Narrow your 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 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
galah_down_to

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…¹ kingdom phylum class issues
##   <chr>       <chr>           <chr>                                                               <chr> <chr>   <chr>   <chr>  <chr> <chr> 
## 1 Reptilia    REPTILIA        https://biodiversity.org.au/afd/taxa/682e1228-5b3c-45ff-833b-550ef… class exactM… Animal… Chord… Rept… noIss…
## # … with abbreviated variable name ¹match_type

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_authorship taxon_concept_id    rank  match…¹ 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            https://biodiversi… genus exactM… Animal… Chord… Aves  Psit… Cacat… Eolo… noIss…
## # … with abbreviated variable name ¹match_type

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 1483960

taxa <- search_taxa(data.frame(genus = "Eolophus", kingdom = "Aves"))

galah_call() |>
 galah_identify(taxa) |>
 atlas_counts()

## # A tibble: 1 × 1
##    count
##    <int>
## 1 948989

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_identify("Bufo") |> 
  galah_group_by(species) |> 
  atlas_counts()

## # A tibble: 2 × 2
##   species       count
##   <chr>         <int>
## 1 Bufo bufo     75771
## 2 Bufo spinosus     1

## Atlas selected: Atlas of Living Australia (ALA) [Australia]

galah_filter

Perhaps 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 72951416

# 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 4093059

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: 83 × 2
##    class          count
##    <chr>          <int>
##  1 Insecta      4130497
##  2 Gastropoda    884000
##  3 Arachnida     575918
##  4 Malacostraca  562801
##  5 Maxillopoda   424030
##  6 Polychaeta    257849
##  7 Bivalvia      216585
##  8 Anthozoa      170859
##  9 Demospongiae  113698
## 10 Ostracoda      59272
## # … with 73 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 65961801

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: 25 × 3
##    basisOfRecord     year    count
##    <chr>             <chr>   <int>
##  1 HUMAN_OBSERVATION 2020  6309237
##  2 HUMAN_OBSERVATION 2019  5517222
##  3 HUMAN_OBSERVATION 2018  5219772
##  4 HUMAN_OBSERVATION 2017  4313434
##  5 HUMAN_OBSERVATION 2016  3483112
##  6 OCCURRENCE        2016   165997
##  7 OCCURRENCE        2018   116242
##  8 OCCURRENCE        2017   102206
##  9 OCCURRENCE        2019    91640
## 10 OCCURRENCE        2020    39429
## # … with 15 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(eventDate, kingdom) |>
  atlas_occurrences()

occurrences |> head()

## # A tibble: 6 × 2
##   eventDate           kingdom 
##   <dttm>              <chr>   
## 1 1929-12-31 14:00:00 Animalia
## 2 1929-12-31 14:00:00 Animalia
## 3 1929-12-31 14:00:00 Animalia
## 4 1929-12-31 14:00:00 Animalia
## 5 1929-12-31 14:00:00 Animalia
## 6 1929-12-31 14:00:00 Animalia

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("elev") & ends_with("n")) |>
  atlas_occurrences()

occurrences |> head()

## # A tibble: 6 × 55
##   recordID  catal…¹ taxon…² verba…³ raw_v…⁴ scien…⁵ taxon…⁶ verna…⁷ kingdom phylum class order family genus species subsp…⁸ dataR…⁹ insti…˟
##   <chr>     <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  <chr> <chr> <chr>  <chr> <chr>   <chr>   <chr>   <chr>  
## 1 050d4cec… J3729   https:… Oxyura… coasta… Oxyura… species Taipan  Animal… Chord… Rept… Squa… Elapi… Oxyu… Oxyura… <NA>    dr1132  <NA>   
## 2 0aee0e05… 391391  https:… Tympan… Lined … Tympan… species Grassl… Animal… Chord… Rept… Squa… Agami… Tymp… Tympan… <NA>    dr361   <NA>   
## 3 0cbfa7cb… R36835  https:… Natrix… <NA>    COLUBR… family  <NA>    Animal… Chord… Rept… Squa… Colub… <NA>  <NA>    <NA>    dr346   in22   
## 4 0fb28f26… <NA>    https:… Notech… easter… Notech… species Tiger … Animal… Chord… Rept… Squa… Elapi… Note… Notech… <NA>    dr1132  <NA>   
## 5 15e65c09… 34102   https:… Emydur… Murray… Emydur… subspe… Macqua… Animal… Chord… Rept… Test… Cheli… Emyd… Emydur… Emydur… dr1132  <NA>   
## 6 170fbb84… 77798   https:… Deniso… orname… Deniso… species Orname… Animal… Chord… Rept… Squa… Elapi… Deni… Deniso… <NA>    dr1132  <NA>   
## # … with 37 more variables: institutionName <chr>, collectionUid <chr>, collectionName <chr>, `dcterms:license` <chr>,
## #   institutionCode <chr>, collectionCode <chr>, locality <chr>, verbatimLatitude <dbl>, verbatimLongitude <dbl>,
## #   verbatimCoordinateSystem <chr>, decimalLatitude <dbl>, decimalLongitude <dbl>, coordinatePrecision <dbl>,
## #   coordinateUncertaintyInMeters <dbl>, country <chr>, stateProvince <chr>, cl959 <chr>, cl21 <chr>, cl1048 <chr>,
## #   minimumElevationInMeters <lgl>, maximumElevationInMeters <lgl>, minimumDepthInMeters <lgl>, maximumDepthInMeters <lgl>,
## #   individualCount <dbl>, recordedBy <chr>, year <dbl>, month <dbl>, day <dbl>, eventDate <dttm>, verbatimBasisOfRecord <chr>,
## #   basisOfRecord <chr>, occurrenceStatus <chr>, raw_sex <chr>, preparations <chr>, informationWithheld <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: 2 × 10
##   kingdom  phylum   class    order           family      genus     species                author               species_guid         verna…¹
##   <chr>    <chr>    <chr>    <chr>           <chr>       <chr>     <chr>                  <chr>                <chr>                <chr>  
## 1 Animalia Chordata Mammalia Peramelemorphia Peramelidae Perameles Perameles eremiana     Spencer, 1897        https://biodiversit… Desert…
## 2 Animalia Chordata Mammalia Peramelemorphia Peramelidae Perameles Perameles bougainville Quoy & Gaimard, 1824 https://biodiversit… Shark …
## # … with abbreviated variable name ¹vernacular_name

galah_down_to

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

Martin Westgate & Dax Kellie