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] 30282795
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] 3862
All georeferenced records in GBIF
occ_count(georeferenced=TRUE)
#> [1] 790874302
Records from Denmark
denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)
#> [1] 26590598
Number of records in a particular dataset
occ_count(datasetKey='9e7ea106-0bf8-4087-bb61-dfe4f29e0f17')
#> [1] 4591
All records from 2012
occ_count(year=2012)
#> [1] 46002702
Records for a particular dataset, and only for preserved specimens
occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')
#> [1] 0
Get possible values to be used in taxonomic rank arguments in functions
taxrank()
#> [1] "kingdom" "phylum" "class" "order"
#> [5] "family" "genus" "species" "subspecies"
#> [9] "infraspecific"
name_lookup() does full text search of name usages covering the scientific and vernacular name, the species description, distribution and the entire classification across all name usages of all or some checklists. Results are ordered by relevance as this search usually returns a lot of results.
By default name_lookup() returns five slots of information: meta, data, facets, hierarchies, and names. hierarchies and names elements are named by their matching GBIF key in the data.frame in the data slot.
out <- name_lookup(query='mammalia')
names(out)
#> [1] "meta" "data" "facets" "hierarchies" "names"
out$meta
#> # A tibble: 1 x 4
#> offset limit endOfRecords count
#> <int> <int> <lgl> <int>
#> 1 0 100 FALSE 1332
head(out$data)
#> # A tibble: 6 x 26
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 114091654 Mammalia 30f55c63-a829-4cb2-9676-3b1b6f981567 359
#> 2 127804986 Mammalia ec532910-dec5-4ca9-9814-f6d1c02ced77 359
#> 3 117910746 Mammalia a43ec6d8-7b8a-4868-ad74-56b824c75698 359
#> 4 134843305 Mammalia 29d2d5a6-db22-4abd-b784-9ab2f9757c3c 359
#> 5 134844570 Mammalia 089ede6e-6496-4638-915e-f28f016c2f89 359
#> 6 134843651 Mammalia 0c0ac27f-c7cb-4a1a-b5d7-6d9a386b2c53 359
#> # ... with 22 more variables: canonicalName <chr>, authorship <chr>,
#> # nameType <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <chr>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, parentKey <int>, parent <chr>,
#> # kingdom <chr>, phylum <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, taxonomicStatus <chr>, rank <chr>, class <chr>,
#> # taxonID <chr>, extinct <lgl>
out$facets
#> NULL
out$hierarchies[1:2]
#> $`127804986`
#> rankkey name
#> 1 127804984 Animalia
#> 2 127804985 Chordata
#>
#> $`117910746`
#> rankkey name
#> 1 117898234 Animalia
#> 2 117910745 Chordata
out$names[2]
#> $`100371916`
#> vernacularName language
#> 1 Säugetiere deu
Search for a genus
head(name_lookup(query='Cnaemidophorus', rank="genus", return="data"))
#> # A tibble: 6 x 34
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 135332978 Cnaemidophorus cbb6498e-8927-405a-916b-576d00a6289b 1858636
#> 2 133063907 Cnaemidophorus 4cec8fef-f129-4966-89b7-4f8439aba058 1858636
#> 3 135538772 Cnaemidophorus 4dd32523-a3a3-43b7-84df-4cda02f15cf7 1858636
#> 4 135795882 Cnaemidophorus 7ddf754f-d193-4cc9-b351-99906754a03b 1858636
#> 5 134031474 Cnaemidophorus 23905003-5ee5-4326-b1bb-3a2fd6250df3 1858636
#> 6 137249919 Cnaemidophorus d16563e0-e718-45a9-a20f-3e9fc20613da 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>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>, nameType <chr>,
#> # taxonID <chr>, authorship <chr>, publishedIn <chr>, extinct <lgl>,
#> # constituentKey <chr>
Search for the class mammalia
head(name_lookup(query='mammalia', return = 'data'))
#> # A tibble: 6 x 26
#> key scientificName datasetKey nubKey
#> <int> <chr> <chr> <int>
#> 1 114091654 Mammalia 30f55c63-a829-4cb2-9676-3b1b6f981567 359
#> 2 127804986 Mammalia ec532910-dec5-4ca9-9814-f6d1c02ced77 359
#> 3 117910746 Mammalia a43ec6d8-7b8a-4868-ad74-56b824c75698 359
#> 4 134843305 Mammalia 29d2d5a6-db22-4abd-b784-9ab2f9757c3c 359
#> 5 134844570 Mammalia 089ede6e-6496-4638-915e-f28f016c2f89 359
#> 6 134843651 Mammalia 0c0ac27f-c7cb-4a1a-b5d7-6d9a386b2c53 359
#> # ... with 22 more variables: canonicalName <chr>, authorship <chr>,
#> # nameType <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <chr>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, parentKey <int>, parent <chr>,
#> # kingdom <chr>, phylum <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, taxonomicStatus <chr>, rank <chr>, class <chr>,
#> # taxonID <chr>, extinct <lgl>
Look up the species Helianthus annuus
head(name_lookup(query = 'Helianthus annuus', rank="species", return = 'data'))
#> # A tibble: 6 x 42
#> key scientificName datasetKey
#> <int> <chr> <chr>
#> 1 3119195 Helianthus annuus d7dddbf4-2cf0-4f39-9b2a-bb099caae36c
#> 2 134854463 Helianthus annuus f82a4f7f-6f84-4b58-82e6-6b41ec9a1f49
#> 3 114910965 Helianthus annuus ee2aac07-de9a-47a2-b828-37430d537633
#> 4 134843454 Helianthus annuus 29d2d5a6-db22-4abd-b784-9ab2f9757c3c
#> 5 127670355 Helianthus annuus 41c06f1a-23da-4445-b859-ec3a8a03b0e2
#> 6 134856103 Helianthus annuus 278c9199-be97-4fd0-9c6c-6c46c4e2369e
#> # ... with 39 more variables: constituentKey <chr>, nubKey <int>,
#> # parentKey <int>, parent <chr>, kingdom <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, species <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> # familyKey <int>, genusKey <int>, speciesKey <int>,
#> # canonicalName <chr>, authorship <chr>, publishedIn <chr>,
#> # nameType <chr>, taxonomicStatus <chr>, rank <chr>, origin <chr>,
#> # numDescendants <int>, numOccurrences <int>, extinct <lgl>,
#> # habitats <chr>, nomenclaturalStatus <chr>, threatStatuses <lgl>,
#> # synonym <lgl>, class <chr>, taxonID <chr>, acceptedKey <int>,
#> # accepted <chr>, accordingTo <chr>, nameKey <int>, 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 x 6
#> taxonKey vernacularName language
#> <int> <chr> <chr>
#> 1 3119195 common sunflower eng
#> 2 3119195 tournesol fra
#> 3 3119195 garden sunflower eng
#> 4 3119195 grand soleil fra
#> 5 3119195 hélianthe annuel fra
#> 6 3119195 soleil fra
#> # ... 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 x 3
#> key canonicalName rank
#> <int> <chr> <chr>
#> 1 2435099 Puma concolor SPECIES
#> 2 8916934 Puma concolor bangsi SUBSPECIES
#> 3 6164599 Puma concolor azteca SUBSPECIES
#> 4 6164589 Puma concolor anthonyi SUBSPECIES
#> 5 8951716 Puma concolor borbensis SUBSPECIES
#> 6 6164618 Puma concolor browni 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=855998194, return='data')
#> name key decimalLatitude decimalLongitude
#> 1 Sciurus vulgaris 855998194 58.40677 12.04386
#> issues
#> 1 cdround,gass84,rdatm
Just taxonomic hierarchy
occ_get(key=855998194, return='hier')
#> name key rank
#> 1 Animalia 1 kingdom
#> 2 Chordata 44 phylum
#> 3 Mammalia 359 class
#> 4 Rodentia 1459 order
#> 5 Sciuridae 9456 family
#> 6 Sciurus 2437489 genus
#> 7 Sciurus vulgaris 8211070 species
All data, or leave return parameter blank
occ_get(key=855998194, return='all')
#> $hierarchy
#> name key rank
#> 1 Animalia 1 kingdom
#> 2 Chordata 44 phylum
#> 3 Mammalia 359 class
#> 4 Rodentia 1459 order
#> 5 Sciuridae 9456 family
#> 6 Sciurus 2437489 genus
#> 7 Sciurus vulgaris 8211070 species
#>
#> $media
#> list()
#>
#> $data
#> name key decimalLatitude decimalLongitude
#> 1 Sciurus vulgaris 855998194 58.40677 12.04386
#> issues
#> 1 cdround,gass84,rdatm
Get many occurrences. occ_get is vectorized
occ_get(key=c(855998194, 1425976049, 240713150), return='data')
#> name key decimalLatitude decimalLongitude
#> 1 Sciurus vulgaris 855998194 58.40677 12.043857
#> 2 Cygnus cygnus 1425976049 58.26546 7.651751
#> 3 Pelosina 240713150 -77.56670 163.582993
#> issues
#> 1 cdround,gass84,rdatm
#> 2 cdround,gass84
#> 3 bri,cdround,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 [9770]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [17]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus, fields=all]
#> # A tibble: 20 x 68
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Ursus americanus 1671722060 33.47152 -91.64752
#> 2 Ursus americanus 1453325042 37.36325 -80.52914
#> 3 Ursus americanus 1453341157 35.44519 -83.75077
#> 4 Ursus americanus 1453341156 35.43836 -83.66423
#> 5 Ursus americanus 1453427952 35.61469 -82.47723
#> 6 Ursus americanus 1453456359 25.31110 -100.96992
#> 7 Ursus americanus 1453476835 29.20453 -103.23501
#> 8 Ursus americanus 1453414927 47.90953 -91.95893
#> 9 Ursus americanus 1453456338 25.30959 -100.96966
#> 10 Ursus americanus 1453445710 35.59506 -82.55149
#> 11 Ursus americanus 1457591001 38.93946 -119.94679
#> 12 Ursus americanus 1500285318 45.51560 -69.35556
#> 13 Ursus americanus 1455592330 46.34195 -83.98219
#> 14 Ursus americanus 1571069736 25.25062 -100.94652
#> 15 Ursus americanus 1500319247 32.89649 -109.48065
#> 16 Ursus americanus 1453476285 35.94219 -76.57357
#> 17 Ursus americanus 1457595069 46.59933 -84.46328
#> 18 Ursus americanus 1500222058 25.34884 -100.91091
#> 19 Ursus americanus 1500223333 45.38687 -76.10825
#> 20 Ursus americanus 1453520782 29.29163 -103.37046
#> # ... 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>,
#> # verbatimLocality <chr>, collectionCode <chr>, gbifID <chr>,
#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, identificationID <chr>,
#> # eventTime <chr>, occurrenceRemarks <chr>, informationWithheld <chr>,
#> # infraspecificEpithet <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 [18377]
#> 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 x 89
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1433793045 59.66859 16.54257
#> 2 Helianthus annuus 1434024463 63.71622 20.31247
#> 3 Helianthus annuus 1563876655 NA NA
#> 4 Helianthus annuus 1436147509 59.85465 17.79089
#> 5 Helianthus annuus 1436223234 59.85509 17.78900
#> 6 Helianthus annuus 1450388036 56.60630 16.64841
#> 7 Helianthus annuus 1499896133 58.76637 16.24997
#> 8 Helianthus annuus 1499929475 59.85530 17.79055
#> 9 Helianthus annuus 1669229145 59.85530 17.79055
#> 10 Helianthus annuus 1669043510 59.74332 17.78161
#> 11 Helianthus annuus 1669900943 57.73119 16.13173
#> 12 Helianthus annuus 1669884935 56.64750 12.85256
#> 13 Helianthus annuus 1669797382 55.72320 13.18386
#> 14 Helianthus annuus 1669882522 58.53514 16.50554
#> 15 Helianthus annuus 1670276641 57.30363 11.90345
#> 16 Helianthus annuus 1675823397 38.38880 -105.00800
#> 17 Helianthus annuus 1669981909 57.72138 11.94415
#> 18 Helianthus annuus 1670011022 59.85046 17.68560
#> 19 Helianthus annuus 1670291603 59.15281 18.17448
#> 20 Helianthus annuus 1670396073 59.70498 16.51416
#> # ... with 85 more variables: issues <chr>, datasetKey <chr>,
#> # publishingOrgKey <chr>, publishingCountry <chr>, protocol <chr>,
#> # lastCrawled <chr>, lastParsed <chr>, crawlId <int>, extensions <chr>,
#> # basisOfRecord <chr>, individualCount <int>, 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>, rightsHolder <chr>, county <chr>, municipality <chr>,
#> # identificationVerificationStatus <chr>, language <chr>, gbifID <chr>,
#> # type <chr>, taxonID <chr>, catalogNumber <chr>,
#> # occurrenceStatus <chr>, vernacularName <chr>, institutionCode <chr>,
#> # taxonConceptID <chr>, eventTime <chr>, identifier <chr>,
#> # informationWithheld <chr>, endDayOfYear <chr>, locality <chr>,
#> # collectionCode <chr>, occurrenceID <chr>, recordedBy <chr>,
#> # startDayOfYear <chr>, datasetID <chr>, accessRights <chr>,
#> # higherClassification <chr>, dateIdentified <chr>, elevation <dbl>,
#> # stateProvince <chr>, references <chr>, recordNumber <chr>,
#> # habitat <chr>, verbatimEventDate <chr>, associatedTaxa <chr>,
#> # verbatimLocality <chr>, verbatimElevation <chr>, identifiedBy <chr>,
#> # identificationID <chr>, occurrenceRemarks <chr>, institutionID <chr>,
#> # higherGeography <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 x 4
#> offset limit endOfRecords count
#> * <int> <int> <lgl> <int>
#> 1 300 200 FALSE 18377
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 [9770]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [17]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus,
#> fields=name,basisOfRecord,protocol]
#> # A tibble: 20 x 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 (578560), 2492010 (3065897), 2498387 (979138)]
#> 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 (5)]
#> 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 x 67
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Cyanocitta stelleri 1453342305 46.14702 -123.6545
#> 2 Cyanocitta stelleri 1453373156 23.67272 -105.4548
#> 3 Cyanocitta stelleri 1453355963 37.76899 -122.4758
#> 4 Cyanocitta stelleri 1453325920 38.48213 -122.8734
#> 5 Cyanocitta stelleri 1453338402 45.50661 -122.7135
#> # ... 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>, 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>,
#> # verbatimLocality <chr>, collectionCode <chr>, gbifID <chr>,
#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, occurrenceRemarks <chr>,
#> # informationWithheld <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)