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Various COVID and flu signals via the COVIDcast endpoint

Source: R/endpoints.R
pub_covidcast.Rd

[Deprecated]

This is a V3/V4 endpoint. As of September 22, 2026, the V3 and V4 APIs no longer receive new data. They still serve the historical data they already have, but current data is only added to the V5 API, accessed via the epidata_snapshot(), epidata_archive(), and epidata_meta() functions. For more details on the changes, refer to vignette("migration-guide"), and visit the V5 signals documentation to see which sources are currently available.

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

The primary endpoint for fetching COVID-19 data, providing access to a wide variety of signals from a wide variety of sources. See the API documentation link above for more. Delphi's COVIDcast public dashboard is powered by this endpoint.

Usage

pub_covidcast(
  source,
  signals,
  geo_type,
  time_type,
  geo_values = "*",
  time_values = "*",
  ...,
  as_of = NULL,
  issues = NULL,
  lag = NULL,
  fetch_args = fetch_args_list()
)

Arguments

source

string. The data source to query (see: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_signals.html).

signals

string. The signals to query from a specific source (see: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_signals.html).

geo_type

string. The geographic resolution of the data (see: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_geography.html).

time_type

string. The temporal resolution of the data (either "day" or "week", depending on signal).

geo_values

character. The geographies to return. Defaults to all ("*") geographies within requested geographic resolution (see: https://cmu-delphi.github.io/delphi-epidata/api/covidcast_geography.html.).

time_values

timeset. Dates or epiweeks 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.

lag

integer. Optionally, the lag of the issues to fetch. See the "Data Versioning" section for details.

fetch_args

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

Value

tibble::tibble

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.

See also

For example queries showing how to discover signals and build calls, see vignette("signal-discovery", package = "epidatr").

See also

pub_covidcast_meta(), covidcast_epidata(), epirange()

Examples


pub_covidcast(
  source = "jhu-csse",
  signals = "confirmed_7dav_incidence_prop",
  geo_type = "state",
  time_type = "day",
  geo_values = c("ca", "fl"),
  time_values = epirange(20200601, 20200801)
)
#> # A tibble: 124 × 15
#>    geo_value signal    source geo_type time_type time_value direction issue     
#>    <chr>     <chr>     <chr>  <fct>    <fct>     <date>         <dbl> <date>    
#>  1 ca        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-10
#>  2 fl        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  3 ca        confirme… jhu-c… state    day       2020-06-02        NA 2023-03-10
#>  4 fl        confirme… jhu-c… state    day       2020-06-02        NA 2023-03-03
#>  5 ca        confirme… jhu-c… state    day       2020-06-03        NA 2023-03-10
#>  6 fl        confirme… jhu-c… state    day       2020-06-03        NA 2023-03-03
#>  7 ca        confirme… jhu-c… state    day       2020-06-04        NA 2023-03-10
#>  8 fl        confirme… jhu-c… state    day       2020-06-04        NA 2023-03-03
#>  9 ca        confirme… jhu-c… state    day       2020-06-05        NA 2023-03-10
#> 10 fl        confirme… jhu-c… state    day       2020-06-05        NA 2023-03-03
#> # ℹ 114 more rows
#> # ℹ 7 more variables: lag <dbl>, missing_value <dbl>, missing_stderr <dbl>,
#> #   missing_sample_size <dbl>, value <dbl>, stderr <dbl>, sample_size <dbl>
pub_covidcast(
  source = "jhu-csse",
  signals = "confirmed_7dav_incidence_prop",
  geo_type = "state",
  time_type = "day",
  geo_values = "*",
  time_values = epirange(20200601, 20200801)
)
#> # A tibble: 3,472 × 15
#>    geo_value signal    source geo_type time_type time_value direction issue     
#>    <chr>     <chr>     <chr>  <fct>    <fct>     <date>         <dbl> <date>    
#>  1 ak        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  2 al        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  3 ar        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  4 as        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  5 az        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  6 ca        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-10
#>  7 co        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  8 ct        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#>  9 dc        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#> 10 de        confirme… jhu-c… state    day       2020-06-01        NA 2023-03-03
#> # ℹ 3,462 more rows
#> # ℹ 7 more variables: lag <dbl>, missing_value <dbl>, missing_stderr <dbl>,
#> #   missing_sample_size <dbl>, value <dbl>, stderr <dbl>, sample_size <dbl>