frictionless: Read and Write Frictionless Data Packages #495
Description
Date accepted: 2022-02-10
Submitting Author Name: Peter Desmet
Submitting Author Github Handle: @peterdesmet
Other Package Authors Github handles: @damianooldoni
Repository: https://github.com/frictionlessdata/frictionless-r
Version submitted: 0.9.0
Submission type: Standard
Editor: @melvidoni
Reviewers: @zambujo, @beatrizmilz
Due date for @beatrizmilz: 2022-02-09
Archive: TBD
Version accepted: TBD
Language: en
- Paste the full DESCRIPTION file inside a code block below:
Package: frictionless
Title: Read and Write Frictionless Data Packages
Version: 0.9.0.9000
Authors@R: c(
person("Peter", "Desmet", , "[email protected]", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-8442-8025")),
person("Damiano", "Oldoni", , "[email protected]", role = "aut",
comment = c(ORCID = "0000-0003-3445-7562")),
person("Research Institute for Nature and Forest (INBO)", , ,
"[email protected]", role = c("cph"))
)
Description: Read and write Frictionless Data Packages. A Data Package
(<https://specs.frictionlessdata.io/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.
License: MIT + file LICENSE
URL: https://github.com/frictionlessdata/frictionless-r,
https://frictionlessdata.github.io/frictionless-r/
BugReports: https://github.com/frictionlessdata/frictionless-r/issues
Imports:
assertthat,
dplyr,
glue,
httr,
jsonlite,
purrr,
readr (>= 2.1.0),
stringr
Suggests:
knitr,
hms,
lubridate,
testthat (>= 3.0.0),
rmarkdown
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2
VignetteBuilder: knitr
Scope
-
Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
- data retrieval
- data extraction
- data munging
- data deposition
- data validation and testing (listed as category, but not in issue template)
- workflow automation
- version control
- citation management and bibliometrics
- scientific software wrappers
- field and lab reproducibility tools
- database software bindings
- geospatial data
- text analysis
-
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
frictionless allows users to read and write Frictionless Data Packages, an open and general-purpose standard to structure and describe (tabular) datasets, typically used to publish FAIR datasets. The package allows users to read (local and remote) Data Packages (data retrieval), load its data resources in data frames (data extraction), return errors if the Data Package is malformed (data validation and testing), add data frames as new resources (data munging) and write Data Packages back to disk (Data deposition).
- Who is the target audience and what are scientific applications of this package?
Anyone who wants to read or create datasets structured as Frictionless Data Packages. The community is referred to as the Frictionless Data community and typical includes researchers, data scientists and data engineers, often interested in (publishing) open data.
- Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?
Yes, datapackage.r: it has an object-oriented design (using a Package class) and offers validation. frictionless on the other hand allows users to quickly read and write Data Package data to and from R data frames, getting out of your way for the rest of your analysis. It is designed to be lightweight, follows tidyverse principles and supports piping. The main functionality (reading data into data frame, adding a data frame as a resource to a package, writing a Data Package to disk) is offered as functions, rather than the class properties in datapackage.r.
- (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
Not applicable
- If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
Not applicable
Technical checks
Confirm each of the following by checking the box.
- I have read the guide for authors and rOpenSci packaging guide.
Note that the link to guide for authors above (in the issue template) returns a 404. It should be https://devguide.ropensci.org/authors-guide.html. I tried to use pkgcheck but I got package ‘pkgcheck’ is not available for this version of R
This package:
- does not violate the Terms of Service of any service it interacts with.
- has a CRAN and OSI accepted license.
- contains a README with instructions for installing the development version.
- includes documentation with examples for all functions, created with roxygen2.
- contains a vignette with examples of its essential functions and uses.
- has a test suite.
- has continuous integration, including reporting of test coverage using services such as Travis CI, Coveralls and/or CodeCov.
Publication options
-
Do you intend for this package to go on CRAN?
-
Do you intend for this package to go on Bioconductor?
-
Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:
MEE Options
- The package is novel and will be of interest to the broad readership of the journal.
- The manuscript describing the package is no longer than 3000 words.
- You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see MEE's Policy on Publishing Code)
- (Scope: Do consider MEE's Aims and Scope for your manuscript. We make no guarantee that your manuscript will be within MEE scope.)
- (Although not required, we strongly recommend having a full manuscript prepared when you submit here.)
- (Please do not submit your package separately to Methods in Ecology and Evolution)
Code of conduct
- I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.
Note that this package falls under the Frictionless Data Code of Conduct.