https://github.com/russHyde/code_as_data
Analysis of code in R dev packages (for a planned talk)
https://github.com/russHyde/code_as_data
Last synced: 4 months ago
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Analysis of code in R dev packages (for a planned talk)
- Host: GitHub
- URL: https://github.com/russHyde/code_as_data
- Owner: russHyde
- Created: 2019-12-16T10:42:04.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2023-10-18T00:12:02.000Z (over 1 year ago)
- Last Synced: 2024-11-27T12:21:41.653Z (5 months ago)
- Language: R
- Size: 295 KB
- Stars: 9
- Watchers: 2
- Forks: 0
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
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README
# code-as-data
## Introduction
Programming projects grow over time. New contributors join, new features are
added, complexity increases. As this happens, the version-control history of the
project becomes a valuable source of data. You might want to know:
- who is the main contributor to this particular file?
- if I change this file, are there any other files that I might need to look at?
- are there any files that should be combined together, or split apart?
Here, we have obtained the source code and version-control history for a range
of R packages. For each package, the changes to individual files can be mapped
out over history.
The packages studied were all related to package-development.
There is a shiny-app that presents the results from the analysis
[at shinyapps.io](https://russ-hyde.shinyapps.io/code-as-data/).
## Environment
This project uses
- `conda` to manage the python-based and command-line tools and to install the
base R release
- and `renv` to manage the installation of any R packages
To set up the project, first install the `conda` environment, activate it, then
install the `renv` environment
Once you've got `conda` installed:
```
# To create an identical environment (req Ubuntu)
conda create --name code-as-data --file conda/requirements.txt
```
```
# To create a similar environment
conda env create --file conda/environment.yml --name code-as-data
```
Activate the conda environment
```
conda activate code-as-data
```
To install the R environment (and also make any non-version-controlled
directories: data, results etc):
```
./setup
```
----
To run the analysis:
Once the environment is setup and activated use the bash script to run the
workflow
```
./run
```
If you want the data or results to be stored to a specific location, set up
links to these positions before running `./run`.