coronavirus: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset
In coronavirus: The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset

coronavirus

R Documentation

The 2019 Novel Coronavirus COVID-19 (2019-nCoV) Dataset

Description

Daily summary of the Coronavirus (COVID-19) cases by state/province.

Usage

coronavirus

Format

A data frame with 7 variables.

date: Date in YYYY-MM-DD format.
province: Name of province/state, for countries where data is provided split across multiple provinces/states.
country: Name of country/region.
lat: Latitude of center of geographic region, defined as either country or, if available, province.
long: Longitude of center of geographic region, defined as either country or, if available, province.
type: An indicator for the type of cases (confirmed, death, recovered).
cases: Number of cases on given date.
uid: Country code
iso2: Officially assigned country code identifiers with two-letter
iso3: Officially assigned country code identifiers with three-letter
code3: UN country code
combined_key: Country and province (if applicable)
population: Country or province population
continent_name: Continent name
continent_code: Continent code

Details

The dataset contains the daily summary of Coronavirus cases (confirmed, death, and recovered), by state/province.

Source

Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus website.

Examples

data(coronavirus)

require(dplyr)

# Get top confirmed cases by state
coronavirus %>%
  filter(type == "confirmed") %>%
  group_by(country) %>%
  summarise(total = sum(cases)) %>%
  arrange(-total) %>%
  head(20)

# Get the number of recovered cases in China by province
coronavirus %>%
  filter(type == "recovered", country == "China") %>%
  group_by(province) %>%
  summarise(total = sum(cases)) %>%
  arrange(-total)

coronavirus documentation built on March 31, 2023, 10:22 p.m.