Object-Oriented Programming

The default method for building queries in galah is to first use galah_call() to create a query object called a “data_request”. This object class is specific to galah.

galah_call() |>
  filter(genus == "Crinia") |>
  class()

## [1] "data_request"

When a piped object is of class data_request, galah can trigger functions to use specific methods for this object class, even if a function name is used by another package. For example, users can use filter() and group_by() functions from dplyr instead of galah_filter() and galah_group_by() to construct a query. Consequently, the following queries are synonymous:

galah_call() |>
  galah_filter(genus == "Crinia", year == 2020) |>
  galah_group_by(species) |>
  atlas_counts()

galah_call() |> 
  filter(genus == "Crinia", year == 2020) |>
  group_by(species) |>
  atlas_counts()

## # A tibble: 16 × 2
##    species                 count
##    <chr>                   <int>
##  1 Crinia signifera        42621
##  2 Crinia parinsignifera    8664
##  3 Crinia glauerti          3111
##  4 Crinia georgiana         1509
##  5 Crinia remota             718
##  6 Crinia sloanei            682
##  7 Crinia insignifera        530
##  8 Crinia tinnula            291
##  9 Crinia deserticola        253
## 10 Crinia pseudinsignifera   223
## 11 Crinia tasmaniensis       181
## 12 Crinia bilingua            74
## 13 Crinia subinsignifera      46
## 14 Crinia riparia             10
## 15 Crinia flindersensis        3
## 16 Crinia nimba                1

Thanks to object-oriented programming, galah “masks” filter() and group_by() functions to use methods defined for data_request objects instead. The full list of masked functions is:

arrange() ({dplyr})
count() ({dplyr})
identify() ({graphics}) as a synonym for galah_identify()
select() ({dplyr}) as a synonym for galah_select()
group_by() ({dplyr}) as a synonym for galah_group_by()
slice_head() ({dplyr}) as a synonym for the limit argument in atlas_counts()
st_crop() ({sf}) as a synonym for galah_polygon()

Note that these functions are all evaluated lazily; they amend the underlying object, but do not amend the nature of the data until the call is evaluated. To actually build and run the query, we’ll need to use one or more of a different set of dplyr verbs: collapse(), compute() and collect().

Advanced query building

The usual way to begin a query to request data in galah is using galah_call(). However, this function now calls one of three types of request_ functions. If you prefer, you can begin your pipe with one of these dedicated request_ functions (rather than galah_call()) depending on the type of data you want to collect.

For example, if you want to download occurrences, use request_data():

x <- request_data("occurrences") |> # note that "occurrences" is the default `type`
  filter(species == "Crinia tinnula", 
         year == 2010) |>
  collect()

You’ll notice that this query differs slightly from the query structure used in earlier versions of galah. The desired data type, "occurrences", is specified at the beginning of the query within request_data() rather than at the end using atlas_occurrences(). Specifying the data type at the start allows users to make use of advanced query building using three newly implemented stages of query building: collapse(), compute() and collect(). These stages mirror existing functions in dplyr for querying databases, and act in the following way:

collapse() converts the object to a query. This allows users to inspect
their API calls before they are sent. Depending on the request, this function may also call ‘supplementary’ APIs to collect required information, such as Taxon Concept Identifiers or field names.
compute() is intended to send the query in question to the requested API for processing. This is particularly important for occurrences, where it can be useful to submit a query and retrieve it at a later time. If the compute() stage is not required, however, compute() simply converts the query to a new class (computed_query).
collect() retrieves the requested data into your workspace, returning a tibble.

We can use these in sequence, or just leap ahead to the stage we want:

x <- request_data() |>
  filter(genus == "Crinia", year == 2020) |>
  group_by(species) |>
  arrange(species) |>
  count()

collapse(x)

## Object of class query with type data/occurrences-count-groupby 
## url: https://api.ala.org.au/occurrences/occurrences/facets?fq=%28genus%3A%2... 
## arrange: species (ascending)

compute(x)

## Object of class computed_query with type data/occurrences-count-groupby 
## url: https://api.ala.org.au/occurrences/occurrences/facets?fq=%28genus%3A%2... 
## arrange: species (ascending)

collect(x) |> head()

## # A tibble: 6 × 2
##   species              count
##   <chr>                <int>
## 1 Crinia bilingua         74
## 2 Crinia deserticola     253
## 3 Crinia flindersensis     3
## 4 Crinia georgiana      1509
## 5 Crinia glauerti       3111
## 6 Crinia insignifera     530

The benefit of using collapse(), compute() and collect() is that queries are more modular. This is particularly useful for large data requests in galah. Users can send their query using compute(), and download data once the query has finished — downloading with collect() later — rather than waiting for the request to finish within R.

# Create and send query to be calculated server-side
request <- request_data() |>
  identify("perameles") |>
  filter(year > 1900) |>
  compute()
  
# Download data
request |>
  collect()

Additionally, functions that are more modular are generally easier to interrogate and debug. Previously some functions did several different things, making it difficult to know which APIs were being called, when, and for what purpose. Partitioning queries into three distinct stages is much more transparent, and allows users to check their query construction prior to sending a request. For example, the query above is constructed with the following information, returned by collapse().

request_data() |>
  identify("perameles") |>
  filter(year > 1900) |>
  collapse()

## Object of class query with type data/occurrences 
## url: https://api.ala.org.au/occurrences/occurrences/offline/download?fq=%28...

The collapse() stage includes an additional argument (.expand) that, when set to TRUE, shows all the APIs called to construct the user-requested query. This is especially useful for debugging.

Object classes

Under the hood, the different query-building verbs each amend the supplied object to a new class:

collapse() returns class query, which is a list containing a type slot and one or more urls
compute() returns a single object of class computed_query
collect() returns a tibble

These can be called directly, or via the method and type arguments of galah_call(), which specify which dedicated request_ function and data type to return. To demonstrate what we mean, take the following calls, which despite using different syntax, all return the number of records available for the year 2020:

# new syntax
request_data() |>
  filter(year == 2020) |>
  count() |>
  collect()

# similar, but using `galah_call()`
galah_call(method = "data",
           type = "occurrences-count") |>
  filter(year == 2020) |>
  collect()

# original syntax
galah_call() |>
  galah_filter(year == 2020) |>
  atlas_counts()

Another example is to list available fields in the selected atlas:

request_metadata(type = "fields") |>
  collect()

galah_call(method = "metadata", 
           type = "fields") |>
  collect()

show_all(fields)

Or to show values for states and territories:

request_metadata() |>
  filter(field == "cl22") |>
  unnest() |>
  collect()

galah_call(method = "metadata", 
           type = "fields-unnest") |>
  galah_filter(id == "cl22") |>
  collect()

search_all(fields, "cl22") |>
  show_values()

While request_metadata() is more modular than show_all(), there is little benefit to using it for most applications. However, in some cases, larger databases like GBIF return huge data.frames of metadata when called via show_all(). Using request_metdata() allows users to specify a slice_head() line within their pipe to get around this issue.

Which syntax should I prefer?

Despite these benefits, we have no plans to require users to call masked functions. Functions prefixed with galah_ or atlas_ are not going away. Indeed, while there is perfect redundancy between old and new syntax in some cases, in others they serve different purposes. In atlas_media() for example, several calls are made and joined in a way that reduces the number of steps required by the user. Under the hood, however, all atlas_ functions are now entirely built using the above syntax.