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Convert European Regional Data · Issue #623 · ropensci/software-review · GitHub

Date accepted: 2024-03-14
Submitting Author Name: Moritz Hennicke
Submitting Author Github Handle: @AAoritz
Other Package Authors Github handles: @krausewe
Repository: https://github.com/AAoritz/nuts/
Version submitted: 0.0.0.9000
Submission type: Standard
Editor: @maelle
Reviewers: @nolwenn, @jospueyo

Archive: TBD
Version accepted: TBD
Language: en

• Paste the full DESCRIPTION file inside a code block below:

Package: nuts
Title: 'nuts': Convert European Regional Data
Version: 0.0.0.9000
Authors@R: c(person("Moritz", "Hennicke", email = "AAoritz@posteo.de", role = c("aut", "cre"),
		   comment = c(ORCID = "0000-0001-6811-1821")),
	     person("Werner", "Krause", email = "werner.krause@uni-potsdam.de", role = c("aut"),
		   comment = c(ORCID = "0000-0002-5069-7964")))
Description: Motivated by changing administrative boundaries over time, the 'nuts' package can convert European regional data with NUTS codes between versions (2006, 2010, 2013, 2016 and 2021) and levels (NUTS 1, NUTS 2 and NUTS 3). The package uses spatial interpolation based on granular (100mx100m) population and land use data provided by the European Commission's Joint Research Center.  
URL: https://AAoritz.github.io/nuts/
BugReports: https://github.com/AAoritz/nuts/
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.1
Suggests: 
    distill,
    eurostat,
    formatR,
    ggalluvial,
    ggfittext,
    ggplot2,
    ggpubr,
    ggrepel,
    gridExtra,
    kableExtra,
    knitr,
    raster,
    RColorBrewer,
    readr,
    rmarkdown,
    sf,
    terra,
    testthat,
    tidyr
Config/testthat/edition: 3
VignetteBuilder: knitr
Imports:
	crayon,
	dplyr,
	stringr
Depends: 
    R (>= 3.5.0)
LazyData: true

• 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
  • 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):

Data munging: Linking regional data from different sources at the level of NUTS codes is often complicated by the usage of different versions or varying levels of geographical granularity across sources. The package can be used to harmonize NUTS versions and levels across different data sources using spatial interpolation.

Data validation and testing: The package includes routine tasks to test for the validity and completeness of NUTS codes.

Geospatial data: NUTS codes are the dominant format for European regional data.

• Who is the target audience and what are scientific applications of this package?

The target audience are academics, journalists and data scientists interested in European regional data. Users who want to exploit changes within NUTS regions over time face the challenge that administrative boundaries are redrawn over time. The package enables the construction of consistent panel data across NUTS regions and over time through the harmonization of NUTS regions to one common version or level of granularity.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

To our knowledge there is currently no package that is targeted at the conversion of NUTS versions using spatial interpolation.

The regions package allows for re-coding of NUTS codes from version to version without spatial interpolation. The package offers some code validation routines, but not an automated detection of the NUTS version used.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

Yes

• 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.

• Explain reasons for any pkgcheck items which your package is unable to pass.

Confirm each of the following by checking the box.

• I have read the rOpenSci packaging guide.
• I have read the author guide and I expect to maintain this package for at least 2 years or to find a replacement.

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.

• Do you intend for this package to go on CRAN?

• I agree to abide by rOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

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