template

This repository offers a robust Quarto template designed for reproducible research and academic writing. It leverages the power of both R and Python for data manipulation, analysis, and visualization, with renv ensuring a fully reproducible R environment and clear directory structures for organized data and figures.

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Prerequisites

Before attempting to render this document or use the template, ensure that the following software and packages are installed on your system:

1. R:

• Make sure you have R installed (version 4.0 or newer is recommended). You can download it from The Comprehensive R Archive Network (CRAN).
• RStudio Desktop is highly recommended as an Integrated Development Environment (IDE) for seamless R and Quarto development. Download it from Posit.

2. R Packages (Managed by renv):

This project uses renv to ensure a fully reproducible R environment. All necessary R packages and their exact versions are locked down in the renv.lock file. Key packages include:

• tidyverse: A collection of R packages designed for data science, including dplyr for data manipulation and ggplot2 for data visualization.
• reticulate: Provides essential tools for seamless interoperability between R and Python.
• scales: Offers functions for numeric and color scales, useful for plots.
• readxl: Enables efficient reading of data from Excel files.
• here: Crucial for constructing file paths relative to the project root, ensuring your code works consistently.

3. Python:

• Make sure you have Python installed (version 3.8 or newer is recommended). Using a distribution like Anaconda is highly recommended as it simplifies package and environment management for scientific libraries.

4. Python Packages:

The following Python packages are required and will be automatically installed by reticulate (from within your Quarto document) if not found in your active Python environment:

• numpy: The fundamental package for numerical computation in Python.
• pandas: Provides powerful data structures and data analysis tools.
• seaborn: A high-level data visualization library based on Matplotlib.
• matplotlib: A comprehensive library for creating static, interactive, and animated visualizations in Python.
• statsmodels: Offers classes and functions for the estimation of various statistical models, tests, and data exploration.
• scienceplots: A collection of scientific plotting styles for Matplotlib.

5. Quarto:

• Make sure you have Quarto installed. Find detailed installation instructions at https://quarto.org/docs/install/.

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Installation & Usage

To generate the template.pdf and begin working with this project, follow these steps:

1. Clone the repository: Open your terminal or command prompt and run:

   git clone [https://github.com/DevonAllies/template.git](https://github.com/DevonAllies/template.git)
   cd template

2. Set up the R environment using renv:

   • Open the template.Rproj file (or simply open the template directory) in RStudio.
   • renv will automatically activate when you open the project. You'll likely see a prompt in the R console suggesting you run renv::restore().
   • Run renv::restore() in the R console. This vital step downloads and installs all necessary R packages at the exact versions specified in the renv.lock file, ensuring a reproducible R environment.

3. Render the document:

   • With your R and Python environments set up and activated, navigate to the project root directory (where template.qmd is located) in your terminal or command prompt.
   • Run the Quarto render command:

     quarto render template.qmd

   • This command executes the R and Python code chunks, generating template.pdf (and potentially template.docx if configured), which will be saved in the output/ directory as configured by your _quarto.yml file.

Acknowledgement

• This template was built using resources from Stellenbosch University.