Introduction
Content
This section covers the following topics:
- Setting up a folder structure
- Using git for version control
- Publishing your project on GitHub
- Choosing a license
Getting Started with Our Project
Welcome to the “Breast Cancer Outcome Analysis” project! This endeavor aims to provide a comprehensive analysis of the expected outcomes for a cohort of 55-year-old women who have undergone tumor excision for localized breast cancer. Breast cancer is a critical health concern worldwide, and understanding its outcomes post-surgery can provide valuable insights for both medical professionals and patients.
Objective
Our primary objective is to model the potential health trajectories of these women using a Markov model. This probabilistic model will allow us to simulate various health states over time, such as remaining localized, recurrence, metastasis, or death. By doing so, we can estimate the quality-adjusted life expectancy for these patients, providing a clearer picture of the potential long-term impacts of the disease and its treatment.
Approach
Throughout this tutorial, we will:
- Set Up Our Project Environment: Using tools like
cookiecutter
, we’ll establish a structured project directory, ensuring that our work remains organized and reproducible. - Data Import and Exploration: We’ll import essential data, such as age-specific hazard rates, and familiarize ourselves with its structure and significance.
- Model Development: Using R, we’ll develop our Markov model, defining various states and transitions based on the provided data and medical knowledge.
- Analysis and Visualization: Once our model is in place, we’ll simulate the health trajectories and visualize the outcomes using packages like
ggplot2
. - Sensitivity Analysis: To understand the robustness of our model, we’ll perform sensitivity analyses on key parameters, observing how changes in these values impact our results.
Collaboration and Reproducibility
Emphasizing computational reproducibility, we’ll employ version control using Git and GitHub. This approach not only ensures that our work can be reliably replicated by others but also facilitates collaboration among researchers.
Exercises
Time for some hands-on practice!
Be sure to ask for help when you need it!
1. Project Setup
Understanding the Project Structure
Navigate to the newly created directory and familiarize yourself with the folders and files.
Adding Data to the Project
Download the necessary data here for the tutorial and place it in the data
folder within your project directory.
2. Version Control with Git and GitHub
Initializing a Git Repository
Navigate to your project directory in the terminal:
git init
Creating a Remote Repository on GitHub
On GitHub, create a new repository with the same name as the one that you just created locally. Link your local repository to this remote:
git remote add origin [your-repository-url]
Pushing to GitHub
Commit and push your changes:
git add .
git commit -m "Initial commit"
git push --set-upstream origin main
Experimenting with GitHub
Make edits directly on GitHub and then pull those changes:
git pull
3. Create a .Rproj
for this repository
What does this do? * Creates a project file (with an .Rproj extension) within the project directory. This file contains various project options (discussed below) and can also be used as a shortcut for opening the project directly from the filesystem.
Creates a hidden directory (named .Rproj.user) where project-specific temporary files (e.g. auto-saved source documents, window-state, etc.) are stored. This directory is also automatically added to .Rbuildignore, .gitignore, etc. if required.
Loads the project into RStudio and display its name in the Projects toolbar (which is located on the far right side of the main toolbar) |
4. Publication & Licensing
Reviewing Your License
Create a LICENSE.md
file in your project directory.
touch LICENSE.md
Exploring Licensing Options
Visit choosealicense.com for other options. Update the LICENSE.md
file and push to GitHub!: