A programmatic interface to many species occurrence data sources, including Global Biodiversity Information Facility ('GBIF'), 'USGSs' Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist', Berkeley 'Ecoinformatics' Engine, 'eBird', Integrated Digitized 'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes functionality for retrieving species occurrence data, and combining those data.
spocc = SPecies OCCurrence data
At rOpenSci, we have been writing R packages to interact with many sources of species occurrence data, including GBIF, Vertnet, BISON, iNaturalist, the Berkeley ecoengine, and eBird. Other databases are out there as well, which we can pull in.
spocc is an R package to query and collect species occurrence data from many sources. The goal is to to create a seamless search experience across data sources, as well as creating unified outputs across data sources.
spocc currently interfaces with nine major biodiversity repositories
Global Biodiversity Information Facility (GBIF) (via
GBIF is a government funded open data repository with several partner organizations with the express goal of providing access to data on Earth's biodiversity. The data are made available by a network of member nodes, coordinating information from various participant organizations and government agencies.
Berkeley Ecoengine (via
The ecoengine is an open API built by the Berkeley Initiative for Global Change Biology. The repository provides access to over 3 million specimens from various Berkeley natural history museums. These data span more than a century and provide access to georeferenced specimens, species checklists, photographs, vegetation surveys and resurveys and a variety of measurements from environmental sensors located at reserves across University of California's natural reserve system.
iNaturalist iNaturalist provides access to crowd sourced citizen science data on species observations.
rgbif, ecoengine, and
rbison (see below), VertNet provides access to more than 80 million vertebrate records spanning a large number of institutions and museums primarly covering four major disciplines (mammology, herpetology, ornithology, and icthyology). Note that we don't currenlty support VertNet data in this package, but we should soon
Biodiversity Information Serving Our Nation (via
Built by the US Geological Survey's core science analytic team, BISON is a portal that provides access to species occurrence data from several participating institutions.
ebird is a database developed and maintained by the Cornell Lab of Ornithology and the National Audubon Society. It provides real-time access to checklist data, data on bird abundance and distribution, and communtiy reports from birders.
iDigBio facilitates the digitization of biological and paleobiological specimens and their associated data, and houses specimen data, as well as providing their specimen data via RESTful web services.
OBIS OBIS (Ocean Biogeographic Information System) allows users to search marine species datasets from all of the world's oceans.
Atlas of Living Australia ALA (Atlas of Living Australia) contains information on all the known species in Australia aggregated from a wide range of data providers: museums, herbaria, community groups, government departments, individuals and universities; it contains more than 50 million occurrence records.
The inspiration for this comes from users requesting a more seamless experience across data sources, and from our work on a similar package for taxonomy data (taxize).
BEWARE: In cases where you request data from multiple providers, especially when including GBIF, there could be duplicate records since many providers' data eventually ends up with GBIF. See
?spocc_duplicates, after installation, for more.
Stable version from CRAN
install.packages("spocc", dependencies = TRUE)
Or the development version from GitHub
Get data from GBIF
(out <- occ(query = 'Accipiter striatus', from = 'gbif', limit = 100))#> Searched: gbif#> Occurrences - Found: 737,289, Returned: 100#> Search type: Scientific#> gbif: Accipiter striatus (100)
Just gbif data
out$gbif#> Species [Accipiter striatus (100)]#> First 10 rows of [Accipiter_striatus]#>#> # A tibble: 100 x 87#> name longitude latitude prov issues key datasetKey publishingOrgKey#> <chr> <dbl> <dbl> <chr> <chr> <int> <chr> <chr>#> 1 Acci… -104. 20.7 gbif cdrou… 1.81e9 50c9509d-… 28eb1a3f-1c15-4…#> 2 Acci… -98.6 33.8 gbif cdrou… 1.81e9 50c9509d-… 28eb1a3f-1c15-4…#> 3 Acci… -74.1 40.1 gbif cdrou… 1.81e9 50c9509d-… 28eb1a3f-1c15-4…#> 4 Acci… -122. 38.0 gbif cdrou… 1.80e9 50c9509d-… 28eb1a3f-1c15-4…...
Get fine-grained detail over each data source by passing on parameters to the packge rebird in this example.
(out <- occ(query = 'Setophaga caerulescens', from = 'gbif', gbifopts = list(country = 'US')))#> Searched: gbif#> Occurrences - Found: 239,219, Returned: 500#> Search type: Scientific#> gbif: Setophaga caerulescens (500)
Get just gbif data
out$gbif#> Species [Setophaga caerulescens (500)]#> First 10 rows of [Setophaga_caerulescens]#>#> # A tibble: 500 x 108#> name longitude latitude prov issues key datasetKey publishingOrgKey#> <chr> <dbl> <dbl> <chr> <chr> <int> <chr> <chr>#> 1 Seto… -80.3 25.7 gbif cdrou… 1.81e9 50c9509d-… 28eb1a3f-1c15-4…#> 2 Seto… -80.3 25.8 gbif cdrou… 1.81e9 50c9509d-… 28eb1a3f-1c15-4…#> 3 Seto… -81.4 28.6 gbif cdrou… 1.84e9 50c9509d-… 28eb1a3f-1c15-4…#> 4 Seto… -77.3 39.0 gbif cdrou… 1.84e9 50c9509d-… 28eb1a3f-1c15-4…#> 5 Seto… -83.2 41.6 gbif cdrou… 1.88e9 50c9509d-… 28eb1a3f-1c15-4…#> 6 Seto… -74.0 40.8 gbif cdrou… 1.84e9 50c9509d-… 28eb1a3f-1c15-4…#> 7 Seto… -80.8 35.5 gbif cdrou… 1.85e9 50c9509d-… 28eb1a3f-1c15-4…#> 8 Seto… -97.2 26.1 gbif cdrou… 1.84e9 50c9509d-… 28eb1a3f-1c15-4…#> 9 Seto… -80.3 25.8 gbif cdrou… 1.85e9 50c9509d-… 28eb1a3f-1c15-4…#> 10 Seto… -77.1 38.9 gbif cdrou… 1.84e9 50c9509d-… 28eb1a3f-1c15-4…#> # ... with 490 more rows, and 100 more variables: networkKeys <list>,#> # installationKey <chr>, publishingCountry <chr>, protocol <chr>,#> # lastCrawled <chr>, lastParsed <chr>, crawlId <int>,#> # basisOfRecord <chr>, taxonKey <int>, kingdomKey <int>,#> # phylumKey <int>, classKey <int>, orderKey <int>, familyKey <int>,#> # genusKey <int>, acceptedTaxonKey <int>, scientificName <chr>,#> # acceptedScientificName <chr>, kingdom <chr>, phylum <chr>,#> # order <chr>, family <chr>, genus <chr>, genericName <chr>,#> # specificEpithet <chr>, taxonRank <chr>, taxonomicStatus <chr>,#> # dateIdentified <chr>, coordinateUncertaintyInMeters <dbl>,#> # stateProvince <chr>, year <int>, month <int>, day <int>,#> # eventDate <date>, modified <chr>, lastInterpreted <chr>,#> # references <chr>, license <chr>, geodeticDatum <chr>, class <chr>,#> # countryCode <chr>, country <chr>, rightsHolder <chr>,#> # identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,#> # verbatimLocality <chr>, gbifID <chr>, collectionCode <chr>,#> # occurrenceID <chr>, taxonID <chr>, catalogNumber <chr>,#> # recordedBy <chr>, `` <chr>,#> # institutionCode <chr>, rights <chr>, eventTime <chr>,#> # occurrenceRemarks <chr>,#> # `` <chr>,#> # identificationID <chr>, informationWithheld <chr>,#> # nomenclaturalCode <chr>, locality <chr>, vernacularName <chr>,#> # fieldNotes <chr>, verbatimElevation <chr>, behavior <chr>,#> # higherClassification <chr>, sex <chr>, lifeStage <chr>,#> # establishmentMeans <chr>, infraspecificEpithet <chr>, continent <chr>,#> # recordNumber <chr>, higherGeography <chr>, dynamicProperties <chr>,#> # endDayOfYear <chr>, georeferenceVerificationStatus <chr>,#> # county <chr>, language <chr>, type <chr>, preparations <chr>,#> # occurrenceStatus <chr>, startDayOfYear <chr>,#> # bibliographicCitation <chr>, accessRights <chr>, institutionID <chr>,#> # dataGeneralizations <chr>, organismID <chr>,#> # ownerInstitutionCode <chr>, datasetID <chr>, collectionID <chr>,#> # habitat <chr>, georeferencedDate <chr>, georeferencedBy <chr>,#> # georeferenceProtocol <chr>, otherCatalogNumbers <chr>,#> # georeferenceSources <chr>, identificationRemarks <chr>,#> # individualCount <int>
Get data from many sources in a single call
ebirdopts <- list(loc = 'CA') # search in Canada onlygbifopts <- list(country = 'US') # search in United States onlyout <- occ(query = 'Setophaga caerulescens', from = c('gbif','bison','inat','ebird'),gbifopts = gbifopts, ebirdopts = ebirdopts, limit = 50)dat <- occ2df(out)head(dat); tail(dat)#> # A tibble: 6 x 6#> name longitude latitude prov date key#> <chr> <chr> <chr> <chr> <date> <chr>#> 1 Setophaga caerulescens -80.347459 25.743763 gbif 2018-01-20 1806338790#> 2 Setophaga caerulescens -80.342233 25.77536 gbif 2018-01-19 1805421161#> 3 Setophaga caerulescens -81.355815 28.569623 gbif 2018-03-14 1837766480#> 4 Setophaga caerulescens -83.192381 41.627135 gbif 2018-04-28 1880571743#> 5 Setophaga caerulescens -77.254868 39.006651 gbif 2018-04-29 1841263350#> 6 Setophaga caerulescens -73.965355 40.782865 gbif 2018-04-29 1841260747#> # A tibble: 6 x 6#> name longitude latitude prov date key#> <chr> <chr> <chr> <chr> <date> <chr>#> 1 Setophaga caerulescens -63.4497222 44.5938889 ebird 2018-11-08 <NA>#> 2 Setophaga caerulescens -97.22659 49.8759422 ebird 2018-11-07 <NA>#> 3 Setophaga caerulescens -97.227492 49.876486 ebird 2018-11-07 <NA>#> 4 Setophaga caerulescens -79.3765 43.6799722 ebird 2018-11-06 <NA>#> 5 Setophaga caerulescens -79.6037 43.516773 ebird 2018-11-03 <NA>#> 6 Setophaga caerulescens -84.3526679 46.5101339 ebird 2018-11-03 <NA>
spoccin R doing
citation(package = 'spocc')
occ()gains new parameter
dateto do date range based searches across data sources without having to know the vagaries of each data source (#181)
print.occdatindso that empty data.frame's don't throw tibble warnings (#184)
stand_dates()due to ALA giving back a timestamp now (#182) (#185)
wicketC++ based package instead. So you no longer need V8 which should make installation easier on some platforms. (#172)
crulfor HTTP reqeusts (#174)
as.*()functions can now pass on curl options to the http client (#177)
foo_ala()- the internal plugin for
occ()that handles ALA queries: changed query from full text query using
q=taxon_name="foo bar"- in addition, improved error handling as sometimes
occurrencesslot is returned in results but is empty, whereas before it seemd to always be absent if no results (#178)
occ2df()more robust to varied inputs - allowing for users that may on purpose or not have a subset of the data source slots normally in the
occdatclass object (#171)
rvertnet, a dependency dealing with data from Vertnet, was failing on certain searches.
rvertnetwas fixed and a new version on CRAN now. No changes here other than requiring the new version of
inherits(), and namespace all
occ()now allows queries that only pass
fromand one of the data source opts params (e.g.,
gbifopts) - allows specifying any options passed down to the internal functions used to do data queries without having to use the other params in
tibblefor representing data.frames (#164)
httr::content()calls to parse raw data from web requests (#160)
ridigbioas its on CRAN - was using internal fxns prior to this (#154)
has_coordsalso fixed. (#161)
data.frame()to set a
data.tablestyle table to a
vertnetas an option to
occ_options()to get the options for passing to
print.occdatind()- which in last version introduced a bug in this print method - wasn't fatal as only applied to empty slots in the output of a call to
occ(), but nonetheless, not good (#159)
data.tablefor fast list to data.frame
as.vertnet()to coerce various inputs (e.g., result from
occ2df(), or a key itself) to occurrence data objects (#142)
occ()gains two parameters
pageto facilitate paging through results across data sources, instead of having to page individually for each data source. Some sources use the
startparameter, while others use the
pageparameter. See Paging section in
?occfor details on Paging (#140)
wkt_vis()now works with WKT polygons with multipe polygons, e.g.,
spocc::wkt_vis("POLYGON((-125 38.4, -121.8 38.4, -121.8 40.9, -125 40.9, -125 38.4), (-115 22.4, -111.8 22.4, -111.8 30.9, -115 30.9, -115 22.4))")(#147)
print.occdatind()to print more helpful info when a geometry search is used as opposed to a taxonomy based search (#149)
print.occdatind()to not fail when first element not present; proceeds to next slot with data (#143)
occ()failed when multiple
geometryelements passed in along with taxonomic names (#146)
occ2df()for combining outputs to not fail when AntWeb doesn't give back dates (#144) (#145) - thanks @timcdlucas
occ2df()to not fail when date field missing (#141)
occ()function. Each data source is taken care of in a separate package or set of wrapper functions, and the man file now details what API parameters are being queried (#138)
Datetimevariable changed to
occurrenceIDvariable changed to
occ()gains new parameter
has_coords- a global parameter (except for ebird and bison) to return only records with lat/long data. (#128)
rank(#133) parameters dropped from
occ()is printed, we now include a message that total count of records found (not returned) is not completely known if ebird is included, because eBird does not include data on records found on their servers with requests to their API (#111)
as.gbif) for most data sources. These functions take in occurrence keys or sets of keys, and retrieve detailed occurrence record data for each key (#112)
occ2df()now returns more fields. This function collapses all essential fields that are easy to get in all data sources:
keyfield is the occurrence key for each record, which you can use to keep track of individual records, get more data on the record, etc. (#103) (#108)
inspect()- takes output from
occ()or individual occurrence keys and gets detailed occurrence data.
methods. No longer importing:
leafletR. Pkgs removed mostly due to splitting off some functionality into
spoccutils. related issues: (#131) (#132)
wkt_vis()now only has an option to view a WKT shape in the browser.
gistrnow to post interactive geojson maps on GitHub gists (#100)
rgbifnow must be
v0.7.7or greater (the latest version on CRAN).
occ2sp()removed. The function
occ_to_sp()function is the working version. (#97)
\dontrunin examples as requested by CRAN maintainers (#99)
occ_names()to search only for taxonomic names. The goal here is to use ths function if there is some question about what names you want to use to search for occurrences with. (#84). Suggested by @jarioksa
occ_names_options()to quickly get parameter options to pass to
summary()method for the
S3object that is output from
occ()documentation file, at package startup), we make it clear that there could be duplicate records returned in certain scenarios. And a new documentation page detailing what to watch out for:
limitto each functions options parameter, and it will work. Each data source can have a different parameter internally from
limit, but now internally within
spocc, we allow you to use
limitso you don't have to know what the data source specific parameter is. (#81)
occ_options()gains new parameter
whereto print either in the console or to open man file in the IDE, or prints to console in command line R.
occ()gains new parameter
calloptsto pass on curl debugging options to
wkt_vis()now by default plots a well known text area (WKT) on an interactive mapbox map in your default browser. New parameter
whichallows you to choose the interactive map or a static ggplot2 map. (#70)
occ()gains new class. In the previous version of this package, a
data.framewas printed. Now the data is assigned the object
occdatind(short for occdat individual).
occ()now uses a print method for the
occdatindclass, adopted from
dplyrthat prints a brief
data.frame, with columns wrapped to fit the width of your console, and additional columns not printed given at bottom with their class type. Note that the print behavior for the resulting object of an
occ()call remains the same. (#69) (#74)
whiskeras a package import to use in the
mapggplot()accepted the output of
occ(), of class
occdat, while the other two functions for mapping,
data.frame. Now all three functions accept the output of
occ(), an object of class
metaslot in each returned object (indexed by
object$meta) contains spots for
found, to designate number of records returned, and number of records found. (#64)
rgbif. A number of input and output parameter names changed. A new version of
rgbifwas pushed to CRAN. (#56)
clean_spocc()started (not finished yet) to attempt to clean data. For example, one use case is removing impossible lat/long values (i.e., longitue values greater than absolute 180). Another, not implemented yet, is to remove points that are not in the country or habitat your points are supposed to be in. (#44)
fixnames()to trim species names with optional input parameters to make data easier to use for mapping.
wkt_vis()to visualize a WKT (well-known text) area on a map. Uses
ggmapto pull down a Google map so that the visualization has some geographic and natural earth context. We'll soon introduce an interactive version of this function that will bring up a small Shiny app to draw a WKT area, then return those coordinates to your R session. (#34)