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R Setup Guide

This guide will get you set up with R for Statistics 224.

Best for: Everyone, especially beginners

Why Posit Cloud?

✓ No installation needed
✓ Works on any computer (even Chromebooks)
✓ Consistent environment for everyone
✓ Access from anywhere
✓ Free tier available

Setup Steps

  1. Go to: posit.cloud
  2. Sign up with your school email
  3. Create a new project
  4. You're ready!

That's it!

Posit Cloud comes with R and RStudio pre-installed. No configuration needed.

💻 Alternative: Install Locally

Best for: Advanced users who want offline access

Step 1: Install R

  1. Go to CRAN
  2. Click "Download R for Windows"
  3. Click "base"
  4. Download and run the installer
  5. Accept all defaults
  1. Go to CRAN
  2. Click "Download R for macOS"
  3. Download the .pkg file
  4. Open and install
  5. Accept all defaults
# Ubuntu/Debian
sudo apt update
sudo apt install r-base

# Fedora
sudo dnf install R

Step 2: Install RStudio

  1. Go to posit.co/download/rstudio-desktop
  2. Download RStudio Desktop (Free)
  3. Install with default settings
  4. Launch RStudio

📦 Essential Packages

Install these packages in R:

# Copy and paste this into R console
install.packages(c(
  "tidyverse",    # Data manipulation
  "effsize",      # Effect sizes
  "effectsize",   # More effect sizes
  "car",          # ANOVA tools
  "lsr",          # Additional stats
  "psych"         # Descriptive stats
))

Run Once

You only need to install packages once. After that, load them with library().

🧪 Test Your Setup

Quick Test

Run this code to verify everything works:

# Create some data
x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)

# Simple plot
plot(x, y, main = "Test Plot", col = "blue", pch = 19)

# Simple statistics
mean(x)
sd(y)
cor(x, y)

# If this runs without errors, you're good! ✓

Expected output: - A plot with blue dots - Numbers printed: 3, 2.236..., 1

📂 Organizing Your Work

Create Project Structure

Stats224/
├── data/           # Your datasets
├── scripts/        # R code files
├── output/         # Plots and results
└── notes/          # Your notes

Best Practices

R Tips

  • Use R Scripts (.R files) not console
  • Comment your code with #
  • Save frequently
  • Use meaningful variable names
  • One analysis per script

🔧 RStudio Interface

Main Panels

┌─────────────────────┬─────────────────────┐
│ Source (Scripts)    │ Environment/History │
│                     │                     │
│ Your code here      │ Your data objects   │
│                     │                     │
├─────────────────────┼─────────────────────┤
│ Console             │ Files/Plots/Help    │
│                     │                     │
│ Code runs here      │ Output appears here │
│                     │                     │
└─────────────────────┴─────────────────────┘

Keyboard Shortcuts

Action Windows/Linux Mac
Run line Ctrl+Enter Cmd+Enter
New script Ctrl+Shift+N Cmd+Shift+N
Save Ctrl+S Cmd+S
Comment/Uncomment Ctrl+Shift+C Cmd+Shift+C

📥 Loading Data

From CSV File

# If file is in your working directory:
data <- read.csv("mydata.csv")

# Check it loaded correctly:
head(data)      # First 6 rows
str(data)       # Structure
summary(data)   # Descriptive stats

Example Datasets

R comes with built-in datasets for practice:

# Load built-in dataset
data(mtcars)

# Explore it
?mtcars         # Help file
head(mtcars)    # Preview
names(mtcars)   # Variable names

🆘 Common Issues

"Package not found"

Solution: Install it first: 

install.packages("package_name")

"Object not found"

Solution: Check spelling, make sure you created it first

"Cannot open file"

Solution: Check file path, use getwd() to see working directory

Console shows + instead of >

Solution: You have incomplete code. Press Esc to cancel.

💡 Learning Resources

Within R

?function_name   # Help for a function
??search_term    # Search all help files
help.start()     # Open help browser

External Resources

✅ Setup Checklist

[ ] R installed (or Posit Cloud account created)
[ ] RStudio running
[ ] Essential packages installed
[ ] Test code runs successfully
[ ] Project folder created
[ ] Ready to start analyzing!

Next: Getting Started Guide | Decision Tree