CDC FluView flu tests from clinical labs — pub_fluview

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

Usage

pub_fluview_clinical(
  regions,
  epiweeks = "*",
  ...,
  issues = NULL,
  lag = NULL,
  fetch_args = fetch_args_list()
)

Arguments

regions: character. Vector of location IDs to fetch. Can be "nat" for national, "hhs1"–"hhs10" for HHS Regions, "cen1"–"cen9" for census divisions, lowercase two-letter state or territory abbreviations for most states and territories,"jfk" for New York City, or "ny_minus_jfk" for upstate New York. Full list of locations is available here.
epiweeks: timeset. Epiweeks to fetch. Supports epirange() and defaults to all ("*") dates. Format as epirange(startweek, endweek), where startweek and endweek are of the form YYYYWW (string or numeric).
...: not used for values, forces later arguments to bind by name
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. 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.

Examples


pub_fluview_clinical(regions = "nat", epiweeks = epirange(201601, 201701))
#> # A tibble: 14 × 11
#>    release_date region issue      epiweek      lag total_specimens total_a
#>    <date>       <chr>  <date>     <date>     <dbl>           <dbl>   <dbl>
#>  1 2018-10-08   nat    2018-09-23 2016-10-02   103           13380     120
#>  2 2018-10-08   nat    2018-09-23 2016-10-09   102           14053     108
#>  3 2018-10-08   nat    2018-09-23 2016-10-16   101           15110     115
#>  4 2018-10-08   nat    2018-09-23 2016-10-23   100           15312     143
#>  5 2018-10-08   nat    2018-09-23 2016-10-30    99           16652     201
#>  6 2018-10-08   nat    2018-09-23 2016-11-06    98           17811     271
#>  7 2018-10-08   nat    2018-09-23 2016-11-13    97           18948     347
#>  8 2018-10-08   nat    2018-09-23 2016-11-20    96           18520     451
#>  9 2018-10-08   nat    2018-09-23 2016-11-27    95           21542     532
#> 10 2018-10-08   nat    2018-09-23 2016-12-04    94           20564     696
#> 11 2018-10-08   nat    2018-09-23 2016-12-11    93           23476    1359
#> 12 2018-10-08   nat    2018-09-23 2016-12-18    92           26775    2556
#> 13 2018-10-08   nat    2018-09-23 2016-12-25    91           33273    4270
#> 14 2018-10-08   nat    2018-09-23 2017-01-01    90           35028    4288
#> # ℹ 4 more variables: total_b <dbl>, percent_positive <dbl>, percent_a <dbl>,
#> #   percent_b <dbl>