Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/ropensci/datapack
An R package to handle data packages
https://github.com/ropensci/datapack
r r-package rstats
Last synced: about 1 month ago
JSON representation
An R package to handle data packages
- Host: GitHub
- URL: https://github.com/ropensci/datapack
- Owner: ropensci
- Created: 2014-09-04T17:53:33.000Z (over 10 years ago)
- Default Branch: main
- Last Pushed: 2022-06-10T22:15:06.000Z (over 2 years ago)
- Last Synced: 2024-11-13T13:55:18.946Z (2 months ago)
- Topics: r, r-package, rstats
- Language: R
- Homepage: https://docs.ropensci.org/datapack
- Size: 1.36 MB
- Stars: 44
- Watchers: 13
- Forks: 9
- Open Issues: 35
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- jimsghstars - ropensci/datapack - An R package to handle data packages (R)
README
## datapack: A Flexible Container to Transport and Manipulate Data and Associated Resources
[](https://cran.r-project.org/package=datapack)
[](https://github.com/ropensci/datapack/actions)
- **Author**: Matthew B. Jones and Peter Slaughter and S. Jeanette Clark ([NCEAS](https://www.nceas.ucsb.edu))
- [doi:10.5063/F1QV3JGM](https://doi.org/10.5063/F1QV3JGM)
- **License**: [Apache 2](https://opensource.org/licenses/Apache-2.0)
- [Package source code on Github](https://github.com/ropensci/datapack)
- [**Submit Bugs and feature requests**](https://github.com/ropensci/datapack/issues)
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.
> Note that this package ('datapack') is not related to the similarly named rOpenSci package 'DataPackageR'.
> Documentation from the DataPackageR github repository states that "DataPackageR is used to reproducibly
> process raw data into packaged, analysis-ready data sets."
## 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:
```
install.packages("datapack")
library(datapack)
```
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](https://github.com/ropensci/redland-bindings/tree/master/R/redland).
## 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 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")
```
## Acknowledgements
Work on this package was supported by:
- NSF-ABI grant #[1262458](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1262458) to C. Gries, M. B. Jones, and S. Collins.
- NSF-DATANET grants #[0830944](https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830944) and #[1430508](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1430508) to W. Michener, M. B. Jones, D. Vieglais, S. Allard and P. Cruse
- NSF DIBBS grant #[1443062](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1443062) to T. Habermann and M. B. Jones
- NSF-PLR grant #[1546024](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546024) to M. B. Jones, S. Baker-Yeboah, J. Dozier, M. Schildhauer, and A. Budden
- NSF-PLR grant #[2042102](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042102) to M. B. Jones, A. Budden, J. Dozier, and M. Schildhauer
Additional support was provided for working group collaboration by the National Center for Ecological Analysis and Synthesis, a Center funded by the University of California, Santa Barbara, and the State of California.
[](https://www.nceas.ucsb.edu)
[](https://www.dataone.org)
[](https://ropensci.org/)