rgbif introduction

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] 44825502

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] 2921

All georeferenced records in GBIF

occ_count(georeferenced=TRUE)
#> [1] 546282258

Records from Denmark

denmark_code <- isocodes[grep("Denmark", isocodes$name), "code"]
occ_count(country=denmark_code)
#> [1] 10260110

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] 38511429

Records for a particular dataset, and only for preserved specimens

occ_count(datasetKey='e707e6da-e143-445d-b41d-529c4a777e8b', basisOfRecord='OBSERVATION')
#> [1] 2120907

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"       "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$meta
#> # A tibble: 1 × 4
#>   offset limit endOfRecords  count
#>    <int> <int>        <lgl>  <int>
#> 1      0   100        FALSE 188461
head(out$data)
#> # A tibble: 6 × 30
#>         key                 scientificName
#>       <int>                          <chr>
#> 1 124551685                       Mammalia
#> 2 124552749                       Mammalia
#> 3 115507497 Mammalia (awaiting allocation)
#> 4       359                       Mammalia
#> 5 100375341                       Mammalia
#> 6 115507496 Mammalia (awaiting allocation)
#> # ... with 28 more variables: datasetKey <chr>, nubKey <int>,
#> #   parentKey <int>, parent <chr>, kingdom <chr>, phylum <chr>,
#> #   kingdomKey <int>, phylumKey <int>, classKey <int>,
#> #   canonicalName <chr>, taxonomicStatus <chr>, rank <chr>,
#> #   numDescendants <int>, numOccurrences <int>, habitats <chr>,
#> #   nomenclaturalStatus <lgl>, threatStatuses <lgl>, synonym <lgl>,
#> #   class <chr>, order <chr>, family <chr>, orderKey <int>,
#> #   familyKey <int>, authorship <chr>, nameType <chr>,
#> #   constituentKey <chr>, extinct <lgl>, taxonID <chr>
out$facets
#> NULL
out$hierarchies[1:2]
#> $`124551685`
#>     rankkey     name
#> 1 124551683 Animalia
#> 2 124551684 Chordata
#> 
#> $`124552749`
#>     rankkey     name
#> 1 124552747 Animalia
#> 2 124552748 Chordata
out$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 × 36
#>         key                  scientificName
#>       <int>                           <chr>
#> 1   1858636 Cnaemidophorus Wallengren, 1862
#> 2 113100610 Cnaemidophorus Wallengren, 1862
#> 3 100555508 Cnaemidophorus Wallengren, 1862
#> 4 120772811                  Cnaemidophorus
#> 5 115196907                  Cnaemidophorus
#> 6 115216121                  Cnaemidophorus
#> # ... with 34 more variables: datasetKey <chr>, constituentKey <chr>,
#> #   nubKey <int>, 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>,
#> #   authorship <chr>, nameType <chr>, taxonomicStatus <chr>, rank <chr>,
#> #   numDescendants <int>, numOccurrences <int>, extinct <lgl>,
#> #   habitats <lgl>, nomenclaturalStatus <chr>, threatStatuses <lgl>,
#> #   synonym <lgl>, class <chr>, taxonID <chr>, publishedIn <chr>,
#> #   accordingTo <chr>, acceptedKey <int>, accepted <chr>

Search for the class mammalia

head(name_lookup(query='mammalia', return = 'data'))
#> # A tibble: 6 × 30
#>         key                 scientificName
#>       <int>                          <chr>
#> 1 124551685                       Mammalia
#> 2 124552749                       Mammalia
#> 3 115507497 Mammalia (awaiting allocation)
#> 4       359                       Mammalia
#> 5 100375341                       Mammalia
#> 6 115507496 Mammalia (awaiting allocation)
#> # ... with 28 more variables: datasetKey <chr>, nubKey <int>,
#> #   parentKey <int>, parent <chr>, kingdom <chr>, phylum <chr>,
#> #   kingdomKey <int>, phylumKey <int>, classKey <int>,
#> #   canonicalName <chr>, taxonomicStatus <chr>, rank <chr>,
#> #   numDescendants <int>, numOccurrences <int>, habitats <chr>,
#> #   nomenclaturalStatus <lgl>, threatStatuses <lgl>, synonym <lgl>,
#> #   class <chr>, order <chr>, family <chr>, orderKey <int>,
#> #   familyKey <int>, authorship <chr>, nameType <chr>,
#> #   constituentKey <chr>, extinct <lgl>, taxonID <chr>

Look up the species Helianthus annuus

head(name_lookup(query = 'Helianthus annuus', rank="species", return = 'data'))
#> # A tibble: 6 × 40
#>         key                           scientificName
#>       <int>                                    <chr>
#> 1 100336353                     Helianthus annuus L.
#> 2   3119195                     Helianthus annuus L.
#> 3 113584542                     Helianthus annuus L.
#> 4 103340289                        Helianthus annuus
#> 5 114910965                        Helianthus annuus
#> 6 115452008 'Helianthus annuus' phyllody phytoplasma
#> # ... with 38 more variables: datasetKey <chr>, nubKey <int>,
#> #   parentKey <int>, parent <chr>, kingdom <chr>, order <chr>,
#> #   family <chr>, genus <chr>, species <chr>, kingdomKey <int>,
#> #   classKey <int>, orderKey <int>, familyKey <int>, genusKey <int>,
#> #   speciesKey <int>, canonicalName <chr>, authorship <chr>,
#> #   nameType <chr>, rank <chr>, numDescendants <int>,
#> #   numOccurrences <int>, habitats <chr>, nomenclaturalStatus <chr>,
#> #   threatStatuses <lgl>, synonym <lgl>, class <chr>,
#> #   constituentKey <chr>, basionymKey <int>, basionym <chr>, phylum <chr>,
#> #   phylumKey <int>, taxonomicStatus <chr>, extinct <lgl>, taxonID <chr>,
#> #   publishedIn <chr>, accordingTo <chr>, acceptedKey <int>,
#> #   accepted <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 × 6
#>            vernacularName language country
#>                     <chr>    <chr>   <chr>
#> 1 Gewöhnliche Sonnenblume      deu      DE
#> 2             Sonnenblume      deu    <NA>
#> 3                 alizeti      swa    <NA>
#> 4        annual sunflower      eng    <NA>
#> 5        common sunflower      eng    <NA>
#> 6                 girasol      spa    <NA>
#> # ... 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 × 3
#>       key             canonicalName       rank
#>     <int>                     <chr>      <chr>
#> 1 6164620      Puma concolor cougar SUBSPECIES
#> 2 6164600       Puma concolor coryi SUBSPECIES
#> 3 6164604  Puma concolor stanleyana SUBSPECIES
#> 4 6164610 Puma concolor hippolestes SUBSPECIES
#> 5 6164622        Puma concolor puma SUBSPECIES
#> 6 7193927    Puma concolor concolor 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=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

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 [8196] 
#> Records returned [20] 
#> No. unique hierarchies [1] 
#> No. media records [17] 
#> No. facets [0] 
#> Args [scientificName=Ursus americanus, limit=20, offset=0, fields=all] 
#> # A tibble: 20 × 68
#>                name        key decimalLatitude decimalLongitude
#>               <chr>      <int>           <dbl>            <dbl>
#> 1  Ursus americanus 1249277297        35.76789        -75.80894
#> 2  Ursus americanus 1229610216        44.06086        -71.92712
#> 3  Ursus americanus 1253300445        44.65481        -72.67270
#> 4  Ursus americanus 1229610234        44.06062        -71.92692
#> 5  Ursus americanus 1253314877        49.25782       -122.82786
#> 6  Ursus americanus 1272078411        44.41793        -72.70709
#> 7  Ursus americanus 1249296297        39.08590       -105.24586
#> 8  Ursus americanus 1249284297        43.68723        -72.32891
#> 9  Ursus americanus 1257415362        44.32746        -72.41007
#> 10 Ursus americanus 1253317181        43.64214        -72.52494
#> 11 Ursus americanus 1262389246        43.80871        -72.20964
#> 12 Ursus americanus 1269541796        41.02228        -74.79251
#> 13 Ursus americanus 1265595722        35.61069        -83.83539
#> 14 Ursus americanus 1269542962        37.36105        -79.87364
#> 15 Ursus americanus 1270054467        44.34083        -72.46110
#> 16 Ursus americanus 1265598494        44.36404        -72.74876
#> 17 Ursus americanus 1269541679        26.15261        -81.47839
#> 18 Ursus americanus 1315062645        34.18098       -118.09706
#> 19 Ursus americanus 1269542955        37.45911        -80.55145
#> 20 Ursus americanus 1269542933        37.40447        -79.97860
#> # ... 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>, infraspecificEpithet <chr>,
#> #   taxonRank <chr>, dateIdentified <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>, collectionCode <chr>,
#> #   verbatimLocality <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>, coordinateUncertaintyInMeters <dbl>,
#> #   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 [20539] 
#> Records returned [20] 
#> No. unique hierarchies [1] 
#> No. media records [16] 
#> No. facets [0] 
#> Args [taxonKey=3119195, limit=20, offset=0, fields=all] 
#> # A tibble: 20 × 67
#>                 name        key decimalLatitude decimalLongitude
#>                <chr>      <int>           <dbl>            <dbl>
#> 1  Helianthus annuus 1249279611        34.04810       -117.79884
#> 2  Helianthus annuus 1315048347        34.04377       -116.94136
#> 3  Helianthus annuus 1253308332        29.67463        -95.44804
#> 4  Helianthus annuus 1249286909        32.58747        -97.10081
#> 5  Helianthus annuus 1305118889        18.40386        -66.04487
#> 6  Helianthus annuus 1262375813        29.82586        -95.45604
#> 7  Helianthus annuus 1262379231        34.04911       -117.80066
#> 8  Helianthus annuus 1262385911        32.78328        -96.70352
#> 9  Helianthus annuus 1265544678        32.58747        -97.10081
#> 10 Helianthus annuus 1270045172        33.92958       -117.37322
#> 11 Helianthus annuus 1265895094        42.87784       -112.43226
#> 12 Helianthus annuus 1265553900        34.12932       -118.20648
#> 13 Helianthus annuus 1269543851        29.50991        -94.50006
#> 14 Helianthus annuus 1265899487        19.45194        -96.95945
#> 15 Helianthus annuus 1265562148        29.47895        -98.51160
#> 16 Helianthus annuus 1305119137        11.86735        -83.93555
#> 17 Helianthus annuus 1265590989        34.19005       -117.31644
#> 18 Helianthus annuus 1265590198        25.76265       -100.25513
#> 19 Helianthus annuus 1305119139        11.86735        -83.93555
#> 20 Helianthus annuus 1315048128        34.03212       -117.47091
#> # ... 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>, 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>,
#> #   verbatimLocality <chr>, gbifID <chr>, occurrenceID <chr>,
#> #   taxonID <chr>, catalogNumber <chr>, recordedBy <chr>,
#> #   http...unknown.org.occurrenceDetails <chr>, institutionCode <chr>,
#> #   rights <chr>, eventTime <chr>, identificationID <chr>,
#> #   coordinateUncertaintyInMeters <dbl>, occurrenceRemarks <chr>,
#> #   informationWithheld <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 20539

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 [8196] 
#> Records returned [20] 
#> No. unique hierarchies [1] 
#> No. media records [17] 
#> No. facets [0] 
#> Args [scientificName=Ursus americanus, limit=20, offset=0,
#>      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 [7192170 (1134), 6173532 (1540), 2498387 (775050)] 
#> Occ. returned [7192170 (5), 6173532 (5), 2498387 (5)] 
#> No. unique hierarchies [7192170 (1), 6173532 (1), 2498387 (1)] 
#> No. media records [7192170 (5), 6173532 (3), 2498387 (5)] 
#> No. facets [] 
#> Args [taxonKey=7192170,6173532,2498387, limit=5, offset=0, fields=all] 
#> First 10 rows of data from 7192170
#> 
#> # A tibble: 5 × 76
#>                  name        key decimalLatitude decimalLongitude  issues
#>                 <chr>      <int>           <dbl>            <dbl>   <chr>
#> 1 Cyanocitta stelleri 1147228297        34.73360        -119.9871        
#> 2 Cyanocitta stelleri 1147146899        39.61584        -120.5881 cdround
#> 3 Cyanocitta stelleri 1147243804        39.61584        -120.5881 cdround
#> 4 Cyanocitta stelleri 1147177967        39.61584        -120.5881 cdround
#> 5 Cyanocitta stelleri 1147187543        39.61584        -120.5881 cdround
#> # ... with 71 more variables: datasetKey <chr>, publishingOrgKey <chr>,
#> #   publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> #   lastParsed <chr>, crawlId <int>, extensions <chr>,
#> #   basisOfRecord <chr>, establishmentMeans <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>, infraspecificEpithet <chr>, taxonRank <chr>,
#> #   continent <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>, institutionID <chr>, county <chr>, language <chr>,
#> #   gbifID <chr>, type <chr>, occurrenceStatus <chr>, catalogNumber <chr>,
#> #   vernacularName <chr>, institutionCode <chr>, rights <chr>,
#> #   behavior <chr>, identifier <chr>, higherGeography <chr>,
#> #   nomenclaturalCode <chr>, verbatimEventDate <chr>, endDayOfYear <chr>,
#> #   georeferenceVerificationStatus <chr>, locality <chr>,
#> #   collectionCode <chr>, verbatimLocality <chr>, occurrenceID <chr>,
#> #   recordedBy <chr>, startDayOfYear <chr>, occurrenceRemarks <chr>,
#> #   accessRights <chr>

Maps

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)

plot of chunk gbifmap1