatar 6 NA NA
#> 43 Argentina 5 NA NA
#> 44 Chile 5 NA NA
#> 45 Hong Kong SAR 5 6 NA
#> 46 Kuwait 5 NA NA
#> 47 Saudi Arabia 5 1 NA
#> 48 Egypt 4 NA NA
#> 49 North Macedonia 4 NA NA
#> 50 Peru 4 NA NA
#> 51 Serbia 4 NA NA
#> 52 Slovakia 4 NA NA
#> 53 South Africa 4 NA NA
#> 54 Belarus 3 2 NA
#> 55 occupied Palestinian territory 3 NA NA
#> 56 Russian Federation 3 NA NA
#> 57 Thailand 3 2 NA
#> 58 Tunisia 3 NA NA
#> 59 Azerbaijan 2 NA NA
#> 60 Bosnia and Herzegovina 2 NA NA
#> 61 Canada 2 NA NA
#> 62 Colombia 2 NA NA
#> 63 Croatia 2 NA NA
#> 64 Estonia 2 NA NA
#> 65 Latvia 2 1 NA
#> 66 Luxembourg 2 NA NA
#> 67 Maldives 2 NA NA
#> 68 Malta 2 NA NA
#> 69 Oman 2 7 NA
#> 70 Republic of Moldova 2 NA NA
#> 71 Taipei and environs 2 2 NA
#> 72 Afghanistan 1 NA NA
#> 73 Burkina Faso 1 NA NA
#> 74 Channel Islands 1 NA NA
#> 75 Cyprus 1 NA NA
#> 76 Mongolia 1 NA NA
#> 77 Morocco 1 NA 1
#> 78 Panama 1 NA NA
#> 79 Viet Nam 1 NA NA
#> 80 Gibraltar NA 1 NA
#> 81 Mexico NA 3 NAThe 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
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