Setting Up a Reproducible R Analysis Environment This repository provides a framework for setting up a robust and reproducible R analysis environment. It focuses on project organization, package management, and best practices to ensure your R projects are well-structured, portable, and easy to share.

Key Features Project Structure: A clear and organized project structure using the here package for relative file paths. This makes your project portable and avoids hardcoded paths. Package Management: Efficient package management with the pacman package. Install packages with p_install() and load them with p_load(). Global Parameters: Centralized global parameters and API keys in a dedicated script. API keys are securely stored as environment variables. Reproducibility: Emphasis on reproducibility with a set random seed and clear documentation. Package Conflicts: Resolve function name conflicts using conflicts_prefer(). Organized Scripts: Well-commented and organized R scripts (e.g., packages.R, global_parameters.R). Project Organization The recommended project structure is as follows:

├── 01_load.R ├── 02_clean.R ├── 03_model.R ├── 04_output.R ├── packages.R ├── global_parameters.R ├── _archive │ └── _anchor.R └── _images └── anchor_R.png

_archive/_anchor.R: This file serves as the anchor for the here package, defining the root of your project. packages.R: This script manages the installation and loading of all required R packages for your project. global_parameters.R: This script stores global settings, API keys (as environment variables), and resolves package conflicts. 01_load.R: This script is for loading in necessary files. 02_clean.R: This script is for data cleaning. 03_model.R: This script is for running models. 04_output.R: This script is for creating output. _images: This folder is for storing images. How to Use Clone the repository: git clone https://github.com/your-username/your-repo-name.git Set up environment variables: Create environment variables for your API keys (e.g., ELSEVIER_API_KEY, CENSUS_API_KEY, GOOGLE_API_KEY). Install packages: Run the packages.R script to install and load the necessary packages. Configure global parameters: Review and modify the global_parameters.R script to match your project settings. Start your analysis: Begin your R analysis, using the provided scripts and folder structure as a foundation. Best Practices Use relative file paths: Always use the here() function to construct file paths. Organize your scripts: Break down your analysis into smaller, well-defined scripts. Document your code: Use comments to explain your code and its purpose. Set a random seed: Use set.seed() for reproducibility. Manage package conflicts: Use conflicts_prefer() to resolve conflicts. Contributing Contributions are welcome! Feel free to open issues or submit pull requests.

License This project is licensed under the MIT License - see the LICENSE file for details.