# Search by species name, using \code{\link{name_backbone}} first to get key
(key <- name_suggest(q='Helianthus annuus', rank='species')$key[1])
occ_search(taxonKey=key, limit=2)
# Return 20 results, this is the default by the way
occ_search(taxonKey=key, limit=20)
# Return just metadata for the search
occ_search(taxonKey=key, limit=100, return='meta')
# Instead of getting a taxon key first, you can search for a name directly
## However, note that using this approach (with \code{scientificName="..."})
## you are getting synonyms too. The results for using \code{scientifcName} and
## \code{taxonKey} parameters are the same in this case, but I wouldn't be surprised if for some
## names they return different results
occ_search(scientificName = 'Ursus americanus', config=verbose())
key <- name_backbone(name = 'Ursus americanus', rank='species')$usageKey
occ_search(taxonKey = key)
# Search by dataset key
occ_search(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a', return='data', limit=20)
# Search by catalog number
occ_search(catalogNumber="49366", limit=20)
occ_search(catalogNumber=c("49366","Bird.27847588"), limit=20)
# Get all data, not just lat/long and name
occ_search(taxonKey=key, fields='all', limit=20)
# Or get specific fields. Note that this isn't done on GBIF's side of things. This
# is done in R, but before you get the return object, so other fields are garbage
# collected
occ_search(taxonKey=key, fields=c('name','basisOfRecord','protocol'), limit=20)
# Use paging parameters (limit and start) to page. Note the different results
# for the two queries below.
occ_search(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a',start=10,limit=5,
return="data")
occ_search(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a',start=20,limit=5,
return="data")
# Many dataset keys
occ_search(datasetKey=c("50c9509d-22c7-4a22-a47d-8c48425ef4a7",
"7b5d6a48-f762-11e1-a439-00145eb45e9a"), limit=20)
# Occurrence data: lat/long data, and associated metadata with occurrences
## If return='data' the output is a data.frame of all data together
## for easy manipulation
occ_search(taxonKey=key, return='data', limit=20)
# Taxonomic hierarchy data
## If return='meta' the output is a list of the hierarch for each record
occ_search(taxonKey=key, return='hier', limit=10)
# Search by recorder
occ_search(recordedBy="smith", limit=20)
# Many collector names
occ_search(recordedBy=c("smith","BJ Stacey"), limit=20)
# Pass in curl options for extra fun
library('httr')
occ_search(taxonKey=key, limit=20, return='hier', config=verbose())
# occ_search(taxonKey=key, limit=20, return='hier', config=progress())
# occ_search(taxonKey=key, limit=20, return='hier', config=timeout(1))
# Search for many species
splist <- c('Cyanocitta stelleri', 'Junco hyemalis', 'Aix sponsa')
keys <- sapply(splist, function(x) name_suggest(x)$key[1], USE.NAMES=FALSE)
occ_search(taxonKey=keys, limit=5, return='data')
# Search using a synonym name
# Note that you'll see a message printing out that the accepted name will be used
occ_search(scientificName = 'Pulsatilla patens', fields = c('name','scientificName'), limit=5)
# Search on latitidue and longitude
occ_search(search="kingfisher", decimalLatitude=50, decimalLongitude=-10)
# Search on a bounding box
## in well known text format
occ_search(geometry='POLYGON((30.1 10.1, 10 20, 20 40, 40 40, 30.1 10.1))', limit=20)
key <- name_suggest(q='Aesculus hippocastanum')$key[1]
occ_search(taxonKey=key, geometry='POLYGON((30.1 10.1, 10 20, 20 40, 40 40, 30.1 10.1))',
limit=20)
## or using bounding box, converted to WKT internally
occ_search(geometry=c(-125.0,38.4,-121.8,40.9), limit=20)
# Search on country
occ_search(country='US', fields=c('name','country'), limit=20)
isocodes[grep("France", isocodes$name),"code"]
occ_search(country='FR', fields=c('name','country'), limit=20)
occ_search(country='DE', fields=c('name','country'), limit=20)
occ_search(country=c('US','DE'), fields=c('name','country'), limit=20)
# Get only occurrences with lat/long data
occ_search(taxonKey=key, hasCoordinate=TRUE, limit=20)
# Get only occurrences that were recorded as living specimens
occ_search(taxonKey=key, basisOfRecord="LIVING_SPECIMEN", hasCoordinate=TRUE, limit=20)
# Get occurrences for a particular eventDate
occ_search(taxonKey=key, eventDate="2013", limit=20)
occ_search(taxonKey=key, year="2013", limit=20)
occ_search(taxonKey=key, month="6", limit=20)
# Get occurrences based on depth
key <- name_backbone(name='Salmo salar', kingdom='animals')$speciesKey
occ_search(taxonKey=key, depth="5", limit=20)
# Get occurrences based on elevation
key <- name_backbone(name='Puma concolor', kingdom='animals')$speciesKey
occ_search(taxonKey=key, elevation=50, hasCoordinate=TRUE, limit=20)
# Get occurrences based on institutionCode
occ_search(institutionCode="TLMF", limit=20)
occ_search(institutionCode=c("TLMF","ArtDatabanken"), limit=20)
# Get occurrences based on collectionCode
occ_search(collectionCode="Floristic Databases MV - Higher Plants", limit=20)
occ_search(collectionCode=c("Floristic Databases MV - Higher Plants","Artport"))
# Get only those occurrences with spatial issues
occ_search(taxonKey=key, hasGeospatialIssue=TRUE, limit=20)
# Search using a query string
occ_search(search="kingfisher", limit=20)
# Range queries
## See Detail for parameters that support range queries
occ_search(depth='50,100') # this is a range depth, with lower/upper limits in character string
occ_search(depth=c(50,100)) # this is not a range search, but does two searches for each depth
## Range search with year
occ_search(year='1999,2000', limit=20)
## Range search with latitude
occ_search(decimalLatitude='29.59,29.6')
# Search by specimen type status
## Look for possible values of the typeStatus parameter looking at the typestatus dataset
occ_search(typeStatus = 'allotype', fields = c('name','typeStatus'))
# Search by specimen record number
## This is the record number of the person/group that submitted the data, not GBIF's numbers
## You can see that many different groups have record number 1, so not super helpful
occ_search(recordNumber = 1, fields = c('name','recordNumber','recordedBy'))
# Search by last time interpreted: Date the record was last modified in GBIF
## The lastInterpreted parameter accepts ISO 8601 format dates, including
## yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Range queries are accepted for lastInterpreted
occ_search(lastInterpreted = '2014-04-02', fields = c('name','lastInterpreted'))
# Search by continent
## One of africa, antarctica, asia, europe, north_america, oceania, or south_america
occ_search(continent = 'south_america', return = 'meta')
occ_search(continent = 'africa', return = 'meta')
occ_search(continent = 'oceania', return = 'meta')
occ_search(continent = 'antarctica', return = 'meta')
# Search for occurrences with images
occ_search(mediaType = 'StillImage', return='media')
occ_search(mediaType = 'MovingImage', return='media')
occ_search(mediaType = 'Sound', return='media')
# Query based on issues - see Details for options
## one issue
occ_search(taxonKey=1, issue='DEPTH_UNLIKELY', fields =
c('name','key','decimalLatitude','decimalLongitude','depth'))
## two issues
occ_search(taxonKey=1, issue=c('DEPTH_UNLIKELY','COORDINATE_ROUNDED'))
# Show all records in the Arizona State Lichen Collection that cant be matched to the GBIF
# backbone properly:
occ_search(datasetKey='84c0e1a0-f762-11e1-a439-00145eb45e9a',
issue=c('TAXON_MATCH_NONE','TAXON_MATCH_HIGHERRANK'))
# Parsing output by issue
(res <- occ_search(geometry='POLYGON((30.1 10.1, 10 20, 20 40, 40 40, 30.1 10.1))', limit = 50))
## what do issues mean, can print whole table, or search for matches
head(gbif_issues())
gbif_issues()[ gbif_issues()$code %in% c('cdround','cudc','gass84','txmathi'), ]
## or parse issues in various ways
### remove data rows with certain issue classes
library('magrittr')
res %>% occ_issues(gass84)
### split issues into separate columns
res %>% occ_issues(mutate = "split")
### expand issues to more descriptive names
res %>% occ_issues(mutate = "expand")
### split and expand
res %>% occ_issues(mutate = "split_expand")
### split, expand, and remove an issue class
res %>% occ_issues(-cudc, mutate = "split_expand")
# If you try multiple values for two different parameters you are wacked on the hand
# occ_search(taxonKey=c(2482598,2492010), recordedBy=c("smith","BJ Stacey"))
# Get a lot of data, here 1500 records for Helianthus annuus
# out <- occ_search(taxonKey=key, limit=1500, return="data")
# nrow(out)
# If you pass in an invalid polygon you get hopefully informative errors
### the WKT string is fine, but GBIF says bad polygon
wkt <- 'POLYGON((-178.59375 64.83258989321493,-165.9375 59.24622380205539,
-147.3046875 59.065977905449806,-130.78125 51.04484764446178,-125.859375 36.70806354647625,
-112.1484375 23.367471303759686,-105.1171875 16.093320185359257,-86.8359375 9.23767076398516,
-82.96875 2.9485268155066175,-82.6171875 -14.812060061226388,-74.8828125 -18.849111862023985,
-77.34375 -47.661687803329166,-84.375 -49.975955187343295,174.7265625 -50.649460483096114,
179.296875 -42.19189902447192,-176.8359375 -35.634976650677295,176.8359375 -31.835565983656227,
163.4765625 -6.528187613695323,152.578125 1.894796132058301,135.703125 4.702353722559447,
127.96875 15.077427674847987,127.96875 23.689804541429606,139.921875 32.06861069132688,
149.4140625 42.65416193033991,159.2578125 48.3160811030533,168.3984375 57.019804336633165,
178.2421875 59.95776046458139,-179.6484375 61.16708631440347,-178.59375 64.83258989321493))'
# occ_search(geometry = gsub("\n", '', wkt))
### unable to parse due to last number pair needing two numbers, not one
# wkt <- 'POLYGON((-178.5 64.8,-165.9 59.2,-147.3 59.0,-130.7 51.0,-125.8))'
# occ_search(geometry = wkt)
### unable to parse due to unclosed string
# wkt <- 'POLYGON((-178.5 64.8,-165.9 59.2,-147.3 59.0,-130.7 51.0))'
# occ_search(geometry = wkt)
### another of the same
# wkt <- 'POLYGON((-178.5 64.8,-165.9 59.2,-147.3 59.0,-130.7 51.0,-125.8 36.7))'
# occ_search(geometry = wkt)
### returns no results
# wkt <- 'LINESTRING(3 4,10 50,20 25)'
# occ_search(geometry = wkt)
### Apparently a point is allowed, but haven't successfully retrieved data, so returns nothing
# wkt <- 'POINT(45 -122)'
# occ_search(geometry = wkt)Run the code above in your browser using DataLab