Each occurrence record contains taxonomic information and
information about the observation itself, like its location and the date
of observation. These pieces of information are recorded and categorised into
respective fields. When you import data using galah
, columns of the
resulting tibble
correspond to these fields.
Data fields are important because they provide a means to manipulate queries
to return only the information that you need, and no more. Consequently, much of
the architecture of galah
has been designed to make narrowing as simple as possible.
These functions include:
galah_identify
or identify
galah_filter
or filter
galah_select
or select
galah_group_by
or group_by
galah_geolocate
or st_crop
These names have been chosen to echo comparable functions from dplyr
; namely
filter
, select
and group_by
. With the exception of galah_geolocate
, they
also use dplyr
tidy evaluation and syntax. This means that how you use
dplyr
functions is also how you use galah_
functions.
Perhaps unsurprisingly, search_taxa
searches for taxonomic information.
It uses fuzzy matching to work a lot like the search bar on the
Atlas of Living Australia website,
and you can use it to search for taxa by their scientific name. Finding your
desired taxon with search_taxa
is an important step to using this taxonomic
information to download data with galah
.
For example, to search for reptiles, we first need to identify whether we have the correct query:
search_taxa("Reptilia")
## # A tibble: 1 × 9 ## search_term scientific_name taxon_concept_id rank match_type kingdom phylum class issues ## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> ## 1 Reptilia REPTILIA https://biodiver… class exactMatch Animal… Chord… Rept… noIss…
If we want to be more specific by providing additional taxonomic information
to search_taxa
, you can provide a tibble
(or data.frame
) containing more
levels of the taxonomic hierarchy:
search_taxa(tibble(genus = "Eolophus", kingdom = "Aves"))
## # A tibble: 1 × 13 ## search_term scientific_name scientific_name_authorship taxon_concept_id rank match_type ## <chr> <chr> <chr> <chr> <chr> <chr> ## 1 Eolophus_Aves Eolophus Bonaparte, 1854 https://biodive… genus exactMatch ## # ℹ 7 more variables: kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>, ## # genus <chr>, issues <chr>
Once we know that our search matches the correct taxon or taxa, we
can use galah_identify
to narrow the results of our queries:
galah_call() |> galah_identify("Reptilia") |> atlas_counts()
## # A tibble: 1 × 1 ## count ## <int> ## 1 1673906
taxa <- search_taxa(tibble(genus = "Eolophus", kingdom = "Aves")) galah_call() |> galah_identify(taxa) |> atlas_counts()
## # A tibble: 1 × 1 ## count ## <int> ## 1 1092638
If you're using an international atlas, search_taxa
will automatically
switch to using the local name-matching service. For example, Portugal uses the
GBIF taxonomic backbone, but integrates seamlessly with our standard workflow.
galah_config(atlas = "Portugal")
## Atlas selected: GBIF Portugal (GBIF.pt) [Portugal]
galah_call() |> galah_identify("Lepus") |> galah_group_by(species) |> atlas_counts()
## # A tibble: 5 × 2 ## species count ## <chr> <int> ## 1 Lepus granatensis 1378 ## 2 Lepus microtis 64 ## 3 Lepus europaeus 10 ## 4 Lepus saxatilis 2 ## 5 Lepus capensis 1
Conversely, the UK's National Biodiversity Network (NBN), has its' own taxonomic backbone, but is supported using the same function call.
galah_config(atlas = "United Kingdom")
## Atlas selected: National Biodiversity Network (NBN) [United Kingdom]
galah_call() |> galah_filter(genus == "Bufo") |> galah_group_by(species) |> atlas_counts()
## # A tibble: 3 × 2 ## species count ## <chr> <int> ## 1 Bufo bufo 94054 ## 2 Bufo spinosus 87 ## 3 Bufo marinus 1
Perhaps the most important function in galah
is galah_filter
, which is used
to filter the rows of queries:
galah_config(atlas = "Australia")
## Atlas selected: Atlas of Living Australia (ALA) [Australia]
# Get total record count since 2000 galah_call() |> galah_filter(year > 2000) |> atlas_counts()
## # A tibble: 1 × 1 ## count ## <int> ## 1 90503179
# Get total record count for iNaturalist in 2021 galah_call() |> galah_filter( year > 2000, dataResourceName == "iNaturalist Australia") |> atlas_counts()
## # A tibble: 1 × 1 ## count ## <int> ## 1 5600557
To find available fields and corresponding valid values, use the field lookup
functions show_all(fields)
, search_all(fields)
& show_values()
.
Finally, a special case of galah_filter
is to make more complex taxonomic
queries than are possible using search_taxa
. By using the taxonConceptID
field, it is possible to build queries that exclude certain taxa, for example.
This can be useful for paraphyletic concepts such as invertebrates:
galah_call() |> galah_filter( taxonConceptID == search_taxa("Animalia")$taxon_concept_id, taxonConceptID != search_taxa("Chordata")$taxon_concept_id ) |> galah_group_by(class) |> atlas_counts()
## # A tibble: 30 × 2 ## class count ## <chr> <int> ## 1 Insecta 5806340 ## 2 Gastropoda 957271 ## 3 Maxillopoda 793194 ## 4 Arachnida 689708 ## 5 Malacostraca 651658 ## 6 Polychaeta 276272 ## 7 Bivalvia 234110 ## 8 Anthozoa 216343 ## 9 Cephalopoda 145929 ## 10 Demospongiae 118375 ## # ℹ 20 more rows
When working with the ALA, a notable feature is the ability to specify a profile
to
remove records that are suspect in some way.
galah_call() |> galah_filter(year > 2000) |> galah_apply_profile(ALA) |> atlas_counts()
## # A tibble: 1 × 1 ## count ## <int> ## 1 81471797
To see a full list of data quality profiles, use show_all(profiles)
.
Use galah_group_by
to group record counts and summarise counts by specified fields:
# Get record counts since 2010, grouped by year and basis of record galah_call() |> galah_filter(year > 2015 & year <= 2020) |> galah_group_by(year, basisOfRecord) |> atlas_counts()
## # A tibble: 36 × 3 ## year basisOfRecord count ## <chr> <chr> <int> ## 1 2020 Human observation 6551035 ## 2 2020 Occurrence 419842 ## 3 2020 Preserved specimen 84136 ## 4 2020 Machine observation 38906 ## 5 2020 Observation 24887 ## 6 2020 Material Sample 1677 ## 7 2020 Living specimen 62 ## 8 2019 Human observation 5730445 ## 9 2019 Occurrence 290610 ## 10 2019 Preserved specimen 165391 ## # ℹ 26 more rows
Use galah_select
to choose which columns are returned when downloading records:
# Get *Reptilia* records from 1930, but only 'eventDate' and 'kingdom' columns occurrences <- galah_call() |> galah_identify("reptilia") |> galah_filter(year == 1930) |> galah_select(kingdom, species, eventDate) |> atlas_occurrences()
## Retrying in 1 seconds.
occurrences |> head()
## # A tibble: 6 × 3 ## kingdom species eventDate ## <chr> <chr> <dttm> ## 1 Animalia Drysdalia coronoides 1930-06-16 00:00:00 ## 2 Animalia Intellagama lesueurii 1930-01-01 00:00:00 ## 3 Animalia <NA> 1930-04-23 00:00:00 ## 4 Animalia <NA> 1930-01-01 00:00:00 ## 5 Animalia Oxyuranus scutellatus 1930-01-01 00:00:00 ## 6 Animalia Tympanocryptis centralis 1930-11-30 00:00:00
You can also use other dplyr
functions that work with dplyr::select()
with
galah_select()
occurrences <- galah_call() |> galah_identify("reptilia") |> galah_filter(year == 1930) |> galah_select(starts_with("accepted") | ends_with("record")) |> atlas_occurrences()
## Retrying in 1 seconds.
occurrences |> head()
## # A tibble: 6 × 6 ## acceptedNameUsage acceptedNameUsageID basisOfRecord raw_basisOfRecord ## <chr> <lgl> <chr> <chr> ## 1 <NA> NA PRESERVED_SPECIMEN Museum specimen ## 2 <NA> NA HUMAN_OBSERVATION HumanObservation ## 3 <NA> NA PRESERVED_SPECIMEN PreservedSpecimen ## 4 <NA> NA HUMAN_OBSERVATION HumanObservation ## 5 <NA> NA PRESERVED_SPECIMEN PreservedSpecimen ## 6 <NA> NA HUMAN_OBSERVATION HumanObservation ## # ℹ 2 more variables: OCCURRENCE_STATUS_INFERRED_FROM_BASIS_OF_RECORD <lgl>, ## # userDuplicateRecord <lgl>
Use galah_geolocate
to specify a geographic area or region to limit your search:
# Get list of perameles species only in area specified: # (Note: This can also be specified by a shapefile) wkt <- "POLYGON((131.36328125 -22.506468769126,135.23046875 -23.396716654542,134.17578125 -27.287832521411,127.40820312499 -26.661206402316,128.111328125 -21.037340349154,131.36328125 -22.506468769126))" galah_call() |> galah_identify("perameles") |> galah_geolocate(wkt) |> atlas_species()
## # A tibble: 1 × 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 Mammalia Peram… Peram… Pera… Perame… Spenc… https://bio… Desert Bandico…
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.