Visual Studio Code Tutorial for Data Scientists and Statistical Programmers.

Visual Studio Code (VS Code) is a lightweight, free code editor developed by Microsoft. It supports many programming languages and offers a range of extensions to enhance productivity. It provides built-in features like debugging and smart code completion. It also integrates easily with version control systems like Git.

Visual Studio Code Blog Post

Your job will be to set up the Visual Studio Code environment and save the world.

You are a scientist and you have to save the world from epidemy. Your company has designed three drugs, and you must check if they work. Your task is to choose which drug works using the first trial's data. However, first, you have to set up your Visual Studio Code environment.

Please Use Python or R.

You SUCCEEDED when GitHub actions pipeline will pass (green color).

Note: If this is a guided tutorial, please—if possible—use two screens: one to display the teacher’s screen and the other for your own work.

Checklist for Success:

• Fork the vsctuto repository on GitHub.
• Inspect the .github/workflows and click "I understand my workflows, go ahead and enable them" in the Actions Tab.

Visual Studio Code Settings (20 minutes)

• Please use GitHub "Fn + ." to access web vscode.
• Stage, Commit and Push the .vscode/settings.json and .vscode/extensions.json files. JSONC is acceptable. Please refer to python_codespace or r_codespace repositories with .vscode directories. The .vscode folder contains environment-specific JSON files for editor settings, debugging configurations, and task automation.
• Reload the window of the web vscode session and inspect what changed.
• Create a LICENSE file. choosealicense can be helpful.
• Change the editor font size in Settings to 15. In settings, Copy the editor font size setting JSON. Add the editor font size setting JSON to the .vscode/settings.json file.
• Please find out the id for vscode pets extension. Add the extension id to the .vscode/extensions.json file.
• Check out what changed in the Extensions tab. Install the vscode pets extension.
• Run the pet session.
• Stage, Commit and Push the changes.

Devcontainer (Secure Cloud Based Solution) (10 minutes)

• Please use GitHub "Fn + ." to access web vscode.
• Stage, Commit and Push the a devcontainer .devcontainer/devcontainer.json file for Python or R. Please refer to python_codespace or r_codespace repositories with .devcontainer directories. The .devcontainer folder holds the container configuration to build a consistent, reproducible development environment.
• Run the Codespaces on GitHub. Now you have a few minutes to explore the .devcontainer/devcontainer.json file.
• Validate your codespace and whether it was built successfully.

Analysis (20+ minutes)

• Open the Codespaces for vsctuto on GitHub.
• Load and explore the Trial data in data/data.csv using your own analysis script to determine which drug works. The installed extensions and settings should hint you the function names and format the file on save. You need to install R or Python dependencies like pandas, although it is possible to use only standard library.
• [BONUS] Validate your results with GitHub Copilot. Use GitHub Copilot Agent mode and ask "Please analyze the data in data.csv to determine which drug has the highest cure rate compared to the placebo. Update the [Python|R] script if necessary, and ensure the correct result ("Drug A" or "Drug B" or "Drug C" or "None") is written to solution.txt.". You can further ask about, for example effect size.
• Please write down a string with the name of the working drug in the solution.txt file, "Drug A" or "Drug B" or "Drug C" or "None".
• Stage, Commit and Push your work.

Thank you for your work