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_identifygalah_filtergalah_selectgalah_group_bygalah_geolocategalah_down_toThese 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 urn:lsid:biodiversity.org.au:afd.taxon:682e1228-5b… class exactMatch Animalia Chordata Repti… noIss…If we want to be more specific by providing additional taxonomic information to search_taxa, you can provide a data.frame containing more levels of the taxonomic hierarchy:
search_taxa(data.frame(genus = "Eolophus", kingdom = "Aves"))## # A tibble: 1 × 13
## search_term scientific_name scientific_name_… taxon_concept_id rank match_type kingdom phylum class order family genus issues
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Eolophus_Aves Eolophus Bonaparte, 1854 urn:lsid:biodiv… genus exactMatch Animal… Chord… Aves Psit… Cacat… Eolo… noIss…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 1317131taxa <- search_taxa(data.frame(genus = "Eolophus", kingdom = "Aves"))
galah_call() |>
galah_identify(taxa) |>
atlas_counts()## # A tibble: 1 × 1
## count
## <int>
## 1 856571If you’re using an international atlas, search_taxa won’t work; instead you need to use the taxize package to look up the relevant identifiers. Most atlases use the GBIF taxonomic backbone, meaning that you can use the get_gbifid function to download the relevant identifiers. These identifiers can then be passed directly to galah_identify.
galah_config(atlas = "Spain")
library(taxize)
id <- get_gbifid("Lepus", messages = FALSE, rows = 1)
galah_call() |>
galah_identify(id) |>
galah_group_by(species) |>
atlas_counts()## # A tibble: 4 × 2
## species count
## <chr> <int>
## 1 Lepus granatensis 8360
## 2 Lepus europaeus 2913
## 3 Lepus castroviejoi 149
## 4 Lepus capensis 41The exception is the UK National Biodiversity Network (NBN), which has its’ own taxonomic backbone (note this information is also given by show_all_atlases()). You can search the NBN taxonomy with get_nbnid.
galah_config(atlas = "UK")
id <- get_nbnid(c("Vulpes vulpes", "Meles meles"), messages = FALSE, rows = 1)
galah_call() |>
galah_identify(id) |>
galah_group_by(species) |>
atlas_counts()## # A tibble: 2 × 2
## species count
## <chr> <int>
## 1 Vulpes vulpes 151307
## 2 Meles meles 87712Perhaps the most important function in galah is galah_filter, which is used to filter the rows of queries:
# Get total record count since 2000
galah_call() |>
galah_filter(year > 2000) |>
atlas_counts()## # A tibble: 1 × 1
## count
## <int>
## 1 63003181# 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 2673224To find available fields and corresponding valid values, use the field lookup functions show_all_fields, search_fields and find_field_values.
A further notable feature of galah_filter is the ability to specify a profile to remove records that are suspect in some way.
galah_call() |>
galah_filter(year > 2000, profile = "ALA") |>
atlas_counts()## # A tibble: 1 × 1
## count
## <int>
## 1 55681954To see a full list of data quality profiles, use show_all_profiles().
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: 83 × 2
## class count
## <chr> <int>
## 1 Insecta 3317770
## 2 Gastropoda 837702
## 3 Arachnida 527555
## 4 Malacostraca 515971
## 5 Maxillopoda 462762
## 6 Polychaeta 256938
## 7 Bivalvia 206242
## 8 Anthozoa 163386
## 9 Demospongiae 107520
## 10 Ostracoda 56295
## # … with 73 more rowsUse 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: 35 × 3
## year basisOfRecord count
## <chr> <chr> <int>
## 1 2020 HUMAN_OBSERVATION 5825030
## 2 2020 PRESERVED_SPECIMEN 13637
## 3 2020 OBSERVATION 3894
## 4 2020 UNKNOWN 365
## 5 2020 MATERIAL_SAMPLE 250
## 6 2020 LIVING_SPECIMEN 127
## 7 2020 MACHINE_OBSERVATION 37
## 8 2019 HUMAN_OBSERVATION 5401216
## 9 2019 UNKNOWN 51747
## 10 2019 PRESERVED_SPECIMEN 38117
## # … with 25 more rowsUse 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(eventDate, kingdom) |>
atlas_occurrences()
occurrences## # A tibble: 29 × 8
## eventDate kingdom decimalLatitude decimalLongitude scientificName taxonConceptID recordID dataResourceName
## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
## 1 1929-12-31T14:00:00Z Animalia -36.4 150. Acanthophis antarcticus urn:lsid:biod… 38dab01… NSW BioNet Atlas
## 2 1929-12-31T14:00:00Z Animalia -26.8 151. Demansia psammophis urn:lsid:biod… c770504… WildNet - Queen…
## 3 1929-12-31T14:00:00Z Animalia -24.4 152. Oxyuranus scutellatus urn:lsid:biod… cfb4279… WildNet - Queen…
## 4 1929-12-31T14:00:00Z Animalia -20.8 145. Lerista wilkinsi urn:lsid:biod… 1b64a15… WildNet - Queen…
## 5 1929-12-31T14:00:00Z Animalia -23.9 150. Furina barnardi urn:lsid:biod… 03e06c9… WildNet - Queen…
## 6 1929-12-31T14:00:00Z Animalia -37.7 145. Tiliqua scincoides urn:lsid:biod… e1e459c… Victorian Biodi…
## 7 1929-12-31T14:00:00Z Animalia -15.5 145. Antaresia maculosa urn:lsid:biod… 084bc0a… WildNet - Queen…
## 8 1929-12-31T14:00:00Z Animalia -37.7 145. Tiliqua scincoides urn:lsid:biod… 675f976… Victorian Biodi…
## 9 1929-12-31T14:00:00Z Animalia -17.3 146. Simalia kinghorni urn:lsid:biod… 0bd4268… WildNet - Queen…
## 10 1930-04-22T14:00:00Z Animalia NA NA COLUBRIDAE urn:lsid:biod… 815d01e… South Australia…
## # … with 19 more rowsYou 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("elev") & ends_with("n")) |>
atlas_occurrences()
occurrencesUse 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: 2 × 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 Peramelemorphia Peramelidae Perameles Perameles eremiana Spence… urn:lsid:biodiv… Desert Bandico…
## 2 Animalia Chordata Mammalia Peramelemorphia Peramelidae Perameles Perameles bougainville Quoy &… urn:lsid:biodiv… Western Barred…Use galah_down_to to specify the lowest taxonomic level to contruct a taxonomic tree:
galah_call() |>
galah_identify("fungi") |>
galah_down_to(phylum) |>
atlas_taxonomy()## levelName
## 1 Fungi
## 2 ¦--Dikarya
## 3 ¦ °--Entorrhizomycota
## 4 ¦--Ascomycota
## 5 ¦--Basidiomycota
## 6 ¦--Blastocladiomycota
## 7 ¦--Chytridiomycota
## 8 ¦--Cryptomycota
## 9 ¦--Glomeromycota
## 10 ¦--Microspora
## 11 ¦--Microsporidia
## 12 ¦--Mucoromycota
## 13 ¦--Neocallimastigomycota
## 14 ¦--Zoopagomycota
## 15 °--Zygomycota