Pre-submission inquiry: rvdat

[mhpob]

Submitting Author Name: Mike O'Brien
Submitting Author Github Handle: @mhpob
Repository: https://github.com/mhpob/rvdat
Submission type: Pre-submission
Language: en

---

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

Package: rvdat
Title: Lightweight Access to 'VDAT' Shell Commands
Version: 0.3.1
Authors@R: c(
    person("Michael", "O'Brien", , "mike@obrien.page", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-1420-6395")),
    person("Benjamin L.", "Hlina", , "benjamin.hlina@gmail.com", role = "ctb")
  )
Description: Provides lightweight, R-friendly syntax for 'VDAT' shell functions.
License: AGPL (>= 3)
URL: https://rvdat.obrien.page, https://github.com/mhpob/rvdat
BugReports: https://github.com/mhpob/rvdat/issues
SystemRequirements: VDAT command line tools
    (https://support.fishtracking.innovasea.com/s/downloads).
Imports: 
    cli,
    sys
Suggests: 
    jsonlite,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
VignetteBuilder: knitr
Config/testthat/edition: 3

Scope

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check one or more appropriate boxes below):

  Data Lifecycle Packages

  • 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
  • translation

  Statistical Packages

  • Bayesian and Monte Carlo Routines
  • Dimensionality Reduction, Clustering, and Unsupervised Learning
  • Machine Learning
  • Regression and Supervised Learning
  • Exploratory Data Analysis (EDA) and Summary Statistics
  • Spatial Analyses
  • Time Series Analyses
  • Probability Distributions

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

This package is primarily a software wrapper around a free, proprietary CLI executable, "Fathom Connect Software" linked here, which translates the binary file offloaded by acoustic animal tracking receivers to text. The package does not aim to provide R import tools as there are a few other packages that do this, just an interface to the CLI, itself. As the software it wraps is proprietary and cannot be shipped with the R package I am curious if this is within scope of ROpenSci.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

N/A

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

Individuals using underwater acoustic telemetry receivers produced by Innovasea, the predominant technology used for underwater animal tracking in North America. This package allows R scripting of data conversion from binary to text formats and quick investigation of file metadata. Innovasea only provides this software on Windows; this allows users on Linux and macOS to run the software within R via a call to Wine.

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

glatos::vdat_convert provides similar functionality (wrapping the vdat executable), but is a large package that is predominantly focused on other tooling and analysis workflows. This package is meant to be light.

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

N/A.

• Any other questions or issues we should be aware of?:

As this relies on an executable that does not allow distribution through other venues, most functions cannot be tested via GitHub Actions and must be run locally.

[ropensci-review-bot]

Thanks for your pre-submission to rOpenSci, our editors will reply soon.

[emilyriederer]

Hi @mhpob - thanks for your submission! I'm the current rotating EiC for rOpenSci.

I'm researching a bit more about fit/scope (as this is not my area of expertise). In the meantime, I had two questions for you:

• I see the README has a TODO list with uncompleted tasks. Do you plan to complete those in the near term? We recommend packages submit for review after they have reached a good baseline "feature complete" state
• Was there AI used in developing this package? We just rolled out a post about how we are approaching this space. This doesn't impact whether the package is a fit, but is helpful as we'd consider our approach to review
• I don't have a sense how easy it would be to find reviewers with experience on these exact needs / softwares. Hypothetically, would you be able to provide examples of the types of files consumed by this tool?
• Do you plan to submit the pkg to CRAN?

[emilyriederer]

Hi @mhpob -- I'm just bumping the questions above! Please let us know your thoughts if you are still interested in proceeding with a review.