In azizka/CoordinateCleaner: Automated Cleaning of Occurrence Records from Biological Collections

CoordinateCleaner v2.0-19

There were some changes to the documentation of CoordinateCleaner with version 2.0-17. You can find three vignettes on cleaning point occurrence records (for instance from www.gbif.org), fossil data and using custom gazetteers with the package now. Additional information on dataset-level cleaning and identifying geographic outliers can be found here.

Automated flagging of common spatial and temporal errors in biological and palaeontological collection data, for the use in conservation, ecology and palaeontology. Specifically includes tests for

General coordinate validity
Country and province centroids
Capital coordinates
Coordinates of biodiversity institutions
Spatial outliers
Temporal outliers
Coordinate-country discordance
Duplicated coordinates per species
Assignment to the location of the GBIF headquarters
Urban areas
Seas
Plain zeros
Equal longitude and latitude
Rounded coordinates
DDMM to DD.DD coordinate conversion errors
Large temporal uncertainty (fossils)
Equal minimum and maximum ages (fossils)
Spatio-temporal outliers (fossils)

CoordinateCleaner can be particularly useful to improve data quality when using data from GBIF (e.g. obtained with rgbif) or the Paleobiology database (e.g. obtained with paleobioDB) for historical biogeography (e.g. with BioGeoBEARS or phytools), automated conservation assessment (e.g. with speciesgeocodeR or conR) or species distribution modelling (e.g. with dismo or sdm). See scrubr and taxize for complementary taxonomic cleaning or biogeo for correcting spatial coordinate errors. You can find a detailed comparison of the functionality of CoordinateCleaner, scrubr, and biogeo here.

See News for update information.

Installation

Stable from CRAN

install.packages("CoordinateCleaner")
library(CoordinateCleaner)

Developmental using devtools

devtools::install_github("ropensci/CoordinateCleaner")
library(CoordinateCleaner)

Usage

A simple example:

# Simulate example data
minages <- runif(250, 0, 65)
exmpl <- data.frame(species = sample(letters, size = 250, replace = TRUE),
                    decimalLongitude = runif(250, min = 42, max = 51),
                    decimalLatitude = runif(250, min = -26, max = -11),
                    min_ma = minages,
                    max_ma = minages + runif(250, 0.1, 65),
                    dataset = "clean")

# Run record-level tests
rl <- clean_coordinates(x = exmpl)
summary(rl)
plot(rl)

# Dataset level 
dsl <- clean_dataset(exmpl)

# For fossils
fl <- clean_fossils(x = exmpl,
                          taxon = "species",
                          lon = "decimalLongitude", 
                          lat = "decimalLatitude")
summary(fl)

# Alternative example using the pipe
library(tidyverse)

cl <- exmpl %>%
  cc_val()%>%
  cc_cap()%>%
  cd_ddmm()%>%
  cf_range(lon = "decimalLongitude", 
           lat = "decimalLatitude", 
           taxon  ="species")

Documentation

Pipelines for cleaning data from the Global Biodiversity Information Facility (GBIF) and the Paleobiology Database (PaleobioDB) are available in here.

Contributing

See the CONTRIBUTING document.

Citation

Zizka A, Silvestro D, Andermann T, Azevedo J, Duarte Ritter C, Edler D, Farooq H, Herdean A, Ariza M, Scharn R, Svanteson S, Wengtrom N, Zizka V & Antonelli A (2019) CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution, 10(5):744-751, doi:10.1111/2041-210X.13152, https://github.com/ropensci/CoordinateCleaner