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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:

Installation

Install the latest released version from CRAN:

install.packages("frictionless")

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")

Usage

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)
#> [1] "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-id` visible timestamp           `location-long` `location-lat`
#>          <dbl> <lgl>   <dttm>                        <dbl>          <dbl>
#>  1 14256075762 TRUE    2018-05-25 16:11:37            4.25           51.3
#>  2 14256075763 TRUE    2018-05-25 16:16:41            4.25           51.3
#>  3 14256075764 TRUE    2018-05-25 16:21:29            4.25           51.3
#>  4 14256075765 TRUE    2018-05-25 16:26:28            4.25           51.3
#>  5 14256075766 TRUE    2018-05-25 16:31:21            4.25           51.3
#>  6 14256075767 TRUE    2018-05-25 16:36:09            4.25           51.3
#>  7 14256075768 TRUE    2018-05-25 16:40:57            4.25           51.3
#>  8 14256075769 TRUE    2018-05-25 16:45:55            4.25           51.3
#>  9 14256075770 TRUE    2018-05-25 16:50:49            4.25           51.3
#> 10 14256075771 TRUE    2018-05-25 16:55:36            4.25           51.3
#> # … with 73,037 more rows, and 16 more variables:
#> #   `bar:barometric-pressure` <dbl>, `external-temperature` <dbl>,
#> #   `gps:dop` <dbl>, `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>, …

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.

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