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:

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.

galah_identify & search_taxa

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

galah_filter

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

galah_apply_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.

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).

galah_group_by

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

galah_select

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>

galah_geolocate

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…