coronavirus
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.
Source: Centers for Disease Control and Prevention’s Public Health Image Library
Installation
Currently, the package available only on Github version:
# install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")Usage
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 Belgium 50.5039 4.4699 2020-01-21 0 confirmed
#> 2 Cambodia 12.5657 104.9910 2020-01-21 0 confirmed
#> 3 Finland 61.9241 25.7482 2020-01-21 0 confirmed
#> 4 France 46.2276 2.2137 2020-01-21 0 confirmed
#> 5 Germany 51.1657 10.4515 2020-01-21 0 confirmed
#> 6 India 20.5937 78.9629 2020-01-21 0 confirmed
tail(coronavirus)
#> Province.State Country.Region Lat Long date cases type
#> 5032 Tibet Mainland China 30.15340 88.78790 2020-02-12 1 recovered
#> 5033 Toronto, ON Canada 43.65320 -79.38320 2020-02-12 0 recovered
#> 5034 Victoria Australia -37.81360 144.96310 2020-02-12 0 recovered
#> 5035 Xinjiang Mainland China 41.11981 85.17822 2020-02-12 0 recovered
#> 5036 Yunnan Mainland China 24.97411 101.48680 2020-02-12 6 recovered
#> 5037 Zhejiang Mainland China 29.18251 120.09850 2020-02-12 42 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> <dbl>
#> 1 Mainland China confirmed 44687
#> 2 Mainland China recovered 5062
#> 3 Mainland China death 1115
#> 4 Others confirmed 175
#> 5 Hong Kong confirmed 50
#> 6 Singapore confirmed 47
#> 7 Thailand confirmed 33
#> 8 Japan confirmed 28
#> 9 South Korea confirmed 28
#> 10 Malaysia confirmed 18
#> 11 Taiwan confirmed 18
#> 12 Germany confirmed 16
#> 13 Australia confirmed 15
#> 14 Vietnam confirmed 15
#> 15 US confirmed 13
#> 16 France confirmed 11
#> 17 Macau confirmed 10
#> 18 Thailand recovered 10
#> 19 Japan recovered 9
#> 20 Singapore recovered 9Data Sources
The 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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