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Interface to subnational and national level COVID-19 data sourced from both official sources, such as Public Health England in the UK, and from other COVID-19 data collections, including the World Health Organisation (WHO), European Centre for Disease Prevention and Control (ECDC), John Hopkins University (JHU), Google Open Data and others. This package is designed to streamline COVID-19 data extraction, cleaning, and processing from a range of data sources in an open and transparent way. This allows users to inspect and scrutinise the data, and tools used to process it, at every step. For all countries supported, data includes a daily time-series of cases and, wherever available, data on deaths, hospitalisations, and tests. National level data is also supported using a range of data sources as well as line list data and links to intervention data sets.


Install from CRAN:


Install the stable development version of the package with:

  repos = ""

Install the unstable development version of the package with:


Quick start


Load covidregionaldata, dplyr, scales, and ggplot2 (all used in this quick start),

Setup data caching

This package can optionally use a data cache from memoise to locally cache downloads. This can be enabled using the following (this will use the temporary directory by default),

#> Using a cache at: /tmp/RtmpPgZXiv

To stop using memoise use,

and to reset the cache (required to download new data),

National data

To get worldwide time-series data by country (sourced from the World Health Organisation (WHO) by default but also optionally from the European Centre for Disease Control (ECDC), John Hopkins University, or the Google COVID-19 open data project), use:

nots <- get_national_data()
#> Downloading data from
#> Rows: 142911 Columns: 8
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr  (3): Country_code, Country, WHO_region
#> dbl  (4): New_cases, Cumulative_cases, New_deaths, Cumulative_deaths
#> date (1): Date_reported
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> Cleaning data
#> Processing data
#> # A tibble: 142,911 × 15
#>    date       un_region who_region country        iso_code cases_new cases_total
#>    <date>     <chr>     <chr>      <chr>          <chr>        <dbl>       <dbl>
#>  1 2020-01-03 Asia      EMRO       Afghanistan    AF               0           0
#>  2 2020-01-03 Europe    EURO       Albania        AL               0           0
#>  3 2020-01-03 Africa    AFRO       Algeria        DZ               0           0
#>  4 2020-01-03 Oceania   WPRO       American Samoa AS               0           0
#>  5 2020-01-03 Europe    EURO       Andorra        AD               0           0
#>  6 2020-01-03 Africa    AFRO       Angola         AO               0           0
#>  7 2020-01-03 Americas  AMRO       Anguilla       AI               0           0
#>  8 2020-01-03 Americas  AMRO       Antigua & Barbuda AG               0           0
#>  9 2020-01-03 Americas  AMRO       Argentina      AR               0           0
#> 10 2020-01-03 Asia      EURO       Armenia        AM               0           0
#> # … with 142,901 more rows, and 8 more variables: deaths_new <dbl>,
#> #   deaths_total <dbl>, recovered_new <dbl>, recovered_total <dbl>,
#> #   hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>, tested_total <dbl>

This can also be filtered for a country of interest,

g7 <- c(
  "United States", "United Kingdom", "France", "Germany",
  "Italy", "Canada", "Japan"
g7_nots <- get_national_data(countries = g7, verbose = FALSE)

Using this data we can compare case information between countries, for example here is the number of deaths over time for each country in the G7:

g7_nots %>%
  ggplot() +
  aes(x = date, y = deaths_new, col = country) +
  geom_line(alpha = 0.4) +
  labs(x = "Date", y = "Reported Covid-19 deaths") +
  scale_y_continuous(labels = comma) +
  theme_minimal() +
  theme(legend.position = "top") +
  guides(col = guide_legend(title = "Country"))

Subnational data

To get time-series data for subnational regions of a specific country, for example by level 1 region in the UK, use:

uk_nots <- get_regional_data(country = "UK", verbose = FALSE)
#> # A tibble: 7,501 × 26
#>    date       region   region_code cases_new cases_total deaths_new deaths_total
#>    <date>     <chr>    <chr>           <dbl>       <dbl>      <dbl>        <dbl>
#>  1 2020-01-30 East Mi… E12000004          NA          NA         NA           NA
#>  2 2020-01-30 East of… E12000006          NA          NA         NA           NA
#>  3 2020-01-30 England  E92000001           2           2         NA           NA
#>  4 2020-01-30 London   E12000007          NA          NA         NA           NA
#>  5 2020-01-30 North E… E12000001          NA          NA         NA           NA
#>  6 2020-01-30 North W… E12000002          NA          NA         NA           NA
#>  7 2020-01-30 Norther… N92000002          NA          NA         NA           NA
#>  8 2020-01-30 Scotland S92000003          NA          NA         NA           NA
#>  9 2020-01-30 South E… E12000008          NA          NA         NA           NA
#> 10 2020-01-30 South W… E12000009          NA          NA         NA           NA
#> # … with 7,491 more rows, and 19 more variables: recovered_new <dbl>,
#> #   recovered_total <dbl>, hosp_new <dbl>, hosp_total <dbl>, tested_new <dbl>,
#> #   tested_total <dbl>, areaType <chr>, cumCasesByPublishDate <dbl>,
#> #   cumCasesBySpecimenDate <dbl>, newCasesByPublishDate <dbl>,
#> #   newCasesBySpecimenDate <dbl>, cumDeaths28DaysByDeathDate <dbl>,
#> #   cumDeaths28DaysByPublishDate <dbl>, newDeaths28DaysByDeathDate <dbl>,
#> #   newDeaths28DaysByPublishDate <dbl>, …

Now we have the data we can create plots, for example the time-series of the number of cases for each region:

uk_nots %>%
  filter(!(region %in% "England")) %>%
  ggplot() +
  aes(x = date, y = cases_new, col = region) +
  geom_line(alpha = 0.4) +
  labs(x = "Date", y = "Reported Covid-19 cases") +
  scale_y_continuous(labels = comma) +
  theme_minimal() +
  theme(legend.position = "top") +
  guides(col = guide_legend(title = "Region"))

See get_available_datasets() for supported regions and subregional levels. To view what datasets we currently have subnationaldata for, along with their current status, check the supported countries page or build the supported countries vignette.

For further examples see the quick start vignette. Additional subnational data are supported via the JHU() and Google() classes. Use the available_regions() method once these data have been downloaded and cleaned (see their examples) for subnational data they internally support.


If using covidregionaldata in your work please consider citing it using the following,

#> To cite covidregionaldata in publications use:
#>   Joseph Palmer, Katharine Sherratt, Richard Martin-Nielsen, Jonnie
#>   Bevan, Hamish Gibbs, Sebastian Funk and Sam Abbott (2021).
#>   covidregionaldata: Subnational data for COVID-19 epidemiology, DOI:
#>   10.21105/joss.03290
#> A BibTeX entry for LaTeX users is
#>   @Article{,
#>     title = {covidregionaldata: Subnational data for COVID-19 epidemiology},
#>     author = {Joseph Palmer and Katharine Sherratt and Richard Martin-Nielsen and Jonnie Bevan and Hamish Gibbs and Sebastian Funk and Sam Abbott},
#>     journal = {Journal of Open Source Software},
#>     year = {2021},
#>     volume = {6},
#>     number = {63},
#>     pages = {3290},
#>     doi = {10.21105/joss.03290},
#>   }



This package is the result of work from a number of contributors (see contributors list here). We would like to thank the CMMID COVID-19 working group for insightful comments and feedback.

We welcome contributions and new contributors! We particularly appreciate help adding new data sources for countries at sub-national level, or work on priority problems in the issues. Please check and add to the issues, and/or add a pull request. For more details, start with the contributing guide. For details of the steps required to add support for a dataset see the adding data guide.