Introduction to rgbif

About the package

rgbif is an R package to search and retrieve data from the Global Biodiverity Information Facilty (GBIF). rgbif wraps R code around the GBIF API to allow you to talk to GBIF from R.

Get rgbif

Install from CRAN

install.packages("rgbif")

Or install the development version from GitHub

remotes::install_github("ropensci/rgbif")

Load rgbif

library("rgbif")

Number of occurrences

Search by type of record, all observational in this case

occ_count(basisOfRecord='OBSERVATION')

Records for Puma concolor with lat/long data (georeferened) only. Note that hasCoordinate in occ_search() is the same as georeferenced in occ_count().

occ_count(taxonKey=2435099, georeferenced=TRUE)

All georeferenced records in GBIF

occ_count(georeferenced=TRUE)

Records from Denmark

denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)

Number of records in a particular dataset

occ_count(datasetKey='9e7ea106-0bf8-4087-bb61-dfe4f29e0f17')

All records from 2012

occ_count(year=2012)

Records for a particular dataset, and only for preserved specimens

occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')

Search for taxon names

Get possible values to be used in taxonomic rank arguments in functions

taxrank()

name_lookup() does full text search of name usages covering the scientific and vernacular name, the species description, distribution and the entire classification across all name usages of all or some checklists. Results are ordered by relevance as this search usually returns a lot of results.

By default name_lookup() returns five slots of information: meta, data, facets, hierarchies, and names. hierarchies and names elements are named by their matching GBIF key in the data.frame in the data slot.

out <- name_lookup(query='mammalia')

names(out)

out$meta

head(out$data)

out$facets

out$hierarchies[1:2]

out$names[2]

Search for a genus

z <- name_lookup(query='Cnaemidophorus', rank="genus")
z$data

Search for the class mammalia

w <- name_lookup(query='mammalia')
w$data

Look up the species Helianthus annuus

m <- name_lookup(query = 'Helianthus annuus', rank="species")
m$data

The function name_usage() works with lots of different name endpoints in GBIF, listed at http://www.gbif.org/developer/species#nameUsages.

name_usage(key=3119195, language="FRENCH", data='vernacularNames')

The function name_backbone() is used to search against the GBIF backbone taxonomy

name_backbone(name='Helianthus', rank='genus', kingdom='plants')

The function name_suggest() is optimized for speed, and gives back suggested names based on query parameters.

head( name_suggest(q='Puma concolor') )

Single occurrence records

Get data for a single occurrence. Note that data is returned as a list, with slots for metadata and data.

occ_get(key=855998194)

Get many occurrences. occ_get is vectorized

occ_get(key=c(855998194, 240713150))

Search for occurrences

Note: The maximum number of records you can get with occ_search() and occ_data() is 100,000. See https://www.gbif.org/developer/occurrence

By default occ_search() returns a dplyr like output summary in which the data printed expands based on how much data is returned, and the size of your window. You can search by scientific name:

occ_search(scientificName = "Ursus americanus", limit = 20)

Or to be more precise, you can search for names first, make sure you have the right name, then pass the GBIF key to the occ_search() function:

key <- name_suggest(q='Helianthus annuus', rank='species')$key[1]
occ_search(taxonKey=key, limit=20)

You can index to different parts of the oupu; here, the metadata:

occ_search(taxonKey=key)$meta

You can choose what fields to return. This isn’t passed on to the API query to GBIF as they don’t allow that, but we filter out the columns before we give the data back to you.

occ_search(scientificName = "Ursus americanus", fields=c('name','basisOfRecord','protocol'), limit = 20)

Most parameters are vectorized, so you can pass in more than one value:

splist <- c('Cyanocitta stelleri', 'Junco hyemalis', 'Aix sponsa')
keys <- sapply(splist, function(x) name_suggest(x)$key[1], USE.NAMES=FALSE)
occ_search(taxonKey=keys, limit=5)

Maps

Using thet GBIF map web tile service, making a raster and visualizing it.

x <- map_fetch(taxonKey = 2480498, year = 2000:2017)
library(raster)
plot(x)