Seach and retrieve data from the Global Biodiverity Information Facilty (GBIF)
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.
Install from CRAN
install.packages("rgbif")
Or install the development version from GitHub
devtools::install_github("ropensci/rgbif")
Load rgbif
library("rgbif")
Search by type of record, all observational in this case
occ_count(basisOfRecord='OBSERVATION')
#> [1] 47653413
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)
#> [1] 3317
All georeferenced records in GBIF
occ_count(georeferenced=TRUE)
#> [1] 629378292
Records from Denmark
denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)
#> [1] 11905048
Number of records in a particular dataset
occ_count(datasetKey='9e7ea106-0bf8-4087-bb61-dfe4f29e0f17')
#> [1] 4591
All records from 2012
occ_count(year=2012)
#> [1] 40175279
Records for a particular dataset, and only for preserved specimens
occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')
#> [1] 2563453
Get possible values to be used in taxonomic rank arguments in functions
taxrank()
#> [1] "kingdom" "phylum" "class" "order"
#> [5] "family" "genus" "species" "infraspecific"
name_lookup()
does full text search of name usages covering the scientific and vernacular name, the species description, distribution and the entire classification across all name usages of all or some checklists. Results are ordered by relevance as this search usually returns a lot of results.
By default name_lookup()
returns five slots of information: meta, data, facets, hierarchies, and names. hierarchies and names elements are named by their matching GBIF key in the data.frame
in the data slot.
out <- name_lookup(query='mammalia')
names(out)
#> [1] "meta" "data" "facets" "hierarchies" "names"
out$meta
#> # A tibble: 1 × 4
#> offset limit endOfRecords count
#> <int> <int> <lgl> <int>
#> 1 0 100 FALSE 1722
head(out$data)
#> # A tibble: 6 × 26
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 120820591 Mammalia 81e739b4-cba8-46d9-8104-5ea487c2dd20 359
#> 2 115197859 Mammalia 672aca30-f1b5-43d3-8a2b-c1606125fa1b 359
#> 3 115199929 Mammalia cbb6498e-8927-405a-916b-576d00a6289b 359
#> 4 115499069 Mammalia 36ad3207-1190-47ad-868e-b09d6c0aeec2 359
#> 5 120674802 Mammalia dbfacc33-350a-4620-976f-4d3a441aa242 359
#> 6 120824215 Mammalia 1570f557-12da-4cb6-ad4f-819bc5963f38 359
#> # ... with 22 more variables: parentKey <int>, parent <chr>,
#> # kingdom <chr>, phylum <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <chr>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>,
#> # constituentKey <chr>, extinct <lgl>, taxonID <chr>
out$facets
#> NULL
out$hierarchies[1:2]
#> $`120820591`
#> rankkey name
#> 1 120820566 Animalia
#> 2 120820567 Chordata
#>
#> $`115197859`
#> rankkey name
#> 1 115197858 Animalia
out$names[2]
#> $`104045725`
#> vernacularName
#> 1 mammals
Search for a genus
head(name_lookup(query='Cnaemidophorus', rank="genus", return="data"))
#> # A tibble: 6 × 34
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 125933331 Cnaemidophorus de8934f4-a136-481c-a87a-b0b202b80a31 1858636
#> 2 115196907 Cnaemidophorus 16c3f9cb-4b19-4553-ac8e-ebb90003aa02 1858636
#> 3 115216121 Cnaemidophorus cbb6498e-8927-405a-916b-576d00a6289b 1858636
#> 4 115346496 Cnaemidophorus cbb6498e-8927-405a-916b-576d00a6289b 1858636
#> 5 125855950 Cnaemidophorus 4cec8fef-f129-4966-89b7-4f8439aba058 1858636
#> 6 123576977 Cnaemidophorus fab88965-e69d-4491-a04d-e3198b626e52 1858636
#> # ... with 30 more variables: parentKey <int>, parent <chr>,
#> # kingdom <chr>, phylum <chr>, order <chr>, family <chr>, genus <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> # familyKey <int>, genusKey <int>, canonicalName <chr>,
#> # taxonomicStatus <chr>, rank <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>, authorship <chr>,
#> # nameType <chr>, taxonID <chr>, constituentKey <chr>, extinct <lgl>,
#> # publishedIn <chr>, accordingTo <chr>
Search for the class mammalia
head(name_lookup(query='mammalia', return = 'data'))
#> # A tibble: 6 × 26
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 120820591 Mammalia 81e739b4-cba8-46d9-8104-5ea487c2dd20 359
#> 2 115197859 Mammalia 672aca30-f1b5-43d3-8a2b-c1606125fa1b 359
#> 3 115199929 Mammalia cbb6498e-8927-405a-916b-576d00a6289b 359
#> 4 115499069 Mammalia 36ad3207-1190-47ad-868e-b09d6c0aeec2 359
#> 5 120674802 Mammalia dbfacc33-350a-4620-976f-4d3a441aa242 359
#> 6 120824215 Mammalia 1570f557-12da-4cb6-ad4f-819bc5963f38 359
#> # ... with 22 more variables: parentKey <int>, parent <chr>,
#> # kingdom <chr>, phylum <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <chr>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>,
#> # constituentKey <chr>, extinct <lgl>, taxonID <chr>
Look up the species Helianthus annuus
head(name_lookup(query = 'Helianthus annuus', rank="species", return = 'data'))
#> # A tibble: 6 × 40
#> key scientificName
#> <int> <chr>
#> 1 103340289 Helianthus annuus
#> 2 114910965 Helianthus annuus
#> 3 124614878 Helianthus annuus L.
#> 4 115452008 'Helianthus annuus' phyllody phytoplasma
#> 5 101321447 Helianthus annuus L.
#> 6 118749457 Helianthus annuus L.
#> # ... with 38 more variables: datasetKey <chr>, parentKey <int>,
#> # parent <chr>, kingdom <chr>, phylum <chr>, order <chr>, family <chr>,
#> # genus <chr>, species <chr>, kingdomKey <int>, phylumKey <int>,
#> # orderKey <int>, familyKey <int>, genusKey <int>, speciesKey <int>,
#> # canonicalName <chr>, nameType <chr>, taxonomicStatus <chr>,
#> # rank <chr>, numDescendants <int>, numOccurrences <int>, taxonID <chr>,
#> # habitats <chr>, nomenclaturalStatus <chr>, threatStatuses <lgl>,
#> # synonym <lgl>, nubKey <int>, classKey <int>, authorship <chr>,
#> # class <chr>, publishedIn <chr>, accordingTo <chr>, extinct <lgl>,
#> # constituentKey <chr>, basionymKey <int>, basionym <chr>,
#> # acceptedKey <int>, accepted <chr>
The function name_usage()
works with lots of different name endpoints in GBIF, listed at http://www.gbif.org/developer/species#nameUsages.
library("plyr")
out <- name_usage(key=3119195, language="FRENCH", data='vernacularNames')
head(out$data)
#> # A tibble: 6 × 6
#> vernacularName language country
#> <chr> <chr> <chr>
#> 1 Gewöhnliche Sonnenblume deu DE
#> 2 Sonnenblume deu <NA>
#> 3 alizeti swa <NA>
#> 4 annual sunflower eng <NA>
#> 5 common sunflower eng <NA>
#> 6 girasol spa <NA>
#> # ... with 3 more variables: source <chr>, sourceTaxonKey <int>,
#> # preferred <lgl>
The function name_backbone()
is used to search against the GBIF backbone taxonomy
name_backbone(name='Helianthus', rank='genus', kingdom='plants')
#> $usageKey
#> [1] 3119134
#>
#> $scientificName
#> [1] "Helianthus L."
#>
#> $canonicalName
#> [1] "Helianthus"
#>
#> $rank
#> [1] "GENUS"
#>
#> $status
#> [1] "ACCEPTED"
#>
#> $confidence
#> [1] 97
#>
#> $matchType
#> [1] "EXACT"
#>
#> $kingdom
#> [1] "Plantae"
#>
#> $phylum
#> [1] "Tracheophyta"
#>
#> $order
#> [1] "Asterales"
#>
#> $family
#> [1] "Asteraceae"
#>
#> $genus
#> [1] "Helianthus"
#>
#> $kingdomKey
#> [1] 6
#>
#> $phylumKey
#> [1] 7707728
#>
#> $classKey
#> [1] 220
#>
#> $orderKey
#> [1] 414
#>
#> $familyKey
#> [1] 3065
#>
#> $genusKey
#> [1] 3119134
#>
#> $synonym
#> [1] FALSE
#>
#> $class
#> [1] "Magnoliopsida"
The function name_suggest()
is optimized for speed, and gives back suggested names based on query parameters.
head( name_suggest(q='Puma concolor') )
#> # A tibble: 6 × 3
#> key canonicalName rank
#> <int> <chr> <chr>
#> 1 2435099 Puma concolor SPECIES
#> 2 7193927 Puma concolor concolor SUBSPECIES
#> 3 6164624 Puma concolor costaricensis SUBSPECIES
#> 4 6164590 Puma concolor couguar SUBSPECIES
#> 5 6164623 Puma concolor cabrerae SUBSPECIES
#> 6 6164589 Puma concolor anthonyi SUBSPECIES
Get data for a single occurrence. Note that data is returned as a list, with slots for metadata and data, or as a hierarchy, or just data.
Just data
occ_get(key=766766824, return='data')
#> name key decimalLatitude decimalLongitude
#> 1 Coloeus monedula 766766824 59.4568 17.9054
#> issues
#> 1 depunl,gass84
Just taxonomic hierarchy
occ_get(key=766766824, return='hier')
#> name key rank
#> 1 Animalia 1 kingdom
#> 2 Chordata 44 phylum
#> 3 Aves 212 class
#> 4 Passeriformes 729 order
#> 5 Corvidae 5235 family
#> 6 Coloeus 4852454 genus
#> 7 Coloeus monedula 6100954 species
All data, or leave return parameter blank
occ_get(key=766766824, return='all')
#> $hierarchy
#> name key rank
#> 1 Animalia 1 kingdom
#> 2 Chordata 44 phylum
#> 3 Aves 212 class
#> 4 Passeriformes 729 order
#> 5 Corvidae 5235 family
#> 6 Coloeus 4852454 genus
#> 7 Coloeus monedula 6100954 species
#>
#> $media
#> list()
#>
#> $data
#> name key decimalLatitude decimalLongitude
#> 1 Coloeus monedula 766766824 59.4568 17.9054
#> issues
#> 1 depunl,gass84
Get many occurrences. occ_get
is vectorized
occ_get(key=c(766766824, 101010, 240713150, 855998194), return='data')
#> name key decimalLatitude decimalLongitude
#> 1 Coloeus monedula 766766824 59.4568 17.9054
#> 2 Platydoras armatulus 101010 NA NA
#> 3 Pelosina 240713150 -77.5667 163.5830
#> 4 Sciurus vulgaris 855998194 58.4068 12.0438
#> issues
#> 1 depunl,gass84
#> 2
#> 3 bri,cdround,gass84
#> 4 depunl,gass84
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)
#> Records found [8424]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [16]
#> No. facets [0]
#> Args [scientificName=Ursus americanus, limit=20, offset=0, fields=all]
#> # A tibble: 20 × 67
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Ursus americanus 1229610234 44.06062 -71.92692
#> 2 Ursus americanus 1253300445 44.65481 -72.67270
#> 3 Ursus americanus 1229610216 44.06086 -71.92712
#> 4 Ursus americanus 1249277297 35.76789 -75.80894
#> 5 Ursus americanus 1249296297 39.08590 -105.24586
#> 6 Ursus americanus 1253314877 49.25782 -122.82786
#> 7 Ursus americanus 1249284297 43.68723 -72.32891
#> 8 Ursus americanus 1272078411 44.41793 -72.70709
#> 9 Ursus americanus 1262389246 43.80871 -72.20964
#> 10 Ursus americanus 1257415362 44.32746 -72.41007
#> 11 Ursus americanus 1253317181 43.64214 -72.52494
#> 12 Ursus americanus 1270045018 44.35271 -72.53303
#> 13 Ursus americanus 1306574101 44.34088 -72.46131
#> 14 Ursus americanus 1265898376 31.42900 -110.41299
#> 15 Ursus americanus 1269541796 41.02228 -74.79251
#> 16 Ursus americanus 1265898452 43.80871 -72.20964
#> 17 Ursus americanus 1269542935 37.45838 -80.55127
#> 18 Ursus americanus 1265595722 35.61069 -83.83539
#> 19 Ursus americanus 1265598494 44.36404 -72.74876
#> 20 Ursus americanus 1315062645 34.18098 -118.09706
#> # ... with 63 more variables: issues <chr>, datasetKey <chr>,
#> # publishingOrgKey <chr>, publishingCountry <chr>, protocol <chr>,
#> # lastCrawled <chr>, lastParsed <chr>, crawlId <int>, extensions <chr>,
#> # basisOfRecord <chr>, taxonKey <int>, kingdomKey <int>,
#> # phylumKey <int>, classKey <int>, orderKey <int>, familyKey <int>,
#> # genusKey <int>, speciesKey <int>, scientificName <chr>, kingdom <chr>,
#> # phylum <chr>, order <chr>, family <chr>, genus <chr>, species <chr>,
#> # genericName <chr>, specificEpithet <chr>, taxonRank <chr>,
#> # dateIdentified <chr>, year <int>, month <int>, day <int>,
#> # eventDate <chr>, modified <chr>, lastInterpreted <chr>,
#> # references <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, rightsHolder <chr>, identifier <chr>,
#> # verbatimEventDate <chr>, datasetName <chr>, verbatimLocality <chr>,
#> # gbifID <chr>, collectionCode <chr>, occurrenceID <chr>, taxonID <chr>,
#> # recordedBy <chr>, catalogNumber <chr>,
#> # http...unknown.org.occurrenceDetails <chr>, institutionCode <chr>,
#> # rights <chr>, eventTime <chr>, identificationID <chr>,
#> # occurrenceRemarks <chr>, infraspecificEpithet <chr>,
#> # coordinateUncertaintyInMeters <dbl>
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)
#> Records found [21970]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [15]
#> No. facets [0]
#> Args [taxonKey=3119195, limit=20, offset=0, fields=all]
#> # A tibble: 20 × 67
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1315048347 34.04377 -116.94136
#> 3 Helianthus annuus 1305118889 18.40386 -66.04487
#> 4 Helianthus annuus 1249286909 32.58747 -97.10081
#> 5 Helianthus annuus 1253308332 29.67463 -95.44804
#> 6 Helianthus annuus 1262375813 29.82586 -95.45604
#> 7 Helianthus annuus 1262385911 32.78328 -96.70352
#> 8 Helianthus annuus 1265544678 32.58747 -97.10081
#> 9 Helianthus annuus 1262379231 34.04911 -117.80066
#> 10 Helianthus annuus 1265560496 34.12861 -118.20700
#> 11 Helianthus annuus 1269541227 NA NA
#> 12 Helianthus annuus 1265895094 42.87784 -112.43226
#> 13 Helianthus annuus 1272087563 28.51021 -96.81979
#> 14 Helianthus annuus 1265590525 29.86693 -95.64667
#> 15 Helianthus annuus 1270045172 33.92958 -117.37322
#> 16 Helianthus annuus 1265553900 34.12932 -118.20648
#> 17 Helianthus annuus 1269543851 29.50991 -94.50006
#> 18 Helianthus annuus 1305119137 11.86735 -83.93555
#> 19 Helianthus annuus 1265590989 34.19005 -117.31644
#> 20 Helianthus annuus 1315048128 34.03212 -117.47091
#> # ... with 63 more variables: issues <chr>, datasetKey <chr>,
#> # publishingOrgKey <chr>, publishingCountry <chr>, protocol <chr>,
#> # lastCrawled <chr>, lastParsed <chr>, crawlId <int>, extensions <chr>,
#> # basisOfRecord <chr>, taxonKey <int>, kingdomKey <int>,
#> # phylumKey <int>, classKey <int>, orderKey <int>, familyKey <int>,
#> # genusKey <int>, speciesKey <int>, scientificName <chr>, kingdom <chr>,
#> # phylum <chr>, order <chr>, family <chr>, genus <chr>, species <chr>,
#> # genericName <chr>, specificEpithet <chr>, taxonRank <chr>,
#> # dateIdentified <chr>, year <int>, month <int>, day <int>,
#> # eventDate <chr>, modified <chr>, lastInterpreted <chr>,
#> # references <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, rightsHolder <chr>, identifier <chr>,
#> # verbatimEventDate <chr>, datasetName <chr>, verbatimLocality <chr>,
#> # gbifID <chr>, collectionCode <chr>, occurrenceID <chr>, taxonID <chr>,
#> # recordedBy <chr>, catalogNumber <chr>,
#> # http...unknown.org.occurrenceDetails <chr>, institutionCode <chr>,
#> # rights <chr>, eventTime <chr>, identificationID <chr>,
#> # coordinateUncertaintyInMeters <dbl>, occurrenceRemarks <chr>,
#> # informationWithheld <chr>
Like many functions in rgbif
, you can choose what to return with the return
parameter, here, just returning the metadata:
occ_search(taxonKey=key, return='meta')
#> # A tibble: 1 × 4
#> offset limit endOfRecords count
#> * <int> <int> <lgl> <int>
#> 1 300 200 FALSE 21970
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)
#> Records found [8424]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [16]
#> No. facets [0]
#> Args [scientificName=Ursus americanus, limit=20, offset=0,
#> fields=name,basisOfRecord,protocol]
#> # A tibble: 20 × 3
#> name protocol basisOfRecord
#> <chr> <chr> <chr>
#> 1 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 2 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 3 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 4 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 5 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 6 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 7 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 8 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 9 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 10 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 11 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 12 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 13 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 14 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 15 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 16 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 17 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 18 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 19 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
#> 20 Ursus americanus DWC_ARCHIVE HUMAN_OBSERVATION
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)
#> Occ. found [2482598 (577278), 2492010 (3058729), 2498387 (973551)]
#> Occ. returned [2482598 (5), 2492010 (5), 2498387 (5)]
#> No. unique hierarchies [2482598 (1), 2492010 (1), 2498387 (1)]
#> No. media records [2482598 (5), 2492010 (5), 2498387 (1)]
#> No. facets []
#> Args [taxonKey=2482598,2492010,2498387, limit=5, offset=0, fields=all]
#> First 10 rows of data from 2482598
#>
#> # A tibble: 5 × 65
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Cyanocitta stelleri 1229615253 49.18573 -123.9761
#> 2 Cyanocitta stelleri 1249299363 38.46463 -120.0399
#> 3 Cyanocitta stelleri 1249289074 49.30474 -123.1404
#> 4 Cyanocitta stelleri 1249293270 40.58712 -111.6279
#> 5 Cyanocitta stelleri 1227772105 49.18573 -123.9761
#> # ... with 61 more variables: issues <chr>, datasetKey <chr>,
#> # publishingOrgKey <chr>, publishingCountry <chr>, protocol <chr>,
#> # lastCrawled <chr>, lastParsed <chr>, crawlId <int>, extensions <chr>,
#> # basisOfRecord <chr>, taxonKey <int>, kingdomKey <int>,
#> # phylumKey <int>, classKey <int>, orderKey <int>, familyKey <int>,
#> # genusKey <int>, speciesKey <int>, scientificName <chr>, kingdom <chr>,
#> # phylum <chr>, order <chr>, family <chr>, genus <chr>, species <chr>,
#> # genericName <chr>, specificEpithet <chr>, taxonRank <chr>,
#> # dateIdentified <chr>, year <int>, month <int>, day <int>,
#> # eventDate <chr>, modified <chr>, lastInterpreted <chr>,
#> # references <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, rightsHolder <chr>, identifier <chr>,
#> # verbatimEventDate <chr>, datasetName <chr>, verbatimLocality <chr>,
#> # gbifID <chr>, collectionCode <chr>, occurrenceID <chr>, taxonID <chr>,
#> # recordedBy <chr>, catalogNumber <chr>,
#> # http...unknown.org.occurrenceDetails <chr>, institutionCode <chr>,
#> # rights <chr>, eventTime <chr>, identificationID <chr>,
#> # coordinateUncertaintyInMeters <dbl>
Static map using the ggplot2 package. Make a map of Puma concolor occurrences.
key <- name_backbone(name='Puma concolor')$speciesKey
dat <- occ_search(taxonKey=key, return='data', limit=300)
gbifmap(dat)