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Read and Write Data Packages
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Data Package in R

Project Status: Inactive – The project has reached a stable, usable state but is no longer being actively developed; support/maintenance will be provided as time allows.

Data-packages is a standard format for describing meta-data for a collection of datasets. The package datapkg provides convenience functions for retrieving and parsing data packages in R. To install in R:


Reading data

The datapkg_read function retrieves and parses data packages from a local or remote sources. A few example packages are available from the datasets and testsuite-py repositories. The path needs to point to a directory on disk or git remote or URL containing the root of the data package.

# Load client

# Clone via git
cities <- datapkg_read("git://")

# Same data but download over http
cities <- datapkg_read("")

The output object contains data and metadata from the data-package, with actual datasets inside the $data field.

# Package info

# Open actual data in RStudio Viewer

In the case of multiple datasets, each one is either referenced by index or, if available, by name (names are optional in data packages).

# Package with many datasets
euribor <- datapkg_read("")

# List datasets in this package

Writing data

The package also has basic functionality to save a data frame into a data package and update the datapackage.json file accordingly.

# Create new data package
pkgdir <- tempfile()
datapkg_write(mtcars, path = pkgdir)
datapkg_write(iris, path = pkgdir)

# Read it back
mypkg <- datapkg_read(pkgdir)

From here you can modify the datapackage.json file with other metadata.


This package is work in progress. Current open issues:

  • Make readr parse 0/1 values for booleans: PR#406
  • Support "year only" dates (%Y). Not sure if this constituates a valid date actually: PR#407
  • R and readr require to specify which strings are interepreted as missing values. Default are empty string "" and NA. A similar property needs to be defined in the spec.
  • It is unclear what to do with parsing errors, or if the fields in datapackage.json does not match the csv data. Examples: s-and-p-500 and currency-codes


  • Writing data packages from data frames.

rOpenSci OKFN

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