Installation and Usage
GitHub
You can install the development version of ArctosR from GitHub with:
install.packages("remotes")
remotes::install_github("hrhwilliams/ArctosR")API key
To use ArctosR, you will need to request an API key from Arctos by filing a GitHub issue in their issue tracker. You can do this with the following steps:
- Navigate to the Arctos GitHub issue tracker and open a new issue
- Fill out the issue by following the template found at https://handbook.arctosdb.org/documentation/api.html:
- Name, institution, position
- Two means of contact: email, phone, address, etc.
- Justification
- Method of access (ArctosR)
- Acknowledge that any granted access is read-only and will expire in one year. There is no limit on renewal or extension requests but it is the responsibility of the user to make them.
- Acknowledge that by requesting and gaining permission to use of Arctos data, you are agreeing to all terms of the Arctos Community Data Policy
Once you have obtained an API key, you can store it as an environment variable inside your .Renviron file. This file can be edited by calling usethis::edit_r_environ(). In it, store your API key as
and functions in ArctosR requiring an API key will be able to read it from there.
Example
library(ArctosR)
# You will have to request an API key from Arctos to get records, and pass it
# to the `get_record_count` and `get_records` functions through the `api_key`
# parameter.
YOUR_API_KEY <- "11111111-2222-3333-4444444444444444"
# Request a list of all result parameters. These are the names that can show up
# as columns in a dataframe returned by ArctosR.
result_params <- get_result_parameters()
# Print the first six rows and first 3 columns to the console.
result_params[1:6, 1:3]
# If using RStudio, view the entire dataframe of result parameters.
View(result_params)
# Request just the number of records matching a query.
count <- get_record_count(
scientific_name = "Canis lupus",
guid_prefix = "MSB:Mamm",
api_key = YOUR_API_KEY
)
# Request to download data. This is limited to 100 records by default.
query <- get_records(
scientific_name = "Canis lupus", guid_prefix = "MSB:Mamm",
columns = list("guid", "parts", "partdetail"),
api_key = YOUR_API_KEY
)
# Request to download all available data.
query <- get_records(
scientific_name = "Canis lupus", guid_prefix = "MSB:Mamm",
columns = list("guid", "parts", "partdetail"),
api_key = YOUR_API_KEY,
all_records = TRUE
)
# Grab the dataframe of records from the response and save that as a csv.
df <- response_data(query)GSoC project description
Student: Harlan Williams
GSoC Mentors: Marlon Cobos, Vijay Barve, Jocelyn Colella, Michelle Koo
Organization: R Project For Statistical Computing
Motivation
Arctos (https://arctosdb.org/) has an extensive database that connects >100 data fields to physical specimen records using standard DarwinCore vocabulary, many of which are only accessible through its web interface. Data can be accessed through the web interface, but downloads are memory intensive, such that only a subset of fields or specimens can be queried at once. The goal of this package is to provide a programmatic way to access these data for researchers, in hopes of improving their workflows and the accessibility of biodiversity data stored on Arctos.
The main difficulties in accessing Arctos via the API is pagination of records, requiring multiple API queries, and hierarchical data where specific columns in Arctos records could themselves be tables or point to other Arctos records. This package was developed specifically to handle these two difficulties for the user. Pagination is handled by package internals so that the user only has to ask for all records pertaining to a query to get all of those records.
The user also is able to expand columns representing hierarchical data and explore that data within RStudio natively, making analysis of that data much more intuitive.
Status of the project
At the time of submission for GSoC 2024 a set of functions for querying from Arctos as well as looking up documentation for Arctos are available to the user. The user is also able to explore downloaded records in a hierarchical manner by expanding columns returned from Arctos which represent tables of tables. These records are then able to be saved in a CSV format or stored as R objects for further data analysis tasks by researchers.
With these functions, ArctosR can be integrated into existing data analysis pipelines to provide updated records. Each query in ArctosR is also accompanied by metadata, allowing for better data documentation and query reproducibility by other researchers. At this stage the project fulfills the goals set out in the proposal, the only thing remaining is for it to be submitted to CRAN.
A complete history of commits can be accessed here.