Welcome to R Markdown!

R Markdown is a powerful tool that combines the simplicity of Markdown with the computational power of R. It enables you to:

🎯 Key Benefits: - Create reproducible documents that combine narrative text and code - Generate professional reports in multiple formats (HTML, PDF, Word) - Build interactive dashboards and presentations - Share your work with both technical and non-technical audiences

Perfect for: - 📊 Data Analysis Reports - 📚 Technical Documentation - 🎓 Academic Papers - 📑 Presentations - 🔍 Exploratory Data Analysis

Part 1: Basic Structure

An R Markdown document consists of three essential components, each serving a specific purpose:

1. YAML Header

The YAML header controls document-wide settings and output format:

---
title: "My First R Markdown"    # Document title
author: "Your Name"             # Author name
date: "2024-03-20"             # Publication date
output: html_document          # Output format (html, pdf, word)
---

Key YAML Options: - title: Main document heading - author: Document creator - date: Publication date (can use “2025-02-14” for automatic dating) - output: Desired output format

2. Text Content

Markdown syntax provides easy-to-use formatting:

Level 1 Heading (Largest)

Level 2 Heading

Level 3 Heading (Smaller)

Text Formatting: - Italics: Surround text with single asterisks (*text*) - Bold: Use double asterisks (**text**) - Code: Use backticks for inline code (`code`) - Links: Create with [text](url)

Lists: - Unordered lists use hyphens (-) * Sub-items use indentation + Can use different markers - Continue main list

  1. Ordered lists use numbers
  2. Numbers auto-increment
  3. Can mix with unordered lists
    • Like this
    • And this

3. Code Chunks

Code chunks are where R code lives and executes:

# Create a simple vector and calculate statistics
x <- 1:10                  # Generate sequence 1 to 10
mean(x)                    # Calculate mean
## [1] 5.5
sd(x)                     # Calculate standard deviation
## [1] 3.02765
summary(x)                # Generate summary statistics
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    1.00    3.25    5.50    5.50    7.75   10.00

Part 2: Code Chunks in Detail

Creating Code Chunks

Three methods to insert code chunks:

  1. 🖱️ GUI Method:
    • Click “Insert” in RStudio
    • Select “R” for R code chunks
  2. ⌨️ Keyboard Shortcut:
    • Mac: Cmd + Option + I
    • Windows/Linux: Ctrl + Alt + I
  3. ✍️ Manual Method:
    • Type: ```{r chunk-name}
    • End with: ```

Chunk Options

Control chunk behavior with options:

# Load required packages
library(tidyverse)

# Create an example plot
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) +
  geom_point(size = 3) +                          # Add points
  labs(title = "Car Weight vs MPG",               # Add labels
       x = "Miles Per Gallon",
       y = "Weight (1000 lbs)",
       color = "Cylinders") +
  theme_minimal()                                 # Use minimal theme

Essential Chunk Options: - echo: Show/hide code (TRUE/FALSE) - eval: Execute code (TRUE/FALSE) - message: Display messages (TRUE/FALSE) - warning: Show warnings (TRUE/FALSE) - fig.width/fig.height: Set plot dimensions - fig.cap: Add figure caption - include: Include chunk output (TRUE/FALSE)

Part 3: Best Practices

1. Document Organization

📝 Structure Guidelines:

✅ Recommended: - Begin with clear introduction - Use logical heading hierarchy - Include navigation aids (TOC) - End with summary/conclusions - Keep sections focused

❌ Avoid: - Skipping heading levels - Writing walls of text - Leaving code unexplained - Mixed formatting styles

2. Code Chunk Management

🔧 Code Organization:

✅ Best Practices:

# Load all required packages at start
library(tidyverse)      # For data manipulation and visualization
library(knitr)         # For nice tables
# Prepare data for analysis
data <- mtcars %>%
  select(mpg, wt, cyl) %>%     # Select relevant columns
  filter(mpg > 20) %>%         # Filter for high MPG
  mutate(                      # Create new variables
    efficiency = mpg/wt,
    cyl = factor(cyl)
  )

3. Text Formatting

📚 Writing Guidelines:

✅ Do: - Use consistent formatting - Break text into paragraphs - Include descriptive lists - Add relevant references

❌ Don’t: - Mix formatting styles - Write dense paragraphs - Leave terms undefined - Skip documentation

4. Output Management

🎨 Presentation Tips:

# Create a professional visualization
ggplot(mtcars, aes(x = mpg, y = wt, color = factor(cyl))) +
  geom_point(size = 3, alpha = 0.7) +            # Add points with transparency
  geom_smooth(method = "lm", se = TRUE) +        # Add trend line with CI
  facet_wrap(~cyl, scales = "free") +            # Split by cylinders
  labs(
    title = "Car Weight vs MPG by Number of Cylinders",
    subtitle = "Analysis of Motor Trend Car Road Tests",
    x = "Miles Per Gallon (MPG)",
    y = "Weight (1000 lbs)",
    color = "Cylinders"
  ) +
  theme_minimal() +                              # Use minimal theme
  theme(
    legend.position = "bottom",                  # Move legend
    plot.title = element_text(size = 16),        # Customize text
    plot.subtitle = element_text(size = 12)
  )
## `geom_smooth()` using formula = 'y ~ x'
Detailed Analysis of MPG vs Weight

Detailed Analysis of MPG vs Weight

Part 4: Common Patterns

Including Tables

# Create a formatted table
library(knitr)
mtcars %>%
  head() %>%                           # Take first 6 rows
  select(mpg, cyl, wt, hp) %>%        # Select key columns
  kable(
    caption = "Sample of Motor Trend Car Data",
    digits = 2,                        # Round numbers
    align = c('r', 'c', 'r', 'r')     # Align columns
  )
Sample of Motor Trend Car Data
mpg cyl wt hp
Mazda RX4 21.0 6 2.62 110
Mazda RX4 Wag 21.0 6 2.88 110
Datsun 710 22.8 4 2.32 93
Hornet 4 Drive 21.4 6 3.21 110
Hornet Sportabout 18.7 8 3.44 175
Valiant 18.1 6 3.46 105

Including Equations

R Markdown supports LaTeX equations:

Inline Math: \(y = \beta_0 + \beta_1x + \epsilon\)

Display Math: \[ \text{MPG} = \beta_0 + \beta_1\text{Weight} + \beta_2\text{Cylinders} + \epsilon \\ \text{where } \epsilon \sim N(0, \sigma^2) \]

Including Images

External images can be included:

RStudio Logo
RStudio Logo

Part 5: Troubleshooting Tips

🔍 Common Issues and Solutions:

  1. Knitting Errors
    • ✓ Check package installation
    • ✓ Verify syntax in all chunks
    • ✓ Confirm file paths
    • ✓ Look for missing dependencies
  2. Figure Issues
    • ✓ Adjust chunk options
    • ✓ Check plot specifications
    • ✓ Verify data integrity
    • ✓ Test device settings
  3. Code Problems
    • ✓ Validate package versions
    • ✓ Ensure unique chunk names
    • ✓ Debug code separately
    • ✓ Check working directory

Practice Exercises

🎯 Hands-on Learning:

  1. Basic Document (15 mins)
    • Create new .Rmd file
    • Add YAML header
    • Include text sections
    • Insert code chunk
  2. Code Integration (20 mins)
    • Load dataset
    • Create summary statistics
    • Generate basic plot
    • Add chunk options
  3. Advanced Features (25 mins)
    • Include equations
    • Create custom table
    • Add figure caption
    • Use cross-references

Next Steps

🚀 Advanced Topics to Explore:

  1. Output Formats
    • PDF documents
    • Word reports
    • HTML slides
    • Dashboards
  2. Advanced Features
    • Custom templates
    • CSS styling
    • Interactive plots
    • Cross-references
  3. Integration
    • Version control
    • Collaboration
    • Automation
    • Publishing