datapack

• Author: Matthew B. Jones and Peter Slaughter (NCEAS)
• doi:10.5063/F1QV3JGM
• License: Apache 2
• Package source code on Github
• Submit Bugs and feature requests

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, which requires the Redland C libraries. The following instructions illustrate how to install datapack and its requirements.

Installing on Mac OS X

On Mac OS X, the required Redland C libraries can be installed with either Mac Ports package manager or the HomeBrew package manager. The HomeBrew package manager can be significantly faster to install but either one will work provided the directions shown below are followed.

You can check if you have MacPorts installed by entering the following command in a terminal window:

port version

If the port command is not found, then skip to the Installing with HomeBrew section.

Mac OS X install option 1: install using Macports

If you are already using the MacPorts package manager, you can install datapack with the following commands, otherwise, it is recommended that you skip to the next section Installing with HomeBrew.

To install the datapack R package with MacPorts, enter this commands at a terminal window:

sudo port install redland

Then enter these commands in the R console:

install.packages("redland", type="source")
install.packages("datapack")
library(datapack)

Please note that the install.packages command specifies a "source" installation. Installing from source is only necessary if Macports is being used, and is not a requirement if Homebrew is used.

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

Mac OS X install option 2: install using HomeBrew

On Mac OS X you can use the package management system HomeBrew to install the necessary libraries. The HomeBrew software can be installed with the following command entered at a terminal window:

ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Once HomeBrew has been installed, you can then enter the following command in a terminal windows to install the Redland C libraries:

brew install redland

Next, install the datapack R package with these commands typed at the R console window:

install.packages("datapack")
library(datapack)

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

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:

install.packages("datapack")
library(datapack)

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:

install.packages("datapack")
library(datapack)

Quick Start

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

library(datapack)
help("datapack")

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

library(datapack)
library(uuid)
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://ecoinformatics.org/eml-2.1.0", 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")