This function access and munges the cumulative time series of confirmed, and deaths from the US data in the repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). Also, Supported by ESRI Living Atlas Team and the Johns Hopkins University Applied Physics Lab (JHU APL).
jhu_us_data()A tidy data.frame (actually, a tbl_df) with columns:
UID: Universal Identifier
iso2: ISO 3166-1 alpha-2 code
iso3: ISO 3166-1 alpha-3 code
code3
fips: Federal Information Processing Standard Publication code
county: County
state: Province or state.
country: US
Lat: Latitude
Long: Longitude
Combined_Key: Comma-separated combination of columns Admin2, ProvinceState, and CountryRegion
date: Date
count: The cumulative count of cases for a given geographic area.
subset: either confirmed or deaths
Data are updated daily by JHU. Each call to this function redownloads the data
from github. No data cleansing is performed. Data are downloaded and then munged
into long-form tidy data.frame.
Uses https://raw.githubusercontent.com/CSSEGISandData/... as data source, then modifies column names and munges to long form table.
Although numbers are meant to be cumulative, there are instances where a day's count might be less than the prior day due to a reclassification of a case. These are not currently corrected in the source data
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(),
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()
res = jhu_data()
colnames(res)
#> [1] "ProvinceState" "CountryRegion" "Lat" "Long"
#> [5] "date" "count" "subset"
head(res)
#> # A tibble: 6 × 7
#> ProvinceState CountryRegion Lat Long date count subset
#> <chr> <chr> <dbl> <dbl> <date> <dbl> <chr>
#> 1 NA Afghanistan 33.9 67.7 2020-01-22 0 confirmed
#> 2 NA Afghanistan 33.9 67.7 2020-01-23 0 confirmed
#> 3 NA Afghanistan 33.9 67.7 2020-01-24 0 confirmed
#> 4 NA Afghanistan 33.9 67.7 2020-01-25 0 confirmed
#> 5 NA Afghanistan 33.9 67.7 2020-01-26 0 confirmed
#> 6 NA Afghanistan 33.9 67.7 2020-01-27 0 confirmed
dplyr::glimpse(res)
#> Rows: 701,406
#> Columns: 7
#> $ ProvinceState <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ CountryRegion <chr> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanista…
#> $ Lat <dbl> 33.93911, 33.93911, 33.93911, 33.93911, 33.93911, 33.939…
#> $ Long <dbl> 67.70995, 67.70995, 67.70995, 67.70995, 67.70995, 67.709…
#> $ date <date> 2020-01-22, 2020-01-23, 2020-01-24, 2020-01-25, 2020-01…
#> $ count <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ subset <chr> "confirmed", "confirmed", "confirmed", "confirmed", "con…
table(res$state)
#> Warning: Unknown or uninitialised column: `state`.
#> < table of extent 0 >