NIDSS flu doctor visits (Taiwan)

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

Obtains information on outpatient inluenza-like-illness from Taiwan National Infectious Disease Statistical System.

Usage

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

Arguments

regions

character. List of regions to fetch.

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_nidss_flu(regions = "taipei", epiweeks = epirange(201501, 201601))
#> # A tibble: 53 × 7
#>    release_date region issue      epiweek      lag visits   ili
#>    <date>       <chr>  <date>     <date>     <dbl>  <dbl> <dbl>
#>  1 2016-01-05   Taipei 2015-12-27 2015-01-04    51  16698  0.96
#>  2 2016-01-12   Taipei 2016-01-03 2015-01-11    51  17146  1.03
#>  3 2016-01-19   Taipei 2016-01-10 2015-01-18    51  17817  1.07
#>  4 2016-01-26   Taipei 2016-01-17 2015-01-25    51  18168  1.13
#>  5 2016-02-02   Taipei 2016-01-24 2015-02-01    51  18321  1.11
#>  6 2016-02-02   Taipei 2016-01-24 2015-02-08    50  19799  1.08
#>  7 2016-02-02   Taipei 2016-01-24 2015-02-15    49  11879  1.25
#>  8 2016-02-02   Taipei 2016-01-24 2015-02-22    48  17397  1.19
#>  9 2016-02-02   Taipei 2016-01-24 2015-03-01    47  17849  1.07
#> 10 2016-02-02   Taipei 2016-01-24 2015-03-08    46  17441  1.08
#> # ℹ 43 more rows