# NOT RUN {
# Search by species name, using \code{\link{name_backbone}} first to get key
(key <- name_suggest(q='Helianthus annuus', rank='species')$key[1])
occ_search(taxonKey=key, limit=2)
# Return 20 results, this is the default by the way
occ_search(taxonKey=key, limit=20)
# Return just metadata for the search
occ_search(taxonKey=key, limit=0, return='meta')
# Instead of getting a taxon key first, you can search for a name directly
## However, note that using this approach (with \code{scientificName="..."})
## you are getting synonyms too. The results for using \code{scientifcName} and
## \code{taxonKey} parameters are the same in this case, but I wouldn't be surprised if for some
## names they return different results
occ_search(scientificName = 'Ursus americanus')
key <- name_backbone(name = 'Ursus americanus', rank='species')$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)
## separate requests: use a vector of strings
occ_search(catalogNumber=c("49366","Bird.27847588"), limit=10)
## one request, many instances of same parameter: use semi-colon sep. string
occ_search(catalogNumber="49366;Bird.27847588", limit=10)
# 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
## separate requests: use a vector of strings
occ_search(datasetKey=c("50c9509d-22c7-4a22-a47d-8c48425ef4a7",
"7b5d6a48-f762-11e1-a439-00145eb45e9a"), limit=20)
## one request, many instances of same parameter: use semi-colon sep. string
v="50c9509d-22c7-4a22-a47d-8c48425ef4a7;7b5d6a48-f762-11e1-a439-00145eb45e9a"
occ_search(datasetKey = v, 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
occ_search(taxonKey=2433407, limit=20, return='hier',
curlopts=list(verbose=TRUE))
occ_search(taxonKey=2433407, limit=20, return='hier',
curlopts = list(
noprogress = FALSE,
progressfunction = function(down, up) {
cat(sprintf("up: %d | down %d\n", up, down))
return(TRUE)
}
)
)
# occ_search(taxonKey=2433407, limit=20, return='hier',
# curlopts = list(timeout_ms = 1))
# Search for many species
splist <- c('Cyanocitta stelleri', 'Junco hyemalis', 'Aix sponsa')
keys <- sapply(splist, function(x) name_suggest(x)$key[1], USE.NAMES=FALSE)
## separate requests: use a vector of strings
occ_search(taxonKey = keys, limit=5)
## one request, many instances of same parameter: use semi-colon sep. string
occ_search(taxonKey = paste0(keys, collapse = ";"), limit=5)
# Search using a synonym name
# Note that you'll see a message printing out that the accepted name will be used
occ_search(scientificName = 'Pulsatilla patens', fields = c('name','scientificName'), limit=5)
# Search on latitidue and longitude
occ_search(search="kingfisher", decimalLatitude=50, decimalLongitude=-10)
# Search on a bounding box
## in well known text format
### polygon
occ_search(geometry='POLYGON((30.1 10.1, 10 20, 20 40, 40 40, 30.1 10.1))', limit=20)
### multipolygon
wkt <- 'MULTIPOLYGON(((-123 38, -123 43, -116 43, -116 38, -123 38)),
((-97 41, -97 45, -93 45, -93 41, -97 41)))'
occ_search(geometry = gsub("\n\\s+", "", wkt), limit = 20)
## taxonKey + WKT
key <- name_suggest(q='Aesculus hippocastanum')$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 a long WKT string - too long for a GBIF search API request
## We internally convert your WKT string to a bounding box
## then do the query
## then clip the results down to just those in the original polygon
## - Alternatively, you can set the parameter `geom_big="bbox"`
## - An additional alternative is to use the GBIF download API, see ?downloads
wkt <- "POLYGON((13.26349675655365 52.53991761181831,18.36115300655365 54.11445544219924,
21.87677800655365 53.80418956368524,24.68927800655365 54.217364774722455,28.20490300655365
54.320018299365124,30.49005925655365 52.85948216284084,34.70880925655365 52.753220564427814,
35.93927800655365 50.46131871049754,39.63068425655365 49.55761261299145,40.86115300655365
46.381388009130845,34.00568425655365 45.279102926537,33.30255925655365 48.636868465271846,
30.13849675655365 49.78513301801265,28.38068425655365 47.2236377039631,29.78693425655365
44.6572866068524,27.67755925655365 42.62220075124676,23.10724675655365 43.77542058000212,
24.51349675655365 47.10412345120368,26.79865300655365 49.55761261299145,23.98615300655365
52.00209943876426,23.63459050655365 49.44345313705238,19.41584050655365 47.580567827212114,
19.59162175655365 44.90682206053508,20.11896550655365 42.36297154876359,22.93146550655365
40.651849782081555,25.56818425655365 39.98171166226459,29.61115300655365 40.78507856230178,
32.95099675655365 40.38459278067577,32.95099675655365 37.37491910393631,26.27130925655365
33.65619609886799,22.05255925655365 36.814081996401605,18.71271550655365 36.1072176729021,
18.53693425655365 39.16878677351903,15.37287175655365 38.346355762190846,15.19709050655365
41.578843777436326,12.56037175655365 41.050735748143424,12.56037175655365 44.02872991212046,
15.19709050655365 45.52594200494078,16.42755925655365 48.05271546733352,17.48224675655365
48.86865641518059,10.62677800655365 47.817178329053135,9.57209050655365 44.154980365192,
8.16584050655365 40.51835445724746,6.05646550655365 36.53210972067291,0.9588092565536499
31.583640057148145,-5.54509699344635 35.68001485298146,-6.77556574344635 40.51835445724746,
-9.41228449344635 38.346355762190846,-12.40056574344635 35.10683619158607,-15.74040949344635
38.07010978950028,-14.68572199344635 41.31532459432774,-11.69744074344635 43.64836179231387,
-8.88494074344635 42.88035509418534,-4.31462824344635 43.52103366008421,-8.35759699344635
47.2236377039631,-8.18181574344635 50.12441989397795,-5.01775324344635 49.55761261299145,
-2.73259699344635 46.25998980446569,-1.67790949344635 44.154980365192,-1.32634699344635
39.30493590580802,2.18927800655365 41.44721797271696,4.47443425655365 43.26556960420879,
2.18927800655365 46.7439668697322,1.83771550655365 50.3492841273576,6.93537175655365
49.671505849335254,5.00177800655365 52.32557322466785,7.81427800655365 51.67627099802223,
7.81427800655365 54.5245591562317,10.97834050655365 51.89375191441792,10.97834050655365
55.43241335888528,13.26349675655365 52.53991761181831))"
wkt <- gsub("\n", " ", wkt)
#### Default option with large WKT string fails
# res <- occ_search(geometry = wkt)
#### if WKT too long, with 'geom_big=bbox': makes into bounding box
res <- occ_search(geometry = wkt, geom_big = "bbox")$data
library("rgeos")
library("sp")
wktsp <- readWKT(wkt)
plot(wktsp)
coordinates(res) <- ~decimalLongitude+decimalLatitude
points(res)
#### Or, use 'geom_big=axe'
(res <- occ_search(geometry = wkt, geom_big = "axe"))
##### manipulate essentially number of polygons that result, so number of requests
###### default geom_size is 40
###### fewer calls
(res <- occ_search(geometry = wkt, geom_big = "axe", geom_size=50))
###### more calls
(res <- occ_search(geometry = wkt, geom_big = "axe", geom_size=30))
# Search on country
occ_search(country='US', fields=c('name','country'), limit=20)
isocodes[grep("France", isocodes$name),"code"]
occ_search(country='FR', fields=c('name','country'), limit=20)
occ_search(country='DE', fields=c('name','country'), limit=20)
### separate requests: use a vector of strings
occ_search(country=c('US','DE'), limit=20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_search(country = 'US;DE', 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)
### separate requests: use a vector of strings
occ_search(institutionCode=c("TLMF","ArtDatabanken"), limit=20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_search(institutionCode = "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)
## spell check - only works with the `search` parameter
### spelled correctly - same result as above call
# occ_search(search = "kingfisher", limit=20, spellCheck = TRUE)
### spelled incorrectly - stops with suggested spelling
# occ_search(search = "kajsdkla", limit=20, spellCheck = TRUE)
### spelled incorrectly - stops with many suggested spellings
### and number of results for each
# occ_search(search = "helir", limit=20, spellCheck = TRUE)
# search on repatriated - doesn't work right now
# occ_search(repatriated = "")
# search on phylumKey
occ_search(phylumKey = 7707728, limit = 5)
# search on kingdomKey
occ_search(kingdomKey = 1, limit = 5)
# search on classKey
occ_search(classKey = 216, limit = 5)
# search on orderKey
occ_search(orderKey = 7192402, limit = 5)
# search on familyKey
occ_search(familyKey = 3925, limit = 5)
# search on genusKey
occ_search(genusKey = 1935496, limit = 5)
# search on establishmentMeans
occ_search(establishmentMeans = "INVASIVE", limit = 5)
occ_search(establishmentMeans = "NATIVE", limit = 5)
occ_search(establishmentMeans = "UNCERTAIN", limit = 5)
# search on protocol
occ_search(protocol = "DIGIR", limit = 5)
# search on license
occ_search(license = "CC_BY_4_0", limit = 5)
# search on organismId
occ_search(organismId = "100", limit = 5)
# search on publishingOrg
occ_search(publishingOrg = "28eb1a3f-1c15-4a95-931a-4af90ecb574d", limit = 5)
# search on stateProvince
occ_search(stateProvince = "California", limit = 5)
# search on waterBody
occ_search(waterBody = "AMAZONAS BASIN, RIO JURUA", limit = 5)
# search on locality
res <- occ_search(locality = c("Trondheim", "Hovekilen"), limit = 5)
res$Trondheim$data
res$Hovekilen$data
# 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 errors
# wkt <- 'POINT(45 -122)'
# occ_search(geometry = wkt)
## Faceting
x <- occ_search(facet = "country", limit = 0)
x$facets
x <- occ_search(facet = "establishmentMeans", limit = 10)
x$facets
x$data
x <- occ_search(facet = c("country", "basisOfRecord"), limit = 10)
x$data
x$facets
x$facets$country
x$facets$basisOfRecord
x$facets$basisOfRecord$count
x <- occ_search(facet = "country", facetMincount = 30000000L, limit = 10)
x$facets
x$data
# paging per each faceted variable
(x <- occ_search(
facet = c("country", "basisOfRecord", "hasCoordinate"),
country.facetLimit = 3,
basisOfRecord.facetLimit = 6,
limit = 0
))
x$facets
# You can set limit=0 to get number of results found
occ_search(datasetKey = '7b5d6a48-f762-11e1-a439-00145eb45e9a', limit = 0)$meta
occ_search(scientificName = 'Ursus americanus', limit = 0)$meta
occ_search(scientificName = 'Ursus americanus', limit = 0, return = "meta")
# }
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