R/coronadatascraper_data.R
coronadatascraper_data.RdCorona Data Scraper pulls COVID-19 Coronavirus case data from verified sources, finds the corresponding GeoJSON features, and adds population data. All sources are cited right in the same row as the data. Covers testing, hospitalization, deaths, cases, icu. Includes population and geospatial coordinates.
coronadatascraper_data()a data.frame
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(),
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(),
owid_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()
Other case-tracking:
align_to_baseline(),
beoutbreakprepared_data(),
bulk_estimate_Rt(),
combined_us_cases_data(),
covidtracker_data(),
ecdc_data(),
estimate_Rt(),
jhu_data(),
nytimes_county_data(),
owid_data(),
plot_epicurve(),
test_and_trace_data(),
usa_facts_data(),
who_cases()
res = coronadatascraper_data()
colnames(res)
#> [1] "name" "level" "city"
#> [4] "county" "state" "country"
#> [7] "population" "lat" "long"
#> [10] "url" "aggregate" "tz"
#> [13] "cases" "deaths" "recovered"
#> [16] "active" "tested" "hospitalized"
#> [19] "hospitalized_current" "discharged" "icu"
#> [22] "icu_current" "growthFactor" "date"
head(res)
#> name level city county state country population
#> 1: Antwerp, Flanders, Belgium county Antwerp Flanders Belgium 1847486
#> 2: Antwerp, Flanders, Belgium county Antwerp Flanders Belgium 1847486
#> 3: Antwerp, Flanders, Belgium county Antwerp Flanders Belgium 1847486
#> 4: Antwerp, Flanders, Belgium county Antwerp Flanders Belgium 1847486
#> 5: Antwerp, Flanders, Belgium county Antwerp Flanders Belgium 1847486
#> 6: Antwerp, Flanders, Belgium county Antwerp Flanders Belgium 1847486
#> lat long url aggregate tz cases
#> 1: 51.2485 4.7175 https://epistat.wiv-isp.be/ Europe/Brussels 4
#> 2: 51.2485 4.7175 https://epistat.wiv-isp.be/ Europe/Brussels 4
#> 3: 51.2485 4.7175 https://epistat.wiv-isp.be/ Europe/Brussels 4
#> 4: 51.2485 4.7175 https://epistat.wiv-isp.be/ Europe/Brussels 4
#> 5: 51.2485 4.7175 https://epistat.wiv-isp.be/ Europe/Brussels 4
#> 6: 51.2485 4.7175 https://epistat.wiv-isp.be/ Europe/Brussels 4
#> deaths recovered active tested hospitalized hospitalized_current discharged
#> 1: NA NA NA NA NA NA NA
#> 2: NA NA NA NA NA NA NA
#> 3: NA NA NA NA NA NA NA
#> 4: NA NA NA NA NA NA NA
#> 5: NA NA NA NA NA NA NA
#> 6: NA NA NA NA NA NA NA
#> icu icu_current growthFactor date
#> 1: NA NA NA 2020-01-22
#> 2: NA NA 1 2020-01-23
#> 3: NA NA 1 2020-01-24
#> 4: NA NA 1 2020-01-25
#> 5: NA NA 1 2020-01-26
#> 6: NA NA 1 2020-01-27
dplyr::glimpse(res)
#> Rows: 428,748
#> Columns: 24
#> $ name <chr> "Antwerp, Flanders, Belgium", "Antwerp, Flanders,…
#> $ level <chr> "county", "county", "county", "county", "county",…
#> $ city <chr> "", "", "", "", "", "", "", "", "", "", "", "", "…
#> $ county <chr> "Antwerp", "Antwerp", "Antwerp", "Antwerp", "Antw…
#> $ state <chr> "Flanders", "Flanders", "Flanders", "Flanders", "…
#> $ country <chr> "Belgium", "Belgium", "Belgium", "Belgium", "Belg…
#> $ population <int> 1847486, 1847486, 1847486, 1847486, 1847486, 1847…
#> $ lat <dbl> 51.2485, 51.2485, 51.2485, 51.2485, 51.2485, 51.2…
#> $ long <dbl> 4.7175, 4.7175, 4.7175, 4.7175, 4.7175, 4.7175, 4…
#> $ url <chr> "https://epistat.wiv-isp.be/", "https://epistat.w…
#> $ aggregate <chr> "", "", "", "", "", "", "", "", "", "", "", "", "…
#> $ tz <chr> "Europe/Brussels", "Europe/Brussels", "Europe/Bru…
#> $ cases <int> 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4…
#> $ deaths <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ recovered <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ active <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ tested <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ hospitalized <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ hospitalized_current <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ discharged <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ icu <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ icu_current <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ growthFactor <dbl> NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
#> $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, …
length(unique(res$names))
#> [1] 0
length(unique(res$county))
#> [1] 1884
length(unique(res$state))
#> [1] 788
length(unique(res$country))
#> [1] 191