galah is an R interface to biodiversity data hosted by the Global Biodiversity Information Facility (GBIF) and its subsidiary node organisations. GBIF and its partner nodes collate and store observations of individual life forms using the ‘Darwin Core’ data standard.

Installation

To install from CRAN:

install.packages("galah")

Or install the development version from GitHub:

install.packages("remotes")
remotes::install_github("AtlasOfLivingAustralia/galah")

Load the package

library(galah)

Getting data

galah is a dplyr extension package; rather than using pipes to amend a tibble in your workspace, you amend a query, which is then sent to your chosen organisation. These pipes differ from traditional syntax in two ways:

they begin with a function - usually galah_call() - instead of a tibble
they end with one of dplyr’s evaluation functions, usually collect()

So an example query might be to find the number of records:

galah_call() |>    # open a pipe
  count() |>       # count the number of rows
  collect()        # retrieve query from the server

## # A tibble: 1 × 1
##       count
##       <int>
## 1 184796858

By default, queries are sent to the Atlas of Living Australia, but this can be changed using the from argument:

galah_call(from = "Spain") |>
  count() |>
  collect()

## # A tibble: 1 × 1
##      count
##      <int>
## 1 59852882

The from argument accepts the acronym, region, or institution fields from show_all(atlases). The full list of supported queries by organisation is as follows:

Figure 1: Organisations (rows) and APIs (columns) supported by galah

Filtering

You can pass taxonomic filters to your query with identify():

galah_call() |>
  identify("Eolophus roseicapilla") |>
  count() |>
  collect()

## # A tibble: 1 × 1
##     count
##     <int>
## 1 1593848

To choose records using non-taxonomic criteria, you’ll need to use filter(). This reqires that you find out:

what fields (columns) are present in the dataset you are searching
what values exist for those fields

You can answer the first question with describe():

galah_call() |>
  describe() |>
  collect()

## # A tibble: 582 × 3
##    id                    description               data_type
##    <chr>                 <chr>                     <chr>    
##  1 acceptedNameUsage     Accepted name             string   
##  2 acceptedNameUsageID   Accepted name             string   
##  3 accessRights          Access rights             string   
##  4 annotationsDoi        <NA>                      string   
##  5 annotationsUid        Referenced by publication string   
##  6 assertionUserId       Assertions by user        string   
##  7 assertions            Record issues             string   
##  8 assertionsCount       <NA>                      int      
##  9 associatedMedia       Associated Media          string   
## 10 associatedOccurrences Associated Occurrences    string   
## # ℹ 572 more rows

Once you find a variable you are interested in, see what values it contains using distinct():

galah_call() |>
  distinct(basisOfRecord) |>
  collect()

## # A tibble: 9 × 1
##   basisOfRecord      
##   <chr>              
## 1 HUMAN_OBSERVATION  
## 2 PRESERVED_SPECIMEN 
## 3 OCCURRENCE         
## 4 OBSERVATION        
## 5 MACHINE_OBSERVATION
## 6 MATERIAL_SAMPLE    
## 7 LIVING_SPECIMEN    
## 8 FOSSIL_SPECIMEN    
## 9 MATERIAL_CITATION

Then you can combine these within a filter() query:

galah_call() |>
  filter(basisOfRecord == "PRESERVED_SPECIMEN") |>
  count() |> 
  collect()

## # A tibble: 1 × 1
##      count
##      <int>
## 1 18419249

These functions can be combined in a number of ways, for example to learn how many species from the genus ‘Crinia’ have records from each country with records in the ALA:

galah_call() |>
  identify("Crinia") |>   # filters by taxonomic names
  group_by(country) |>    # grouping variable
  distinct(species) |>    # keep only unique values
  count() |>
  collect()

## # A tibble: 5 × 2
##   country          count
##   <chr>            <int>
## 1 Australia           17
## 2 Papua New Guinea     2
## 3 New Zealand          1
## 4 Indonesia            0
## 5 Solomon Islands      1

You can glimpse() a data download before you run it, to check all the data you need is included:

galah_call() |>
  identify("Eolophus roseicapilla") |> 
  filter(year == 2010) |>
  glimpse() |>
  collect()

## Rows: 26,430
## Columns: 8
## $ taxonConceptID   <chr> "https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea", "https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea", "https://biodiversity.org.au/afd/taxa/a38ac5ac-3049-4fde-8391-faf8bf7aeb7f"
## $ eventDate        <dbl> 1.278833e+12, 1.286170e+12, 1.292803e+12
## $ scientificName   <chr> "Eolophus roseicapilla", "Eolophus roseicapilla", "Eolophus roseicapilla albiceps"
## $ decimalLatitude  <dbl> -27.57463, -16.06593, -35.88444
## $ decimalLongitude <dbl> 153.4350, 136.3067, 145.9089
## $ basisOfRecord    <chr> "HUMAN_OBSERVATION", "HUMAN_OBSERVATION", "HUMAN_OBSERVATION"
## $ dataResourceName <chr> "eBird Australia", "eBird Australia", "BirdLife Australia, Birdata"
## $ otherProperties  <list> ["PRESENT"], ["PRESENT"], ["PRESENT"]

And, once satisfied that your parameters are correct, download the records themselves:

galah_call() |>
  authenticate(email = "registered_email@wherever.com") |>
  identify("Eolophus roseicapilla") |> 
  filter(year == 2010) |>
  select(eventDate, decimalLatitude, species) |>
  collect()

## Request for 26430 occurrences placed in queue.

## Current queue length: 1

## --

## 
ℹ Downloading

## 
✔ Downloading [253ms]

## # A tibble: 26,430 × 3
##    eventDate decimalLatitude species              
##    <dttm>              <dbl> <chr>                
##  1 NA                  -37.1 Eolophus roseicapilla
##  2 NA                  -37.0 Eolophus roseicapilla
##  3 NA                  -37.1 Eolophus roseicapilla
##  4 NA                  -37.1 Eolophus roseicapilla
##  5 NA                  -37.2 Eolophus roseicapilla
##  6 NA                  -37.0 Eolophus roseicapilla
##  7 NA                  -37.0 Eolophus roseicapilla
##  8 NA                  -37.0 Eolophus roseicapilla
##  9 NA                  -37.0 Eolophus roseicapilla
## 10 NA                  -37.1 Eolophus roseicapilla
## # ℹ 26,420 more rows

Architecture

This works because many of the functions in dplyr are “generic”, meaning it is possible to write extensions that apply them to new object classes. In our case, request_data() creates a new object class called a data_request for which we have written new extensions. This means that galah will not interfere with your use of filter() and friends on your tibbles. Supported dplyr verbs that modify queries are as follows:

arrange.data_request()
count.data_request()
distinct.data_request()
filter.data_request()
glimpse.data_request()
group_by.data_request()
select.data_request()
slice_head.data_request()

Additional verbs are:

apply_profile()
describe()
geolocate() or st_crop.data_request()
identify.data_request()
unnest()

It is good practice to download your data in as few steps as possible, to minimize impacts on the server, and to ensure you can get a single DOI for your data. See the download data reproducibly vignette for details.

Wrapper functions

While dplyr syntax is very flexible, there are cases where it is easier to simply say the sort of data you want, rather than create a database query to implement it. For this reason, several common use cases have their own wrapper functions.

The atlas_ family of functions act like collect(), but enforce a particular type of data to be returned, such as record counts:

galah_call() |>
  filter(year == 2025) |>
  atlas_counts()   # note no need for a `count()` function

## # A tibble: 1 × 1
##      count
##      <int>
## 1 10781991

Or occurrences:

galah_call() |>
  identify("Eolophus roseicapilla") |>
  filter(year == 2000,
         cl22 == "Australian Capital Territory") |>
  atlas_occurrences() |>
  print(n = 6)

## -----

## # A tibble: 2,318 × 9
##   recordID                             scientificName        taxonConceptID                                                            decimalLatitude decimalLongitude eventDate           basisOfRecord     occurrenceStatus dataResourceName   
##   <chr>                                <chr>                 <chr>                                                                               <dbl>            <dbl> <dttm>              <chr>             <chr>            <chr>              
## 1 0026d29f-b6ab-4a1d-9c57-6ee12cfde3a0 Eolophus roseicapilla https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea           -35.4             149. 2000-08-07 00:00:00 HUMAN_OBSERVATION PRESENT          Garden Bird Surveys
## 2 00a62ee0-1e08-4114-b0d8-9b7905472d53 Eolophus roseicapilla https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea           -35.2             149. 2000-01-29 00:00:00 HUMAN_OBSERVATION PRESENT          Garden Bird Surveys
## 3 00ab2f4d-326f-4b01-9a8a-1a10c1f77e3c Eolophus roseicapilla https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea           -35.4             149. 2000-09-25 00:00:00 HUMAN_OBSERVATION PRESENT          Garden Bird Surveys
## 4 00b6c8ec-e7b9-4d9f-9638-d962b1b4acfa Eolophus roseicapilla https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea           -35.2             149. 2000-02-05 00:00:00 HUMAN_OBSERVATION PRESENT          Garden Bird Surveys
## 5 00e36517-6518-42d5-8b69-90c526095fef Eolophus roseicapilla https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea           -35.4             149. 2000-01-01 00:00:00 HUMAN_OBSERVATION PRESENT          Garden Bird Surveys
## 6 010b463f-d34f-45ec-8733-91f676a299d8 Eolophus roseicapilla https://biodiversity.org.au/afd/taxa/9b4ad548-8bb3-486a-ab0a-905506c463ea           -35.2             149. 2000-03-12 00:00:00 HUMAN_OBSERVATION PRESENT          Garden Bird Surveys
## # ℹ 2,312 more rows

atlas_species() replaces the need for distinct() call, while atlas_media() is a shortcut to a more complex workflow that incorporates both data and metadata calls. Finally, metadata calls can be made more efficiently using the show_all() and show_values() functions. These take the same arguments as the type argument in request_metadata(), but us non-standard evaluation, so they don’t require quotes. They are also evaluated immediately rather than lazily:

show_all(fields)

## # A tibble: 582 × 3
##    id                    description               type  
##    <chr>                 <chr>                     <chr> 
##  1 acceptedNameUsage     Accepted name             fields
##  2 acceptedNameUsageID   Accepted name             fields
##  3 accessRights          Access rights             fields
##  4 annotationsDoi        <NA>                      fields
##  5 annotationsUid        Referenced by publication fields
##  6 assertionUserId       Assertions by user        fields
##  7 assertions            Record issues             fields
##  8 assertionsCount       <NA>                      fields
##  9 associatedMedia       Associated Media          fields
## 10 associatedOccurrences Associated Occurrences    fields
## # ℹ 572 more rows

You can check the look up information vignette for further details.

Quick start guide

Martin Westgate & Dax Kellie

2026-09-16

Installation

Getting data

Filtering

Architecture

Wrapper functions