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 ten 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.
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.
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: 617,192, 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]
#>
#> # A tibble: 500 × 97
#> name longitude latitude prov issues
#> <chr> <dbl> <dbl> <chr> <chr>
#> 1 Accipiter striatus -106.31531 31.71593 gbif cdround,gass84
#> 2 Accipiter striatus -97.81493 26.03150 gbif cdround,cucdmis,gass84
#> 3 Accipiter striatus -81.85267 28.81852 gbif gass84
#> 4 Accipiter striatus -81.85329 28.81806 gbif cdround,gass84
#> 5 Accipiter striatus -95.50117 29.76086 gbif cdround,gass84
#> 6 Accipiter striatus -73.23131 44.28476 gbif cdround,gass84
#> 7 Accipiter striatus -97.94314 30.04580 gbif cdround,gass84
#> 8 Accipiter striatus -77.05161 38.87834 gbif cdround,gass84
#> 9 Accipiter striatus -123.44703 48.54571 gbif cdround,gass84
#> 10 Accipiter striatus -96.74874 33.03102 gbif cdround,gass84
#> # ... with 490 more rows, and 92 more variables: key <int>,
#> # datasetKey <chr>, publishingOrgKey <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>, scientificName <chr>, kingdom <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, genericName <chr>,
#> # specificEpithet <chr>, taxonRank <chr>, dateIdentified <chr>,
#> # coordinateUncertaintyInMeters <dbl>, 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>, informationWithheld <chr>, verbatimEventDate <chr>,
#> # datasetName <chr>, verbatimLocality <chr>, gbifID <chr>,
#> # collectionCode <chr>, occurrenceID <chr>, taxonID <chr>,
#> # recordedBy <chr>, catalogNumber <chr>,
#> # `http://unknown.org/occurrenceDetails` <chr>, institutionCode <chr>,
#> # rights <chr>, occurrenceRemarks <chr>, identificationID <chr>,
#> # eventTime <chr>, individualCount <int>, elevation <dbl>,
#> # elevationAccuracy <dbl>, continent <chr>, stateProvince <chr>,
#> # institutionID <chr>, county <chr>,
#> # identificationVerificationStatus <chr>, language <chr>, type <chr>,
#> # preparations <chr>, locationAccordingTo <chr>, identifiedBy <chr>,
#> # georeferencedDate <chr>, nomenclaturalCode <chr>,
#> # higherGeography <chr>, georeferencedBy <chr>,
#> # georeferenceProtocol <chr>, georeferenceVerificationStatus <chr>,
#> # endDayOfYear <chr>, verbatimCoordinateSystem <chr>, locality <chr>,
#> # otherCatalogNumbers <chr>, organismID <chr>,
#> # previousIdentifications <chr>, identificationQualifier <chr>,
#> # samplingProtocol <chr>, accessRights <chr>,
#> # higherClassification <chr>, georeferenceSources <chr>, sex <chr>,
#> # dynamicProperties <chr>, vernacularName <chr>,
#> # reproductiveCondition <chr>, lifeStage <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)
df$gbif$data$Accipiter_striatus
#> # A tibble: 25 × 62
#> name longitude latitude issues prov
#> <chr> <dbl> <dbl> <chr> <chr>
#> 1 Accipiter striatus -106.31531 31.71593 cdround,gass84 gbif
#> 2 Accipiter striatus -97.81493 26.03150 cdround,cucdmis,gass84 gbif
#> 3 Accipiter striatus -81.85267 28.81852 gass84 gbif
#> 4 Accipiter striatus -81.85329 28.81806 cdround,gass84 gbif
#> 5 Accipiter striatus -95.50117 29.76086 cdround,gass84 gbif
#> 6 Accipiter striatus -73.23131 44.28476 cdround,gass84 gbif
#> 7 Accipiter striatus -97.94314 30.04580 cdround,gass84 gbif
#> 8 Accipiter striatus -77.05161 38.87834 cdround,gass84 gbif
#> 9 Accipiter striatus -123.44703 48.54571 cdround,gass84 gbif
#> 10 Accipiter striatus -96.74874 33.03102 cdround,gass84 gbif
#> # ... with 15 more rows, and 57 more variables: key <int>,
#> # datasetKey <chr>, publishingOrgKey <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>, scientificName <chr>, kingdom <chr>, phylum <chr>,
#> # order <chr>, family <chr>, genus <chr>, genericName <chr>,
#> # specificEpithet <chr>, taxonRank <chr>, dateIdentified <chr>,
#> # coordinateUncertaintyInMeters <dbl>, 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>, informationWithheld <chr>, verbatimEventDate <chr>,
#> # datasetName <chr>, verbatimLocality <chr>, gbifID <chr>,
#> # collectionCode <chr>, occurrenceID <chr>, taxonID <chr>,
#> # recordedBy <chr>, catalogNumber <chr>,
#> # `http://unknown.org/occurrenceDetails` <chr>, institutionCode <chr>,
#> # rights <chr>, occurrenceRemarks <chr>, identificationID <chr>,
#> # eventTime <chr>
df$ecoengine$data$Accipiter_striatus
#> # A tibble: 25 × 17
#> longitude latitude
#> * <dbl> <dbl>
#> 1 -123.4474 40.4757
#> 2 NA NA
#> 3 NA NA
#> 4 -87.5932 41.7945
#> 5 -86.9241 41.2665
#> 6 -118.3016 34.0320
#> 7 -118.3016 34.0320
#> 8 -118.3016 34.0320
#> 9 -118.3016 34.0320
#> 10 -118.4415 34.2677
#> # ... with 15 more rows, and 15 more variables: url <chr>, key <chr>,
#> # observation_type <chr>, name <chr>, country <chr>,
#> # state_province <chr>, begin_date <date>, end_date <chr>, source <chr>,
#> # remote_resource <chr>, locality <chr>,
#> # coordinate_uncertainty_in_meters <int>, recorded_by <chr>,
#> # last_modified <chr>, prov <chr>
We provide a function occ2df that pulls out a few key columns needed for making maps:
occ2df(df)
#> # A tibble: 50 × 6
#> name longitude latitude prov date key
#> <chr> <dbl> <dbl> <chr> <date> <chr>
#> 1 Accipiter striatus -106.31531 31.71593 gbif 2016-01-20 1233597063
#> 2 Accipiter striatus -97.81493 26.03150 gbif 2016-01-14 1229927719
#> 3 Accipiter striatus -81.85267 28.81852 gbif 2016-01-18 1253301153
#> 4 Accipiter striatus -81.85329 28.81806 gbif 2016-01-18 1249295043
#> 5 Accipiter striatus -95.50117 29.76086 gbif 2016-01-09 1229610478
#> 6 Accipiter striatus -73.23131 44.28476 gbif 2016-01-03 1227769707
#> 7 Accipiter striatus -97.94314 30.04580 gbif 2016-01-24 1233600470
#> 8 Accipiter striatus -77.05161 38.87834 gbif 2016-01-02 1270044795
#> 9 Accipiter striatus -123.44703 48.54571 gbif 2016-01-31 1249281424
#> 10 Accipiter striatus -96.74874 33.03102 gbif 2016-01-28 1257416040
#> # ... with 40 more rows
One problem you often run in to is that there can be various names for the same taxon in any one source. For example:
df <- occ(query = 'Pinus contorta', from = c('gbif', 'ecoengine'), limit = 50)
df$gbif$data$Pinus_contorta$name
#> [1] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [5] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [9] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [13] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [17] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [21] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [25] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [29] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [33] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [37] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [41] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [45] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [49] "Pinus contorta" "Pinus contorta"
df$ecoengine$data$Pinus_contorta$name
#> [1] "Pinus contorta" "Pinus contorta"
#> [3] "Pinus contorta" "Pinus contorta"
#> [5] "Pinus contorta" "Pinus contorta"
#> [7] "Pinus contorta" "Pinus contorta"
#> [9] "Pinus contorta" "Pinus contorta"
#> [11] "Pinus contorta" "Pinus contorta"
#> [13] "Pinus contorta" "Pinus contorta"
#> [15] "Pinus contorta" "Pinus contorta"
#> [17] "Pinus contorta" "Pinus contorta"
#> [19] "Pinus contorta subsp. murrayana" "Pinus contorta"
#> [21] "Pinus contorta" "Pinus contorta"
#> [23] "Pinus contorta" "Pinus contorta"
#> [25] "Pinus contorta" "Pinus contorta"
#> [27] "Pinus contorta" "Pinus contorta"
#> [29] "Pinus contorta" "Pinus contorta"
#> [31] "Pinus contorta subsp. murrayana" "Pinus contorta subsp. murrayana"
#> [33] "Pinus contorta" "Pinus contorta subsp. murrayana"
#> [35] "Pinus contorta subsp. murrayana" "Pinus contorta subsp. murrayana"
#> [37] "Pinus contorta" "Pinus contorta"
#> [39] "Pinus contorta" "Pinus contorta"
#> [41] "Pinus contorta" "Pinus contorta"
#> [43] "Pinus contorta" "Pinus contorta"
#> [45] "Pinus contorta" "Pinus contorta"
#> [47] "Pinus contorta" "Pinus contorta"
#> [49] "Pinus contorta" "Pinus contorta"
This is fine, but when trying to make a map in which points are colored for each taxon, you can have many colors for a single taxon, where instead one color per taxon is more appropriate. There is a function in spocc called fixnames, which has a few options in which you can take the shortest names (usually just the plain binomials like Homo sapiens), or the original name queried, or a vector of names supplied by the user.
df <- fixnames(df, how = 'query')
df$gbif$data$Pinus_contorta$name
#> [1] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [5] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [9] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [13] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [17] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [21] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [25] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [29] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [33] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [37] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [41] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [45] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [49] "Pinus contorta" "Pinus contorta"
df$ecoengine$data$Pinus_contorta$name
#> [1] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [5] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [9] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [13] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [17] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [21] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [25] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [29] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [33] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [37] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [41] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [45] "Pinus contorta" "Pinus contorta" "Pinus contorta" "Pinus contorta"
#> [49] "Pinus contorta" "Pinus contorta"
df_comb <- occ2df(df)
head(df_comb); tail(df_comb)
#> # A tibble: 6 × 6
#> name longitude latitude prov date key
#> <chr> <dbl> <dbl> <chr> <date> <chr>
#> 1 Pinus contorta 168.85006 -44.94818 gbif 2016-01-30 1269541527
#> 2 Pinus contorta -120.33987 39.34308 gbif 2016-01-03 1249276846
#> 3 Pinus contorta -123.98210 46.20296 gbif 2016-02-07 1249288703
#> 4 Pinus contorta 7.01607 62.86770 gbif 2016-02-20 1272958740
#> 5 Pinus contorta 176.32093 -39.33307 gbif 2016-02-16 1249301037
#> 6 Pinus contorta -123.35278 48.90594 gbif 2016-02-29 1253314823
#> # A tibble: 6 × 6
#> name longitude latitude prov date
#> <chr> <dbl> <dbl> <chr> <date>
#> 1 Pinus contorta -120.3358 39.1632 ecoengine <NA>
#> 2 Pinus contorta -119.9564 38.7905 ecoengine <NA>
#> 3 Pinus contorta -121.2308 40.3064 ecoengine <NA>
#> 4 Pinus contorta -121.2308 40.3064 ecoengine <NA>
#> 5 Pinus contorta -119.5066 37.6013 ecoengine <NA>
#> 6 Pinus contorta -119.5158 37.6024 ecoengine <NA>
#> # ... with 1 more variables: key <chr>
All data cleaning functionality is in a new package scrubr. On CRAN.
All mapping functionality is now in a separate package mapr (formerly known as spoccutils), to make spocc easier to maintain. On CRAN.