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] 19756387
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] 4628
All georeferenced records in GBIF
occ_count(georeferenced=TRUE)
#> [1] 991539311
Records from Denmark
denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)
#> [1] 29429637
Number of records in a particular dataset
occ_count(datasetKey='9e7ea106-0bf8-4087-bb61-dfe4f29e0f17')
#> [1] 4591
All records from 2012
occ_count(year=2012)
#> [1] 52829793
Records for a particular dataset, and only for preserved specimens
occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')
#> [1] 0
Get possible values to be used in taxonomic rank arguments in functions
taxrank()
#> [1] "kingdom" "phylum" "class" "order"
#> [5] "family" "genus" "species" "subspecies"
#> [9] "infraspecific"
name_lookup()
does full text search of name usages covering the scientific and vernacular name, the species description, distribution and the entire classification across all name usages of all or some checklists. Results are ordered by relevance as this search usually returns a lot of results.
By default name_lookup()
returns five slots of information: meta, data, facets, hierarchies, and names. hierarchies and names elements are named by their matching GBIF key in the data.frame
in the data slot.
out <- name_lookup(query='mammalia')
names(out)
#> [1] "meta" "data" "facets" "hierarchies" "names"
out$meta
#> # A tibble: 1 x 4
#> offset limit endOfRecords count
#> <int> <int> <lgl> <int>
#> 1 0 100 FALSE 1044
head(out$data)
#> # A tibble: 6 x 25
#> key scientificName datasetKey nubKey parentKey parent kingdom phylum
#> <int> <chr> <chr> <int> <int> <chr> <chr> <chr>
#> 1 1.35e8 Mammalia 7b3f4866-… 359 135080672 Chord… Animal… Chord…
#> 2 1.35e8 Mammalia 1ddab917-… 359 135215962 Chord… Animal… Chord…
#> 3 1.35e8 Mammalia 97b24147-… 359 135216923 Chord… Animal… Chord…
#> 4 1.35e8 Mammalia f3a1e772-… 359 135229685 Chord… Animal… Chord…
#> 5 1.44e8 Mammalia 3c188f76-… 359 143969855 Chord… Animal… Chord…
#> 6 1.35e8 Mammalia 128fd844-… 359 135080507 Chord… Animal… Chord…
#> # … with 17 more variables: kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>, taxonID <chr>
out$facets
#> NULL
out$hierarchies[1:2]
#> $`135080673`
#> rankkey name
#> 1 135080671 Animalia
#> 2 135080672 Chordata
#>
#> $`135215963`
#> rankkey name
#> 1 135215961 Animalia
#> 2 135215962 Chordata
out$names[2]
#> NULL
Search for a genus
head(name_lookup(query='Cnaemidophorus', rank="genus", return="data"))
#> # A tibble: 6 x 35
#> key scientificName datasetKey nubKey parentKey parent phylum order
#> <int> <chr> <chr> <int> <int> <chr> <chr> <chr>
#> 1 1.33e8 Cnaemidophorus 4cec8fef-… 1.86e6 133063901 Ptero… Arthr… Lepi…
#> 2 1.53e8 Cnaemidophorus 7ddf754f-… 1.86e6 153048732 Ptero… Arthr… Lepi…
#> 3 1.53e8 Cnaemidophorus d16563e0-… 1.86e6 152672840 Ptero… Arthr… Lepi…
#> 4 1.24e8 Cnaemidophorus fab88965-… 1.86e6 104446806 Ptero… Arthr… Lepi…
#> 5 1.53e8 Cnaemidophorus 4dd32523-… 1.86e6 152565575 Ptero… Arthr… Lepi…
#> 6 1.52e8 Cnaemidophorus 23905003-… 1.86e6 152121874 Ptero… Arthr… Lepi…
#> # … with 27 more variables: family <chr>, genus <chr>, phylumKey <int>,
#> # classKey <int>, orderKey <int>, familyKey <int>, genusKey <int>,
#> # canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>, kingdom <chr>,
#> # kingdomKey <int>, taxonID <chr>, publishedIn <chr>, extinct <lgl>,
#> # accordingTo <chr>, constituentKey <chr>
Search for the class mammalia
head(name_lookup(query='mammalia', return = 'data'))
#> # A tibble: 6 x 25
#> key scientificName datasetKey nubKey parentKey parent kingdom phylum
#> <int> <chr> <chr> <int> <int> <chr> <chr> <chr>
#> 1 1.35e8 Mammalia 7b3f4866-… 359 135080672 Chord… Animal… Chord…
#> 2 1.35e8 Mammalia 1ddab917-… 359 135215962 Chord… Animal… Chord…
#> 3 1.35e8 Mammalia 97b24147-… 359 135216923 Chord… Animal… Chord…
#> 4 1.35e8 Mammalia f3a1e772-… 359 135229685 Chord… Animal… Chord…
#> 5 1.44e8 Mammalia 3c188f76-… 359 143969855 Chord… Animal… Chord…
#> 6 1.35e8 Mammalia 128fd844-… 359 135080507 Chord… Animal… Chord…
#> # … with 17 more variables: kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, canonicalName <chr>, authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, rank <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, class <chr>, taxonID <chr>
Look up the species Helianthus annuus
head(name_lookup(query = 'Helianthus annuus', rank="species", return = 'data'))
#> # A tibble: 6 x 40
#> key scientificName datasetKey nubKey parentKey parent kingdom phylum
#> <int> <chr> <chr> <int> <int> <chr> <chr> <chr>
#> 1 1.35e8 Helianthus an… 29d2d5a6-… 9.21e6 148402516 Aster… Plantae Trach…
#> 2 1.28e8 Helianthus an… 41c06f1a-… 9.21e6 146770884 Amara… Plantae <NA>
#> 3 1.46e8 Helianthus an… 6a97172b-… 9.21e6 147653302 Helia… <NA> <NA>
#> 4 1.15e8 Helianthus an… ee2aac07-… 9.21e6 144238801 Helia… Plantae Trach…
#> 5 1.35e8 Helianthus an… f82a4f7f-… 9.21e6 152142298 Aster… Plantae Trach…
#> 6 1.35e8 Helianthus an… 278c9199-… 9.21e6 147652468 Aster… Plantae Trach…
#> # … with 32 more variables: order <chr>, family <chr>, species <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> # familyKey <int>, speciesKey <int>, canonicalName <chr>,
#> # nameType <chr>, taxonomicStatus <chr>, rank <chr>, origin <chr>,
#> # numDescendants <int>, numOccurrences <int>, taxonID <chr>,
#> # habitats <chr>, nomenclaturalStatus <chr>, threatStatuses <chr>,
#> # synonym <lgl>, class <chr>, genus <chr>, genusKey <int>,
#> # authorship <chr>, acceptedKey <int>, accepted <chr>,
#> # publishedIn <chr>, accordingTo <chr>, constituentKey <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.
name_usage(key=3119195, language="FRENCH", data='vernacularNames')
#> $meta
#> # A tibble: 1 x 3
#> offset limit endOfRecords
#> <int> <int> <lgl>
#> 1 0 100 TRUE
#>
#> $data
#> # A tibble: 0 x 0
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 6164622 Puma concolor puma SUBSPECIES
#> 3 6164589 Puma concolor anthonyi SUBSPECIES
#> 4 6164618 Puma concolor browni SUBSPECIES
#> 5 8951716 Puma concolor borbensis SUBSPECIES
#> 6 8860878 Puma concolor capricornensis 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')
#> key scientificName decimalLatitude
#> 1 855998194 Sciurus vulgaris Linnaeus, 1758 58.40677
#> decimalLongitude issues
#> 1 12.04386 cdround,gass84
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
#> key scientificName decimalLatitude
#> 1 855998194 Sciurus vulgaris Linnaeus, 1758 58.40677
#> decimalLongitude issues
#> 1 12.04386 cdround,gass84
Get many occurrences. occ_get
is vectorized
occ_get(key=c(855998194, 1425976049, 240713150), return='data')
#> key scientificName decimalLatitude
#> 1 855998194 Sciurus vulgaris Linnaeus, 1758 58.40677
#> 2 1425976049 Cygnus cygnus (Linnaeus, 1758) 58.26546
#> 3 240713150 Pelosina Brady, 1879 -77.56670
#> decimalLongitude issues
#> 1 12.043857 cdround,gass84
#> 2 7.651751 cdround,gass84
#> 3 163.583000 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 [12042]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [20]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus, fields=all]
#> # A tibble: 20 x 74
#> key scientificName decimalLatitude decimalLongitude issues datasetKey
#> <int> <chr> <dbl> <dbl> <chr> <chr>
#> 1 1.99e9 Ursus america… 29.2 -81.8 cdrou… 50c9509d-…
#> 2 1.99e9 Ursus america… 45.3 -76.8 cdrou… 50c9509d-…
#> 3 1.99e9 Ursus america… 47.7 -122. cdrou… 50c9509d-…
#> 4 1.99e9 Ursus america… 32.6 -109. cdrou… 50c9509d-…
#> 5 1.99e9 Ursus america… 33.1 -91.9 cdrou… 50c9509d-…
#> 6 1.99e9 Ursus america… 27.7 -81.5 gass84 50c9509d-…
#> 7 1.99e9 Ursus america… 30.1 -103. cdrou… 50c9509d-…
#> 8 1.99e9 Ursus america… 39.4 -120. cdrou… 50c9509d-…
#> 9 1.99e9 Ursus america… 35.7 -76.6 cdrou… 50c9509d-…
#> 10 1.99e9 Ursus america… 33.1 -91.9 cdrou… 50c9509d-…
#> 11 1.99e9 Ursus america… 45.5 -93.1 cdrou… 50c9509d-…
#> 12 1.99e9 Ursus america… 45.4 -93.1 cdrou… 50c9509d-…
#> 13 1.99e9 Ursus america… 31.9 -94.7 cdrou… 50c9509d-…
#> 14 1.99e9 Ursus america… 45.4 -93.2 cdrou… 50c9509d-…
#> 15 1.99e9 Ursus america… 45.4 -93.2 cdrou… 50c9509d-…
#> 16 1.99e9 Ursus america… 44.9 -62.7 cdrou… 50c9509d-…
#> 17 1.99e9 Ursus america… 40.9 -121. gass84 50c9509d-…
#> 18 1.99e9 Ursus america… 39.0 -120. cdrou… 50c9509d-…
#> 19 1.99e9 Ursus america… 38.9 -120. cdrou… 50c9509d-…
#> 20 1.99e9 Ursus america… 35.6 -82.9 cdrou… 50c9509d-…
#> # … with 68 more variables: publishingOrgKey <chr>, networkKeys <chr>,
#> # installationKey <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>, acceptedTaxonKey <int>,
#> # acceptedScientificName <chr>, kingdom <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, species <chr>,
#> # genericName <chr>, specificEpithet <chr>, taxonRank <chr>,
#> # taxonomicStatus <chr>, dateIdentified <chr>, stateProvince <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>,
#> # gbifID <chr>, verbatimLocality <chr>, collectionCode <chr>,
#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, name <chr>,
#> # coordinateUncertaintyInMeters <dbl>, occurrenceRemarks <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 [43757]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [16]
#> No. facets [0]
#> Args [limit=20, offset=0, taxonKey=9206251, fields=all]
#> # A tibble: 20 x 91
#> key scientificName decimalLatitude decimalLongitude issues datasetKey
#> <int> <chr> <dbl> <dbl> <chr> <chr>
#> 1 1.99e9 Helianthus an… 34.0 -117. cdrou… 50c9509d-…
#> 2 1.99e9 Helianthus an… 33.4 -118. cdrou… 50c9509d-…
#> 3 1.99e9 Helianthus an… 33.8 -118. cdrou… 50c9509d-…
#> 4 1.99e9 Helianthus an… 53.9 10.9 cdrou… 6ac3f774-…
#> 5 1.99e9 Helianthus an… 27.7 -97.3 cdrou… 50c9509d-…
#> 6 1.99e9 Helianthus an… 52.6 10.1 cdrou… 6ac3f774-…
#> 7 1.99e9 Helianthus an… 26.2 -98.2 cdrou… 50c9509d-…
#> 8 2.01e9 Helianthus an… 31.5 -97.1 cdrou… 50c9509d-…
#> 9 1.99e9 Helianthus an… 29.8 -95.2 cdrou… 50c9509d-…
#> 10 2.01e9 Helianthus an… 31.6 -106. cdrou… 50c9509d-…
#> 11 2.01e9 Helianthus an… 27.5 -99.5 cdrou… 50c9509d-…
#> 12 1.95e9 Helianthus an… -37.8 175. gass84 50c9509d-…
#> 13 1.82e9 Helianthus an… 59.8 17.5 gass84 38b4c89f-…
#> 14 1.95e9 Helianthus an… -37.8 175. gass84 50c9509d-…
#> 15 1.82e9 Helianthus an… 56.6 16.4 cdrou… 38b4c89f-…
#> 16 1.84e9 Helianthus an… 34.1 -116. gass84 50c9509d-…
#> 17 1.82e9 Helianthus an… 56.6 16.6 cdrou… 38b4c89f-…
#> 18 1.81e9 Helianthus an… 25.7 -100. cdrou… 50c9509d-…
#> 19 1.81e9 Helianthus an… 25.6 -100. cdrou… 50c9509d-…
#> 20 1.84e9 Helianthus an… 33.9 -117. cdrou… 50c9509d-…
#> # … with 85 more variables: publishingOrgKey <chr>, networkKeys <chr>,
#> # installationKey <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>, acceptedTaxonKey <int>,
#> # acceptedScientificName <chr>, kingdom <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, species <chr>,
#> # genericName <chr>, specificEpithet <chr>, taxonRank <chr>,
#> # taxonomicStatus <chr>, dateIdentified <chr>, stateProvince <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>,
#> # gbifID <chr>, verbatimLocality <chr>, collectionCode <chr>,
#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, name <chr>,
#> # coordinateUncertaintyInMeters <dbl>, occurrenceRemarks <chr>,
#> # locality <chr>, individualCount <int>, continent <chr>, county <chr>,
#> # municipality <chr>, identificationVerificationStatus <chr>,
#> # language <chr>, type <chr>, occurrenceStatus <chr>,
#> # vernacularName <chr>, taxonConceptID <chr>, informationWithheld <chr>,
#> # endDayOfYear <chr>, startDayOfYear <chr>, datasetID <chr>,
#> # accessRights <chr>, higherClassification <chr>,
#> # identificationRemarks <chr>, habitat <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 43757
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 [12042]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [20]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus,
#> fields=name,basisOfRecord,protocol]
#> # A tibble: 20 x 2
#> protocol basisOfRecord
#> <chr> <chr>
#> 1 DWC_ARCHIVE HUMAN_OBSERVATION
#> 2 DWC_ARCHIVE HUMAN_OBSERVATION
#> 3 DWC_ARCHIVE HUMAN_OBSERVATION
#> 4 DWC_ARCHIVE HUMAN_OBSERVATION
#> 5 DWC_ARCHIVE HUMAN_OBSERVATION
#> 6 DWC_ARCHIVE HUMAN_OBSERVATION
#> 7 DWC_ARCHIVE HUMAN_OBSERVATION
#> 8 DWC_ARCHIVE HUMAN_OBSERVATION
#> 9 DWC_ARCHIVE HUMAN_OBSERVATION
#> 10 DWC_ARCHIVE HUMAN_OBSERVATION
#> 11 DWC_ARCHIVE HUMAN_OBSERVATION
#> 12 DWC_ARCHIVE HUMAN_OBSERVATION
#> 13 DWC_ARCHIVE HUMAN_OBSERVATION
#> 14 DWC_ARCHIVE HUMAN_OBSERVATION
#> 15 DWC_ARCHIVE HUMAN_OBSERVATION
#> 16 DWC_ARCHIVE HUMAN_OBSERVATION
#> 17 DWC_ARCHIVE HUMAN_OBSERVATION
#> 18 DWC_ARCHIVE HUMAN_OBSERVATION
#> 19 DWC_ARCHIVE HUMAN_OBSERVATION
#> 20 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 (709564), 9362842 (3822355), 2498387 (1248740)]
#> Occ. returned [2482598 (5), 9362842 (5), 2498387 (5)]
#> No. unique hierarchies [2482598 (1), 9362842 (1), 2498387 (1)]
#> No. media records [2482598 (5), 9362842 (5), 2498387 (5)]
#> No. facets [2482598 (0), 9362842 (0), 2498387 (0)]
#> Args [limit=5, offset=0, taxonKey=2482598,9362842,2498387, fields=all]
#> 3 requests; First 10 rows of data from 2482598
#>
#> # A tibble: 5 x 71
#> key scientificName decimalLatitude decimalLongitude issues datasetKey
#> <int> <chr> <dbl> <dbl> <chr> <chr>
#> 1 1.99e9 Cyanocitta st… 16.7 -92.7 cdrou… 50c9509d-…
#> 2 1.99e9 Cyanocitta st… 32.9 -106. cdrou… 50c9509d-…
#> 3 1.99e9 Cyanocitta st… 32.9 -106. cdrou… 50c9509d-…
#> 4 1.99e9 Cyanocitta st… 32.9 -106. cdrou… 50c9509d-…
#> 5 1.99e9 Cyanocitta st… 32.9 -106. cdrou… 50c9509d-…
#> # … with 65 more variables: publishingOrgKey <chr>, networkKeys <chr>,
#> # installationKey <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>, acceptedTaxonKey <int>,
#> # acceptedScientificName <chr>, kingdom <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, species <chr>,
#> # genericName <chr>, specificEpithet <chr>, taxonRank <chr>,
#> # taxonomicStatus <chr>, dateIdentified <chr>,
#> # coordinateUncertaintyInMeters <dbl>, stateProvince <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>,
#> # gbifID <chr>, verbatimLocality <chr>, collectionCode <chr>,
#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, name <chr>
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)