Frictionless is an R package to read and write Frictionless Data Packages. A Data Package is a simple container format and standard to describe and package a collection of (tabular) data. It is typically used to publish FAIR and open datasets.
To get started, see:
- Get started: an introduction to the package’s main functionalities.
- Function reference: overview of all functions.
Install the latest released version from CRAN:
Or the development version from GitHub or R-universe:
# install.packages("devtools") devtools::install_github("frictionlessdata/frictionless-r") # Or rOpenSci R-universe install.packages("frictionless", repos = "https://ropensci.r-universe.dev")
With frictionless you can read data from a Data Package (local or remote) into your R environment. Here we read bird GPS tracking data from a Data Package published on Zenodo:
library(frictionless) # Read the datapackage.json file # This gives you access to all Data Resources of the Data Package without # reading them, which is convenient and fast. package <- read_package("https://zenodo.org/record/5879096/files/datapackage.json") #> Please make sure you have the right to access data from this Data Package for your intended use. #> Follow applicable norms or requirements to credit the dataset and its authors. #> For more information, see https://doi.org/10.5281/zenodo.5879096 # List resources resources(package) #>  "reference-data" "gps" "acceleration" # Read data from the resource "gps" # This will return a single data frame, even though the data are split over # multiple zipped CSV files. read_resource(package, "gps") #> # A tibble: 73,047 × 21 #> event-i…¹ visible timestamp locat…² locat…³ bar:b…⁴ exter…⁵ gps:d…⁶ #> <dbl> <lgl> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 1.43e10 TRUE 2018-05-25 16:11:37 4.25 51.3 NA 32.5 2 #> 2 1.43e10 TRUE 2018-05-25 16:16:41 4.25 51.3 NA 32.8 2.1 #> 3 1.43e10 TRUE 2018-05-25 16:21:29 4.25 51.3 NA 34.1 2.1 #> 4 1.43e10 TRUE 2018-05-25 16:26:28 4.25 51.3 NA 34.5 2.2 #> 5 1.43e10 TRUE 2018-05-25 16:31:21 4.25 51.3 NA 34.1 2.2 #> 6 1.43e10 TRUE 2018-05-25 16:36:09 4.25 51.3 NA 32.5 2.2 #> 7 1.43e10 TRUE 2018-05-25 16:40:57 4.25 51.3 NA 32.1 2.2 #> 8 1.43e10 TRUE 2018-05-25 16:45:55 4.25 51.3 NA 33.3 2.1 #> 9 1.43e10 TRUE 2018-05-25 16:50:49 4.25 51.3 NA 32.6 2.1 #> 10 1.43e10 TRUE 2018-05-25 16:55:36 4.25 51.3 NA 31.7 2 #> # … with 73,037 more rows, 13 more variables: `gps:satellite-count` <dbl>, #> # `gps-time-to-fix` <dbl>, `ground-speed` <dbl>, heading <dbl>, #> # `height-above-msl` <dbl>, `location-error-numerical` <dbl>, #> # `manually-marked-outlier` <lgl>, `vertical-error-numerical` <dbl>, #> # `sensor-type` <chr>, `individual-taxon-canonical-name` <chr>, #> # `tag-local-identifier` <chr>, `individual-local-identifier` <chr>, #> # `study-name` <chr>, and abbreviated variable names ¹`event-id`, …
You can also create your own Data Package, add data and write it to disk:
# Create a Data Package and add the "iris" data frame as a resource my_package <- create_package() %>% add_resource(resource_name = "iris", data = iris) # Write the Data Package to disk my_package %>% write_package("my_directory")
For more functionality, see get started or the function reference.
frictionless vs datapackage.r
datapackage.r is an alternative R package to work with Data Packages. It has an object-oriented design (using a
Package class) and offers validation. frictionless on the other hand allows you to quickly read and write Data Packages to and from data frames, getting out of the way for the rest of your analysis. It is designed to be lightweight, follows tidyverse principles and supports piping.
- We welcome contributions including bug reports.
- License: MIT
- Get citation information for frictionless in R doing
- Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.