covidregionaldata is designed to extract national and subnational Covid-19 data from publicly available sources, both daily and aggregated over time.
For example, we could:
We will demonstrate some of these examples below, as well as some useful data manipulation and visualisation.
Let’s say that we want to see the evolution of the pandemic over time in all countries. To do this, we need to get the number of cases (positive test results) and deaths for each country and date since the pandemic started. Firstly load
covidregionaldata, then call
library(covidregionaldata) all_countries <- get_national_data() #> Downloading data from https://covid19.who.int/WHO-COVID-19-global-data.csv #> Rows: 148599 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
We can take a look at this data by printing it to the console:
all_countries #> # A tibble: 148,599 × 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 148,589 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>
We can see that this provides information about the number of cases and deaths for each country over time. As a tibble is returned, we can easily manipulate and plot this using
tidyverse packages. For example, we can plot the total deaths in Italy over time.
We could have also filtered by country using
get_national_data(countries = "Italy") to achieve the same result.
To plot the evolution of cases for several countries since the start of the pandemic, we could perform something like the following:
get_national_data(totals = TRUE) we can obtain total cases and deaths for all nations up to the latest date reported. This is useful to get a snapshot of the current running total for each country. Note how the data is sorted by the total number of cases:
all_countries_totals <- get_national_data(totals = TRUE, verbose = FALSE) all_countries_totals #> # A tibble: 236 × 7 #> country iso_code cases_total deaths_total recovered_total hosp_total #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 United States US 41716516 667244 0 0 #> 2 India IN 33478424 445133 0 0 #> 3 Brazil BR 21230325 590508 0 0 #> 4 United Kingdom GB 7429750 135203 0 0 #> 5 Russia RU 7294672 198996 0 0 #> 6 Turkey TR 6847259 61574 0 0 #> 7 France FR 6746220 114001 0 0 #> 8 Iran IR 5424835 117182 0 0 #> 9 Argentina AR 5238610 114367 0 0 #> 10 Colombia CO 4939251 125860 0 0 #> # … with 226 more rows, and 1 more variable: tested_total <dbl>
As well as national data, we can also retrieve subnational data, such as states and counties in the US, or regions and local authorities in the UK, and perform similar manipulations. See the README for a current list of countries with subnational data.
To do this, we use
get_regional_data() (we could also use the underlying method as follows
us <- USA$new(get = TRUE); us$return()). We’ll get the state-level data for the United States over time:
usa_states <- get_regional_data(country = "USA") #> Downloading data from https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv #> Rows: 31144 Columns: 5 #> ── Column specification ──────────────────────────────────────────────────────── #> Delimiter: "," #> chr (2): state, fips #> dbl (2): cases, deaths #> date (1): date #> #> 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
This retrieves the new and total cases and deaths for all states over time in the United States. Note that the
country argument to the
get_regional_data() function must be specified. Let’s plot out how the number of cases has evolved for a selection of states:
totals argument can also be used in
get_regional_data to return the cumulative number of cases or deaths over time.
usa_states_totals <- get_regional_data( country = "USA", totals = TRUE, verbose = FALSE )
Let’s plot the total deaths for each state, ordered by total deaths:
usa_states_totals %>% ggplot() + aes(x = reorder(state, -deaths_total), y = deaths_total) + geom_bar(stat = "identity") + labs(x = "U.S. states", y = "All reported Covid-19 deaths") + theme_minimal() + theme( axis.text.x = element_text(angle = 90), axis.title.y = element_text(hjust = 1), legend.position = "bottom" )
Most countries have a hierarchy of political subdivisions. Where data are available, use the
level = "2" argument to
get_regional_data. We can dig down further into each US state by looking at data by county:
usa_counties <- get_regional_data(country = "USA", level = "2", verbose = FALSE)
This returns data for each county on each day of reported data. As there are more than 3,000 counties in the US, with some reporting data since the 21st January, this makes for a very large download.
The same method will work for different countries where data are available, returning relevant sub-national units for each country. For example, in Germany data are available for Bundesland (federated state) and Kreise (district) areas.
Additional subnational data are supported via the
Google() classes. Use the
available_regions() method once these data have been downloaded and cleaned (see their examples) for subnational data they internally support.
We provide the relevant national and subnational ISO codes, or local alternatives, to enable mapping. We can map Covid-19 data at national or subnational level, using any standard shapefile. In R, the
rnaturalearth package already provides shapefiles for all country borders, and the largest (administrative level 1) subnational units for most countries.
For simplicity here we will use the
rworldmap package, which uses data from
rnaturalearth for national country borders and inbuilt mapping. We can join Covid-19 data to the map using the
# Get latest worldwide WHO data map_data <- get_national_data(totals = TRUE, verbose = FALSE) %>% rworldmap::joinCountryData2Map( joinCode = "ISO2", nameJoinColumn = "iso_code" ) #> 225 codes from your data successfully matched countries in the map #> 11 codes from your data failed to match with a country code in the map #> 16 codes from the map weren't represented in your data # Produce map rworldmap::mapCountryData(map_data, nameColumnToPlot = "deaths_total", catMethod = "fixedWidth", mapTitle = "Total Covid-19 deaths to date", addLegend = TRUE )