The package can either be installed from CRAN, from our
r-universe repository, or from GitHub. See the README for details. Once installed load the package using the following,
Both the World Health Organisation (WHO) and European Centre for Disease Control (ECDC) provide worldwide national data. Access national level data for any country using:
This returns daily new and cumulative (total) cases, and where available, deaths, hospitalisations, and tests. For a complete list of variables returned, see section 5, “Data glossary” below. See the documentation (
?get_national_data) for details of optional arguments.
Data is returned with no gaps in the structure of the data by country over time, and NAs fill in where data are not available.
Access sub-national level data for a specific country over time using
get_available_datasets() to explore the currently supported sub-national datasets and select the data set of interest using the
country (selects the country of interest), and
level (selects the spatial scale of the data) arguments of
This function returns daily new and cumulative (total) cases, and where available, deaths, hospitalisations, and tests. For a complete list of variables returned, see section 5, “Data glossary” below. See the documentation (
?get_regional_data) for details of optional arguments.
As for national level data any gaps in reported data are filled with NAs.
For example, data for France Level 1 regions over time can be accessed using:
get_regional_data(country = "france")
This data then has the following format:
|2021-07-01||Saint-Pierre et Miquelon||FR-MF||8||2317||NA||NA||NA||NA||NA||NA||234||42628||08|
Alternatively, the same data can be accessed using the underlying class as follows (the France object now contains data at each processing step and the methods used at each step),
<- France$new(get = TRUE) france $return()france
All countries included in the package (see below,“Coverage”) have data for regions at the admin-1 level, the largest administrative unit of the country (e.g. state in the USA). Some countries also have data for smaller areas at the admin-2 level (e.g. county in the USA).
Data for Level 2 units can be returned by using the
level = "2" argument. The dataset will still show the corresponding Level 1 region.
An example of a country with Level 2 units is France, where Level 2 units are French departments:
get_regional_data(country = "france", level = "2")
This data again has the following format:
|2021-07-04||Saint-Pierre et Miquelon||FR-MF||Saint-Pierre et Miquelon||FR-975||NA||21||NA||0||NA||NA||NA||0||NA||1417||NA|
For totalled data up to the most recent date available, use the
get_regional_data("france", totals = TRUE)
This data now has no date variable and reflects the latest total:
|Saint-Pierre et Miquelon||FR-MF||2317||0||0||0||42628|
The data columns that will be returned by
get_regional_data() are listed below.
To standardise across countries and regions, the columns returned for each country will always be the same. If the corresponding data was missing from the original source then that data field is filled with NA values (or 0 if accessing totals data).
Note that Date is not included if the
totals argument is set to TRUE. Level 2 region/level 2 region code are not included if the
level = "1".
date: the date that the counts were reported (YYYY-MM-DD).
level_1_region: the level 1 region name. This column will be named differently for different countries (e.g. state, province).
level_1_region_code: a standard code for the level 1 region. The column name reflects the specific administrative code used. Typically data returns the iso_3166_2 standard, although where not available the column will be named differently to reflect its source.
level_2_region: the level 2 region name. This column will be named differently for different countries (e.g. city, county).
level_2_region_code: a standard code for the level 2 region. The column will be named differently for different countries (e.g.
fips in the USA).
cases_new: new reported cases for that day.
cases_total: total reported cases up to and including that day.
deaths_new: new reported deaths for that day.
deaths_total: total reported deaths up to and including that day.
recovered_new: new reported recoveries for that day.
recovered_total: total reported recoveries up to and including that day.
hosp_new: new reported hospitalisations for that day.
hosp_total: total reported hospitalisations up to and including that day (note this is cumulative total of new reported, not total currently in hospital).
tested_new: tests for that day.
tested_total: total tests completed up to and including that day.
In addition to the above, the following columns are included when using
un_region: country geographical region defined by the United Nations.
who_region: only included when
source = "WHO". Country geographical region defined by WHO.
population_2019: only included when
source = "ECDC". Total country population estimate in 2019.