From version 2.0 onwards, galah is built around object-oriented programming principles. In practice, this won't change the experience for most users. However, it does enable new ways of programming that were not previously available.
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() |> galah_filter(genus == "Crinia") |> class()
## [1] "data_request"
When an 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 57878 ## 2 Crinia parinsignifera 12478 ## 3 Crinia glauerti 3090 ## 4 Crinia georgiana 1490 ## 5 Crinia remota 784 ## 6 Crinia sloanei 644 ## 7 Crinia insignifera 536 ## 8 Crinia tinnula 417 ## 9 Crinia deserticola 278 ## 10 Crinia pseudinsignifera 235 ## 11 Crinia tasmaniensis 189 ## 12 Crinia bilingua 75 ## 13 Crinia subinsignifera 45 ## 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()
.
The usual way to begin a query to request data in galah is using galah_call()
.
From version 2.0, to make galah more flexible and modular, the underlying
architecture of galah_call()
has been divided into several types of request_
functions. 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 typical query structure
users of galah are familiar with. 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 theircompute()
sends queries to any required APIs. This includes sending a query
for data, determining whether field names are valid, or finding how much
data has been requested (which is necessary for pagination)collect()
retrieves the requested data into your workspaceWe 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_set` containing 3 queries:
## • metadata/fields data: galah:::check_internal_cache()$fields ## • metadata/assertions data: galah:::check_internal_cache()$assertions ## • data/occurrences-count-groupby url: https://biocache-ws.ala.org.au/ws/occurre...
compute(x)
## Object of class query with type data/occurrences-count-groupby ## url: https://biocache-ws.ala.org.au/ws/occurrences/facets?fq=%28genus%3A%22... ## arrange: species (ascending)
collect(x) |> head()
## # A tibble: 6 × 2 ## species count ## <chr> <int> ## 1 Crinia bilingua 75 ## 2 Crinia deserticola 278 ## 3 Crinia flindersensis 3 ## 4 Crinia georgiana 1490 ## 5 Crinia glauerti 3090 ## 6 Crinia insignifera 536
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_set` containing 5 queries:
## • metadata/fields data: galah:::check_internal_cache()$fields ## • metadata/assertions data: galah:::check_internal_cache()$assertions ## • metadata/reasons url: https://api.ala.org.au/logger/service/logger/reasons ## • metadata/taxa-single url: https://api.ala.org.au/namematching/api/search?q=pe... ## • data/occurrences url: https://biocache-ws.ala.org.au/ws/occurrences/offline/d...
The collapse()
stage displays useful information about which APIs will be pinged
by a query in galah when it is sent using compute()
.
Under the hood, the different query-building verbs amend our object to a new class:
collapse()
returns class query_set
, which is a list of one or more query
objectscompute()
returns a single object of class query
collect()
doesn't add a new data class (returns class 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()
Although there is little reason to use request_metadata()
rather than
show_all()
most of the time, in some cases larger databases like
GBIF return huge data.frame
s of metadata. galah allows users to use
collapse()
, compute()
and collect()
on all types of requests, meaning
users are able to download large metadata queries using the process detailed
above in "Advanced query building" to get around this issue.
Despite these benefits, we have no plans to require this new syntax; 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.
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