The rOpenSci projects aims to provide programmatic access to scientific data repositories on the web. A vast majority of the packages in our current suite retrieve some form of biodiversity or taxonomic data. Since several of these datasets have been georeferenced, it provides numerous opportunities for visualizing species distributions, building species distribution maps, and for using it analyses such as species distribution models. In an effort to streamline access to these data, we have developed a package called spocc, which provides a unified API to all the biodiversity sources that we provide. The obvious advantage is that a user can interact with a common API and not worry about the nuances in syntax that differ between packages. As more data sources come online, users can access even more data without significant changes to their code. However, it is important to note that spocc will never replicate the full functionality that exists within specific packages. Therefore users with a strong interest in one of the specific data sources listed below would benefit from familiarising themselves with the inner working of the appropriate packages.
spocc currently interfaces with eight major biodiversity repositories
Global Biodiversity Information Facility (GBIF) (via rgbif)
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 ecoengine)
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.
VertNet (via rvertnet)
Similar to 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 rbison)
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 (via rebird)
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.
AntWeb (via AntWeb)
AntWeb is the world's largest online database of images, specimen records, and natural history information on ants. It is community driven and open to contribution from anyone with specimen records, natural history comments, or images.
iDigBio (via ridigbio)
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.
Important Note: It's important to keep in mind that several data providers interface with many of the above mentioned repositories. This means that occurence data obtained from BISON may be duplicates of data that are also available through GBIF. We do not have a way to resolve these duplicates or overlaps at this time but it is an issue we are hoping to resolve in future versions of the package. See ?spocc_duplicates, after installation, for more.
The most significant function in spocc is the occ (short for occurrence) function. occ takes a query, often a species name, and searches across all data sources specified in the from argument. For example, one can search for all occurrences of Sharp-shinned Hawks (Accipiter striatus) from the GBIF database with the following R call.
library('spocc')
(df <- occ(query = 'Accipiter striatus', from = 'gbif'))
#> Searched: gbif
#> Occurrences - Found: 529,063, Returned: 500
#> Search type: Scientific
#> gbif: Accipiter striatus (500)
The data returned are part of a S3 class called occdat. This class has slots for each of the data sources described above. One can easily switch the source by changing the from parameter in the function call above.
Within each data source is the set of species queried. In the above example, we only asked for occurrence data for one species, but we could have asked for any number. Let's say we asked for data for two species: Accipiter striatus, and Pinus contorta. Then the structure of the response would be
response -- |
| -- gbif ------- |
| -- Accipiter_striatus
| -- Pinus_contorta
| -- ecoengine -- |
| -- Accipiter_striatus
| -- Pinus_contorta
... and so on for each data source
If you only request data from gbif, like from = 'gbif', then the other four source slots are present in the response object, but have no data.
You can quickly get just the GBIF data by indexing to it, like
df$gbif
#> Species [Accipiter striatus (500)]
#> First 10 rows of [Accipiter_striatus]
#>
#> name longitude latitude prov issues
#> 1 Accipiter striatus -73.23131 44.28476 gbif cdround,cudc,gass84
#> 2 Accipiter striatus -97.63810 30.24674 gbif cdround,cudc,gass84
#> 3 Accipiter striatus -97.81493 26.03150 gbif cdround,cudc,gass84
#> 4 Accipiter striatus -135.32684 57.05398 gbif cdround,cudc,gass84
#> 5 Accipiter striatus -116.67145 32.94147 gbif cdround,cudc,gass84
#> 6 Accipiter striatus -95.50117 29.76086 gbif cdround,cudc,gass84
#> 7 Accipiter striatus -96.91463 32.82949 gbif cdround,cudc,gass84
#> 8 Accipiter striatus -75.65139 45.44557 gbif cdround,cudc,gass84
#> 9 Accipiter striatus -103.01232 36.38905 gbif cdround,cudc,gass84
#> 10 Accipiter striatus -98.24809 26.10815 gbif cdround,cudc,gass84
#> .. ... ... ... ... ...
#> Variables not shown: key (int), datasetKey (chr), publishingOrgKey (chr),
#> publishingCountry (chr), protocol (chr), lastCrawled (chr),
#> lastParsed (chr), extensions (chr), basisOfRecord (chr), taxonKey
#> (int), kingdomKey (int), phylumKey (int), classKey (int), orderKey
#> (int), familyKey (int), genusKey (int), speciesKey (int),
#> scientificName (chr), kingdom (chr), phylum (chr), order (chr),
#> family (chr), genus (chr), species (chr), genericName (chr),
#> specificEpithet (chr), taxonRank (chr), dateIdentified (chr), year
#> (int), month (int), day (int), eventDate (time), modified (chr),
#> lastInterpreted (chr), references (chr), identifiers (chr), facts
#> (chr), relations (chr), geodeticDatum (chr), class (chr), countryCode
#> (chr), country (chr), rightsHolder (chr), identifier (chr),
#> verbatimEventDate (chr), datasetName (chr), gbifID (chr),
#> collectionCode (chr), verbatimLocality (chr), occurrenceID (chr),
#> taxonID (chr), catalogNumber (chr), recordedBy (chr),
#> http...unknown.org.occurrenceDetails (chr), institutionCode (chr),
#> rights (chr), eventTime (chr), occurrenceRemarks (chr),
#> identificationID (chr), informationWithheld (chr), sex (chr),
#> establishmentMeans (chr), continent (chr), stateProvince (chr),
#> institutionID (chr), county (chr), language (chr), type (chr),
#> preparations (chr), occurrenceStatus (chr), nomenclaturalCode (chr),
#> higherGeography (chr), endDayOfYear (chr), locality (chr),
#> disposition (chr), otherCatalogNumbers (chr), startDayOfYear (chr),
#> accessRights (chr), higherClassification (chr), elevation (dbl),
#> elevationAccuracy (dbl), http...unknown.org.organismID (chr),
#> identificationVerificationStatus (chr), locationAccordingTo (chr),
#> identifiedBy (chr), georeferencedDate (chr), georeferencedBy (chr),
#> georeferenceProtocol (chr), georeferenceVerificationStatus (chr),
#> verbatimCoordinateSystem (chr), previousIdentifications (chr),
#> identificationQualifier (chr), samplingProtocol (chr),
#> georeferenceSources (chr), dynamicProperties (chr),
#> infraspecificEpithet (chr), georeferenceRemarks (chr), collectionID
#> (chr), habitat (chr), identificationRemarks (chr), vernacularName
#> (chr), recordNumber (chr)
When you get data from multiple providers, the fields returned are slightly different, e.g.:
df <- occ(query = 'Accipiter striatus', from = c('gbif', 'ecoengine'), limit = 25)
head(df$gbif$data$Accipiter_striatus)[1:6,1:10]
#> name longitude latitude issues prov
#> 1 Accipiter striatus -73.23131 44.28476 cdround,cudc,gass84 gbif
#> 2 Accipiter striatus -97.63810 30.24674 cdround,cudc,gass84 gbif
#> 3 Accipiter striatus -97.81493 26.03150 cdround,cudc,gass84 gbif
#> 4 Accipiter striatus -135.32684 57.05398 cdround,cudc,gass84 gbif
#> 5 Accipiter striatus -116.67145 32.94147 cdround,cudc,gass84 gbif
#> 6 Accipiter striatus -95.50117 29.76086 cdround,cudc,gass84 gbif
#> key datasetKey
#> 1 1227769707 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 2 1229927481 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 3 1229927719 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 4 1229612615 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 5 1229613664 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 6 1229610478 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> publishingOrgKey publishingCountry protocol
#> 1 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE
#> 2 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE
#> 3 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE
#> 4 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE
#> 5 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE
#> 6 28eb1a3f-1c15-4a95-931a-4af90ecb574d US DWC_ARCHIVE
head(df$ecoengine$data$Accipiter_striatus)
#> longitude latitude
#> 1 -122.1706 37.4289
#> 2 -122.2238 37.4698
#> 3 -122.2238 37.4698
#> 4 -122.2238 37.4698
#> 5 -122.2238 37.4698
#> 6 -122.2238 37.4698
#> url
#> 1 https://ecoengine.berkeley.edu/api/observations/CAS%3AORN%3A73314/
#> 2 https://ecoengine.berkeley.edu/api/observations/CAS%3AORN%3A73315/
#> 3 https://ecoengine.berkeley.edu/api/observations/CAS%3AORN%3A73338/
#> 4 https://ecoengine.berkeley.edu/api/observations/CAS%3AORN%3A73318/
#> 5 https://ecoengine.berkeley.edu/api/observations/CAS%3AORN%3A73319/
#> 6 https://ecoengine.berkeley.edu/api/observations/CAS%3AORN%3A73320/
#> key observation_type name country
#> 1 CAS:ORN:73314 specimen Accipiter striatus velox United States
#> 2 CAS:ORN:73315 specimen Accipiter striatus velox United States
#> 3 CAS:ORN:73338 specimen Accipiter striatus velox United States
#> 4 CAS:ORN:73318 specimen Accipiter striatus velox United States
#> 5 CAS:ORN:73319 specimen Accipiter striatus velox United States
#> 6 CAS:ORN:73320 specimen Accipiter striatus velox United States
#> state_province begin_date end_date
#> 1 California <NA> 1895-01-25T00:00:00
#> 2 California <NA> 1922-11-22T00:00:00
#> 3 California <NA> 1892-11-18T00:00:00
#> 4 California <NA> 1914-10-11T00:00:00
#> 5 California <NA> 1922-11-22T00:00:00
#> 6 California <NA> 1922-10-25T00:00:00
#> source remote_resource
#> 1 https://ecoengine.berkeley.edu/api/sources/8/
#> 2 https://ecoengine.berkeley.edu/api/sources/8/
#> 3 https://ecoengine.berkeley.edu/api/sources/8/
#> 4 https://ecoengine.berkeley.edu/api/sources/8/
#> 5 https://ecoengine.berkeley.edu/api/sources/8/
#> 6 https://ecoengine.berkeley.edu/api/sources/8/
#> locality coordinate_uncertainty_in_meters recorded_by
#> 1 Stanford University 1000 C. J. Pierson
#> 2 Redwood City 1000 C. Littlejohn
#> 3 Redwood City 1000 C. Littlejohn
#> 4 Redwood City 1000 C. Littlejohn
#> 5 Redwood City 1000 C. Littlejohn
#> 6 Redwood City 1000 C. Littlejohn
#> last_modified prov
#> 1 2014-06-02T11:39:10.808198 ecoengine
#> 2 2014-06-02T11:39:10.855111 ecoengine
#> 3 2014-06-02T11:39:11.459178 ecoengine
#> 4 2014-06-02T11:39:10.929030 ecoengine
#> 5 2014-06-02T11:39:10.956478 ecoengine
#> 6 2014-06-02T11:39:10.988688 ecoengine
We provide a function occ2df that pulls out a few key columns needed for making maps:
head(occ2df(df))
#> name longitude latitude prov date
#> 1 Accipiter striatus -73.23131 44.28476 gbif 2016-01-03 17:46:00
#> 2 Accipiter striatus -97.63810 30.24674 gbif 2016-01-16 14:25:40
#> 3 Accipiter striatus -97.81493 26.03150 gbif 2016-01-14 16:57:29
#> 4 Accipiter striatus -135.32684 57.05398 gbif 2016-01-09 18:01:00
#> 5 Accipiter striatus -116.67145 32.94147 gbif 2016-01-12 11:45:00
#> 6 Accipiter striatus -95.50117 29.76086 gbif 2016-01-09 17:05:47
#> key
#> 1 1227769707
#> 2 1229927481
#> 3 1229927719
#> 4 1229612615
#> 5 1229613664
#> 6 1229610478
One problem you often run in to is that there can be various names for the same taxon in any one source. For example:
df <- occ(query = 'Pinus contorta', from = c('gbif', 'ecoengine'), limit = 50)
head(df$gbif$data$Pinus_contorta)[1:6, 1:5]
#> name longitude latitude issues prov
#> 1 Pinus contorta 16.66390 56.63950 cudc,depunl,gass84 gbif
#> 2 Pinus contorta -110.69412 44.72325 cdround,cudc,gass84 gbif
#> 3 Pinus contorta 11.78660 58.16500 cudc,depunl,gass84 gbif
#> 4 Pinus contorta 9.39020 62.56215 cdround,gass84 gbif
#> 5 Pinus contorta 14.48530 61.23390 cudc,depunl,gass84 gbif
#> 6 Pinus contorta 9.38997 62.56203 cdround,gass84 gbif
head(df$ecoengine$data$Pinus_contorta)[1:6, 1:5]
#> longitude latitude
#> 1 -119.4967 38.0990
#> 2 -117.7989 34.3485
#> 3 -117.8033 34.3531
#> 4 -119.6149 38.0754
#> 5 -119.6120 38.0923
#> 6 -120.6422 39.6801
#> url
#> 1 https://ecoengine.berkeley.edu/api/observations/CalPhotos%3A5555%2B5555%2B0000%2B1112%3A2/
#> 2 https://ecoengine.berkeley.edu/api/observations/CalPhotos%3A5555%2B5555%2B0000%2B1122%3A2/
#> 3 https://ecoengine.berkeley.edu/api/observations/CalPhotos%3A5555%2B5555%2B0000%2B1121%3A1/
#> 4 https://ecoengine.berkeley.edu/api/observations/CalPhotos%3A5555%2B5555%2B0000%2B1109%3A1/
#> 5 https://ecoengine.berkeley.edu/api/observations/CalPhotos%3A5555%2B5555%2B0000%2B1108%3A1/
#> 6 https://ecoengine.berkeley.edu/api/observations/CalPhotos%3A5555%2B5555%2B0000%2B0939%3A3/
#> key observation_type
#> 1 CalPhotos:5555+5555+0000+1112:2 photo
#> 2 CalPhotos:5555+5555+0000+1122:2 photo
#> 3 CalPhotos:5555+5555+0000+1121:1 photo
#> 4 CalPhotos:5555+5555+0000+1109:1 photo
#> 5 CalPhotos:5555+5555+0000+1108:1 photo
#> 6 CalPhotos:5555+5555+0000+0939:3 photo
This is fine, but when trying to make a map in which points are colored for each taxon, you can have many colors for a single taxon, where instead one color per taxon is more appropriate. There is a function in spocc called fixnames, which has a few options in which you can take the shortest names (usually just the plain binomials like Homo sapiens), or the original name queried, or a vector of names supplied by the user.
df <- fixnames(df, how = 'shortest')
head(df$gbif$data$Pinus_contorta[,1:2])
#> name longitude
#> 1 Pinus contorta 16.66390
#> 2 Pinus contorta -110.69412
#> 3 Pinus contorta 11.78660
#> 4 Pinus contorta 9.39020
#> 5 Pinus contorta 14.48530
#> 6 Pinus contorta 9.38997
head(df$ecoengine$data$Pinus_contorta[,1:2])
#> longitude latitude
#> 1 -119.4967 38.0990
#> 2 -117.7989 34.3485
#> 3 -117.8033 34.3531
#> 4 -119.6149 38.0754
#> 5 -119.6120 38.0923
#> 6 -120.6422 39.6801
df_comb <- occ2df(df)
head(df_comb); tail(df_comb)
#> name longitude latitude prov date key
#> 1 Pinus contorta 16.66390 56.63950 gbif 2015-01-03 23:00:00 1051515518
#> 2 Pinus contorta -110.69412 44.72325 gbif 2015-01-01 23:00:00 1088897277
#> 3 Pinus contorta 11.78660 58.16500 gbif 2015-01-17 23:00:00 1052933649
#> 4 Pinus contorta 9.39020 62.56215 gbif 2015-02-19 23:00:00 1092518647
#> 5 Pinus contorta 14.48530 61.23390 gbif 2015-02-15 23:00:00 1065763672
#> 6 Pinus contorta 9.38997 62.56203 gbif 2015-02-19 23:00:00 1092518927
#> name longitude latitude prov date
#> 95 Pinus contorta -120.3358 39.1632 ecoengine <NA>
#> 96 Pinus contorta -119.9564 38.7905 ecoengine <NA>
#> 97 Pinus contorta -121.2308 40.3064 ecoengine <NA>
#> 98 Pinus contorta -121.2308 40.3064 ecoengine <NA>
#> 99 Pinus contorta -119.5066 37.6013 ecoengine <NA>
#> 100 Pinus contorta -119.5158 37.6024 ecoengine <NA>
#> key
#> 95 CalPhotos:5555+5555+0000+0436:5
#> 96 CalPhotos:5555+5555+0000+0225:1
#> 97 CalPhotos:6666+6666+0513+0077:1
#> 98 CalPhotos:6666+6666+0513+0078:1
#> 99 CalPhotos:5555+5555+0000+1417:3
#> 100 CalPhotos:5555+5555+0000+1418:4
All data cleaning functionality is in a new package scrubr - not yet on CRAN.
All mapping functionality is now in a separate package mapr (formerly known as spoccutils), to make spocc easier to maintain.