if (FALSE) {
(key <- name_backbone(name='Encelia californica')$speciesKey)
occ_data(taxonKey = key, limit = 4)
(res <- occ_data(taxonKey = key, limit = 400))
# Return 20 results, this is the default by the way
(key <- name_suggest(q='Helianthus annuus', rank='species')$data$key[1])
occ_data(taxonKey=key, limit=20)
# Instead of getting a taxon key first, you can search for a name directly
## However, note that using this approach (with \code{scientificName="..."})
## you are getting synonyms too. The results for using \code{scientifcName}
## and \code{taxonKey} parameters are the same in this case, but I wouldn't
## be surprised if for some names they return different results
occ_data(scientificName = 'Ursus americanus', curlopts=list(verbose=TRUE))
key <- name_backbone(name = 'Ursus americanus', rank='species')$usageKey
occ_data(taxonKey = key)
# Search by dataset key
occ_data(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a', limit=10)
# Search by catalog number
occ_data(catalogNumber="49366", limit=10)
## separate requests: use a vector of strings
occ_data(catalogNumber=c("49366","Bird.27847588"), limit=10)
## one request, many instances of same parameter: use semi-colon sep. string
occ_data(catalogNumber="49366;Bird.27847588", limit=10)
# Use paging parameters (limit and start) to page. Note the different results
# for the two queries below.
occ_data(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a',start=10,limit=5)
occ_data(datasetKey='7b5d6a48-f762-11e1-a439-00145eb45e9a',start=20,limit=5)
# Many dataset keys
## separate requests: use a vector of strings
occ_data(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_data(datasetKey = v, limit=20)
# Search by recorder
occ_data(recordedBy="smith", limit=20)
# Many collector names
## separate requests: use a vector of strings
occ_data(recordedBy=c("smith","BJ Stacey"), limit=10)
## one request, many instances of same parameter: use semi-colon sep. string
occ_data(recordedBy="smith;BJ Stacey", limit=10)
# recordedByID
occ_data(recordedByID="https://orcid.org/0000-0003-1691-239X", limit=20)
## many at once
### separate searches
ids <- c("https://orcid.org/0000-0003-1691-239X",
"https://orcid.org/0000-0001-7569-1828",
"https://orcid.org/0000-0002-0596-5376")
res <- occ_data(recordedByID=ids, limit=20)
res[[1]]$data$recordedByIDs[[1]]
res[[2]]$data$recordedByIDs[[1]]
res[[3]]$data$recordedByIDs[[1]]
### all in one search
res <- occ_data(recordedByID=paste0(ids, collapse=";"), limit=20)
unique(vapply(res$data$recordedByIDs, "[[", "", "value"))
# identifiedByID
occ_data(identifiedByID="https://orcid.org/0000-0003-4710-2648", limit=20)
# Pass in curl options for extra fun
occ_data(taxonKey=2433407, limit=20, curlopts=list(verbose=TRUE))
occ_data(taxonKey=2433407, limit=20,
curlopts = list(
noprogress = FALSE,
progressfunction = function(down, up) {
cat(sprintf("up: %d | down %d\n", up, down))
return(TRUE)
}
)
)
# occ_data(taxonKey=2433407, limit=20, 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)$data$key[1], USE.NAMES=FALSE)
## separate requests: use a vector of strings
occ_data(taxonKey = keys, limit=5)
## one request, many instances of same parameter: use semi-colon sep. string
occ_data(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_data(scientificName = 'Pulsatilla patens', limit=5)
# Search on latitidue and longitude
occ_data(decimalLatitude=40, decimalLongitude=-120, limit = 10)
# Search on a bounding box
## in well known text format
### polygon
occ_data(geometry='POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))',
limit=20)
### multipolygon
wkt <- 'MULTIPOLYGON(((-123 38,-116 38,-116 43,-123 43,-123 38)),
((-97 41,-93 41,-93 45,-97 45,-97 41)))'
occ_data(geometry = gsub("\n\\s+", "", wkt), limit = 20)
### polygon and taxonkey
key <- name_suggest(q='Aesculus hippocastanum')$data$key[1]
occ_data(taxonKey=key,
geometry='POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))',
limit=20)
## or using bounding box, converted to WKT internally
occ_data(geometry=c(-125.0,38.4,-121.8,40.9), limit=20)
## you can seaerch on many geometry objects
### separate requests: use a vector of strings
wkts <-
c('POLYGON((-102.2 46,-102.2 43.7,-93.9 43.7,-93.9 46,-102.2 46))',
'POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 10.1))')
occ_data(geometry = wkts, limit=20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(geometry = paste0(wkts, collapse = ";"), limit=20)
# Search on a long WKT string - too long for a GBIF search API request
## By default, a very long WKT string will likely cause a request failure as
## GBIF only handles strings up to about 1500 characters long. You can leave as is, or
## - Alternatively, you can choose to break up your polygon into many, and do a
## data request on each piece, and the output is put back together (see below)
## - Or, 2nd alternatively, you could use the GBIF download API
wkt <- "POLYGON((-9.178796777343678 53.22769021556159,
-12.167078027343678 51.56540789297837,
-12.958093652343678 49.78333685689162,-11.024499902343678 49.21251756301334,
-12.079187402343678 46.68179685941719,-15.067468652343678 45.83103608186854,
-15.770593652343678 43.58271629699817,-15.067468652343678 41.57676278827219,
-11.815515527343678 40.44938999172728,-12.958093652343678 37.72112962230871,
-11.639734277343678 36.52987439429357,-8.299890527343678 34.96062625095747,
-8.739343652343678 32.62357394385735,-5.223718652343678 30.90497915232165,
1.1044063476563224 31.80562077746643,1.1044063476563224 30.754036557416256,
6.905187597656322 32.02942785462211,5.147375097656322 32.99292810780193,
9.629796972656322 34.164474406524725,10.860265722656322 32.91918014319603,
14.551671972656322 33.72700959356651,13.409093847656322 34.888564192275204,
16.748937597656322 35.104560368110114,19.561437597656322 34.81643887792552,
18.594640722656322 36.38849705969625,22.989171972656322 37.162874858929854,
19.825109472656322 39.50651757842751,13.760656347656322 38.89353140585116,
14.112218847656322 42.36091601976124,10.596593847656322 41.11488736647705,
9.366125097656322 43.70991402658437,5.059484472656322 42.62015372417812,
2.3348750976563224 45.21526500321446,-0.7412967773436776 46.80225692528942,
6.114171972656322 47.102229890207894,8.047765722656322 45.52399303437107,
12.881750097656322 48.22681126957933,9.190343847656322 48.693079457106684,
8.750890722656322 50.68283120621287,5.059484472656322 50.40356146487845,
4.268468847656322 52.377558897655156,1.4559688476563224 53.28027243658647,
0.8407344726563224 51.62000971578333,0.5770625976563224 49.32721423860726,
-2.5869999023436776 49.49875947592088,-2.4991092773436776 51.18135535408638,
-2.0596561523436776 52.53822562473851,-4.696374902343678 51.67454591918756,
-5.311609277343678 50.009802108095776,-6.629968652343678 48.75106196817059,
-7.684656152343678 50.12263634382465,-6.190515527343678 51.83776110910459,
-5.047937402343678 54.267098895684235,-6.893640527343678 53.69860705549198,
-8.915124902343678 54.77719740243195,-12.079187402343678 54.52294465763567,
-13.573328027343678 53.437631551347174,
-11.288171777343678 53.48995552517918,
-9.178796777343678 53.22769021556159))"
wkt <- gsub("\n", " ", wkt)
#### Default option with large WKT string fails
# res <- occ_data(geometry = wkt)
#### if WKT too long, with 'geom_big=bbox': makes into bounding box
if (interactive()){
res <- occ_data(geometry = wkt, geom_big = "bbox")
}
#### Or, use 'geom_big=axe'
(res <- occ_data(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_data(geometry = wkt, geom_big = "axe", geom_size=50))
###### more calls
(res <- occ_data(geometry = wkt, geom_big = "axe", geom_size=30))
# Search on country
occ_data(country='US', limit=20)
isocodes[grep("France", isocodes$name),"code"]
occ_data(country='FR', limit=20)
occ_data(country='DE', limit=20)
### separate requests: use a vector of strings
occ_data(country=c('US','DE'), limit=20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(country = 'US;DE', limit=20)
# Get only occurrences with lat/long data
occ_data(taxonKey=key, hasCoordinate=TRUE, limit=20)
# Get only occurrences that were recorded as living specimens
occ_data(basisOfRecord="LIVING_SPECIMEN", hasCoordinate=TRUE, limit=20)
## multiple values in a vector = a separate request for each value
occ_data(taxonKey=key,
basisOfRecord=c("OBSERVATION", "HUMAN_OBSERVATION"), limit=20)
## mutiple values in a single string, ";" separated = one request including all values
occ_data(taxonKey=key,
basisOfRecord="OBSERVATION;HUMAN_OBSERVATION", limit=20)
# Get occurrences for a particular eventDate
occ_data(taxonKey=key, eventDate="2013", limit=20)
occ_data(taxonKey=key, year="2013", limit=20)
occ_data(taxonKey=key, month="6", limit=20)
# Get occurrences based on depth
key <- name_backbone(name='Salmo salar', kingdom='animals')$speciesKey
occ_data(taxonKey=key, depth=1, limit=20)
# Get occurrences based on elevation
key <- name_backbone(name='Puma concolor', kingdom='animals')$speciesKey
occ_data(taxonKey=key, elevation=50, hasCoordinate=TRUE, limit=20)
# Get occurrences based on institutionCode
occ_data(institutionCode="TLMF", limit=20)
### separate requests: use a vector of strings
occ_data(institutionCode=c("TLMF","ArtDatabanken"), limit=20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(institutionCode = "TLMF;ArtDatabanken", limit=20)
# Get occurrences based on collectionCode
occ_data(collectionCode="Floristic Databases MV - Higher Plants", limit=20)
### separate requests: use a vector of strings
occ_data(collectionCode=c("Floristic Databases MV - Higher Plants",
"Artport"), limit = 20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(collectionCode = "Floristic Databases MV - Higher Plants;Artport",
limit = 20)
# Get only those occurrences with spatial issues
occ_data(taxonKey=key, hasGeospatialIssue=TRUE, limit=20)
# Search using a query string
occ_data(search="kingfisher", limit=20)
# search on repatriated - doesn't work right now
# occ_data(repatriated = "")
# search on phylumKey
occ_data(phylumKey = 7707728, limit = 5)
# search on kingdomKey
occ_data(kingdomKey = 1, limit = 5)
# search on classKey
occ_data(classKey = 216, limit = 5)
# search on orderKey
occ_data(orderKey = 7192402, limit = 5)
# search on familyKey
occ_data(familyKey = 3925, limit = 5)
# search on genusKey
occ_data(genusKey = 1935496, limit = 5)
# search on establishmentMeans
occ_data(establishmentMeans = "INVASIVE", limit = 5)
occ_data(establishmentMeans = "NATIVE", limit = 5)
occ_data(establishmentMeans = "UNCERTAIN", limit = 5)
### separate requests: use a vector of strings
occ_data(establishmentMeans = c("INVASIVE", "NATIVE"), limit = 5)
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(establishmentMeans = "INVASIVE;NATIVE", limit = 5)
# search on protocol
occ_data(protocol = "DIGIR", limit = 5)
# search on license
occ_data(license = "CC_BY_4_0", limit = 5)
# search on organismId
occ_data(organismId = "100", limit = 5)
# search on publishingOrg
occ_data(publishingOrg = "28eb1a3f-1c15-4a95-931a-4af90ecb574d", limit = 5)
# search on stateProvince
occ_data(stateProvince = "California", limit = 5)
# search on waterBody
occ_data(waterBody = "pacific ocean", limit = 5)
# search on locality
occ_data(locality = "Trondheim", limit = 5)
### separate requests: use a vector of strings
res <- occ_data(locality = c("Trondheim", "Hovekilen"), limit = 5)
res$Trondheim$data
res$Hovekilen$data
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(locality = "Trondheim;Hovekilen", limit = 5)
# Range queries
## See Detail for parameters that support range queries
occ_data(depth='50,100', limit = 20)
### this is not a range search, but does two searches for each depth
occ_data(depth=c(50,100), limit = 20)
## Range search with year
occ_data(year='1999,2000', limit=20)
## Range search with latitude
occ_data(decimalLatitude='29.59,29.6', limit = 20)
## Range search with distanceFromCentroidInMeters
occ_data(distanceFromCentroidInMeters = "2000,*") # at least 2km from centroids
occ_data(distanceFromCentroidInMeters = "0,2000") # close to centroids within 2km
occ_data(distanceFromCentroidInMeters = 0) # exactly on centroids
# Search by specimen type status
## Look for possible values of the typeStatus parameter looking at the typestatus dataset
occ_data(typeStatus = 'allotype', limit = 20)$data[,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_data(recordNumber = 1, limit = 20)$data[,c('name','recordNumber','recordedBy')]
# Search by last time interpreted: Date the record was last modified in GBIF
## The lastInterpreted parameter accepts ISO 8601 format dates, including
## yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Range queries are accepted for lastInterpreted
occ_data(lastInterpreted = '2016-04-02', limit = 20)
# Search for occurrences with images
occ_data(mediaType = 'StillImage', limit = 20)
occ_data(mediaType = 'MovingImage', limit = 20)
occ_data(mediaType = 'Sound', limit = 20)
# Search by continent
## One of africa, antarctica, asia, europe, north_america, oceania, or
## south_america
occ_data(continent = 'south_america', limit = 20)$meta
occ_data(continent = 'africa', limit = 20)$meta
occ_data(continent = 'oceania', limit = 20)$meta
occ_data(continent = 'antarctica', limit = 20)$meta
### separate requests: use a vector of strings
occ_data(continent = c('south_america', 'oceania'), limit = 20)
### one request, many instances of same parameter: use semi-colon sep. string
occ_data(continent = 'south_america;oceania', limit = 20)
# Query based on issues - see Details for options
## one issue
x <- occ_data(taxonKey=1, issue='DEPTH_UNLIKELY', limit = 20)
x$data[,c('name','key','decimalLatitude','decimalLongitude','depth')]
## two issues
occ_data(taxonKey=1, issue=c('DEPTH_UNLIKELY','COORDINATE_ROUNDED'), limit = 20)
# Show all records in the Arizona State Lichen Collection that cant be matched to the GBIF
# backbone properly:
occ_data(datasetKey='84c0e1a0-f762-11e1-a439-00145eb45e9a',
issue=c('TAXON_MATCH_NONE','TAXON_MATCH_HIGHERRANK'), limit = 20)
# Parsing output by issue
(res <- occ_data(geometry='POLYGON((30.1 10.1,40 40,20 40,10 20,30.1 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")
}
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