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
As this an ongoing situation, frequent changes in the data format may occur, please visit the package news to get updates about those changes
Install the CRAN version:
install.packages("coronavirus")Install the Github version (refreshed on a daily bases):
# install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")While 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 data_type value lat long
#> 1 2020-10-10 Alberta, Canada state CA-AB iso_3166_2 deaths_new 0 53.9333 -116.5765
#> 2 2020-08-09 Alberta, Canada state CA-AB iso_3166_2 cases_new 0 53.9333 -116.5765
#> 3 2020-10-21 Alberta, Canada state CA-AB iso_3166_2 cases_new 406 53.9333 -116.5765
#> 4 2020-06-15 Alberta, Canada state CA-AB iso_3166_2 deaths_new 1 53.9333 -116.5765
#> 5 2020-06-13 Alberta, Canada state CA-AB iso_3166_2 cases_new 37 53.9333 -116.5765
#> 6 2020-06-21 Alberta, Canada state CA-AB iso_3166_2 cases_new 31 53.9333 -116.5765A supporting dashboard is available here
data("coronavirus")This coronavirus dataset has the following fields:
date- The date of the summaryprovince- The province or state, when applicablecountry- The country or region namelat- Latitude pointlong- Longitude pointtype- the type of case (i.e., confirmed, death)cases- the number of daily cases (corresponding to the case type)
head(coronavirus)
#> date province country lat long type cases
#> 1 2020-01-22 <NA> Afghanistan 33.93911 67.709953 confirmed 0
#> 2 2020-01-22 <NA> Albania 41.15330 20.168300 confirmed 0
#> 3 2020-01-22 <NA> Algeria 28.03390 1.659600 confirmed 0
#> 4 2020-01-22 <NA> Andorra 42.50630 1.521800 confirmed 0
#> 5 2020-01-22 <NA> Angola -11.20270 17.873900 confirmed 0
#> 6 2020-01-22 <NA> Antigua and Barbuda 17.06080 -61.796400 confirmed 0Summary 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 x 2
#> country total_cases
#> <chr> <dbl>
#> 1 US 24821813
#> 2 India 10639684
#> 3 Brazil 8753920
#> 4 Russia 3637862
#> 5 United Kingdom 3594094
#> 6 France 3069695
#> 7 Spain 2499560
#> 8 Italy 2441854
#> 9 Turkey 2418472
#> 10 Germany 2125261
#> 11 Colombia 1987418
#> 12 Argentina 1853830
#> 13 Mexico 1732290
#> 14 Poland 1464448
#> 15 South Africa 1392568
#> 16 Iran 1360852
#> 17 Ukraine 1222459
#> 18 Peru 1082907
#> 19 Indonesia 965283
#> 20 Netherlands 951747Summary of new cases during the past 24 hours by country and type (as of 2021-01-22):
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: 192 x 4
#> # Groups: country [192]
#> country confirmed death recovered
#> <chr> <dbl> <dbl> <dbl>
#> 1 US 189925 3758 0
#> 2 Brazil 56552 1096 73818
#> 3 Spain 42885 400 0
#> 4 United Kingdom 40321 1401 97
#> 5 France 23324 649 1287
#> 6 Russia 21182 566 26976
#> 7 Mexico 21007 1440 27160
#> 8 Germany 16366 837 16246
#> 9 Colombia 15073 399 10418
#> 10 India 14256 152 17136
#> 11 Portugal 13987 234 7319
#> 12 Italy 13633 472 27676
#> 13 Indonesia 13632 250 8357
#> 14 South Africa 11761 575 17841
#> 15 Argentina 10753 220 11071
#> 16 Peru 9693 230 0
#> 17 Czechia 7488 157 13320
#> 18 Poland 6693 347 0
#> 19 Iran 6332 75 7127
#> 20 Israel 6159 21 7243
#> 21 Turkey 5967 149 6018
#> 22 Canada 5827 123 6939
#> 23 Netherlands 5799 89 103
#> 24 Ukraine 5679 177 14581
#> 25 Japan 5045 108 6027
#> 26 Chile 4959 84 3031
#> 27 Sweden 4214 84 0
#> 28 Malaysia 3631 18 2554
#> 29 United Arab Emirates 3552 10 3945
#> 30 Lebanon 3220 57 3185
#> 31 Romania 2699 74 4635
#> 32 Belgium 2444 55 0
#> 33 Tunisia 2389 103 2720
#> 34 Ireland 2357 52 0
#> 35 Philippines 2170 20 245
#> 36 Switzerland 2156 63 0
#> 37 Austria 2088 42 2048
#> 38 Panama 2041 36 3288
#> 39 Pakistan 1927 43 1737
#> 40 Bolivia 1864 53 1006
#> # … with 152 more rowsPlotting the total cases by type worldwide:
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 - recovered) %>%
mutate(active_total = cumsum(active),
recovered_total = cumsum(recovered),
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")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.
https://ncov.dxy.cn/ncovh5/view/pneumonia.
- BNO News:
https://bnonews.com/index.php/2020/04/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/2019-novel-coronavirus-infection/symptoms.html
- Australia Government Department of
Health:https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert
- European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
- Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
- Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
- 1Point3Arces: https://coronavirus.1point3acres.com/en
- WorldoMeters: https://www.worldometers.info/coronavirus/
- COVID Tracking Project: https://covidtracking.com/data. (US Testing and Hospitalization Data. We use the maximum reported value from “Currently” and “Cumulative” Hospitalized for our hospitalization number reported for each state.)
- French Government: https://dashboard.covid19.data.gouv.fr/
- COVID Live (Australia): https://covidlive.com.au/
- Washington State Department of Health:https://www.doh.wa.gov/Emergencies/COVID19
- Maryland Department of Health: https://coronavirus.maryland.gov/
- New York State Department of Health: https://health.data.ny.gov/Health/New-York-State-Statewide-COVID-19-Testing/xdss-u53e/data
- NYC Department of Health and Mental Hygiene: https://www1.nyc.gov/site/doh/covid/covid-19-data.page and https://github.com/nychealth/coronavirus-data
- Florida Department of Health Dashboard: https://services1.arcgis.com/CY1LXxl9zlJeBuRZ/arcgis/rest/services/Florida_COVID19_Cases/FeatureServer/0 and https://fdoh.maps.arcgis.com/apps/opsdashboard/index.html#/8d0de33f260d444c852a615dc7837c86
- Palestine (West Bank and Gaza): https://corona.ps/details
- Israel: https://govextra.gov.il/ministry-of-health/corona/corona-virus/
- Colorado: https://covid19.colorado.gov/data)