December 23, 2016
finch has just been released to CRAN (binaries should be up soon).
finch is a package to parse Darwin Core files. Darwin Core (DwC) is:
a body of standards. It includes a glossary of terms (in other contexts these might be called properties, elements, fields, columns, attributes, or concepts) intended to facilitate the sharing of information about biological diversity by providing reference definitions, examples, and commentaries. The Darwin Core is primarily based on taxa, their occurrence in nature as documented by observations, specimens, samples, and related information. … The Simple Darwin Core [SIMPLEDWC] is a specification for one particular way to use the terms - to share data about taxa and their occurrences in a simply structured way - and is probably what is meant if someone suggests to “format your data according to the Darwin Core”.
GBIF (Global Biodiversity Information Facility) is the biggest holder of biodiversity data. When you request
data in bulk format from GBIF they call give it to you in what’s called a Darwin Core Archive, or
DwC-A. GBIF has a validator for DwC-A files as well: https://tools.gbif.org/dwca-validator/
One of our most used packages is probably rgbif, a client to interact with GBIF’s web services.
There’s a series of functions in rgbif to request data in bulk format (see functions starting
with occ_download), and from this you get a DwC-A file. This is where finch comes in:
it can parse these DwC-A files into something useable inside R.
Installation
install.packages("finch")
# or from source if binary not available yet
install.packages("finch", type = "source")
library("finch")
To parse a simple darwin core file like
<?xml version="1.0" encoding="UTF-8"?>
<SimpleDarwinRecordSet
xmlns="http://rs.tdwg.org/dwc/xsd/simpledarwincore/"
xmlns:dc="http://purl.org/dc/terms/"
xmlns:dwc="http://rs.tdwg.org/dwc/terms/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://rs.tdwg.org/dwc/xsd/simpledarwincore/ ../../xsd/tdwg_dwc_simple.xsd">
<SimpleDarwinRecord>
<dwc:occurrenceID>urn:catalog:YPM:VP.057488</dwc:occurrenceID>
<dc:type>PhysicalObject</dc:type>
<dc:modified>2009-02-12T12:43:31</dc:modified>
<dc:language>en</dc:language>
<dwc:basisOfRecord>FossilSpecimen</dwc:basisOfRecord>
<dwc:institutionCode>YPM</dwc:institutionCode>
<dwc:collectionCode>VP</dwc:collectionCode>
<dwc:catalogNumber>VP.057488</dwc:catalogNumber>
<dwc:individualCount>1</dwc:individualCount>
<dwc:locationID xsi:nil="true"/>
<dwc:continent>North America</dwc:continent>
<dwc:country>United States</dwc:country>
<dwc:countryCode>US</dwc:countryCode>
<dwc:stateProvince>Montana</dwc:stateProvince>
<dwc:county>Garfield</dwc:county>
<dwc:scientificName>Tyrannosourus rex</dwc:scientificName>
<dwc:genus>Tyrannosourus</dwc:genus>
<dwc:specificEpithet>rex</dwc:specificEpithet>
<dwc:earliestPeriodOrHighestSystem>Creataceous</dwc:earliestPeriodOrHighestSystem>
<dwc:latestPeriodOrHighestSystem>Creataceous</dwc:latestPeriodOrHighestSystem>
<dwc:earliestEonOrHighestEonothem>Late Cretaceous</dwc:earliestEonOrHighestEonothem>
<dwc:latestEonOrHighestEonothem>Late Cretaceous</dwc:latestEonOrHighestEonothem>
</SimpleDarwinRecord>
</SimpleDarwinRecordSet>
This file is in this package as an example file, get the file, then simple()
file <- system.file("examples", "example_simple_fossil.xml", package = "finch")
out <- simple_read(file)
Index to meta, dc or dwc
out$dc
#> [[1]]
#> [[1]]$type
#> [1] "PhysicalObject"
#>
#>
#> [[2]]
#> [[2]]$modified
#> [1] "2009-02-12T12:43:31"
#>
#>
#> [[3]]
#> [[3]]$language
#> [1] "en"
Parse Darwin Core Archive
To parse a Darwin Core Archive like can be gotten from GBIF use dwca_read()
dwca_read() can parse a DwC-A file as a directory, zipped file, or from a URL.
There’s an example Darwin Core Archive:
file <- system.file("examples", "0000154-150116162929234.zip", package = "finch")
(out <- dwca_read(file, read = TRUE))
#> <gbif dwca>
#> Package ID: 6cfaaf9c-d518-4ca3-8dc5-f5aadddc0390
#> No. data sources: 10
#> No. datasets: 3
#> Dataset occurrence.txt: [225 X 443]
#> Dataset multimedia.txt: [15 X 1]
#> Dataset verbatim.txt: [209 X 443]
List files in the archive
out$files
#> $xml_files
#> [1] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/meta.xml"
#> [2] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/metadata.xml"
#>
#> $txt_files
#> [1] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/citations.txt"
#> [2] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/multimedia.txt"
#> [3] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/occurrence.txt"
#> [4] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/rights.txt"
#> [5] "/Library/Frameworks/R.framework/Versions/3.3/Resources/library/finch/examples/0000154-150116162929234/verbatim.txt"
...
High level metadata for the whole archive (printing a subset for brevity)
out$emlmeta
#> <eml packageId="6cfaaf9c-d518-4ca3-8dc5-f5aadddc0390" system="http://gbif.org" scope="system" xml:lang="en" xsi:schemaLocation="eml://ecoinformatics.org/eml-2.1.1 http://rs.gbif.org/schema/eml-gbif-profile/1.0.2/eml.xsd">
#> <dataset>
#> <title>GBIF Occurrence Download 0000154-150116162929234</title>
#> <creator>
#> <individualName>
#> <surName>GBIF Download Service</surName>
#> </individualName>
#> </creator>
#> <metadataProvider>
#> <individualName>
#> <surName>GBIF Download Service</surName>
#> </individualName>
#> </metadataProvider>
#> <associatedParty>
#> <organizationName>OZCAM (Online Zoological Collections of Australian Museums) Provider</organizationName>
#> <onlineUrl>http://www.ozcam.org.au/</onlineUrl>
#> <role>CONTENT_PROVIDER</role>
#> </associatedParty>
#> <associatedParty>
#> <individualName>
...
High level metadata for each data file, there’s many files, but we’ll just look at one
hm <- out$highmeta
head( hm$occurrence.txt )
#> index term delimitedBy
#> 1 0 http://rs.gbif.org/terms/1.0/gbifID <NA>
#> 2 1 http://purl.org/dc/terms/abstract <NA>
#> 3 2 http://purl.org/dc/terms/accessRights <NA>
#> 4 3 http://purl.org/dc/terms/accrualMethod <NA>
#> 5 4 http://purl.org/dc/terms/accrualPeriodicity <NA>
#> 6 5 http://purl.org/dc/terms/accrualPolicy <NA>
You can get the same metadata as above for each dataset that went into the tabular dataset downloaded
out$dataset_meta[[1]]
View one of the datasets, brief overview.
head(out$data[[1]][,c(1:5)])
#> gbifID abstract accessRights accrualMethod accrualPeriodicity
#> 1 50280003 NA NA NA
#> 2 477550574 NA NA NA
#> 3 239703844 NA NA NA
#> 4 239703843 NA NA NA
#> 5 239703833 NA NA NA
#> 6 477550692 NA NA NA
names(out$data[[1]])[1:20]
#> [1] "gbifID" "abstract"
#> [3] "accessRights" "accrualMethod"
#> [5] "accrualPeriodicity" "accrualPolicy"
#> [7] "alternative" "audience"
#> [9] "available" "bibliographicCitation"
#> [11] "conformsTo" "contributor"
#> [13] "coverage" "created"
#> [15] "creator" "date"
#> [17] "dateAccepted" "dateCopyrighted"
#> [19] "dateSubmitted" "description"
Using with rgbif
Now that we have finch we can make working with GBIF bulk downloads from R a
richer experience.
Right now, we make it easy to import just the occurrence data from DwC-A files
via occ_download_import(), e.g.,
library(rgbif)
res <- occ_download('taxonKey = 7264332', 'hasCoordinate = TRUE')
dat <- occ_download_get(res)
occ_download_import(dat)
#> Download file size: 0.26 MB
#>
#> gbifID abstract accessRights accrualMethod accrualPeriodicity accrualPolicy
#> 1 1269880600 NA NA NA NA
#> 2 1269861719 NA NA NA NA
#> 3 1269850111 NA NA NA NA
#> 4 1265524086 NA NA NA NA
#> 5 1257400209 NA NA NA NA
#> 6 1257396860 NA NA NA NA
#> 7 1257391874 NA NA NA NA
#> 8 1257390731 NA NA NA NA
#> 9 1257383844 NA NA NA NA
#> 10 1257375500 NA NA NA NA
#> .. ... ... ... ... ... ...
#> Variables not shown: accrualPolicy (lgl), alternative (lgl), audience (lgl),
#> available (lgl), bibliographicCitation (lgl), conformsTo (lgl),
#> contributor (lgl), coverage (lgl), created (lgl), creator (lgl), date
#> (lgl), dateAccepted (lgl), dateCopyrighted (lgl), dateSubmitted (lgl),
#> description (lgl), educationLevel (lgl), extent (lgl), format (lgl),
#> hasFormat (lgl), hasPart (lgl), hasVersion (lgl), identifier (chr),
#> instructionalMethod (lgl), isFormatOf (lgl), isPartOf (lgl),
#> isReferencedBy (lgl), isReplacedBy (lgl), isRequiredBy (lgl), ...
With finch, you can access all the data in the DwC-A file. finch is not integrated
into rgbif, though we may in the future.
The object returned from occ_download_get is just a path, so we can use that
with finch
library(finch)
(out <- dwca_read(dat[1], read = TRUE))
#> <gbif dwca>
#> Package ID: 10.15468/dl.mmecqc
#> No. data sources: 8
#> No. datasets: 3
#> Dataset occurrence.txt: [235 X 1371]
#> Dataset multimedia.txt: [15 X 0]
#> Dataset verbatim.txt: [217 X 1371]
Now we have access to not just the occurrence data
head(out$data$occurrence.txt)[,1:5]
#> gbifID abstract accessRights accrualMethod accrualPeriodicity
#> 1 1269880600 NA NA NA
#> 2 1269861719 NA NA NA
#> 3 1269850111 NA NA NA
#> 4 1265524086 NA NA NA
#> 5 1257400209 NA NA NA
#> 6 1257396860 NA NA NA
But all the rich metadata in the other files. Yay!
Future work
DwC-A files can be very large - This is for sure going to be a pain point for some. We’ll continue to test and refine on big data files.
Feedback?
We’d love to know what people think about this package.
Documentation can be better, e.g., there’s no vignette yet (but adding that soon).
