Pre-submission Inquiry: unifir, A Unifying API for Working with Unity in R

[mikemahoney218]

Reviewers list: Editor:

Submitting Author Name: Mike Mahoney Submitting Author Github Handle: !--author1-->@mikemahoney218<!--end-author1-- Repository: https://github.com/mikemahoney218/unifir Submission type: Pre-submission Language: en

---

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

Type: Package
Package: unifir
Title: A Unifying API for Calling 'Unity' from R
Version: 0.1.0.9001
Authors@R: 
    person("Michael", "Mahoney", , "mike.mahoney.218@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2402-304X"))
Description: Functions for the creation and manipulation of scenes and objects
    within the 'Unity' '3D' video game engine (<https://unity.com/>). Specific
    focuses include the creation and import of terrain data and 'GameObjects' as
    well as scene management.
License: MIT + file LICENSE
Depends:
    R (>= 3.5.0)
Imports: 
    glue,
    methods,
    proceduralnames,
    R6,
    utils
Suggests: 
    terrainr,
    covr,
    knitr,
    lintr,
    pkgdown,
    rmarkdown,
    styler,
    testthat (>= 3.0.0),
    terra
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
Config/testthat/edition: 3
Config/testthat/parallel: true
URL: https://mikemahoney218.github.io/unifir/, https://github.com/mikemahoney218/unifir
BugReports: https://github.com/mikemahoney218/unifir/issues
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

  • [x] 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:

This package provides bindings to the Unity video game engine API from R, for the production of 3D/VR "environments" entirely from R code. It enables producing a new type of visualization in an efficient and reproducible manner, and provides specific methods for visualizing spatial data stored in standard spatial formats. However, I could understand if the package itself is too general for rOpenSci in particular; while we are designing it for research applications, Unity is not explicitly scientific software.

• 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?

The target audience is anyone looking to produce data-driven immersive virtual environments in Unity, with a primary focus on visualizing natural environments. I work directly with ecologists and landscape architects, and so the initial feature set is oriented towards making the package useful for that audience.

Immersive virtual environments are an active area of research (see for instance https://www.mm218.dev/papers/vrs_2021.pdf, https://joss.theoj.org/papers/10.21105/joss.04060, https://link.springer.com/article/10.1007/s42489-020-00069-6 ). At the moment, the current standard for the field is producing hand-designed "artistic" environments tailored for each purpose, which makes assessing this visualization method difficult. Our aim with this project is to produce a standard set of tooling for creating immersive virtual environments, making it both easier to produce visualization "bases" for applications and giving us a way to produce reproducible visualizations as a baseline for assessing visualization effectiveness directly.

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

I'm not aware of any. The closest is likely the rayverse (rayshader/rayrender), which makes fantastic 3D visualizations in R directly, without incorporating Unity; the incorporation of Unity makes adding player controllers (for interactive exploration of the environment) much easier.

• (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?:

[jooolia]

Dear @mikemahoney218, Thank you for your pre-submission. I am discussing with the other editors to determine if this package is in-scope for rOpenSci. Thanks, Julia

[mikemahoney218]

In case it's useful, a few more links about the use of VR (and specifically immersive virtual environments, the focus of our work) in research settings:

https://www.sciencedirect.com/science/article/pii/S0360131519303276 https://www.frontiersin.org/articles/10.3389/frvir.2021.603750/full https://journals.sagepub.com/doi/abs/10.1177/1937586720924787 https://www.jantsje.nl/publication/mol-2019/postprint-mol-2019.pdf

[jooolia]

Dear @mikemahoney218, We have deemed this package to be in-scope. We would recommend adding information about the research application(s) in the Readme. I will now close this issue and we look forward to your full submission. Thanks, Julia

[mikemahoney218]

Thank you! Will do.