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An abstraction that holds information needed to make an epidata request

Source: R/epidatacall.R
epidata_call.Rd

epidata_call objects are generated internally by endpoint functions like pub_covidcast; by default, they are piped directly into the fetch function to fetch and format the data. For most endpoints this will return a tibble, but a few non-COVIDCAST endpoints will return a JSON-like list instead.

Usage

create_epidata_call(
  endpoint,
  params,
  meta = NULL,
  api_version = c("classic", "cast"),
  response_format = c("classic", "json", "csv")
)

fetch(epidata_call, fetch_args = fetch_args_list())

Arguments

endpoint

the epidata endpoint to call

params

the parameters to pass to the epidata endpoint

meta

meta data to attach to the epidata call

api_version

string. The API version to use. One of "classic" or "cast".

response_format

string. The expected format of the response. One of "classic", "json", or "csv".

epidata_call

an instance of epidata_call

fetch_args

a fetch_args object

Value

  • For create_epidata_call: an epidata_call object

  • For fetch: a tibble

Details

create_epidata_call is the constructor for epidata_call objects, but you should not need to use it directly; instead, use an endpoint function, e.g., pub_covidcast, to generate an epidata_call for the data of interest.

Examples

library(magrittr)

call <- pub_covidcast(
  source = "jhu-csse",
  signals = "confirmed_7dav_incidence_prop",
  time_type = "day",
  geo_type = "state",
  time_values = epirange(20200601, 20200801),
  geo_values = c("ca", "fl"),
  fetch_args = fetch_args_list(dry_run = TRUE)
)
call %>% fetch()
#> # 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>