Seach and retrieve data from the Global Biodiverity Information Facilty (GBIF)
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
Number of occurrences
Search by type of record, all observational in this case
occ_count(basisOfRecord='OBSERVATION')
#> [1] 19898700Records 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] 9852All georeferenced records in GBIF
occ_count(georeferenced=TRUE)
#> [1] 1800318338Records from Denmark
denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)
#> [1] 46363801Number of records in a particular dataset
occ_count(datasetKey='9e7ea106-0bf8-4087-bb61-dfe4f29e0f17')
#> [1] 4591All records from 2012
occ_count(year=2012)
#> [1] 64919530Records for a particular dataset, and only for preserved specimens
occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')
#> [1] 0Search for taxon names
Get possible values to be used in taxonomic rank arguments in functions
taxrank()
#> [1] "kingdom" "phylum" "class" "order"
#> [5] "family" "genus" "species" "subspecies"
#> [9] "infraspecific"name_lookup() does full text search of name usages covering the scientific and vernacular name, the species description, distribution and the entire classification across all name usages of all or some checklists. Results are ordered by relevance as this search usually returns a lot of results.
By default name_lookup() returns five slots of information: meta, data, facets, hierarchies, and names. hierarchies and names elements are named by their matching GBIF key in the data.frame in the data slot.
out <- name_lookup(query='mammalia')names(out)
#> [1] "meta" "data" "facets" "hierarchies" "names"out$meta
#> # A tibble: 1 x 4
#> offset limit endOfRecords count
#> <int> <int> <lgl> <int>
#> 1 0 100 FALSE 2956head(out$data)
#> # A tibble: 6 x 24
#> key scientificName datasetKey nubKey parentKey parent canonicalName
#> <int> <chr> <chr> <int> <int> <chr> <chr>
#> 1 114079693 Mammalia bd0a2b6d-69d1-~ 359 114079410 Anima~ Mammalia
#> 2 168233342 Mammalia 2e76af52-48a9-~ 359 168233332 Chord~ Mammalia
#> 3 168239467 Mammalia ed581dc4-7008-~ 359 168239466 Chord~ Mammalia
#> 4 190557477 Mammalia 777f49d9-27c7-~ 359 190557476 Chord~ Mammalia
#> 5 190653816 Mammalia 69c434f8-762e-~ 359 190653815 Chord~ Mammalia
#> 6 190793428 Mammalia b3293c54-11ba-~ 359 190793427 Chord~ Mammalia
#> # ... with 17 more variables: authorship <chr>, nameType <chr>,
#> # taxonomicStatus <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, kingdom <chr>, phylum <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, rank <chr>, class <chr>out$facets
#> NULLout$hierarchies[1:2]
#> $`114079693`
#> rankkey name
#> 1 114079410 Animalia
#>
#> $`168233342`
#> rankkey name
#> 1 168233318 Animalia
#> 2 168233332 Chordataout$names[2]
#> NULLSearch for a genus
z <- name_lookup(query='Cnaemidophorus', rank="genus")
z$data
#> # A tibble: 31 x 37
#> key scientificName datasetKey nubKey parentKey parent kingdom phylum order
#> <int> <chr> <chr> <int> <int> <chr> <chr> <chr> <chr>
#> 1 1.58e8 Cnaemidophorus 4cec8fef-~ 1.86e6 157904443 Ptero~ Animal~ Arthr~ Lepi~
#> 2 1.69e8 Cnaemidophorus 4b3e4a71-~ 1.86e6 168525701 Ptero~ Animal~ Arthr~ Lepi~
#> 3 1.91e8 Cnaemidophorus 23905003-~ 1.86e6 191419707 Ptero~ Animal~ Arthr~ Lepi~
#> 4 1.91e8 Cnaemidophorus 23905003-~ 1.86e6 190527758 Ptero~ Animal~ Arthr~ Lepi~
#> 5 1.90e8 Cnaemidophorus 16c3f9cb-~ 1.86e6 190348092 Ptero~ <NA> Arthr~ Lepi~
#> 6 1.91e8 Cnaemidophorus 848271aa-~ 1.86e6 190866933 Lepid~ Animal~ <NA> Lepi~
#> 7 1.24e8 Cnaemidophorus fab88965-~ 1.86e6 104446806 Ptero~ Metazoa Arthr~ Lepi~
#> 8 1.78e8 Cnaemidophorus 6b6b2923-~ 1.86e6 177881660 Ptero~ Metazoa Arthr~ Lepi~
#> 9 1.91e8 Cnaemidophorus 4dd32523-~ 1.86e6 190585832 Ptero~ Animal~ Arthr~ Lepi~
#> 10 1.91e8 Cnaemidophorus 4dd32523-~ 1.86e6 191472572 Ptero~ Animal~ Arthr~ Lepi~
#> # ... with 21 more rows, and 28 more variables: family <chr>, genus <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> # familyKey <int>, genusKey <int>, canonicalName <chr>, authorship <chr>,
#> # nameType <chr>, taxonomicStatus <chr>, rank <chr>, origin <chr>,
#> # numDescendants <int>, numOccurrences <int>, habitats <chr>,
#> # nomenclaturalStatus <chr>, threatStatuses <chr>, synonym <lgl>,
#> # class <chr>, taxonID <chr>, acceptedKey <int>, accepted <chr>, ...Search for the class mammalia
w <- name_lookup(query='mammalia')
w$data
#> # A tibble: 100 x 24
#> key scientificName datasetKey nubKey parentKey parent canonicalName
#> <int> <chr> <chr> <int> <int> <chr> <chr>
#> 1 114079693 Mammalia bd0a2b6d-69d1~ 359 114079410 Anima~ Mammalia
#> 2 168233342 Mammalia 2e76af52-48a9~ 359 168233332 Chord~ Mammalia
#> 3 168239467 Mammalia ed581dc4-7008~ 359 168239466 Chord~ Mammalia
#> 4 190557477 Mammalia 777f49d9-27c7~ 359 190557476 Chord~ Mammalia
#> 5 190653816 Mammalia 69c434f8-762e~ 359 190653815 Chord~ Mammalia
#> 6 190793428 Mammalia b3293c54-11ba~ 359 190793427 Chord~ Mammalia
#> 7 190800965 Mammalia c52dfeb7-f89d~ 359 190800963 Chord~ Mammalia
#> 8 159140593 Mammalia 9d926eab-7be1~ 359 159140592 Chord~ Mammalia
#> 9 144203186 Mammalia 39c18d15-4c48~ 359 144203185 Chord~ Mammalia
#> 10 167583005 Mammalia 8e77944c-d207~ 359 167583004 Chord~ Mammalia
#> # ... with 90 more rows, and 17 more variables: authorship <chr>,
#> # nameType <chr>, taxonomicStatus <chr>, origin <chr>, numDescendants <int>,
#> # numOccurrences <int>, habitats <lgl>, nomenclaturalStatus <lgl>,
#> # threatStatuses <lgl>, synonym <lgl>, kingdom <chr>, phylum <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, rank <chr>, class <chr>Look up the species Helianthus annuus
m <- name_lookup(query = 'Helianthus annuus', rank="species")
m$data
#> # A tibble: 100 x 41
#> key scientificName datasetKey nubKey parentKey parent kingdom phylum order
#> <int> <chr> <chr> <int> <int> <chr> <chr> <chr> <chr>
#> 1 1.35e8 Helianthus an~ f82a4f7f-~ 9.21e6 188456849 Aster~ Plantae Trach~ Aste~
#> 2 1.15e8 Helianthus an~ ee2aac07-~ 9.21e6 144238801 Helia~ Plantae Trach~ Aste~
#> 3 1.46e8 Helianthus an~ 3f5e930b-~ 9.21e6 157140516 Helia~ Plantae Angio~ <NA>
#> 4 1.35e8 Helianthus an~ 29d2d5a6-~ 9.21e6 188628564 Aster~ Plantae Trach~ Aste~
#> 5 1.03e8 Helianthus an~ fab88965-~ 9.21e6 103340270 Helia~ Viridi~ Strep~ Aste~
#> 6 1.79e8 Helianthus an~ 6b6b2923-~ 9.21e6 178978795 Helia~ Viridi~ Strep~ Aste~
#> 7 1.28e8 Helianthus an~ 41c06f1a-~ 9.21e6 146770884 Amara~ Plantae <NA> <NA>
#> 8 1.63e8 Helianthus an~ 88217638-~ 9.21e6 163398972 Aster~ Plantae Trach~ Aste~
#> 9 1.35e8 Helianthus an~ 83ca3188-~ 9.21e6 188536859 Aster~ Plantae Trach~ Aste~
#> 10 1.35e8 Helianthus an~ 3cabcf37-~ 9.21e6 168335901 Aster~ Plantae Trach~ Aste~
#> # ... with 90 more rows, and 32 more variables: family <chr>, species <chr>,
#> # kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> # familyKey <int>, speciesKey <int>, canonicalName <chr>, authorship <chr>,
#> # nameType <chr>, taxonomicStatus <chr>, rank <chr>, origin <chr>,
#> # numDescendants <int>, numOccurrences <int>, habitats <chr>,
#> # nomenclaturalStatus <chr>, threatStatuses <chr>, synonym <lgl>,
#> # class <chr>, genus <chr>, genusKey <int>, taxonID <chr>, ...The function name_usage() works with lots of different name endpoints in GBIF, listed at https://www.gbif.org/developer/species#nameUsages
name_usage(key=3119195, language="FRENCH", data='vernacularNames')
#> Records returned [0]
#> Args [offset=0, limit=100, language=FRENCH]
#> # A tibble: 0 x 0The function name_backbone() is used to search against the GBIF backbone taxonomy
name_backbone(name='Helianthus', rank='genus', kingdom='plants')
#> # A tibble: 1 x 23
#> usageKey scientificName canonicalName rank status confidence matchType
#> * <int> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 3119134 Helianthus L. Helianthus GENUS ACCEPTED 97 EXACT
#> # ... with 16 more variables: kingdom <chr>, phylum <chr>, order <chr>,
#> # family <chr>, genus <chr>, kingdomKey <int>, phylumKey <int>,
#> # classKey <int>, orderKey <int>, familyKey <int>, genusKey <int>,
#> # synonym <lgl>, class <chr>, verbatim_name <chr>, verbatim_rank <chr>,
#> # verbatim_kingdom <chr>The function name_suggest() is optimized for speed, and gives back suggested names based on query parameters.
head( name_suggest(q='Puma concolor') )
#> $data
#> # A tibble: 32 x 3
#> key canonicalName rank
#> <int> <chr> <chr>
#> 1 2435099 Puma concolor SPECIES
#> 2 8860878 Puma concolor capricornensis SUBSPECIES
#> 3 6164618 Puma concolor browni SUBSPECIES
#> 4 7193927 Puma concolor concolor SUBSPECIES
#> 5 8944801 Puma concolor acrocodia SUBSPECIES
#> 6 9104297 Puma concolor greeni SUBSPECIES
#> 7 6164623 Puma concolor cabrerae SUBSPECIES
#> 8 6164608 Puma concolor californica SUBSPECIES
#> 9 9045222 Puma concolor araucanus SUBSPECIES
#> 10 6164594 Puma concolor vancouverensis SUBSPECIES
#> # ... with 22 more rows
#>
#> $hierarchy
#> list()Single occurrence records
Get data for a single occurrence. Note that data is returned as a list, with slots for metadata and data.
occ_get(key=855998194)
#> [[1]]
#> [[1]]$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
#>
#> [[1]]$media
#> [[1]]$media$`855998194`
#> [[1]]$media$`855998194`[[1]]
#> [[1]]$media$`855998194`[[1]][[1]]
#> [1] "none"
#>
#>
#> [[1]]$media$`855998194`$key
#> [1] "855998194"
#>
#> [[1]]$media$`855998194`$species
#> [1] "Sciurus vulgaris"
#>
#> [[1]]$media$`855998194`$decimalLatitude
#> [1] 58.40677
#>
#> [[1]]$media$`855998194`$decimalLongitude
#> [1] 12.04386
#>
#> [[1]]$media$`855998194`$country
#> [1] "Sweden"
#>
#>
#>
#> [[1]]$data
#> key scientificName decimalLatitude decimalLongitude
#> 1 855998194 Sciurus vulgaris Linnaeus, 1758 58.40677 12.04386
#> issues
#> 1 cdround,gass84Get many occurrences. occ_get is vectorized
occ_get(key=c(855998194, 240713150))
#> [[1]]
#> [[1]]$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
#>
#> [[1]]$media
#> [[1]]$media$`855998194`
#> [[1]]$media$`855998194`[[1]]
#> [[1]]$media$`855998194`[[1]][[1]]
#> [1] "none"
#>
#>
#> [[1]]$media$`855998194`$key
#> [1] "855998194"
#>
#> [[1]]$media$`855998194`$species
#> [1] "Sciurus vulgaris"
#>
#> [[1]]$media$`855998194`$decimalLatitude
#> [1] 58.40677
#>
#> [[1]]$media$`855998194`$decimalLongitude
#> [1] 12.04386
#>
#> [[1]]$media$`855998194`$country
#> [1] "Sweden"
#>
#>
#>
#> [[1]]$data
#> key scientificName decimalLatitude decimalLongitude
#> 1 855998194 Sciurus vulgaris Linnaeus, 1758 58.40677 12.04386
#> issues
#> 1 cdround,gass84
#>
#>
#> [[2]]
#> [[2]]$hierarchy
#> name key rank
#> 1 Chromista 4 kingdom
#> 2 Foraminifera 8376456 phylum
#> 3 Astrorhizida 8142878 order
#> 4 Astrorhizidae 7747923 family
#> 5 Pelosina 7822114 genus
#>
#> [[2]]$media
#> [[2]]$media$`240713150`
#> [[2]]$media$`240713150`[[1]]
#> [[2]]$media$`240713150`[[1]][[1]]
#> [1] "none"
#>
#>
#> [[2]]$media$`240713150`$key
#> [1] "240713150"
#>
#> [[2]]$media$`240713150`$decimalLatitude
#> [1] -77.5667
#>
#> [[2]]$media$`240713150`$decimalLongitude
#> [1] 163.583
#>
#> [[2]]$media$`240713150`$country
#> [1] "Antarctica"
#>
#>
#>
#> [[2]]$data
#> key scientificName decimalLatitude decimalLongitude issues
#> 1 240713150 Pelosina Brady, 1879 -77.5667 163.583 gass84Search 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)
#> Records found [24356]
#> 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 77
#> key scientificName decimalLatitude decimalLongitude issues datasetKey
#> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 3017958083 Ursus american~ 34.5 -120. "cdro~ 50c9509d-~
#> 2 3017969110 Ursus american~ 41.9 -73.5 "cdro~ 50c9509d-~
#> 3 3017987007 Ursus american~ 38.4 -122. "cdro~ 50c9509d-~
#> 4 3018016971 Ursus american~ 37.5 -120. "cdro~ 50c9509d-~
#> 5 3018054750 Ursus american~ 37.5 -120. "" 50c9509d-~
#> 6 3018145073 Ursus american~ 42.7 -72.3 "cdro~ 50c9509d-~
#> 7 3018152095 Ursus american~ 41.9 -73.6 "cdro~ 50c9509d-~
#> 8 3031756180 Ursus american~ 42.2 -123. "cdro~ 50c9509d-~
#> 9 3031764831 Ursus american~ 25.2 -101. "cdro~ 50c9509d-~
#> 10 3031812193 Ursus american~ 42.3 -72.4 "cdro~ 50c9509d-~
#> 11 3031813862 Ursus american~ 42.7 -73.2 "cdro~ 50c9509d-~
#> 12 3031892711 Ursus american~ 51.9 -120. "cdro~ 50c9509d-~
#> 13 3031997275 Ursus american~ 42.2 -123. "cdro~ 50c9509d-~
#> 14 3032081365 Ursus american~ 43.6 -72.6 "cdro~ 50c9509d-~
#> 15 3032083390 Ursus american~ 43.4 -71.9 "cdro~ 50c9509d-~
#> 16 3032124304 Ursus american~ 45.3 -84.5 "cdro~ 50c9509d-~
#> 17 3032163654 Ursus american~ 34.8 -120. "cdro~ 50c9509d-~
#> 18 3039162886 Ursus american~ 29.2 -81.6 "cdro~ 50c9509d-~
#> 19 3039240363 Ursus american~ 48.5 -124. "" 50c9509d-~
#> 20 3039358219 Ursus american~ 29.3 -103. "cdro~ 50c9509d-~
#> # ... with 71 more variables: publishingOrgKey <chr>, installationKey <chr>,
#> # publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> # lastParsed <chr>, crawlId <int>, hostingOrganizationKey <chr>,
#> # basisOfRecord <chr>, occurrenceStatus <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>, ...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')$data$key[1]
occ_search(taxonKey=key, limit=20)
#> Records found [93861]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [20]
#> No. facets [0]
#> Args [limit=20, offset=0, taxonKey=9206251, fields=all]
#> # A tibble: 20 x 84
#> key scientificName decimalLatitude decimalLongitude issues datasetKey
#> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 3017947125 Helianthus ann~ 29.4 -98.5 "cdro~ 50c9509d-~
#> 2 3018105046 Helianthus ann~ -29.6 30.4 "cdro~ 50c9509d-~
#> 3 3018133231 Helianthus ann~ 50.8 4.10 "cdro~ 50c9509d-~
#> 4 3031652179 Helianthus ann~ 34.3 -118. "cdro~ 50c9509d-~
#> 5 3031677301 Helianthus ann~ 3.04 102. "cdro~ 50c9509d-~
#> 6 3031889761 Helianthus ann~ 35.1 -107. "cdro~ 50c9509d-~
#> 7 3031913527 Helianthus ann~ 33.9 -117. "cdro~ 50c9509d-~
#> 8 3031929875 Helianthus ann~ -38.0 -59.2 "cdro~ 50c9509d-~
#> 9 3032001377 Helianthus ann~ 8.60 -83.4 "cdro~ 50c9509d-~
#> 10 3032031919 Helianthus ann~ 44.3 -78.4 "cdro~ 50c9509d-~
#> 11 3032049747 Helianthus ann~ 28.3 -105. "cdro~ 50c9509d-~
#> 12 3032125750 Helianthus ann~ -37.4 145. "cdro~ 50c9509d-~
#> 13 3032138018 Helianthus ann~ 45.4 -114. "cdro~ 50c9509d-~
#> 14 3032149602 Helianthus ann~ 29.6 -95.1 "cdro~ 50c9509d-~
#> 15 3032156328 Helianthus ann~ 50.4 33.4 "cdro~ 50c9509d-~
#> 16 3032173372 Helianthus ann~ -32.2 142. "cdro~ 50c9509d-~
#> 17 3039128940 Helianthus ann~ -25.4 -49.2 "cdro~ 50c9509d-~
#> 18 3039139177 Helianthus ann~ NA NA "" 50c9509d-~
#> 19 3039249873 Helianthus ann~ 22.8 -98.5 "cdro~ 50c9509d-~
#> 20 3039366142 Helianthus ann~ 25.8 -109. "cdro~ 50c9509d-~
#> # ... with 78 more variables: publishingOrgKey <chr>, installationKey <chr>,
#> # publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> # lastParsed <chr>, crawlId <int>, hostingOrganizationKey <chr>,
#> # basisOfRecord <chr>, occurrenceStatus <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>, ...You can index to different parts of the oupu; here, the metadata:
occ_search(taxonKey=key)$meta
#> $offset
#> [1] 300
#>
#> $limit
#> [1] 200
#>
#> $endOfRecords
#> [1] FALSE
#>
#> $count
#> [1] 93861You can choose what fields to return. This isn’t passed on to the API query to GBIF as they don’t allow that, but we filter out the columns before we give the data back to you.
occ_search(scientificName = "Ursus americanus", fields=c('name','basisOfRecord','protocol'), limit = 20)
#> Records found [24356]
#> Records returned [20]
#> No. unique hierarchies [1]
#> No. media records [20]
#> No. facets [0]
#> Args [limit=20, offset=0, scientificName=Ursus americanus,
#> fields=name,basisOfRecord,protocol]
#> # A tibble: 20 x 2
#> protocol basisOfRecord
#> <chr> <chr>
#> 1 DWC_ARCHIVE HUMAN_OBSERVATION
#> 2 DWC_ARCHIVE HUMAN_OBSERVATION
#> 3 DWC_ARCHIVE HUMAN_OBSERVATION
#> 4 DWC_ARCHIVE HUMAN_OBSERVATION
#> 5 DWC_ARCHIVE HUMAN_OBSERVATION
#> 6 DWC_ARCHIVE HUMAN_OBSERVATION
#> 7 DWC_ARCHIVE HUMAN_OBSERVATION
#> 8 DWC_ARCHIVE HUMAN_OBSERVATION
#> 9 DWC_ARCHIVE HUMAN_OBSERVATION
#> 10 DWC_ARCHIVE HUMAN_OBSERVATION
#> 11 DWC_ARCHIVE HUMAN_OBSERVATION
#> 12 DWC_ARCHIVE HUMAN_OBSERVATION
#> 13 DWC_ARCHIVE HUMAN_OBSERVATION
#> 14 DWC_ARCHIVE HUMAN_OBSERVATION
#> 15 DWC_ARCHIVE HUMAN_OBSERVATION
#> 16 DWC_ARCHIVE HUMAN_OBSERVATION
#> 17 DWC_ARCHIVE HUMAN_OBSERVATION
#> 18 DWC_ARCHIVE HUMAN_OBSERVATION
#> 19 DWC_ARCHIVE HUMAN_OBSERVATION
#> 20 DWC_ARCHIVE HUMAN_OBSERVATIONMost parameters are vectorized, so you can pass in more than one value:
splist <- c('Cyanocitta stelleri', 'Junco hyemalis', 'Aix sponsa')
keys <- sapply(splist, function(x) name_suggest(x)$data$key[1], USE.NAMES=FALSE)
occ_search(taxonKey=keys, limit=5)
#> Records found [2482598 (1506272), 9362842 (7816056), 2498387 (4210168)]
#> Records 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 78
#> key scientificName decimalLatitude decimalLongitude issues datasetKey
#> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 3017946721 Cyanocitta ste~ 49.0 -123. cdrou~ 50c9509d-2~
#> 2 3017965708 Cyanocitta ste~ 47.7 -122. cdrou~ 50c9509d-2~
#> 3 3017970720 Cyanocitta ste~ 37.6 -122. cdrou~ 50c9509d-2~
#> 4 3017987157 Cyanocitta ste~ 36.8 -122. cdrou~ 50c9509d-2~
#> 5 3017988765 Cyanocitta ste~ 50.6 -115. cdrou~ 50c9509d-2~
#> # ... with 72 more variables: publishingOrgKey <chr>, installationKey <chr>,
#> # publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> # lastParsed <chr>, crawlId <int>, hostingOrganizationKey <chr>,
#> # basisOfRecord <chr>, occurrenceStatus <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>, ...