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

devtools::install_github("ropensci/rgbif")

Load rgbif

library("rgbif")

Number of occurrences

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

Search 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$meta
#> # A tibble: 1 x 4
#>   offset limit endOfRecords count
#>    <int> <int> <lgl>        <int>
#> 1      0   100 FALSE         1044
head(out$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

Single occurrence records

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

Search for occurrences

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>

Maps

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)

plot of chunk gbifmap1