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128 changes: 98 additions & 30 deletions methodshub.qmd
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---
title: rang - Reconstructing Reproducible R Computational Environments
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---

## Description
# rang - Reconstructing Reproducible R Computational Environments
<!--
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<!-- - Provide a brief and clear description of the method, its purpose, and what it aims to achieve. Add a link to a related paper from social science domain and show how your method can be applied to solve that research question. -->
## Description
<!--
1. Provide a brief and exact description of the method clearly mentioning its purpose i.e., what the method does or aims to achieve in abstract terms (avoiding technical details).
2. The focus should be on explaining the method in a way that helps users with different levels of expertise understand what it does, without going into technical details. It should clearly describe what inputs are needed and what outputs can be expected.
3. Briefly explain the input and output of the method and its note worthy features.
4. Provide link(s) to related papers from the social science domain using the method or similar methods for solving social science research questions.
5. In a separate paragraph, highlight the reproducibility aspect of the method providing details or references to the resources used by the method, the data used in building the pre-trained modules etc.
6. It should also discuss the decisions and parameters controlling the behavior of the method.
-->

Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.

Expand All @@ -20,20 +46,45 @@ Resolve the dependency graph of R packages at a specific time point based on the
* Computational Reproducibility
* Open Science

## Science Usecase(s)
## Use Cases
<!--
1. The use cases section should contain a list of use cases relevant to the social sciences.
2. Each use case should start with a description of a task and then detail how one can use the method to assist in the task.
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-->

<!-- - Include usecases from social sciences that would make this method applicable in a certain scenario. -->
<!-- The use cases or research questions mentioned should arise from the latest social science literature cited in the description. -->
This package is designed to retrospectively construct a constant computational environment for running shared R scripts, in which the computational environment is **not** specified. Additional functions are provided for creating executable [research compendia](https://research-compendium.science/).

<!-- This is an example -->
## Input Data
<!--
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-->

This package is designed to retrospectively construct a constant computational environment for running shared R scripts, in which the computational environment is **not** specified. Additional functions are provided for creating executable [research compendia](https://research-compendium.science/).
The main function `resolve()` accepts various input data. One example is a path to a directory of R scripts.

## Repository structure
## Output Data
<!--
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2. The output data section should link to a small example output file in the same repository that can be re-created (as far as the method is non-random) from the input data (as described in the section "How to Use").
-->

This repository follows [the standard structure of an R package](https://cran.r-project.org/doc/FAQ/R-exts.html#Package-structure).
The main function `resolve()` gives an S3 object of dependency graph. Please refer to @sec-touse.

## Hardware Requirements
<!--
1. The hardware requirements section should list all requirements (storage, memory, compute, GPUs, cluster software, ...) that exceed the capabilities of a cheap virtual machine provided by cloud computing company (2 x86 CPU core, 4 GB RAM, 40GB HDD).
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-->

Rang runs on any hardware that can run R.

## Environment Setup
<!--
1. The environment setup section should list all requirements and provide all further steps to prepare an environment for running the method (installing requirements, downloading files, creating directoriees, etc.).
2. The environment setup section should recommend to use a virtual environment or similar if the programming language supports one.
-->

With R installed:

Expand All @@ -43,19 +94,15 @@ install.packages("rang")

Installation of [Docker](https://www.docker.com/) or [Singularity](https://sylabs.io/singularity/) is strongly recommended.

## Input Data

The main function `resolve()` accepts various input data. One example is a path to a directory of R scripts.

## Output Data

The main function `resolve()` gives an S3 object of dependency graph. Please refer to @sec-touse.

## Hardware Requirements
## Repository structure

Rang runs on any hardware that can run R.
This repository follows [the standard structure of an R package](https://cran.r-project.org/doc/FAQ/R-exts.html#Package-structure).

## How to Use {#sec-touse}
## How to Use
<!--
1. The how to use section should provide the list of steps that are necessary to produce the example output file (see section Output Data) after having set up the environment (see section Environment Setup).
2. The how to use section should explain how to customize the steps to one's own needs, usually through configuration files or command line parameters, or refer to the appropriate open documentation.
-->

Suppose you would like to run this code snippet in [this 2018 paper](https://joss.theoj.org/papers/10.21105/joss.00774) of the R package `quanteda` (an R package for text analysis).

Expand Down Expand Up @@ -103,21 +150,42 @@ The launched container is based on R 3.5.1 and `quanteda` 1.3.4 and is able to r
Please refer to the [official website](https://gesistsa.github.io/rang/) for further information.

## Technical Details
<!--
1. The technical details section should proview a process overview, linking to key source code files at every step of the process.
2. In case a publication provides the details mentioned below, the technical details section should link to this publication using a sentence like "See the [publication](url-of-publication-best-using-doi) for ...". In this case, the mentioned technical details can be omitted from the section.
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See the [publication](https://doi.org/10.1371/journal.pone.0286761) for information about technical details.

## Contact Details
## References
<!--
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-->

Maintainer: Chung-hong Chan <chainsawtiney@gmail.com>
Chan, C. H., & Schoch, D. (2023). rang: Reconstructing reproducible R computational environments. PLoS ONE, 18(6): e0286761. <https://doi.org/10.1371/journal.pone.0286761>.

Issue Tracker: [https://github.com/gesistsa/rang/issues](https://github.com/gesistsa/rang/issues)
<!-- ## Acknowledgements -->
<!--
1. The acknowledgments section is optional.
2. The acknowledgments section should list expressions of gratitude to people or organizations who contributed, supported or guided.
-->

## Publication
<!-- ## Disclaimer-->
<!--
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2. The disclaimer section should list disclaimers, legal notices, or usage restrictions for the method.
-->

Chan, C. H., & Schoch, D. (2023). rang: Reconstructing reproducible R computational environments. PLoS ONE, 18(6): e0286761. <https://doi.org/10.1371/journal.pone.0286761>.
## Contact Details
<!--
1. The contact details section should specify whom to contact for questions or contributions and how (can be separate entitites; for example email addresses or links to the GitHub issue board).
-->

<!-- ## Acknowledgements -->
<!-- - Acknowledgements if any -->
Maintainer: Chung-hong Chan <chainsawtiney@gmail.com>

<!-- ## Disclaimer -->
<!-- - Add any disclaimers, legal notices, or usage restrictions for the method, if necessary. -->
Issue Tracker: [https://github.com/gesistsa/rang/issues](https://github.com/gesistsa/rang/issues)
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