The coronavirus package provides a tidy format dataset of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic and the vaccination efforts by country. The raw data is being 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
Additional documentation available on the followng vignettes:
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 provides the following two datasets:
coronavirus - tidy (long) format of the JHU CCSE datasets. That includes the following columns:
date - The date of the observation, using
Date classprovince - Name of province/state, for countries where
data is provided split across multiple provinces/statescountry - Name of country/regionlat - The latitude codelong - The longitude codetype - An indicator for the type of cases (confirmed,
death, recovered)cases - Number of cases on given dateuid - Country codeprovince_state - Province or state if applicableiso2 - Officially assigned country code identifiers
with two-letteriso3 - Officially assigned country code identifiers
with three-lettercode3 - UN country codefips - Federal Information Processing Standards code
that uniquely identifies counties within the USAcombined_key - Country and province (if
applicable)population - Country or province populationcontinent_name - Continent namecontinent_code - Continent codecovid19_vaccine - a tidy (long) format of the the Johns Hopkins Centers for Civic Impact global vaccination dataset by country. This dataset includes the following columns:
country_region - Country or region namedate - Data collection date in YYYY-MM-DD formatdoses_admin - Cumulative number of doses administered.
When a vaccine requires multiple doses, each one is counted
independentlypeople_partially_vaccinated - Cumulative number of
people who received at least one vaccine dose. When the person receives
a prescribed second dose, it is not counted twicepeople_fully_vaccinated - Cumulative number of people
who received all prescribed doses necessary to be considered fully
vaccinatedreport_date_string - Data report date in YYYY-MM-DD
formatuid - Country codeprovince_state - Province or state if applicableiso2 - Officially assigned country code identifiers
with two-letteriso3 - Officially assigned country code identifiers
with three-lettercode3 - UN country codefips - Federal Information Processing Standards code
that uniquely identifies counties within the USAlat - Latitudelong - Longitudecombined_key - Country and province (if
applicable)population - Country or province populationcontinent_name - Continent namecontinent_code - Continent codeWhile the coronavirus CRAN version
is updated every month or two, the Github (Dev)
version is updated on a daily bases. The update_dataset
function enables to overcome this gap and keep the installed version
with the most recent data available on the Github version:
library(coronavirus)
update_dataset()Note: must restart the R session to have the updates available
Alternatively, you can pull the data using the Covid19R project data
standard format with the refresh_coronavirus_jhu
function:
covid19_df <- refresh_coronavirus_jhu()
head(covid19_df)
#> date location location_type location_code location_code_type
#> 1 2022-04-21 Afghanistan country AF iso_3166_2
#> 2 2022-04-20 Afghanistan country AF iso_3166_2
#> 3 2021-12-26 Afghanistan country AF iso_3166_2
#> 4 2022-04-17 Afghanistan country AF iso_3166_2
#> 5 2022-04-23 Afghanistan country AF iso_3166_2
#> 6 2022-04-24 Afghanistan country AF iso_3166_2
#> data_type value lat long
#> 1 deaths_new 0 33.93911 67.70995
#> 2 deaths_new 0 33.93911 67.70995
#> 3 deaths_new 5 33.93911 67.70995
#> 4 deaths_new 2 33.93911 67.70995
#> 5 deaths_new 1 33.93911 67.70995
#> 6 deaths_new 1 33.93911 67.70995data("coronavirus")
head(coronavirus)
#> date province country lat long type cases uid iso2 iso3
#> 1 2020-01-22 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
#> 2 2020-01-23 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
#> 3 2020-01-24 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
#> 4 2020-01-25 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
#> 5 2020-01-26 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
#> 6 2020-01-27 Alberta Canada 53.9333 -116.5765 confirmed 0 12401 CA CAN
#> code3 combined_key population continent_name continent_code
#> 1 124 Alberta, Canada 4413146 North America NA
#> 2 124 Alberta, Canada 4413146 North America NA
#> 3 124 Alberta, Canada 4413146 North America NA
#> 4 124 Alberta, Canada 4413146 North America NA
#> 5 124 Alberta, Canada 4413146 North America NA
#> 6 124 Alberta, Canada 4413146 North America NASummary of the total confrimed cases by country (top 20):
library(dplyr)
summary_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases)
summary_df %>% head(20)
#> # A tibble: 20 × 2
#> country total_cases
#> <chr> <int>
#> 1 US 86636306
#> 2 India 43344958
#> 3 Brazil 31890733
#> 4 France 30555038
#> 5 Germany 27573585
#> 6 United Kingdom 22751393
#> 7 Korea, South 18305783
#> 8 Russia 18137759
#> 9 Italy 18014202
#> 10 Turkey 15085742
#> 11 Spain 12613634
#> 12 Vietnam 10739855
#> 13 Argentina 9341492
#> 14 Japan 9178003
#> 15 Netherlands 8247488
#> 16 Australia 7919844
#> 17 Iran 7235440
#> 18 Colombia 6131657
#> 19 Indonesia 6072918
#> 20 Poland 6011984Summary of new cases during the past 24 hours by country and type (as of 2022-06-22):
library(tidyr)
coronavirus %>%
filter(date == max(date)) %>%
select(country, type, cases) %>%
group_by(country, type) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type,
values_from = total_cases) %>%
arrange(-confirmed)
#> # A tibble: 199 × 4
#> # Groups: country [199]
#> country confirmed death recovery
#> <chr> <int> <int> <int>
#> 1 US 184074 860 0
#> 2 Germany 119360 98 0
#> 3 France 78123 66 0
#> 4 Brazil 71906 140 0
#> 5 Italy 54873 50 0
#> 6 Taiwan* 52218 171 0
#> 7 United Kingdom 33406 77 0
#> 8 Australia 32034 52 0
#> 9 Japan 17263 15 0
#> 10 Portugal 15372 21 0
#> # … with 189 more rowsPlotting daily confirmed and death cases in Brazil:
library(plotly)
coronavirus %>%
group_by(type, date) %>%
summarise(total_cases = sum(cases)) %>%
pivot_wider(names_from = type, values_from = total_cases) %>%
arrange(date) %>%
mutate(active = confirmed - death - recovery) %>%
mutate(active_total = cumsum(active),
recovered_total = cumsum(recovery),
death_total = cumsum(death)) %>%
plot_ly(x = ~ date,
y = ~ active_total,
name = 'Active',
fillcolor = '#1f77b4',
type = 'scatter',
mode = 'none',
stackgroup = 'one') %>%
add_trace(y = ~ death_total,
name = "Death",
fillcolor = '#E41317') %>%
add_trace(y = ~recovered_total,
name = 'Recovered',
fillcolor = 'forestgreen') %>%
layout(title = "Distribution of Covid19 Cases Worldwide",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of Cases"),
xaxis = list(title = "Source: Johns Hopkins University Center for Systems Science and Engineering"))
Plot the confirmed cases distribution by counrty with treemap plot:
conf_df <- coronavirus %>%
filter(type == "confirmed") %>%
group_by(country) %>%
summarise(total_cases = sum(cases)) %>%
arrange(-total_cases) %>%
mutate(parents = "Confirmed") %>%
ungroup()
plot_ly(data = conf_df,
type= "treemap",
values = ~total_cases,
labels= ~ country,
parents= ~parents,
domain = list(column=0),
name = "Confirmed",
textinfo="label+value+percent parent")
data(covid19_vaccine)
head(covid19_vaccine)
#> country_region date doses_admin people_partially_vaccinated
#> 1 Canada 2020-12-14 5 0
#> 2 World 2020-12-14 5 0
#> 3 Canada 2020-12-15 723 0
#> 4 China 2020-12-15 1500000 0
#> 5 Russia 2020-12-15 28500 28500
#> 6 World 2020-12-15 1529223 28500
#> people_fully_vaccinated report_date_string uid province_state iso2 iso3 code3
#> 1 0 2020-12-14 124 <NA> CA CAN 124
#> 2 0 2020-12-14 NA <NA> <NA> <NA> NA
#> 3 0 2020-12-15 124 <NA> CA CAN 124
#> 4 0 2020-12-15 156 <NA> CN CHN 156
#> 5 0 2020-12-15 643 <NA> RU RUS 643
#> 6 0 2020-12-15 NA <NA> <NA> <NA> NA
#> fips lat long combined_key population continent_name continent_code
#> 1 <NA> 60.00000 -95.0000 Canada 37855702 North America NA
#> 2 <NA> NA NA <NA> NA <NA> <NA>
#> 3 <NA> 60.00000 -95.0000 Canada 37855702 North America NA
#> 4 <NA> 35.86170 104.1954 China 1404676330 Asia AS
#> 5 <NA> 61.52401 105.3188 Russia 145934460 Europe EU
#> 6 <NA> NA NA <NA> NA <NA> <NA>Plot the top 20 vaccinated countries:
covid19_vaccine %>%
filter(date == max(date),
!is.na(population)) %>%
mutate(fully_vaccinated_ratio = people_fully_vaccinated / population) %>%
arrange(- fully_vaccinated_ratio) %>%
slice_head(n = 20) %>%
arrange(fully_vaccinated_ratio) %>%
mutate(country = factor(country_region, levels = country_region)) %>%
plot_ly(y = ~ country,
x = ~ round(100 * fully_vaccinated_ratio, 2),
text = ~ paste(round(100 * fully_vaccinated_ratio, 1), "%"),
textposition = 'auto',
orientation = "h",
type = "bar") %>%
layout(title = "Percentage of Fully Vaccineted Population - Top 20 Countries",
yaxis = list(title = ""),
xaxis = list(title = "Source: Johns Hopkins Centers for Civic Impact",
ticksuffix = "%"))
Note: Currently, the dashboard is under maintenance due to recent changes in the data structure. Please see this issue
A supporting dashboard is available here
The raw data pulled and arranged by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from the following resources: