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] 48001480
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] 3465
All georeferenced records in GBIF
occ_count(georeferenced=TRUE)
#> [1] 652903162
Records from Denmark
denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)
#> [1] 11312029
Number of records in a particular dataset
occ_count(datasetKey='9e7ea106-0bf8-4087-bb61-dfe4f29e0f17')
#> [1] 4591
All records from 2012
occ_count(year=2012)
#> [1] 40925784
Records for a particular dataset, and only for preserved specimens
occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')
#> [1] 2563453
Get possible values to be used in taxonomic rank arguments in functions
taxrank()
#> [1] "kingdom" "phylum" "class" "order"
#> [5] "family" "genus" "species" "infraspecific"
name_lookup() does full text search of name usages covering the scientific and vernacular name, the species description, distribution and the entire classification across all name usages of all or some checklists. Results are ordered by relevance as this search usually returns a lot of results.
By default name_lookup() returns five slots of information: meta, data, facets, hierarchies, and names. hierarchies and names elements are named by their matching GBIF key in the data.frame in the data slot.
out <- name_lookup(query='mammalia')
names(out)
#> [1] "meta" "data" "facets" "hierarchies" "names"
out$meta
#> # A tibble: 1 × 4
#> offset limit endOfRecords count
#> <int> <int> <lgl> <int>
#> 1 0 100 FALSE 1795
head(out$data)
#> # A tibble: 6 × 26
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 114079693 Mammalia bd0a2b6d-69d1-4650-8bb1-829c8f92035f 359
#> 2 123227024 Mammalia 90d9e8a6-0ce1-472d-b682-3451095dbc5a 359
#> 3 127687936 Mammalia 11f5ca15-8de9-4499-9fd2-368b62ea45cb 359
#> 4 127804685 Mammalia d573804a-0521-41c4-9a8d-ffa2b807a1c6 359
#> 5 127805888 Mammalia 6fa16a5a-560e-432f-ad9e-f93e8bd3e90d 359
#> 6 127820433 Mammalia 3385d731-b373-4d9b-a167-633902b43069 359
#> # ... with 22 more variables: parentKey <int>, parent <chr>,
#> # canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # numDescendants <int>, numOccurrences <int>, habitats <lgl>,
#> # nomenclaturalStatus <lgl>, threatStatuses <lgl>, synonym <lgl>,
#> # kingdom <chr>, phylum <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, taxonomicStatus <chr>, rank <chr>, class <chr>,
#> # extinct <lgl>, order <chr>, orderKey <int>
out$facets
#> NULL
out$hierarchies[1:2]
#> $`114079693`
#> rankkey name
#> 1 114079410 Animalia
#>
#> $`123227024`
#> rankkey name
#> 1 123221530 Gnathostomata
out$names[2]
#> $`100375341`
#> vernacularName language
#> 1 Säugetiere deu
#> 2 Triconodont cat
#> 3 Triconodonta ces
#> 4 Triconodonta nld
#> 5 Triconodonta por
#> 6 Trykonodonty pol
#> 7 Триконодонты rus
Search for a genus
head(name_lookup(query='Cnaemidophorus', rank="genus", return="data"))
#> # A tibble: 6 × 33
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 128171003 Cnaemidophorus 4cec8fef-f129-4966-89b7-4f8439aba058 1858636
#> 2 125933331 Cnaemidophorus de8934f4-a136-481c-a87a-b0b202b80a31 1858636
#> 3 128091787 Cnaemidophorus 4dd32523-a3a3-43b7-84df-4cda02f15cf7 1858636
#> 4 123576977 Cnaemidophorus fab88965-e69d-4491-a04d-e3198b626e52 1858636
#> 5 123625903 Cnaemidophorus 7ddf754f-d193-4cc9-b351-99906754a03b 1858636
#> 6 115196907 Cnaemidophorus 16c3f9cb-4b19-4553-ac8e-ebb90003aa02 1858636
#> # ... with 29 more variables: parentKey <int>, parent <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, phylumKey <int>,
#> # classKey <int>, orderKey <int>, familyKey <int>, genusKey <int>,
#> # canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>, kingdom <chr>,
#> # kingdomKey <int>, publishedIn <chr>, accordingTo <chr>, extinct <lgl>,
#> # constituentKey <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 114079693 Mammalia bd0a2b6d-69d1-4650-8bb1-829c8f92035f 359
#> 2 123227024 Mammalia 90d9e8a6-0ce1-472d-b682-3451095dbc5a 359
#> 3 127687936 Mammalia 11f5ca15-8de9-4499-9fd2-368b62ea45cb 359
#> 4 127804685 Mammalia d573804a-0521-41c4-9a8d-ffa2b807a1c6 359
#> 5 127805888 Mammalia 6fa16a5a-560e-432f-ad9e-f93e8bd3e90d 359
#> 6 127820433 Mammalia 3385d731-b373-4d9b-a167-633902b43069 359
#> # ... with 22 more variables: parentKey <int>, parent <chr>,
#> # canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # numDescendants <int>, numOccurrences <int>, habitats <lgl>,
#> # nomenclaturalStatus <lgl>, threatStatuses <lgl>, synonym <lgl>,
#> # kingdom <chr>, phylum <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, taxonomicStatus <chr>, rank <chr>, class <chr>,
#> # extinct <lgl>, order <chr>, orderKey <int>
Look up the species Helianthus annuus
head(name_lookup(query = 'Helianthus annuus', rank="species", return = 'data'))
#> # A tibble: 6 × 39
#> key scientificName datasetKey
#> <int> <chr> <chr>
#> 1 103340289 Helianthus annuus fab88965-e69d-4491-a04d-e3198b626e52
#> 2 127670355 Helianthus annuus 41c06f1a-23da-4445-b859-ec3a8a03b0e2
#> 3 3119195 Helianthus annuus d7dddbf4-2cf0-4f39-9b2a-bb099caae36c
#> 4 114910965 Helianthus annuus ee2aac07-de9a-47a2-b828-37430d537633
#> 5 127670357 Helianthus annuus 41c06f1a-23da-4445-b859-ec3a8a03b0e2
#> 6 101321447 Helianthus annuus L. 66dd0960-2d7d-46ee-a491-87b9adcfe7b1
#> # ... with 36 more variables: 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>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <chr>, nomenclaturalStatus <chr>,
#> # threatStatuses <lgl>, synonym <lgl>, nubKey <int>,
#> # constituentKey <chr>, classKey <int>, publishedIn <chr>,
#> # extinct <lgl>, class <chr>, acceptedKey <int>, accepted <chr>,
#> # accordingTo <chr>, basionymKey <int>, basionym <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 × 5
#> vernacularName language source
#> <chr> <chr> <chr>
#> 1 common sunflower eng Database of Vascular Plants of Canada (VASCAN)
#> 2 tournesol fra Database of Vascular Plants of Canada (VASCAN)
#> 3 garden sunflower eng Database of Vascular Plants of Canada (VASCAN)
#> 4 grand soleil fra Database of Vascular Plants of Canada (VASCAN)
#> 5 hélianthe annuel fra Database of Vascular Plants of Canada (VASCAN)
#> 6 soleil fra Database of Vascular Plants of Canada (VASCAN)
#> # ... with 2 more variables: 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 8836300 Puma concolor discolor SUBSPECIES
#> 3 7193927 Puma concolor concolor SUBSPECIES
#> 4 6164624 Puma concolor costaricensis SUBSPECIES
#> 5 6164590 Puma concolor couguar SUBSPECIES
#> 6 6164623 Puma concolor cabrerae 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 [8707]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [5]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus, fields=all]
#> # A tibble: 20 × 68
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Ursus americanus 1453325042 37.36325 -80.52914
#> 2 Ursus americanus 1453341157 35.44519 -83.75077
#> 3 Ursus americanus 1453341156 35.43836 -83.66423
#> 4 Ursus americanus 1453427952 35.61469 -82.47723
#> 5 Ursus americanus 1453414927 47.90953 -91.95893
#> 6 Ursus americanus 1453456338 25.30959 -100.96966
#> 7 Ursus americanus 1453445710 35.59506 -82.55149
#> 8 Ursus americanus 1453476835 29.24034 -103.30502
#> 9 Ursus americanus 1453456359 25.31110 -100.96992
#> 10 Ursus americanus 1453520782 29.28037 -103.30340
#> 11 Ursus americanus 1455592330 46.34195 -83.98219
#> 12 Ursus americanus 1453471882 43.85200 -72.41200
#> 13 Ursus americanus 1453476285 35.94219 -76.57357
#> 14 Ursus americanus 1453181964 41.79962 -124.14862
#> 15 Ursus americanus 1229610234 44.06062 -71.92692
#> 16 Ursus americanus 1229610216 44.06086 -71.92712
#> 17 Ursus americanus 1253300445 44.65481 -72.67270
#> 18 Ursus americanus 1249277297 35.76789 -75.80894
#> 19 Ursus americanus 1453074812 NA NA
#> 20 Ursus americanus 1453181995 41.76532 -124.10842
#> # ... with 64 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>, coordinateUncertaintyInMeters <dbl>, 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>,
#> # collectionCode <chr>, gbifID <chr>, verbatimLocality <chr>,
#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # occurrenceRemarks <chr>, identificationID <chr>,
#> # infraspecificEpithet <chr>, informationWithheld <chr>
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 [14820]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [1]
#> No. facets [0]
#> Args [limit=20, offset=0, taxonKey=9206251, fields=all]
#> # A tibble: 20 × 77
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1437798345 51.03513 4.518690
#> 2 Helianthus annuus 1437786250 50.91574 3.579770
#> 3 Helianthus annuus 1454554504 50.22000 9.630000
#> 4 Helianthus annuus 1273001624 59.32739 10.803912
#> 5 Helianthus annuus 1454554470 49.32000 12.000000
#> 6 Helianthus annuus 1272997264 58.66189 6.721671
#> 7 Helianthus annuus 1454553080 49.75000 9.300000
#> 8 Helianthus annuus 1272995969 59.83241 10.763219
#> 9 Helianthus annuus 1454553865 NA NA
#> 10 Helianthus annuus 1454555306 48.21000 12.080000
#> 11 Helianthus annuus 1323229476 59.08521 11.036315
#> 12 Helianthus annuus 1454553832 48.42000 10.620000
#> 13 Helianthus annuus 1323241585 59.57240 10.847597
#> 14 Helianthus annuus 1273024475 59.08521 11.036315
#> 15 Helianthus annuus 1454554421 49.31000 12.360000
#> 16 Helianthus annuus 1454554404 49.35000 12.440000
#> 17 Helianthus annuus 1323261789 58.66189 6.721671
#> 18 Helianthus annuus 1305561325 48.57490 7.759700
#> 19 Helianthus annuus 1454554608 49.21000 12.350000
#> 20 Helianthus annuus 1454554593 49.34000 12.530000
#> # ... with 73 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>,
#> # coordinateUncertaintyInMeters <dbl>, continent <chr>, year <int>,
#> # month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, identifier <chr>, verbatimEventDate <chr>,
#> # nomenclaturalCode <chr>, dataGeneralizations <chr>,
#> # verbatimCoordinateSystem <chr>, datasetName <chr>, language <chr>,
#> # gbifID <chr>, occurrenceID <chr>, type <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, institutionCode <chr>, ownerInstitutionCode <chr>,
#> # datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>,
#> # locality <chr>, collectionCode <chr>, individualCount <int>,
#> # elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> # municipality <chr>, county <chr>, coordinatePrecision <dbl>,
#> # habitat <chr>, dateIdentified <chr>, identifiedBy <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 14820
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 [8707]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [5]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus,
#> 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 (577582), 2492010 (3060247), 2498387 (974005)]
#> Occ. returned [2482598 (5), 2492010 (5), 2498387 (5)]
#> No. unique hierarchies [2482598 (1), 2492010 (1), 2498387 (1)]
#> No. media records [2482598 (1), 2492010 (1), 2498387 (1)]
#> No. facets [2482598 (0), 2492010 (0), 2498387 (0)]
#> Args [limit=5, offset=0, taxonKey=2482598,2492010,2498387, fields=all]
#> 3 requests; First 10 rows of data from 2482598
#>
#> # A tibble: 5 × 66
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Cyanocitta stelleri 1453335137 45.53538 -122.7850
#> 2 Cyanocitta stelleri 1453386383 48.16790 -122.0787
#> 3 Cyanocitta stelleri 1453369911 38.61128 -122.7838
#> 4 Cyanocitta stelleri 1453339220 47.65927 -122.0961
#> 5 Cyanocitta stelleri 1453338402 45.50661 -122.7135
#> # ... with 62 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>, coordinateUncertaintyInMeters <dbl>, 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>, informationWithheld <chr>, verbatimEventDate <chr>,
#> # datasetName <chr>, collectionCode <chr>, gbifID <chr>,
#> # verbatimLocality <chr>, occurrenceID <chr>, taxonID <chr>,
#> # catalogNumber <chr>, recordedBy <chr>,
#> # http...unknown.org.occurrenceDetails <chr>, institutionCode <chr>,
#> # rights <chr>, eventTime <chr>, identificationID <chr>
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)