https://github.com/alexkyllo/workbench-r
An R data science project template for use with the cookiecutter templating tool
https://github.com/alexkyllo/workbench-r
Last synced: 9 days ago
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An R data science project template for use with the cookiecutter templating tool
- Host: GitHub
- URL: https://github.com/alexkyllo/workbench-r
- Owner: alexkyllo
- License: mit
- Created: 2020-09-01T05:12:35.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-09-02T05:49:44.000Z (over 4 years ago)
- Last Synced: 2025-03-20T21:21:04.936Z (25 days ago)
- Language: R
- Homepage:
- Size: 58.6 KB
- Stars: 4
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- jimsghstars - alexkyllo/workbench-r - An R data science project template for use with the cookiecutter templating tool (R)
README
# Workbench R
A cookiecutter template for R projects based on a streamlined version of
the
[Cookiecutter Data Science](https://drivendata.github.io/cookiecutter-data-science/)
template.
Uses Azure Data Explorer as a source database and Azure Blob Storage
to store CSV data files.
### Requirements to use the cookiecutter template:
-----------
- Python 2.7 or 3.5
- [Cookiecutter Python package](http://cookiecutter.readthedocs.org/en/latest/installation.html) >= 1.4.0:
This can be installed with pip or conda depending on how you manage your Python packages:
``` bash
$ pip install cookiecutter
```
or
``` bash
$ conda config --add channels conda-forge
$ conda install -y cookiecutter
```
### To start a new project, run:
------------
cookiecutter https://github.com/alexkyllo/workbench-r
### The resulting directory structure
------------
The directory structure of your new project looks like this:
```
├── R/ <- R source code for use in this project.
│ ├── clean.R <- Clean data in data/raw/ and output result to /data/processed
│ ├── explore.R <- Exploratory data analysis script for interactive work
│ ├── get.R <- Script to get data from the source
│ ├── install.R <- Script to install R packages (that are not available from conda)
│ ├── plot.R <- Script to plot visuals
│ ├── render.R <- Script to render the report.Rmd RMarkdown file into a document
│ ├── report.Rmd <- Analysis report writeup RMarkdown file
│ ├── score.R <- Script to score a trained model on test data
│ ├── theme.R <- A minimal blue/gray style theme for ggplot2 plots
│ ├── train.R <- Script to train a predictive model and save the model to a file
│ └── utilities.R <- Utility functions for use by other scripts.
├── data/
│ ├── processed/ <- The cleaned, transformed data for modeling
│ └── raw/ <- The original source data
├── figures/ <- Generated .png plot figures
├── models/ <- Trained and serialized models, model predictions, or model summaries
├── notebooks/ <- Jupyter notebooks
├── queries/ <- SQL and Kusto (Azure Data Explorer) queries used to retrive data
├── references/ <- Data dictionaries, manuals, and all other explanatory materials
├── reports/ <- Generated analysis as HTML, PDF, DOCX, etc.
├── .env <- Environment variables including configurations and any secret keys
├── .gitignore <- List of file patterns for Git to ignore from version control
├── .here <- Tells the "here" package that this is the project root directory
├── LICENSE <- MIT License
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
└── environment.yml <- conda environment configuration file.
```
### Installing development requirements
------------
pip install -r requirements.txt
### Running the tests
------------
py.test tests