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GitHub · Where software is built

Date accepted: 2022-01-12
Submitting Author Name: Will Landau
Submitting Author Github Handle: @wlandau
Repository: https://github.com/wlandau/gittargets
Version submitted: 0.0.0.9000
Submission type: Standard
Editor: @adamhsparks
Reviewers: @smwindecker, @mdneuzerling

Due date for

: 2021-12-20

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

Package: gittargets
Title: Data Version Control for the Targets Package
Description: Version control systems such as Git help researchers
  track changes and history in data science projects,
  and the 'targets' package (2021, <doi:10.21105/joss.02959>)
  minimizes the computational cost of keeping the latest results
  reproducible and up to date. The 'gittargets' package
  combines these two capabilities. The 'targets' data store
  becomes a version control repository and stays synchronized
  with the Git repository of the source code.
  Users can switch commits and branches
  without invalidating the 'targets' pipeline.
Version: 0.0.0.9000
License: MIT + file LICENSE
URL: https://wlandau.github.io/gittargets/, https://github.com/wlandau/gittargets
BugReports: https://github.com/wlandau/gittargets/issues
Authors@R: c(
  person(
    given = c("William", "Michael"),
    family = "Landau",
    role = c("aut", "cre"),
    email = "will.landau@gmail.com",
    comment = c(ORCID = "0000-0003-1878-3253")
  ),
  person(
    family = "Eli Lilly and Company",
    role = "cph"
  ))
Depends:
  R (>= 3.5.0)
Imports:
  cli (>= 3.0.0),
  data.table (>= 1.12.8),
  gert (>= 1.0.0),
  processx (>= 3.0.0),
  stats,
  targets (>= 0.8.1.9000),
  tibble (>= 3.0.0),
  utils,
  uuid (>= 1.0.0)
Suggests:
  knitr (>= 1.30),
  markdown (>= 1.1),
  rmarkdown (>= 2.4),
  testthat (>= 3.0.0)
Remotes:
  ropensci/targets
SystemRequirements: Git (>= 2.0.0)
Encoding: UTF-8
Language: en-US
VignetteBuilder: knitr
Config/testthat/edition: 3
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2

• 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
  • 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):

Version control systems such as Git help researchers track changes and history in data science projects, and the targets package minimizes the computational cost of keeping the latest results reproducible and up to date. The gittargets package combines these two capabilities. The targets data store becomes a version control repository and stays synchronized with the Git repository of the source code. Users can switch commits and branches without invalidating the targets pipeline.

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

gittargets is for people who use targets for reproducible analysis pipelines and Git for tracking the code files of those pipelines.

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

Packages gh, gert, git2r, and git2rdata all work with Git from R, but in a way that is more general, less opinionated, and less targets-specific than gittargets. gittargets establishes a separate repository for the targets data that is linked to the project's code repository. This allows the code and data to synchronize without affecting the efficiency or the size of the code repository.

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

N/A

• 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.

N/A

Confirm each of the following by checking the box.

• I have read the guide for authors and rOpenSci packaging guide.

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.

• 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:

• [n/a] The package is novel and will be of interest to the broad readership of the journal.
• [n/a] The manuscript describing the package is no longer than 3000 words.
• [n/a] 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)

• 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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