Narrow Results#
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 arguments include:
taxa
filters
group_by
taxa#
Perhaps unsurprisingly, galah.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 galah.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:
>>> import galah
>>> galah.search_taxa(taxa="Reptilia")
scientificName taxonConceptID rank kingdom phylum issues
0 REPTILIA https://biodiversity.org.au/afd/taxa/682e1228-5b3c-45ff-833b-550efd40c399 class Animalia Chordata noIssue
Once we know that our search matches the correct taxon or taxa, we can use it as an argument to narrow the results of our queries:
>>> galah.atlas_counts(taxa="Reptilia")
totalRecords
0 1844001
If you’re using an international atlas, galah.search_taxa()
will automatically switch to using the local name-matching
service. We have the Brazilian atlas as an example here:
>>> galah.galah_config(atlas="Brazil")
>>> galah.atlas_counts(taxa="Ramphastos")
totalRecords
0 199882
filters#
Perhaps the most important argument in galah is filters
, which is used to filter the rows of queries:
>>> # Get total record count since 2000
>>> galah.atlas_counts(filters="year>2000")
totalRecords
0 104835559
>>> # Get total record count for iNaturalist in 2021
>>> galah.atlas_counts(filters=["dataResourceName=iNaturalist Australia","year=2021"])
totalRecords
0 1034406
To find available fields and corresponding valid values, use the field lookup functions
galah.show_all()
, galah.search_all()
& show_values()
.
Finally, a special case of filters
is to make more complex taxonomic queries than are possible using galah.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:
>>> animalia_id = galah.search_taxa(taxa="Animalia")["taxonConceptID"][0]
>>> chordata_id = galah.search_taxa(taxa="Chordata")["taxonConceptID"][0]
>>> galah.atlas_counts(filters=["taxonConceptID={}".format(animalia_id),"taxonConceptID!={}".format(chordata_id)],group_by="class",expand=False)
here
class count
0 Acoela 116
1 Anthozoa 228869
2 Aplacophora 646
3 Arachnida 883466
4 Archiacanthocephala 42
.. ... ...
65 Staurozoa 102
66 Stenolaemata 1312
67 Symphyla 657
68 Tentaculata 725
69 Trematoda 33446
[70 rows x 2 columns]
use_data_profile#
When working with the ALA, a notable feature is the ability to specify a profile to remove records that are suspect in some way. Profiles are groups of data quality filters.
galah.galah_config(data_profile="ALA")
galah.atlas_counts(filter="year>2000",use_data_profile=True)
totalRecords
0 92023694
To see a full list of data quality profiles, use galah.show_all(profiles=True)
.