README

corella is an R package that helps users standardize their data using the Darwin Core data standard, used for biodiversity data like species occurrences. corella provides tools to prepare, manipulate and validate data against the standard’s criteria. Once standardized, data can be subsequently shared as a Darwin Core Archive and published to open data infrastructures like the Atlas of Living Australia and GBIF.

Installation

install.packages("corella")

# install.packages("devtools")
devtools::install_github("AtlasOfLivingAustralia/corella")

Usage

Here we have a small sample of example data containing observations of cockatoos. Using corella we can convert our data to use Darwin Core Standard.

library(corella)
library(tibble)

# A simple example of species occurrence data
df <- tibble(
  species = c("Callocephalon fimbriatum", "Eolophus roseicapilla"),
  latitude = c(-35.310, "-35.273"), # deliberate error for demonstration purposes
  longitude = c(149.125, 149.133),
  eventDate = c("14-01-2023", "15-01-2023"),
  status = c("present", "present")
)

df
#> # A tibble: 2 × 5
#>   species                  latitude longitude eventDate  status 
#>   <chr>                    <chr>        <dbl> <chr>      <chr>  
#> 1 Callocephalon fimbriatum -35.31        149. 14-01-2023 present
#> 2 Eolophus roseicapilla    -35.273       149. 15-01-2023 present

One of the most important aspects of Darwin Core Standard is using standard column names (Darwin Core terms). We can update column names in our data to match Darwin Core terms with set_ functions.

Each set_ function name corresponds to the type of data, and argument names correspond to the available Darwin Core terms to use as column names. set_ functions support data wrangling operations & dplyr::mutate() functionality, meaning columns can be changed or fixed in your pipe. set_ functions will indicate if anything needs fixing because they also automatically run checks on each column data to make sure each column is in the correct format.

suppressMessages( # for readability

df |>
  set_coordinates(
    decimalLatitude = as.numeric(latitude), # fix latitude
    decimalLongitude = longitude
    ) |>
  set_scientific_name(
    scientificName = species
    ) |>
  set_datetime(
    eventDate = lubridate::dmy(eventDate) # specify date format
    ) |>
  set_occurrences(occurrenceStatus = status)

)
#> # A tibble: 2 × 5
#>   eventDate  decimalLatitude decimalLongitude scientificName    occurrenceStatus
#>   <date>               <dbl>            <dbl> <chr>             <chr>           
#> 1 2023-01-14           -35.3             149. Callocephalon fi… present         
#> 2 2023-01-15           -35.3             149. Eolophus roseica… present

Not sure where to start? Use suggest_workflow() to know what steps you need to make to make your data Darwin Core compliant.

df |> 
  suggest_workflow()
#> 
#> ── Matching Darwin Core terms ──────────────────────────────────────────────────
#> Matched 1 of 5 column names to DwC terms:
#> ✔ Matched: eventDate
#> ✖ Unmatched: latitude, longitude, species, status
#> 
#> ── Minimum required Darwin Core terms ──────────────────────────────────────────
#> 
#>   Type                      Matched term(s)  Missing term(s)                                                                
#> ✔ Date/Time                 eventDate        -                                                                               
#> ✖ Identifier (at least one) -                occurrenceID, catalogNumber, recordNumber                                       
#> ✖ Record type               -                basisOfRecord                                                                   
#> ✖ Scientific name           -                scientificName                                                                  
#> ✖ Location                  -                decimalLatitude, decimalLongitude, geodeticDatum, coordinateUncertaintyInMeters
#> 
#> ── Suggested workflow ──────────────────────────────────────────────────────────
#> 
#> To make your data Darwin Core compliant, use the following workflow:
#> df |>
#>   set_occurrences() |>
#>   set_coordinates() |>
#>   set_scientific_name()
#> 
#> ── Additional functions
#> ℹ See all `set_` functions at
#>   http://corella.ala.org.au/reference/index.html#add-rename-or-edit-columns-to-match-darwin-core-terms

Or, if your data is nearly ready and you want to run checks over all columns that match Darwin Core terms, run check_dataset().

df |>
  check_dataset()
#> ℹ Testing data
#> ✔ | E P | Column
#> ⠙ | 0 eventDate
#> ⠹ | 1 ✖ | eventDate ✔ | 1 ✖ | eventDate  [111ms]
#> ══ Results ═════════════════════════════════════════════════════════════════════
#> 
#> [ Errors: 1 | Pass: 0 ]
#> ℹ Checking Darwin Core compliance
#> ✖ Data does not meet minimum Darwin Core column requirements
#> ℹ Use `suggest_workflow()` to see more information.
#> ── Error in term ───────────────────────────────────────────────────────────────
#> 
#> eventDate must be a Date vector, not a character.
#> ℹ Specify date format with lubridate functions e.g. `ymd()`, `mdy()`, or
#> `dmy()`.

corella

Overview

Installation

Usage

Citing corella