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# CoordinateCleaner v2.0-19
[](https://travis-ci.org/ropensci/CoordinateCleaner)
[](https://codecov.io/github/ropensci/CoordinateCleaner)
[](https://cran.r-project.org/package=CoordinateCleaner)
[](http://cranlogs.r-pkg.org/badges/grand-total/CoordinateCleaner)
[](http://cranlogs.r-pkg.org/badges/CoordinateCleaner)
[](https://www.repostatus.org/#active)
[](https://doi.org/10.5281/zenodo.2539408)
[](https://github.com/ropensci/software-review/issues/210)
**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](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13152).**
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]( https://github.com/ropensci/rgbif)) or the Paleobiology database (e.g. obtained with [paleobioDB](https://github.com/ropensci/paleobioDB)) for historical biogeography (e.g. with [BioGeoBEARS](https://CRAN.R-project.org/package=BioGeoBEARS) or [phytools](https://CRAN.R-project.org/package=phytools)), automated conservation assessment (e.g. with [speciesgeocodeR](https://github.com/azizka/speciesgeocodeR/wiki) or [conR](https://CRAN.R-project.org/package=ConR)) or species distribution modelling (e.g. with [dismo](https://CRAN.R-project.org/package=dismo) or [sdm](https://CRAN.R-project.org/package=sdm)). See [scrubr](https://github.com/ropensci/scrubr) and [taxize](https://github.com/ropensci/taxize) for complementary taxonomic cleaning or [biogeo](https://github.com/cran/biogeo) for correcting spatial coordinate errors. You can find a detailed comparison of the functionality of `CoordinateCleaner`, `scrubr`, and `biogeo` [here](https://ropensci.github.io/CoordinateCleaner/articles/comparison_other_software.html).
See [News](https://github.com/ropensci/CoordinateCleaner/blob/master/NEWS.md) for update information.
# Installation
## Stable from CRAN
```r
install.packages("CoordinateCleaner")
library(CoordinateCleaner)
```
## Developmental using devtools
```r
devtools::install_github("ropensci/CoordinateCleaner")
library(CoordinateCleaner)
```
# Usage
A simple example:
```r
# 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](https://ropensci.github.io/CoordinateCleaner/articles/).
# Contributing
See the [CONTRIBUTING](https://github.com/ropensci/CoordinateCleaner/blob/master/CONTRIBUTING.md) 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
[](https://ropensci.org)