README.md

In dmglandon/coronavirus_test: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset

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.

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

Installation

Install the CRAN version:

install.packages("coronavirus") 

Install the Github version (refreshed on a daily bases):

# 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                   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
#> 45355       Zhejiang          China 29.1832 120.0934 2020-03-12     2 recovered
#> 45356       Zhejiang          China 29.1832 120.0934 2020-03-13     0 recovered
#> 45357       Zhejiang          China 29.1832 120.0934 2020-03-14    14 recovered
#> 45358       Zhejiang          China 29.1832 120.0934 2020-03-15     0 recovered
#> 45359       Zhejiang          China 29.1832 120.0934 2020-03-16     5 recovered
#> 45360       Zhejiang          China 29.1832 120.0934 2020-03-17     0 recovered

Here 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 [14]
#>    Country.Region type      total_cases
#>    <chr>          <chr>           <int>
#>  1 China          confirmed       81058
#>  2 China          recovered       68798
#>  3 Italy          confirmed       31506
#>  4 Iran           confirmed       16169
#>  5 Spain          confirmed       11748
#>  6 Germany        confirmed        9257
#>  7 Korea, South   confirmed        8320
#>  8 France         confirmed        7699
#>  9 US             confirmed        6421
#> 10 Iran           recovered        5389
#> 11 China          death            3230
#> 12 Italy          recovered        2941
#> 13 Switzerland    confirmed        2700
#> 14 Italy          death            2503
#> 15 United Kingdom confirmed        1960
#> 16 Netherlands    confirmed        1708
#> 17 Norway         confirmed        1463
#> 18 Korea, South   recovered        1407
#> 19 Austria        confirmed        1332
#> 20 Belgium        confirmed        1243

Summary of new cases during the past 24 hours by country and type (as of 2020-03-17):

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: 152 x 4
#> # Groups:   country [152]
#>    country        confirmed death recovered
#>    <chr>              <int> <int>     <int>
#>  1 Italy               3526   345       192
#>  2 Germany             1985     7         0
#>  3 Spain               1806   191       498
#>  4 US                  1789    23         0
#>  5 Iran                1178   135       799
#>  6 France              1031     0         0
#>  7 Switzerland          500    13         0
#>  8 United Kingdom       409     0        32
#>  9 Austria              314     0        -5
#> 10 Netherlands          294    19         0
#> 11 Belgium              185     5         0
#> 12 Norway               130     0         0
#> 13 Brazil               121     1         1
#> 14 Portugal             117     1         0
#> 15 Malaysia             107     2         7
#> 16 Pakistan             100     0         0
#> 17 Czechia               98     0         0
#> 18 Denmark               92     1         0
#> 19 Sweden                87     1         0
#> 20 Korea, South          84     6       270
#> 21 Israel                82     0         7
#> 22 Australia             75     2         0
#> 23 Canada                63     1         0
#> 24 Luxembourg            63     0         0
#> 25 Poland                61     1         0
#> 26 Greece                56     1         0
#> 27 Ireland               54     0         5
#> 28 Japan                 53     2         0
#> 29 Saudi Arabia          53     0         4
#> 30 Chile                 46     0         0
#> 31 Egypt                 46     2         5
#> 32 Philippines           45     0         3
#> 33 Finland               44     0         0
#> 34 Iceland               40     1         0
#> 35 Indonesia             38     0         0
#> 36 Andorra               37     0         0
#> 37 Peru                  31     0         1
#> 38 Iraq                  30     1         6
#> 39 Thailand              30     0         6
#> 40 Mexico                29     0         0
#> # … with 112 more rows

Data 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

dmglandon/coronavirus_test documentation built on March 23, 2020, 12:44 a.m.