README.md

In adityasinhak/coronavirus: 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

Important Note

As this an ongoing situation, frequent changes in the data format may occur, please visit the package news to get updates about those changes

Installation

Install the CRAN version:

install.packages("coronavirus")

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

# install.packages("devtools")
devtools::install_github("covid19r/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
#> 56457       Zhejiang          China 29.1832 120.0934 2020-03-30     0 recovered
#> 56458       Zhejiang          China 29.1832 120.0934 2020-03-31     1 recovered
#> 56459       Zhejiang          China 29.1832 120.0934 2020-04-01     0 recovered
#> 56460       Zhejiang          China 29.1832 120.0934 2020-04-02     2 recovered
#> 56461       Zhejiang          China 29.1832 120.0934 2020-04-03     0 recovered
#> 56462       Zhejiang          China 29.1832 120.0934 2020-04-04     1 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 [12]
#>    Country.Region type      total_cases
#>    <chr>          <chr>           <int>
#>  1 US             confirmed      308850
#>  2 Spain          confirmed      126168
#>  3 Italy          confirmed      124632
#>  4 Germany        confirmed       96092
#>  5 France         confirmed       90848
#>  6 China          confirmed       82543
#>  7 China          recovered       76946
#>  8 Iran           confirmed       55743
#>  9 United Kingdom confirmed       42477
#> 10 Spain          recovered       34219
#> 11 Germany        recovered       26400
#> 12 Turkey         confirmed       23934
#> 13 Italy          recovered       20996
#> 14 Switzerland    confirmed       20505
#> 15 Iran           recovered       19736
#> 16 Belgium        confirmed       18431
#> 17 Netherlands    confirmed       16727
#> 18 France         recovered       15572
#> 19 Italy          death           15362
#> 20 US             recovered       14652

Summary of new cases during the past 24 hours by country and type (as of 2020-04-04):

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: 181 x 4
#> # Groups:   country [181]
#>    country        confirmed death recovered
#>    <chr>              <int> <int>     <int>
#>  1 US                 33264  1320      4945
#>  2 France             25646  1054      1437
#>  3 Spain               6969   749      3706
#>  4 Germany             4933   169      1825
#>  5 Italy               4805   681      1238
#>  6 United Kingdom      3788   709         7
#>  7 Turkey              3013    76       302
#>  8 Iran                2560   158      1801
#>  9 Belgium             1661   140       375
#> 10 Brazil              1304    86         0
#> # … with 171 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

adityasinhak/coronavirus documentation built on April 21, 2020, 7:28 a.m.