The OurWoldInData dataset includes country-level testing, deaths, and confirmed cases for most countries in the world.
owid_data()a data.frame
Max Roser, Hannah Ritchie, Esteban Ortiz-Ospina and Joe Hasell (2020) - "Coronavirus Pandemic (COVID-19)". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/coronavirus'
Other case-tracking:
align_to_baseline(),
beoutbreakprepared_data(),
bulk_estimate_Rt(),
combined_us_cases_data(),
coronadatascraper_data(),
covidtracker_data(),
ecdc_data(),
estimate_Rt(),
jhu_data(),
nytimes_county_data(),
plot_epicurve(),
test_and_trace_data(),
usa_facts_data(),
who_cases()
Other data-import:
acaps_government_measures_data(),
acaps_secondary_impact_data(),
apple_mobility_data(),
beoutbreakprepared_data(),
cci_us_vaccine_data(),
cdc_aggregated_projections(),
cdc_excess_deaths(),
cdc_social_vulnerability_index(),
coronadatascraper_data(),
coronanet_government_response_data(),
cov_glue_lineage_data(),
cov_glue_newick_data(),
cov_glue_snp_lineage(),
covidtracker_data(),
descartes_mobility_data(),
ecdc_data(),
econ_tracker_consumer_spending,
econ_tracker_employment,
econ_tracker_unemp_data,
economist_excess_deaths(),
financial_times_excess_deaths(),
google_mobility_data(),
government_policy_timeline(),
jhu_data(),
jhu_us_data(),
kff_icu_beds(),
nytimes_county_data(),
oecd_unemployment_data(),
param_estimates_published(),
test_and_trace_data(),
us_county_geo_details(),
us_county_health_rankings(),
us_healthcare_capacity(),
us_hospital_details(),
us_state_distancing_policy(),
usa_facts_data(),
who_cases()
res = owid_data()
colnames(res)
#> [1] "iso3c"
#> [2] "continent"
#> [3] "country"
#> [4] "date"
#> [5] "confirmed"
#> [6] "new_cases"
#> [7] "deaths"
#> [8] "new_deaths"
#> [9] "total_cases_per_million"
#> [10] "new_cases_per_million"
#> [11] "total_deaths_per_million"
#> [12] "new_deaths_per_million"
#> [13] "reproduction_rate"
#> [14] "icu_patients"
#> [15] "icu_patients_per_million"
#> [16] "hosp_patients"
#> [17] "hosp_patients_per_million"
#> [18] "weekly_icu_admissions"
#> [19] "weekly_icu_admissions_per_million"
#> [20] "weekly_hosp_admissions"
#> [21] "weekly_hosp_admissions_per_million"
#> [22] "tests"
#> [23] "new_tests"
#> [24] "total_tests_per_thousand"
#> [25] "new_tests_per_thousand"
#> [26] "positive_rate"
#> [27] "tests_per_case"
#> [28] "tests_units"
#> [29] "total_vaccinations"
#> [30] "people_vaccinated"
#> [31] "people_fully_vaccinated"
#> [32] "total_boosters"
#> [33] "new_vaccinations"
#> [34] "total_vaccinations_per_hundred"
#> [35] "people_vaccinated_per_hundred"
#> [36] "people_fully_vaccinated_per_hundred"
#> [37] "total_boosters_per_hundred"
#> [38] "stringency_index"
#> [39] "population"
#> [40] "population_density"
#> [41] "median_age"
#> [42] "aged_65_older"
#> [43] "aged_70_older"
#> [44] "gdp_per_capita"
#> [45] "extreme_poverty"
#> [46] "cardiovasc_death_rate"
#> [47] "diabetes_prevalence"
#> [48] "female_smokers"
#> [49] "male_smokers"
#> [50] "handwashing_facilities"
#> [51] "hospital_beds_per_thousand"
#> [52] "life_expectancy"
#> [53] "human_development_index"
#> [54] "excess_mortality_cumulative_absolute"
#> [55] "excess_mortality_cumulative"
#> [56] "excess_mortality"
#> [57] "excess_mortality_cumulative_per_million"
head(res)
#> iso3c continent country date confirmed new_cases deaths new_deaths
#> 1: AFG Asia Afghanistan 2020-02-24 5 5 NA NA
#> 2: AFG Asia Afghanistan 2020-02-25 5 0 NA NA
#> 3: AFG Asia Afghanistan 2020-02-26 5 0 NA NA
#> 4: AFG Asia Afghanistan 2020-02-27 5 0 NA NA
#> 5: AFG Asia Afghanistan 2020-02-28 5 0 NA NA
#> 6: AFG Asia Afghanistan 2020-02-29 5 0 NA NA
#> total_cases_per_million new_cases_per_million total_deaths_per_million
#> 1: 0.126 0.126 NA
#> 2: 0.126 0.000 NA
#> 3: 0.126 0.000 NA
#> 4: 0.126 0.000 NA
#> 5: 0.126 0.000 NA
#> 6: 0.126 0.000 NA
#> new_deaths_per_million reproduction_rate icu_patients
#> 1: NA NA NA
#> 2: NA NA NA
#> 3: NA NA NA
#> 4: NA NA NA
#> 5: NA NA NA
#> 6: NA NA NA
#> icu_patients_per_million hosp_patients hosp_patients_per_million
#> 1: NA NA NA
#> 2: NA NA NA
#> 3: NA NA NA
#> 4: NA NA NA
#> 5: NA NA NA
#> 6: NA NA NA
#> weekly_icu_admissions weekly_icu_admissions_per_million
#> 1: NA NA
#> 2: NA NA
#> 3: NA NA
#> 4: NA NA
#> 5: NA NA
#> 6: NA NA
#> weekly_hosp_admissions weekly_hosp_admissions_per_million tests new_tests
#> 1: NA NA NA NA
#> 2: NA NA NA NA
#> 3: NA NA NA NA
#> 4: NA NA NA NA
#> 5: NA NA NA NA
#> 6: NA NA NA NA
#> total_tests_per_thousand new_tests_per_thousand positive_rate tests_per_case
#> 1: NA NA NA NA
#> 2: NA NA NA NA
#> 3: NA NA NA NA
#> 4: NA NA NA NA
#> 5: NA NA NA NA
#> 6: NA NA NA NA
#> tests_units total_vaccinations people_vaccinated people_fully_vaccinated
#> 1: NA NA NA
#> 2: NA NA NA
#> 3: NA NA NA
#> 4: NA NA NA
#> 5: NA NA NA
#> 6: NA NA NA
#> total_boosters new_vaccinations total_vaccinations_per_hundred
#> 1: NA NA NA
#> 2: NA NA NA
#> 3: NA NA NA
#> 4: NA NA NA
#> 5: NA NA NA
#> 6: NA NA NA
#> people_vaccinated_per_hundred people_fully_vaccinated_per_hundred
#> 1: NA NA
#> 2: NA NA
#> 3: NA NA
#> 4: NA NA
#> 5: NA NA
#> 6: NA NA
#> total_boosters_per_hundred stringency_index population population_density
#> 1: NA 8.33 39835428 54.422
#> 2: NA 8.33 39835428 54.422
#> 3: NA 8.33 39835428 54.422
#> 4: NA 8.33 39835428 54.422
#> 5: NA 8.33 39835428 54.422
#> 6: NA 8.33 39835428 54.422
#> median_age aged_65_older aged_70_older gdp_per_capita extreme_poverty
#> 1: 18.6 2.581 1.337 1803.987 NA
#> 2: 18.6 2.581 1.337 1803.987 NA
#> 3: 18.6 2.581 1.337 1803.987 NA
#> 4: 18.6 2.581 1.337 1803.987 NA
#> 5: 18.6 2.581 1.337 1803.987 NA
#> 6: 18.6 2.581 1.337 1803.987 NA
#> cardiovasc_death_rate diabetes_prevalence female_smokers male_smokers
#> 1: 597.029 9.59 NA NA
#> 2: 597.029 9.59 NA NA
#> 3: 597.029 9.59 NA NA
#> 4: 597.029 9.59 NA NA
#> 5: 597.029 9.59 NA NA
#> 6: 597.029 9.59 NA NA
#> handwashing_facilities hospital_beds_per_thousand life_expectancy
#> 1: 37.746 0.5 64.83
#> 2: 37.746 0.5 64.83
#> 3: 37.746 0.5 64.83
#> 4: 37.746 0.5 64.83
#> 5: 37.746 0.5 64.83
#> 6: 37.746 0.5 64.83
#> human_development_index excess_mortality_cumulative_absolute
#> 1: 0.511 NA
#> 2: 0.511 NA
#> 3: 0.511 NA
#> 4: 0.511 NA
#> 5: 0.511 NA
#> 6: 0.511 NA
#> excess_mortality_cumulative excess_mortality
#> 1: NA NA
#> 2: NA NA
#> 3: NA NA
#> 4: NA NA
#> 5: NA NA
#> 6: NA NA
#> excess_mortality_cumulative_per_million
#> 1: NA
#> 2: NA
#> 3: NA
#> 4: NA
#> 5: NA
#> 6: NA
dplyr::glimpse(res)
#> Rows: 184,751
#> Columns: 57
#> $ iso3c <chr> "AFG", "AFG", "AFG", "AFG", "A…
#> $ continent <chr> "Asia", "Asia", "Asia", "Asia"…
#> $ country <chr> "Afghanistan", "Afghanistan", …
#> $ date <date> 2020-02-24, 2020-02-25, 2020-…
#> $ confirmed <dbl> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
#> $ new_cases <dbl> 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
#> $ deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ new_deaths <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ total_cases_per_million <dbl> 0.126, 0.126, 0.126, 0.126, 0.…
#> $ new_cases_per_million <dbl> 0.126, 0.000, 0.000, 0.000, 0.…
#> $ total_deaths_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ new_deaths_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ reproduction_rate <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ icu_patients <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ icu_patients_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ hosp_patients <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ hosp_patients_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ weekly_icu_admissions <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ weekly_icu_admissions_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ weekly_hosp_admissions <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ weekly_hosp_admissions_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ tests <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ new_tests <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ total_tests_per_thousand <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ new_tests_per_thousand <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ positive_rate <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ tests_per_case <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ tests_units <chr> "", "", "", "", "", "", "", ""…
#> $ total_vaccinations <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ people_vaccinated <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ people_fully_vaccinated <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ total_boosters <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ new_vaccinations <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ total_vaccinations_per_hundred <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ people_vaccinated_per_hundred <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ people_fully_vaccinated_per_hundred <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ total_boosters_per_hundred <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ stringency_index <dbl> 8.33, 8.33, 8.33, 8.33, 8.33, …
#> $ population <dbl> 39835428, 39835428, 39835428, …
#> $ population_density <dbl> 54.422, 54.422, 54.422, 54.422…
#> $ median_age <dbl> 18.6, 18.6, 18.6, 18.6, 18.6, …
#> $ aged_65_older <dbl> 2.581, 2.581, 2.581, 2.581, 2.…
#> $ aged_70_older <dbl> 1.337, 1.337, 1.337, 1.337, 1.…
#> $ gdp_per_capita <dbl> 1803.987, 1803.987, 1803.987, …
#> $ extreme_poverty <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ cardiovasc_death_rate <dbl> 597.029, 597.029, 597.029, 597…
#> $ diabetes_prevalence <dbl> 9.59, 9.59, 9.59, 9.59, 9.59, …
#> $ female_smokers <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ male_smokers <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ handwashing_facilities <dbl> 37.746, 37.746, 37.746, 37.746…
#> $ hospital_beds_per_thousand <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, …
#> $ life_expectancy <dbl> 64.83, 64.83, 64.83, 64.83, 64…
#> $ human_development_index <dbl> 0.511, 0.511, 0.511, 0.511, 0.…
#> $ excess_mortality_cumulative_absolute <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ excess_mortality_cumulative <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ excess_mortality <dbl> NA, NA, NA, NA, NA, NA, NA, NA…
#> $ excess_mortality_cumulative_per_million <dbl> NA, NA, NA, NA, NA, NA, NA, NA…