Returns the values the catalogue can be filtered on, together with the number of datasets carrying each. This is the starting point for browsing the portal rather than searching it: it answers what themes and keywords exist before passing them to [list_cov_datasets()] as a `refine` filter.
The catalogue facets are `theme`, `keyword`, `features`, `custom.data-owner` and `custom.data-team`. The `features` facet is worth knowing about: its `geo` value identifies the datasets [get_cov_data()] returns as `sf` objects, and `timeserie` those carrying a date field.
Results are cached for the duration of the R session.
Usage
list_cov_facets(
facet = NULL,
where = NULL,
refine = NULL,
exclude = NULL,
apikey = getOption("VancouverOpenDataApiKey"),
refresh = FALSE
)Arguments
- facet
Name(s) of the catalogue facets to return. Default `NULL` returns every facet.
- where
Filter expression using ODSQL syntax, restricting the datasets the counts are computed over. Default `NULL`.
- refine
Facet filter(s) of the form `"facet:value"`. Default `NULL`.
- exclude
Facet exclusion(s) of the form `"facet:value"`. Default `NULL`.
- apikey
the CoV open data API key, optional
- refresh
Bypass the session cache and re-download, default `FALSE`
Value
A tibble with columns `facet` (the facet name), `value`, and `count` (the number of datasets). Returns `NULL` with a warning if the API cannot be reached.
See also
[list_cov_datasets()] to filter the catalogue on these values, [get_cov_facets()] for the facets of an individual dataset
Examples
# \donttest{
# Every facet of the catalogue
list_cov_facets()
#> # A tibble: 86 × 3
#> facet value count
#> <chr> <chr> <int>
#> 1 features analyze 152
#> 2 features custom_view 8
#> 3 features geo 135
#> 4 features image 2
#> 5 features timeserie 42
#> 6 theme Business and economy 7
#> 7 theme Culture and education 5
#> 8 theme Demographics 5
#> 9 theme Food and housing 6
#> 10 theme Geography and imagery 19
#> # ℹ 76 more rows
# Just the themes, and how many datasets each holds
list_cov_facets(facet = "theme")
#> # A tibble: 12 × 3
#> facet value count
#> <chr> <chr> <int>
#> 1 theme Business and economy 7
#> 2 theme Culture and education 5
#> 3 theme Demographics 5
#> 4 theme Food and housing 6
#> 5 theme Geography and imagery 19
#> 6 theme Government and finance 44
#> 7 theme Parks, recreation, and pets 13
#> 8 theme Property and development 32
#> 9 theme Safety 6
#> 10 theme Streets and transportation 49
#> 11 theme Sustainability 2
#> 12 theme Water and sewer 13
# }