README

The R package redland provides methods to create, query and write information stored in the Resource Description Framework (RDF). An introduction to RDF can be found at https://www.w3.org/TR/rdf-primer. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

This package is implemented as R scripts that provide an R interface (aka “wrapper”) to the Redland RDF C libraries that are described at https://librdf.org/docs/api/index.html. Additional information about the software that provides the connection or ‘bindings’ between R and the Redland RDF C libraries is available at https://github.com/ropensci/redland-bindings/tree/master/R.

In this redland R package, S4 classes are used to model RDF data structures. A redland::Statement is composed of redland::Nodes representing the subject, predicate, and object of each triple statement. Statements can be composed into a graph by adding them to a redland::Model, which in turn can be serialized and deserialized to RDF’s text formats using redland::Serializer and redland::Parser, respectively.

Installation Notes

The redland R package is now available from CRAN. Before the redland R package can be installed, the Redland C libraries may need to be installed, depending on the platform type you are installing to and whether or not you wish to install from source.

Installing on Mac OS X

On Mac OS X, the binary build from CRAN can be installed without installing the Redland C libraries by typing the following commands in the R console:

Alternatively, to install redland from source, please continue reading this section.

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:

Installing on Mac OS X with Macports

If you are already using the MacPorts package manager, you can install redland with the following commands, otherwise, it is recommended that you skip to the next section Installing with HomeBrew. To install the redland R package with MacPorts, enter these commands at a terminal window:

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.

Installing on Mac OS X with 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:

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

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

Installing on Ubuntu

Installing on Windows

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

Example Usage

The redland library can be used for a wide variety of RDF parsing and creation tasks. Some examples are provided in the redland_overview vignette. As a quick start, here is an example that creates an RDF graph using an in-memory storage model, adds some triples, and then serializes the graph to disk.

library(redland)

# World is the redland mechanism for scoping models
world <- new("World")

# Storage provides a mechanism to store models; in-memory hashes are convenient for small models
storage <- new("Storage", world, "hashes", name="", options="hash-type='memory'")

# A model is a set of Statements, and is associated with a particular Storage instance
model <- new("Model", world=world, storage, options="")

# Add some Dublin Core properties to the model
dc <- "https://purl.org/dc/elements/1.1/"
stmt <- new("Statement", world=world, 
        subject="https://ropensci.org/", predicate=paste0(dc, "title"), object="ROpenSci")
addStatement(model, stmt)
stmt <- new("Statement", world=world, 
        subject="https://ropensci.org/", predicate=paste0(dc, "language"), object="en")
addStatement(model, stmt)
stmt <- new("Statement", world=world, 
        subject="https://ropensci.org/", predicate=paste0(dc, "license"), 
        object="https://creativecommons.org/licenses/by/2.0/")
addStatement(model, stmt)

# Serialize the model to a TTL file
serializer <- new("Serializer", world, name="turtle", mimeType="text/turtle")
status <- setNameSpace(serializer, world, namespace="https://purl.org/dc/elements/1.1/", prefix="dc")  
filePath <- tempfile(pattern = "file", tmpdir = tempdir(), fileext = ".ttl")
status <- serializeToFile(serializer, world, model, filePath)
readLines(file(filePath))

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