Comments are essential for readable, maintainable code. While R doesn’t have a native block comment syntax (like /* */ in other languages), there are multiple ways to create multi-line comments. Good comments explain why your code does something, not what it does.

When to Use Comments

You’ll use comments when:

  • Explaining complex logic
  • Documenting assumptions or data structures
  • Noting performance considerations
  • Temporarily disabling code
  • Adding references or citations
  • Noting TODO items

Methods for Multi-Line Comments

Method 1: Repeat # Symbol (recommended)

# This is a multi-line comment
# You can explain your code here
# Each line needs its own # symbol

Method 2: IDE Keyboard Shortcut

  • RStudio: Ctrl + Shift + C (Windows/Linux) or Cmd + Shift + C (Mac)
  • Automatically adds # to multiple selected lines

Method 3: Block comments with repeated # (formatting)

################################
# This is a block comment
# It stands out visually
################################

Examples with Explanations

Create Multi-Line Comments Using # Symbol

Let’s see how we can add multiline comment in R.

# print("This shows before creating multi line command")
# print("After creating multi line command")
# print("Hello R Language")

print("Hello R Language")

Output:

[1] "Hello R Language"

Here the output shows the string we printed using print(). The other statements are commented out with # symbols, so they don’t execute.

Common Mistakes to Avoid

Mistake 1: Forgetting # on each line

# ❌ WRONG - Second line isn't commented!
# This is a comment
This is NOT a comment - will cause error!

# ✅ CORRECT - Each line needs #
# This is a comment
# This is also a comment

Mistake 2: Using /* */ block comments (from other languages)

# ❌ WRONG - R doesn't support block comments
/* This is NOT a comment in R
   It will cause an error */

# ✅ CORRECT - Use # for each line
# This is a comment in R
# Multiple lines like this

Mistake 3: Comments that don’t add value

# ❌ WRONG - States the obvious
x <- 5  # Assign 5 to x
y <- x + 1  # Add 1 to x and assign to y

# ✅ BETTER - Explain the "why"
x <- 5  # Initialization value for algorithm
y <- x + 1  # Increment by 1 per iteration

Mistake 4: Unbalanced commented code

# ❌ CONFUSING - Hard to find where comment ends
# result <- function(x) {
#   if (x > 0) return(x * 2)
# }
# Some code here

# ✅ CLEARER - Use block markers
# ===== COMMENTED OUT =====
# result <- function(x) {
#   if (x > 0) return(x * 2)
# }
# ========================

Pro Tips

  1. Use comments for future you: Explain why you chose an approach, not what code does
  2. Document assumptions: “Assumes data is sorted”, “Requires positive values”
  3. Mark TODOs: 
    # TODO: Optimize this loop for large datasets
  4. Use in RStudio: Select lines, press Ctrl+Shift+C to toggle comments
  5. Document regex patterns: 
    # Pattern: matches email addresses ([email protected])
email_pattern <- "^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"

Comment Best Practices

# BAD: Too many obvious comments
x <- 1  # Set x to 1
if (x > 0) {  # If x is greater than 0
  print(x)  # Print x
}

# GOOD: Comments explain intent
# Initialize counter for main loop
x <- 1
if (x > 0) {  # Positive values only (per requirement)
  print(x)
}

See Also