Presubmission inquiry: octolog

[assignUser]

Submitting Author Jacob Wujciak-Jens (@assignUser) Reviewers: Editor: Language Code en Repository: https://github.com/assignUser/octolog/ Submission type: Pre-submission

---

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

Package: octolog
Title: Better 'Github Action' Logging
Version: 0.1.1.9000
Authors@R: 
    person("Jacob", "Wujciak-Jens", , "jacob@wujciak.de", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-7281-3989"))
Description: Provides an API for 'Github' <https://github.com> workflow
    commands and makes it possible to signal conditions from within R that
    will create annotations when used within 'Github Actions'
    <https://github.com/features/actions> but raise normal R conditions
    when used interactively.
License: MIT + file LICENSE
URL: https://github.com/assignUser/octolog,
    https://assignuser.github.io/octolog/
BugReports: https://github.com/assignUser/octolog/issues
Imports: 
    cli (>= 3.0.0),
    fs,
    glue,
    magrittr,
    openssl,
    rlang (>= 1.0.0),
    utils,
    withr
Suggests: 
    knitr,
    covr,
    mockery (>= 0.4.2.9000),
    testthat (>= 3.0.0),
    rmarkdown
Remotes: 
    r-lib/mockery
Config/testthat/edition: 3
Config/testthat/parallel: true
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2
VignetteBuilder: knitr

Scope

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

  Data Lifecycle Packages

  • [ ] data retrieval

  • [ ] data extraction

  • [ ] database access

  • [ ] data munging

  • [ ] data deposition

  • [x] workflow automation

  • [ ] version control

  • [ ] citation management and bibliometrics

  • [ ] scientific software wrappers

  • [ ] database software bindings

  • [ ] geospatial data

  • [ ] text data

    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

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

Octolog aims to make it easier for R author's to use the full power of Github Actions as a CI service.

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

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

Using CI is recognized as an important part in the development process of (scientific) R software and octlog aims to make this easier and more powerful.

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

As far as I know there are none.

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

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

Disclosure: We use octolog in https://github.com/ropensci-review-tools/pkgcheck-action

[ropensci-review-bot]

The submission template is missing the following values: author1, language

[jooolia]

Dear @assignUser, Thanks for the pre-submission. We consider octolog in-scope and welcome a full submission. We consider it a workflow automation package as it increases the utility and reporting of Github actions for R developers. Thanks and let us know if you have any further questions.