While there are reasons why users may need to check every record meeting their
search criteria (i.e. using atlas_occurrences()
), a common use case
is to simply identify which species occur in a specified region, time period,
or taxonomic group. This function returns a data.frame
with one row
per species, and columns giving associated taxonomic information.
atlas_species(
request = NULL,
identify = NULL,
filter = NULL,
geolocate = NULL,
refresh_cache = FALSE
)
optional data_rquest
object: generated by a call to
galah_call()
.
data.frame
: generated by a call to
galah_identify()
.
data.frame
: generated by a call to
galah_filter()
string
: generated by a call to
galah_geolocate()
logical
: if set to TRUE
and
galah_config(caching = TRUE)
then files cached from a previous query will
be replaced by the current query
An object of class tbl_df
and data.frame
(aka a tibble),
returning matching species The data.frame
object has attributes listing of
the user-supplied arguments of the data_request
(i.e., identify, filter, geolocate, columns)
First, look up a genus of interest in the ALA with search_taxa()
search_taxa("Heleioporus") #> # A tibble: 1 x 13 #> search_term scientific_name scientific_name_~ taxon_concept_id rank match_type kingdom phylum class order family genus #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 Heleioporus Heleioporus Gray, 1841 urn:lsid:biodiver~ genus exactMatch Animal~ Chord~ Amph~ Anura Limno~ Hele~ #> # ... with 1 more variable: issues <chr>
It's a good idea to find how many species there are for the taxon you are
interested in - in our case, genus Heleioporus - with atlas_counts()
galah_call() |> galah_identify("Heleioporus") |> atlas_counts(type = "species") #> # A tibble: 1 x 1 #> count #> <int> #> 1 6
Now get taxonomic information on all species within this genus with
atlas_species()
# (every row is a species with associated taxonomic data) galah_call() |> galah_identify("Heleioporus") |> atlas_species() #> # A tibble: 6 x 10 #> kingdom phylum class order family genus species author species_guid vernacular_name #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Gray, 1~ urn:lsid:biodiversity~ Moaning Frog #> 2 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Shaw & ~ urn:lsid:biodiversity~ Giant Burrowin~ #> 3 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ Gray, 18~ urn:lsid:biodiversity~ Western Spotte~ #> 4 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Lee & M~ urn:lsid:biodiversity~ Sand Frog #> 5 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Lee & M~ urn:lsid:biodiversity~ Plains Frog #> 6 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ Lee, 1967 urn:lsid:biodiversity~ Western Marsh ~
You can also get taxonomic information on species by piping with %>%
or
|>
. Just begin your query with galah_call()
galah_call() |> galah_identify("Heleioporus") |> atlas_species() #> # A tibble: 6 x 10 #> kingdom phylum class order family genus species author species_guid vernacular_name #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Gray, 1~ urn:lsid:biodiversity~ Moaning Frog #> 2 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Shaw & ~ urn:lsid:biodiversity~ Giant Burrowin~ #> 3 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ Gray, 18~ urn:lsid:biodiversity~ Western Spotte~ #> 4 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Lee & M~ urn:lsid:biodiversity~ Sand Frog #> 5 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ (Lee & M~ urn:lsid:biodiversity~ Plains Frog #> 6 Animalia Chordata Amphibia Anura Limnodynastidae Heleioporus Heleioporu~ Lee, 1967 urn:lsid:biodiversity~ Western Marsh ~
The primary use case of this function is to extract species-level information
given a set of criteria defined by search_taxa()
,
galah_filter()
or galah_geolocate()
. If the purpose
is simply to get taxonomic information that is not restricted by filtering,
then search_taxa()
is more efficient. Similarly, if counts are
required that include filter but without returning taxonomic detail, then
atlas_counts()
is more efficient (see examples).