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# rang - Reconstructing Reproducible R Computational Environments
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## Description
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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>.

## Keywords

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* Computational Environment
* Computational Reproducibility
* Open Science

## Use Cases
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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/).

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

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

## Hardware Requirements
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Rang runs on any hardware that can run R.

## Environment Setup
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With R installed:

```r
install.packages("rang")
```

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

## How to Use {#sec-touse}
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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).

```r
library("quanteda")
# construct the feature co-occurrence matrix
examplefcm <-
tokens(data_corpus_irishbudget2010, remove_punct = TRUE) %>%
tokens_tolower() %>%
tokens_remove(stopwords("english"), padding = FALSE) %>%
fcm(context = "window", window = 5, tri = FALSE)
# choose 30 most frequency features
topfeats <- names(topfeatures(examplefcm, 30))
# select the top 30 features only, plot the network
set.seed(100)
textplot_network(fcm_select(examplefcm, topfeats), min_freq = 0.8)
```

This code cannot be executed with a recent version of `quanteda`. As the above code was written in 2018, one can get the dependency graph of `quanteda` in 2018:

```{r}
library(rang)
graph <- resolve(pkgs = "quanteda",
                 snapshot_date = "2018-10-06",
                 os = "ubuntu-18.04")
graph
```

This dependency graph can be used to create a dockerized computational environment (in form of `Dockerfile`) for running the abovementioned code. Suppose one would like to generate the `Dockerfile` in the directory "quanteda_docker".

```r
dockerize(graph, "quanteda_docker", method = "evercran")
```

A Docker container can then be built and launched, e.g. from the shell:

```sh
cd quanteda_docker
docker build -t rang .
docker run --rm --name "rangtest" -ti rang
```

The launched container is based on R 3.5.1 and `quanteda` 1.3.4 and is able to run the abovementioned code snippet.

Please refer to the [official website](https://gesistsa.github.io/rang/) for further information.

## Technical Details
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See the [publication](https://doi.org/10.1371/journal.pone.0286761) for information about technical details.

## References
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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>.

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## Contact Details
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Maintainer: Chung-hong Chan <chainsawtiney@gmail.com>

Issue Tracker: [https://github.com/gesistsa/rang/issues](https://github.com/gesistsa/rang/issues)