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COVID hospitalizations by state

Source: R/endpoints.R
pub_covid_hosp_state_timeseries.Rd

API docs: https://cmu-delphi.github.io/delphi-epidata/api/covid_hosp.html.

Obtains the COVID-19 reported patient impact and hospital capacity data by state. This dataset is provided by the US Department of Health & Human Services.

Usage

pub_covid_hosp_state_timeseries(
  states,
  dates = "*",
  ...,
  as_of = NULL,
  issues = NULL,
  fetch_args = fetch_args_list()
)

Arguments

states

character. List of states to fetch, formatted as two letter state abbreviations.

dates

timeset. Dates to fetch. Supports epirange() and defaults to all ("*") dates.

...

not used for values, forces later arguments to bind by name

as_of

Date. Optionally, the as-of date for the issues to fetch. See the "Data Versioning" section for details.

issues

timeset. Optionally, the issue(s) of the data to fetch. See the "Data Versioning" section for details.

fetch_args

fetch_args. Additional arguments to pass to fetch(). See fetch_args_list() for details.

Value

tibble::tibble

Details

Starting October 1, 2022, some facilities are only required to report annually.

Data Versioning

Several endpoints support retrieving historical versions of the data. The following parameters control this and are mutually exclusive (only one can be provided at a time).

  • as_of: (Date) Retrieve the data as it was on this date.

  • issues: timeset Retrieve data from a specific issue date or range of dates.

  • lag: (integer) Retrieve data with a specific lag from its issue date.

If none of these is specified, the most recent version of the data is returned.

See vignette("versioned-data") for details and more ways to specify versioned data.

Examples


pub_covid_hosp_state_timeseries(
  states = "fl",
  dates = epirange(20200101, 20200501)
)
#> # A tibble: 51 × 118
#>    state geocoded_state issue      date       critical_staffing_shortage_today…¹
#>    <chr> <lgl>          <date>     <date>     <lgl>                             
#>  1 FL    NA             2024-05-03 2020-03-12 FALSE                             
#>  2 FL    NA             2024-05-03 2020-03-13 FALSE                             
#>  3 FL    NA             2024-05-03 2020-03-14 FALSE                             
#>  4 FL    NA             2024-05-03 2020-03-15 FALSE                             
#>  5 FL    NA             2024-05-03 2020-03-16 FALSE                             
#>  6 FL    NA             2024-05-03 2020-03-17 FALSE                             
#>  7 FL    NA             2024-05-03 2020-03-18 FALSE                             
#>  8 FL    NA             2024-05-03 2020-03-19 FALSE                             
#>  9 FL    NA             2024-05-03 2020-03-20 FALSE                             
#> 10 FL    NA             2024-05-03 2020-03-21 FALSE                             
#> # ℹ 41 more rows
#> # ℹ abbreviated name: ¹​critical_staffing_shortage_today_yes
#> # ℹ 113 more variables: critical_staffing_shortage_today_no <lgl>,
#> #   critical_staffing_shortage_today_not_reported <lgl>,
#> #   critical_staffing_shortage_anticipated_within_week_yes <lgl>,
#> #   critical_staffing_shortage_anticipated_within_week_no <lgl>,
#> #   critical_staffing_shortage_anticipated_within_week_not_reported <lgl>, …