Search for GBIF occurrences - simplified for speed

Search for GBIF occurrences - simplified for speed

Usage

occ_data(
  taxonKey = NULL,
  scientificName = NULL,
  country = NULL,
  publishingCountry = NULL,
  hasCoordinate = NULL,
  typeStatus = NULL,
  recordNumber = NULL,
  lastInterpreted = NULL,
  continent = NULL,
  geometry = NULL,
  geom_big = "asis",
  geom_size = 40,
  geom_n = 10,
  recordedBy = NULL,
  recordedByID = NULL,
  identifiedByID = NULL,
  basisOfRecord = NULL,
  datasetKey = NULL,
  eventDate = NULL,
  catalogNumber = NULL,
  year = NULL,
  month = NULL,
  decimalLatitude = NULL,
  decimalLongitude = NULL,
  elevation = NULL,
  depth = NULL,
  institutionCode = NULL,
  collectionCode = NULL,
  hasGeospatialIssue = NULL,
  issue = NULL,
  search = NULL,
  mediaType = NULL,
  subgenusKey = NULL,
  repatriated = NULL,
  phylumKey = NULL,
  kingdomKey = NULL,
  classKey = NULL,
  orderKey = NULL,
  familyKey = NULL,
  genusKey = NULL,
  speciesKey = NULL,
  establishmentMeans = NULL,
  degreeOfEstablishment = NULL,
  protocol = NULL,
  license = NULL,
  organismId = NULL,
  publishingOrg = NULL,
  stateProvince = NULL,
  waterBody = NULL,
  locality = NULL,
  limit = 500,
  start = 0,
  skip_validate = TRUE,
  occurrenceStatus = "PRESENT",
  gadmGid = NULL,
  coordinateUncertaintyInMeters = NULL,
  verbatimScientificName = NULL,
  eventId = NULL,
  identifiedBy = NULL,
  networkKey = NULL,
  verbatimTaxonId = NULL,
  occurrenceId = NULL,
  organismQuantity = NULL,
  organismQuantityType = NULL,
  relativeOrganismQuantity = NULL,
  iucnRedListCategory = NULL,
  lifeStage = NULL,
  isInCluster = NULL,
  distanceFromCentroidInMeters = NULL,
  curlopts = list(http_version = 2)
)

Arguments

taxonKey

(numeric) A taxon key from the GBIF backbone. All included and synonym taxa are included in the search, so a search for aves with taxononKey=212 will match all birds, no matter which species. You can pass many keys to occ_search(taxonKey=c(1,212)).

scientificName

A scientific name from the GBIF backbone. All included and synonym taxa are included in the search.

country

(character) The 2-letter country code (ISO-3166-1) in which the occurrence was recorded. enumeration_country().

publishingCountry

The 2-letter country code (as per ISO-3166-1) of the country in which the occurrence was recorded. See enumeration_country().

hasCoordinate

(logical) Return only occurrence records with lat/long data (TRUE) or all records (FALSE, default).

typeStatus

Type status of the specimen. One of many options.

recordNumber

Number recorded by collector of the data, different from GBIF record number.

lastInterpreted

Date the record was last modified in GBIF, in ISO 8601 format: yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Supports range queries, 'smaller,larger' (e.g., '1990,1991', whereas '1991,1990' wouldn't work).

continent

The source supplied continent.

• "africa"

• "antarctica"

• "asia"

• "europe"

• "north_america"

• "oceania"

• "south_america"

Continent is not inferred but only populated if provided by the dataset publisher. Applying this filter may exclude many relevant records.

geometry

(character) Searches for occurrences inside a polygon in Well Known Text (WKT) format. A WKT shape written as either

• "POINT"

• "LINESTRING"

• "LINEARRING"

• "POLYGON"

• "MULTIPOLYGON"

For Example, "POLYGON((37.08 46.86,38.06 46.86,38.06 47.28,37.08 47.28, 37.0 46.8))". See also the section WKT below.

geom_big

(character) One of "axe", "bbox", or "asis" (default).

geom_size

(integer) An integer indicating size of the cell. Default: 40.

geom_n

(integer) An integer indicating number of cells in each dimension. Default: 10.

recordedBy

(character) The person who recorded the occurrence.

recordedByID

(character) Identifier (e.g. ORCID) for the person who recorded the occurrence

identifiedByID

(character) Identifier (e.g. ORCID) for the person who provided the taxonomic identification of the occurrence.

basisOfRecord

(character) The specific nature of the data record. See here.

• "FOSSIL_SPECIMEN"

• "HUMAN_OBSERVATION"

• "MATERIAL_CITATION"

• "MATERIAL_SAMPLE"

• "LIVING_SPECIMEN"

• "MACHINE_OBSERVATION"

• "OBSERVATION"

• "PRESERVED_SPECIMEN"

• "OCCURRENCE"

datasetKey

(character) The occurrence dataset uuid key. That can be found in the dataset page url. For example, "7e380070-f762-11e1-a439-00145 eb45e9a" is the key for Natural History Museum (London) Collection Specimens.

eventDate

(character) Occurrence date in ISO 8601 format: yyyy, yyyy-MM, yyyy-MM-dd, or MM-dd. Supports range queries, 'smaller,larger' ('1990,1991', whereas '1991,1990' wouldn't work).

catalogNumber

(character) An identifier of any form assigned by the source within a physical collection or digital dataset for the record which may not unique, but should be fairly unique in combination with the institution and collection code.

year

The 4 digit year. A year of 98 will be interpreted as AD 98. Supports range queries, 'smaller,larger' (e.g., '1990,1991', whereas 1991, 1990' wouldn't work).

month

The month of the year, starting with 1 for January. Supports range queries, 'smaller,larger' (e.g., '1,2', whereas '2,1' wouldn't work).

decimalLatitude

Latitude in decimals between -90 and 90 based on WGS84. Supports range queries, 'smaller,larger' (e.g., '25,30', whereas '30,25' wouldn't work).

decimalLongitude

Longitude in decimals between -180 and 180 based on WGS84. Supports range queries (e.g., '-0.4,-0.2', whereas '-0.2,-0.4' wouldn't work).

elevation

Elevation in meters above sea level. Supports range queries, 'smaller,larger' (e.g., '5,30', whereas '30,5' wouldn't work).

depth

Depth in meters relative to elevation. For example 10 meters below a lake surface with given elevation. Supports range queries, 'smaller,larger' (e.g., '5,30', whereas '30,5' wouldn't work).

institutionCode

An identifier of any form assigned by the source to identify the institution the record belongs to.

collectionCode

(character) An identifier of any form assigned by the source to identify the physical collection or digital dataset uniquely within the text of an institution.

hasGeospatialIssue

(logical) Includes/excludes occurrence records which contain spatial issues (as determined in our record interpretation), i.e. hasGeospatialIssue=TRUE returns only those records with spatial issues while hasGeospatialIssue=FALSE includes only records without spatial issues. The absence of this parameter returns any record with or without spatial issues.

issue

(character) One or more of many possible issues with each occurrence record. Issues passed to this parameter filter results by the issue. One of many options. See here for definitions.

search

(character) Query terms. The value for this parameter can be a simple word or a phrase. For example, search="puma"

mediaType

(character) Media type of "MovingImage", "Sound", or "StillImage".

subgenusKey

(numeric) Subgenus classification key.

repatriated

(character) Searches for records whose publishing country is different to the country where the record was recorded in.

phylumKey

(numeric) Phylum classification key.

kingdomKey

(numeric) Kingdom classification key.

classKey

(numeric) Class classification key.

orderKey

(numeric) Order classification key.

familyKey

(numeric) Family classification key.

genusKey

(numeric) Genus classification key.

speciesKey

(numeric) Species classification key.

establishmentMeans

(character) provides information about whether an organism or organisms have been introduced to a given place and time through the direct or indirect activity of modern humans.

• "Introduced"

• "Native"

• "NativeReintroduced"

• "Vagrant"

• "Uncertain"

• "IntroducedAssistedColonisation"

degreeOfEstablishment

(character) Provides information about degree to which an Organism survives, reproduces, and expands its range at the given place and time. One of many options.

protocol

(character) Protocol or mechanism used to provide the occurrence record. One of many options.

license

(character) The type license applied to the dataset or record.

• "CC0_1_0"

• "CC_BY_4_0"

• "CC_BY_NC_4_0"

organismId

(numeric) An identifier for the Organism instance (as opposed to a particular digital record of the Organism). May be a globally unique identifier or an identifier specific to the data set.

publishingOrg

(character) The publishing organization key (a UUID).

stateProvince

(character) The name of the next smaller administrative region than country (state, province, canton, department, region, etc.) in which the Location occurs.

waterBody

(character) The name of the water body in which the locations occur

locality

(character) The specific description of the place.

limit

Number of records to return. Default: 500. Note that the per request maximum is 300, but since we set it at 500 for the function, we do two requests to get you the 500 records (if there are that many). Note that there is a hard maximum of 100,000, which is calculated as the limit+start, so start=99,000 and limit=2000 won't work

start

Record number to start at. Use in combination with limit to page through results. Note that we do the paging internally for you, but you can manually set the start parameter

skip_validate

(logical) whether to skip wellknown::validate_wkt call or not. passed down to check_wkt(). Default: TRUE

occurrenceStatus

(character) Default is "PRESENT". Specify whether search should return "PRESENT" or "ABSENT" data.

gadmGid

(character) The gadm id of the area occurrences are desired from. https://gadm.org/.

coordinateUncertaintyInMeters

A number or range between 0-1,000,000 which specifies the desired coordinate uncertainty. A coordinateUncertainty InMeters=1000 will be interpreted all records with exactly 1000m. Supports range queries, 'smaller,larger' (e.g., '1000,10000', whereas '10000,1000' wouldn't work).

verbatimScientificName

(character) Scientific name as provided by the source.

eventId

(character) identifier(s) for a sampling event.

identifiedBy

(character) names of people, groups, or organizations.

networkKey

(character) The occurrence network key (a uuid) who assigned the Taxon to the subject.

verbatimTaxonId

(character) The taxon identifier provided to GBIF by the data publisher.

occurrenceId

(character) occurrence id from source.

organismQuantity

A number or range which specifies the desired organism quantity. An organismQuantity=5 will be interpreted all records with exactly 5. Supports range queries, smaller,larger (e.g., '5,20', whereas '20,5' wouldn't work).

organismQuantityType

(character) The type of quantification system used for the quantity of organisms. For example, "individuals" or "biomass".

relativeOrganismQuantity

(numeric) A relativeOrganismQuantity=0.1 will be interpreted all records with exactly 0.1 The relative measurement of the quantity of the organism (a number between 0-1). Supports range queries, "smaller,larger" (e.g., '0.1,0.5', whereas '0.5,0.1' wouldn't work).

iucnRedListCategory

(character) The IUCN threat status category.

• "NE" (Not Evaluated)

• "DD" (Data Deficient)

• "LC" (Least Concern)

• "NT" (Near Threatened)

• "VU" (Vulnerable)

• "EN" (Endangered)

• "CR" (Critically Endangered)

• "EX" (Extinct)

• "EW" (Extinct in the Wild)

lifeStage

(character) the life stage of the occurrence. One of many options.

isInCluster

(logical) identify potentially related records on GBIF.

distanceFromCentroidInMeters

A number or range. A value of "2000,*" means at least 2km from known centroids. A value of "0" would mean occurrences exactly on known centroids. A value of "0,2000" would mean within 2km of centroids. Max value is 5000.

curlopts

list of named curl options passed on to HttpClient. see curl::curl_options for curl options

Value

An object of class gbif_data, which is a S3 class list, with slots for metadata (meta) and the occurrence data itself (data), and with attributes listing the user supplied arguments and whether it was a "single" or "many" search; that is, if you supply two values of the datasetKey parameter to searches are done, and it's a "many". meta is a list of length four with offset, limit, endOfRecords and count fields. data is a tibble (aka data.frame)

Note

Maximum number of records you can get with this function is 100,000. See https://www.gbif.org/developer/occurrence

Multiple values passed to a parameter

There are some parameters you can pass multiple values to in a vector, each value of which produces a different request (multiple different requests = c("a","b")). Some parameters allow multiple values to be passed in the same request (multiple same request = "a;b") in a semicolon separated string (e.g., 'a;b'); if given we'll do a single request with that parameter repeated for each value given (e.g., foo=a&foo=b if the parameter is foo).

See article Multiple Values.

Hierarchies

Hierarchies are returned with each occurrence object. There is no option to return them from the API. However, within the occ_search function you can select whether to return just hierarchies, just data, all of data and hierarchies and metadata, or just metadata. If all hierarchies are the same we just return one for you.

curl debugging

You can pass parameters not defined in this function into the call to the GBIF API to control things about the call itself using curlopts. See an example below that passes in the verbose function to get details on the http call.

WKT

Examples of valid WKT objects:

• 'POLYGON((-19.5 34.1, 27.8 34.1, 35.9 68.1, -25.3 68.1, -19.5 34.1))'

• 'MULTIPOLYGON(((-123 38,-116 38,-116 43,-123 43,-123 38)),((-97 41,-93 41,-93 45,-97 45,-97 41)))'

• 'POINT(-120 40)'

• 'LINESTRING(3 4,10 50,20 25)'

Note that GBIF expects counter-clockwise winding order for WKT. You can supply clockwise WKT, but GBIF treats it as an exclusion, so you get all data not inside the WKT area. occ_download() behaves differently in that you should simply get no data back at all with clockwise WKT.

Long WKT

Options for handling long WKT strings: Note that long WKT strings are specially handled when using occ_search or occ_data. Here are the three options for long WKT strings (> 1500 characters), set one of these three via the parameter geom_big:

• asis - the default setting. This means we don't do anything internally. That is, we just pass on your WKT string just as we've done before in this package.

• axe - this option uses the sf package to chop up your WKT string in to many polygons, which then leads to a separate data request for each polygon piece, then we combine all dat back together to give to you. Note that if your WKT string is not of type polygon, we drop back to asisas there's no way to chop up linestrings, etc. This option will in most cases be slower than the other two options. However, this polygon splitting approach won't have the problem of the disconnect between how many records you want and what you actually get back as with the bbox option.

  This method uses sf::st_make_grid and sf::st_intersection, which has two parameters cellsize and n. You can tweak those parameters here by tweaking geom_size and geom_n. geom_size seems to be more useful in toggling the number of WKT strings you get back.

  See wkt_parse to manually break make WKT bounding box from a larger WKT string, or break a larger WKT string into many smaller ones.

• bbox - this option checks whether your WKT string is longer than 1500 characters, and if it is we create a bounding box from the WKT, do the GBIF search with that bounding box, then prune the resulting data to only those occurrences in your original WKT string. There is a big caveat however. Because we create a bounding box from the WKT, and the limit parameter determines some subset of records to get, then when we prune the resulting data to the WKT, the number of records you get could be less than what you set with your limit parameter. However, you could set the limit to be high enough so that you get all records back found in that bounding box, then you'll get all the records available within the WKT.

Counts

There is a slight difference in the way records are counted here vs. results from occ_count. For equivalent outcomes, in this function use hasCoordinate=TRUE, and hasGeospatialIssue=FALSE to have the same outcome using occ_count with isGeoreferenced=TRUE

occ_data vs. occ_search

This does nearly the same thing as occ_search(), but is simplified for speed, and is for the most common use case where user just wants occurrence data, and not other information like taxon hierarchies and media (e.g., images). Alot of time in occ_search() is used parsing data to be more useable downstream. We do less of that in this function.

There are a number of data fields GBIF returns that we drop to speed up processing time within R. These fields take extra time to process because they are deeply nested and so take extra time to check if they are empty or not, and if not, figure out how to parse them into a data.frame. The fields are:

• gadm

• media

• facts

• relations

• extensions

• identifiers

• recordedByIDs

• identifiedByIDs

To get these fields use occ_search() instead.

References

https://www.gbif.org/developer/occurrence#search

See also

downloads(), occ_search()

Examples

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")
}