An R package to handle data packages
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R Fix minor doc problem revealed by check() Feb 17, 2017
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DESCRIPTION Update publish date Feb 8, 2017
NEWS Update installation instructions. May 6, 2016 Update test environments, changes Nov 23, 2016
datapack.Rproj Updated tabs to standard 4 spaces. Mar 24, 2016



The datapack R package provides an abstraction for collating heterogeneous collections of data objects and metadata into a bundle that can be transported and loaded into a single composite file. The methods in this package provide a convenient way to load data from common repositories such as DataONE into the R environment, and to document, serialize, and save data from R to data repositories worldwide.

Installation Notes

The datapack R package requires the R package redland. If you are installing on Ubuntu then the Redland C libraries must be installed before the redland and datapack package can be installed. If you are installing on Mac OS X or Windows then installing these libraries is not required.

The following instructions illustrate how to install datapack and its requirements.

Installing on Mac OS X

On Mac OS X datapack can be installed with the following commands:


The datapack R package should be available for use at this point.

Note: if you wish to build the required redland package from source before installing datapack, please see the redland installation instructions.

Installing on Ubuntu

For ubuntu, install the required Redland C libraries by entering the following commands in a terminal window:

sudo apt-get update
sudo apt-get install librdf0 librdf0-dev

Then install the R packages from the R console:


The datapack R package should be available for use at this point

Installing on Windows

For windows, the required redland R package is distributed as a binary release, so it is not necessary to install any additional system libraries.

To install the R packages from the R console:


Quick Start

See the full manual for documentation, but once installed, the package can be run in R using:


Create a DataPackage and add metadata and data DataObjects to it:

dp <- new("DataPackage")
mdFile <- system.file("extdata/sample-eml.xml", package="datapack")
mdId <- paste("urn:uuid:", UUIDgenerate(), sep="")
md <- new("DataObject", id=mdId, format="eml://", file=mdFile)
addData(dp, md)

csvfile <- system.file("extdata/sample-data.csv", package="datapack")
sciId <- paste("urn:uuid:", UUIDgenerate(), sep="")
sciObj <- new("DataObject", id=sciId, format="text/csv", filename=csvfile)
dp <- addData(dp, sciObj)
ids <- getIdentifiers(dp)

Add a relationship to the DataPackage that shows that the metadata describes, or "documents", the science data:

dp <- insertRelationship(dp, subjectID=mdId, objectIDs=sciId)
relations <- getRelationships(dp)

Create an Resource Description Framework representation of the relationships in the package:

serializationId <- paste("resourceMap", UUIDgenerate(), sep="")
filePath <- file.path(sprintf("%s/%s.rdf", tempdir(), serializationId))
status <- serializePackage(dp, filePath, id=serializationId, resolveURI="")

Save the DataPackage to a file, using the BagIt packaging format:

bagitFile <- serializeToBagIt(dp) 

Note that the dataone R package can be used to upload a DataPackage to a DataONE Member Node using the uploadDataPackage method. Please see the documentation for the dataone R package, for example:

vignette("upload-data", package="dataone")