Data Simulation Lab 1

Welcome to the first data simulation lab for Psych 201a! This lab introduces you to statistical distributions and data simulation techniques commonly used in psychological research.

Lab Goals

• Understand distributions: Learn how normal, binomial, and lognormal distributions describe behavioral data (accuracy, RTs, individual variability)
• Practice simulation: Generate and visualize synthetic datasets using tidyverse functions
• Reproducible reports: Learn how to write reproducible lab reports with inline statistics in Quarto

Prerequisites

Make sure you have the following installed:

• Homebrew (macOS package manager)
• Git and GitHub CLI
• Pixi (package manager for R and Python)

Getting Started

1. Fork this Repository

1. Go to https://github.com/psyc-201/data_simulation_lab_1
2. Click the "Fork" button in the top-right corner
3. This creates a copy of the repository under your own GitHub account

2. Clone Your Fork

git clone https://github.com/YOUR_USERNAME/data_simulation_lab_1.git
cd data_simulation_lab_1

Replace YOUR_USERNAME with your actual GitHub username.

3. Install Dependencies

pixi install

4. Setup R & Python Kernels

pixi run setup

5. Open in VS Code

1. Open VS Code and use the account icon to login to your GitHub account
2. Open the folder you cloned: File > Open
3. Accept any pop-ups to configure VS Code with necessary extensions

6. Start Working

• R Console: pixi run r
• Live Preview: pixi run render
• Preview specific file: pixi run preview filename.qmd

Lab Structure

This lab includes multiple versions to support different learning styles:

File	Description
code/index.qmd	Introduction and overview
code/distributions-lab.qmd	Complete lab with full instructions
code/distributions-lab-intermediate.qmd	Scaffolded version with placeholders to fill in
code/distributions-lab-withsolutions.qmd	Complete solutions for reference
code/distributions_lab.py	Python implementation using pandas and matplotlib
code/distributions_lab_withsolutions.py	Python solutions

Working with the Lab

R/Quarto Workflow

1. Open code/distributions-lab-intermediate.qmd in VS Code
2. Use the command palette (cmd+shift+p) to search: "Terminal: Create New Terminal"
3. Start the R console: pixi run r
4. Fill in the ___ placeholders in the intermediate version
5. Use "Run cell" buttons to execute code chunks

Python Workflow

1. Open code/distributions_lab.py in VS Code
2. Use the command palette to search: "Jupyter: Create Interactive Window"
3. Use "Run cell" buttons to execute code

Converting Between Formats

• Convert .qmd to .ipynb: pixi run convert filename.qmd
• Convert .ipynb to .qmd: pixi run convert filename.ipynb

Adding/Removing Libraries

Use these commands to manage packages (they auto-update pixi.toml):

• Python: pixi add package or pixi add --pypi package
• R: pixi add r-package
• Remove: pixi remove package or pixi remove r-package

Key Topics Covered

• Normal distributions: Simulating continuous data
• Binomial distributions: Modeling accuracy and binary outcomes
• Lognormal distributions: Simulating reaction times
• Multi-participant experiments: Individual differences and group-level analysis
• Data visualization: Creating publication-ready plots

Troubleshooting

Common Issues

1. R not found: Make sure you're using pixi run r not just r
2. Package not found: Use pixi add instead of install.packages() or pip install
3. Render errors: The intermediate version cannot be rendered due to placeholder syntax - fill in the ___ placeholders first

Reset Environment

If anything goes wrong, you can safely reset:

rm -rf .pixi/ pixi.lock
pixi install
pixi run setup

Resources

• Quarto Documentation
• Tidyverse Documentation
• Pandas Documentation
• Matplotlib Documentation
• Pixi Documentation

Important Notes

Always prefer using pixi add and pixi remove instead of install.packages() in R or pip install / conda install in Python

This will save you from many unexpected headaches and ensure reproducible environments!

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Happy coding! 🎉