R: R Control Flow

Q: What are the key differences between R’s if/else and Python’s?

In R, code blocks are enclosed in {} (while Python uses indentation); R’s if/else is an expression (which can be assigned to a variable), whereas Python’s is a statement; R’s else must be on the same line as } .

Q: How do I choose between if and ifelse ?

Use if to evaluate a single value; use ifelse() to evaluate an entire vector. A regular if statement will throw an error if the condition involves a length greater than 1.

Q: When should you use while instead of for ?

Use while when the number of iterations is unknown (e.g., waiting for an event, a number-guessing game), and use for when the number of iterations is known (e.g., iterating over a vector). Use repeat + break for "infinite loops with early termination."

Q: What is the difference between next and break ?

next skips the current iteration and proceeds to the next one (in R, continue is called next ); break exits the entire loop. next is used for filtering, while break is used to exit early.

Last updated: 2026-08-26

In the previous lesson, we learned about operators, and our R programs can now "determine whether something is true or false." But simply being able to make such determinations isn't enough—programs also need to be able to repeat tasks (loops) and make branching decisions (conditions). In this lesson, we'll learn about "control flow" in R: if/else conditions, for/while loops, and the repeat statement.

Control flow in R is a bit different from Python: R encloses code blocks in {} (instead of using indentation), and the entire if/else statement is a single expression. In this lesson, we’ll learn these differences.

1. What You'll Learn

if / else / else if conditional branching
Use a for loop to iterate through a vector or list
while loop
repeat: infinite loop (use break to exit)
ifelse() vectorized conditional evaluation
next: Skip this round; break: Exit the loop
Key Differences in Control Flow Between R and Python

2. A Story About Data Filtering

(1) Pain Point: Manual Categorization

Xiao Zhao is a data analyst. His manager asked him to segment 100 customers by age:

Under 18: Minors
Ages 18–30: Young adults
Ages 31–50: Middle-aged
51 years of age and older: elderly

If you use Excel, you’d have to write four nested IF formulas; if you use Python, you’d have to write loops and if-else statements. But R has ifelse(), which is specifically designed for vectors—

(2) Solution using R

# 5 The age of each customer
ages <- c(15, 22, 35, 50, 65)

# Done in one line of code 4 File Categories(Vectorization ifelse)
labels <- ifelse(ages < 18, "Minor",
          ifelse(ages < 31, "Youth",
            ifelse(ages < 51, "Middle Age", "Old Age")))

cat("Customer Segmentation:\n")
print(data.frame(Age = ages, Category = labels))

Solve a 4-class classification problem with just 3 lines of code. This is the "vectorized" advantage of R's control flow—in short, a single statement is a decision vector.

3. if/else: Conditional Branching

(1) Basic Syntax of the `if` Statement

# R uses { } to enclose a code block (Python uses indentation)
if (Conditions) {
  Execute when the condition is true
}

age <- 18

if (age >= 18) {
  cat("You're an adult now.\n")
}
# Output:You're an adult now.

(2) if-else: Binary Choice

if (Conditions) {
  Execute when the condition is true
} else {
  Execute when the condition is false
}

score <- 45

if (score >= 60) {
  cat("Passed!\n")
} else {
  cat("Fail,Need to take a makeup exam\n")
}
# Output:Fail,Need to take a makeup exam

(3) if-else if-else: Multi-branch

if (Conditions1) {
  Branch1
} else if (Conditions2) {
  Branch2
} else {
  Other
}

score <- 85

if (score >= 90) {
  cat("Excellent\n")
} else if (score >= 80) {
  cat("Good\n")
} else if (score >= 60) {
  cat("Passing Grade\n")
} else {
  cat("Fail\n")
}
# Output:Good

(4) Key Differences Between R and Python

Feature	R	Python
Code block	`{}` Curly braces	Indentation (4 spaces)
if syntax	`if (x > 0) { ... }`	`if x > 0:`
Overall Expression	if/else is an expression (assignable)	if/else is a statement (not assignable)
Ternary Operations	`ifelse()` Special Functions	`x if x > 0 else -x`

# R Special Usage of:ifelse It is an expression
result <- if (score >= 60) "Passing Grade" else "Fail"
result
# [1] "Passing Grade"

⚠️ Note: In R, the if/else statement must include else on the same line (if treated as a single expression). If it is split across two lines, R will report a syntax error.

4. ifelse(): Vectorized Decision-Making

(1) Why do we need ifelse()?

The standard if can only evaluate a single value, but in R, you often need to evaluate an entire vector. In that case, use ifelse():

graph LR
    A["x = 1, 2, 3, 4, 5"] --> B["ifelse x > 3 yes no"]
    B --> C["no no no yes yes"]
    
    style A fill:#cce5ff
    style C fill:#d4edda

# Regular if It will throw an error(Only accept lengths 1)
x <- c(1, 2, 3, 4, 5)
if (x > 3) "L" else "S"   # Error: condition has length > 1

# Use ifelse() to resolve
ifelse(x > 3, "L", "S")
# [1] "S" "S" "S" "L" "L"

(2) ifelse() Syntax

ifelse(Conditions, The value when the condition is true, Value when the condition is false)

(3) Nested ifelse(): Multi-level Classification

ages <- c(15, 22, 35, 50, 65)

# 4 File Categories
labels <- ifelse(ages < 18, "Minor",
          ifelse(ages < 31, "Youth",
            ifelse(ages < 51, "Middle Age", "Old Age")))

print(data.frame(Age = ages, Category = labels))

Output:

TEXT 📖 Display only

  Age  Category
1   15 Minor
2   22   Youth
3   35   Middle Age
4   50   Middle Age
5   65   Old Age

💡 Tip: ifelse() If you have more than 3 levels of nesting, we recommend using dplyr::case_when() (covered in Lesson 10) for greater clarity.

5. for Loop: Iterating Through Elements

(1) Basic Syntax of the "for" Loop

for (Variable in Vector) {
  Code executed in each iteration
}

(2) Iterating Through a Vector

# Iteration 1:5
for (i in 1:5) {
  cat("Iteration", i, "of the loop\n")
}
# Output:
# Iteration 1 of the loop
# Iteration 2 of the loop
# Iteration 3 of the loop
# Iteration 4 of the loop
# Iteration 5 of the loop

# Iterating Through a Character Vector
fruits <- c("Apple", "Banana", "Cherries")
for (fruit in fruits) {
  cat("I like to eat", fruit, "\n")
}

(3) Cumulative Sum (Classic Example)

# 1+2+3+...+100
total <- 0
for (i in 1:100) {
  total <- total + i
}
total
# [1] 5050

(4) ⚠️ R has poor performance in loops

R's for loops are 10 to 100 times slower than Python's. R encourages the use of vectorized operations instead of loops:

# Slow (Loop)
system.time({
  total <- 0
  for (i in 1:1e6) {
    total <- total + i
  }
})
# User  System  Passing 
# 0.40   0.00   0.40  ← 0.4 s

# Fast (Vectorization)
system.time({
  total <- sum(1:1e6)
})
# User  System  Passing 
# 0.01   0.00   0.01  <- 0.01 s (40x faster)

⚠️ Note: R encourages "vectorization first"—if you can solve a problem using sum() mean() ifelse(), don't write a loop.

6. while Loop: Conditional Loop

(1) while Syntax

while (Conditions) {
  Execute repeatedly when the condition is true
}

(2) Classic Example: The "Guess the Number" Game

# Target Number
target <- 42
guess <- 0
attempts <- 0

while (guess != target) {
  guess <- sample(1:100, 1)  # Guess at Random
  attempts <- attempts + 1
  if (attempts > 1000) break  # More than 1000 Second attempt
}
cat("Used", attempts, "Second guess\n")

(3) Differences from `for`

Feature	for	while
Purpose	Loops with a known number of iterations	Unknown number of iterations, triggered by conditions
Termination	End of vector traversal	Termination due to false condition
Applicable	Iterate through data	Wait for events, polling

7. repeat and break: Infinite Loops

(1) `repeat` Syntax

repeat {
  Code
  if (Conditions) break  # Must have break,Otherwise, an infinite loop
}

choice <- 0
repeat {
  cat("\n=== Main Menu ===\n")
  cat("1. Check the weather\n")
  cat("2. Look up stocks\n")
  cat("0. Exit\n")
  choice <- as.integer(readline("Please select:"))
  
  if (choice == 0) {
    cat("Goodbye!\n")
    break
  } else if (choice == 1) {
    cat("It's sunny today,25°C\n")
  } else if (choice == 2) {
    cat("Shanghai Stock Exchange 3000 pt\n")
  } else {
    cat("Invalid input\n")
  }
}

⚠️ Note: repeat must be used in conjunction with break; otherwise, an infinite loop will occur (the program will hang).

8. next and break: Loop Control

Keyword	Function	Scenario
`next`	Skip this round and proceed to the next	Skip certain items
`break`	Exit the entire loop	Exit immediately upon finding the target

# next:Skip even numbers
for (i in 1:10) {
  if (i %% 2 == 0) next
  cat(i, " ")
}
# Output:1 3 5 7 9

cat("\n")

# break:Find the first one 7 Just log out
for (i in 1:10) {
  if (i == 7) break
  cat(i, " ")
}
# Output:1 2 3 4 5 6

9. Common Pitfalls

(1) Pitfalls of Floating-Point Comparisons

# Seemingly equal,The actual values are not equal
0.1 + 0.2 == 0.3
# [1] FALSE  ← Actually, it is 0.30000000000000004

# The Correct Way to Do It:Comparison of Tolerances
abs(0.1 + 0.2 - 0.3) < 1e-9
# [1] TRUE

(2) The `else` statement must be on a new line or on the same line

# ❌ Error:else Traveling Alone
if (TRUE) {
  cat("yes")
}
else {
  cat("no")
}
# Error: unexpected 'else' in "else"

# Correct: else must follow if block }
if (TRUE) {
  cat("yes")
} else {
  cat("no")
}
# Output:yes

💡 Tip: Write } else { on the same line to avoid parsing ambiguities.

(3) if does not print automatically

# In R, if has no "Automatic Printing" (Not like a function call)
if (TRUE) "yes"
# No output  ← Do not print

# Resolve: Use cat() or print() for explicit output
if (TRUE) cat("yes\n")
# Output:yes

10. Complete Example: Customer Tier Management

Below is an example of a complete workflow that ties together all the control flows from this lesson.

▶ Example: Customer Segmentation and Marketing Strategies

# ============================================
# Customer Segmentation and Marketing Strategies
# Features:By age+Classify Customers by Spending Amount
# ============================================

# 1. Preparation 6 Customer Data
customers <- data.frame(
  name = c("Alice", "Bob", "Charlie", "Diana", "Eve", "Frank"),
  age = c(15, 22, 35, 50, 65, 28),
  spend = c(100, 500, 2000, 5000, 10000, 800)
)

# 2. Use ifelse() Batch Grading (Vectorization)
customers$tier <- ifelse(customers$spend < 500, "Regular",
                  ifelse(customers$spend < 3000, "Silver Card",
                    ifelse(customers$spend < 8000, "Gold Card", "Diamond")))

# 3. Go through the customer list,Output Marketing Strategy(for Loop)
cat("=== Customer Segmentation and Marketing Strategies ===\n\n")
for (i in 1:nrow(customers)) {
  name <- customers$name[i]
  age <- customers$age[i]
  spend <- customers$spend[i]
  tier <- customers$tier[i]
  
  # Skip Diamond Members(Optimized)
  if (tier == "Diamond") {
    cat(name, "(", tier, "):", spend, "USD,Existing Customers,Priority Maintenance\n")
    next
  }
  
  # Exit Conditions(Demo break)
  if (spend > 5000) {
    cat(name, ": High-spending customers! Enter VIP Process\n")
  }
  
  # Age-Based Marketing Recommendations
  if (age < 18) {
    cat(name, "(", tier, "):Minor,Recommended Parent Card\n")
  } else if (age < 30) {
    cat(name, "(", tier, "):Youth,New Product Releases\n")
  } else if (age < 50) {
    cat(name, "(", tier, "):Middle Age,Promote Family Plans\n")
  } else {
    cat(name, "(", tier, "):Middle-Aged and Older Adults,Promote Health Products\n")
  }
}

▶ Try it Yourself

Expected Output (Excerpt):

TEXT 📖 Display only

=== Customer Segmentation and Marketing Strategies ===

Alice( Regular ):Minor,Recommended Parent Card
Bob( Regular ):Youth,New Product Releases
Charlie( Silver Card ):Middle Age,Promote Family Plans
Diana( Gold Card ):Middle Age,Promote Family Plans
Eve( Diamond ):10000 USD,Existing Customers,Priority Maintenance
Frank( Silver Card ):Youth,New Product Releases

❓ FAQ

Q What are the key differences between R’s if/else and Python’s?

A In R, code blocks are enclosed in {} (while Python uses indentation); R’s if/else is an expression (which can be assigned to a variable), whereas Python’s is a statement; R’s else must be on the same line as }.

Q How do I choose between if and ifelse?

A Use if to evaluate a single value; use ifelse() to evaluate an entire vector. A regular if statement will throw an error if the condition involves a length greater than 1.

Q What should I do if a for loop is too slow?

A R encourages "vectorization first"—if you can solve the problem using sum() mean() ifelse(), don’t write a for loop. R loops are 10 to 100 times slower than Python, so use them with caution when working with more than 100,000 elements.

Q When should you use while instead of for?

A Use while when the number of iterations is unknown (e.g., waiting for an event, a number-guessing game), and use for when the number of iterations is known (e.g., iterating over a vector). Use repeat + break for "infinite loops with early termination."

Q What is the difference between next and break?

A next skips the current iteration and proceeds to the next one (in R, continue is called next); break exits the entire loop. next is used for filtering, while break is used to exit early.

📖 Summary

if/else/else if conditional branching: In R, enclose the code block in {} (do not indent)
ifelse() is a vectorized conditional statement that can evaluate an entire vector; a regular if statement can only evaluate a single value.
A for loop iterates a known number of times; a while loop iterates an unknown number of times; a repeat loop is an infinite loop and must be used with a break statement.
next: Skip this round; break: Exit the entire loop
R encourages vectorization first—for loops are 10–100 times slower than in Python; for big data, use sum() and ifelse() instead.
Use the tolerance for floating-point comparisons (abs(x - y) < 1e-9); do not use == directly.
else Must be used in conjunction with } (to avoid ambiguity in parsing)

📝 Exercises

Basic Problem: Write an R script using if/else if/else to implement the following: Given a score score, output the grade (90+: Excellent, 80+: Good, 60+: Pass, otherwise: Fail). Test the script with four scores: 95, 75, 60, and 40, and save a screenshot of the output.
Basic Problem: Use ifelse() to divide the vector c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) into odd and even elements, and output the result.
Basic Problem: Use for to calculate 5! (5 factorial = 5 × 4 × 3 × 2 × 1) in a loop, and save screenshots of the process and the result.
Advanced Exercise: Write a "Guess the Number" game: ① Use sample(1:100, 1) to generate a random number between 1 and 100 as the target; ② Use while to loop through user inputs (readline); ③ Display the hint "Too high" or "Too low"; ④ When the number is guessed correctly, output "Congratulations! It took N attempts." Run and test the program.
Challenge: Write a script that processes integers from 1 to 100 as follows: ① Skip multiples of 3 (next); ② Exit when it reaches 50 (break); ③ Sum the remaining numbers. Verify the result (it should be 1+2+4+5+7+8+...+49 = ?). Save a screenshot of your code and the output.

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About Us

R: R Control Flow

1. What You'll Learn

2. A Story About Data Filtering

(1) Pain Point: Manual Categorization

(2) Solution using R

3. if/else: Conditional Branching

(1) Basic Syntax of the if Statement

(2) if-else: Binary Choice

(3) if-else if-else: Multi-branch

(4) Key Differences Between R and Python

4. ifelse(): Vectorized Decision-Making

(1) Why do we need ifelse()?

(2) ifelse() Syntax

(3) Nested ifelse(): Multi-level Classification

5. for Loop: Iterating Through Elements

(1) Basic Syntax of the "for" Loop

(2) Iterating Through a Vector

(3) Cumulative Sum (Classic Example)

(4) ⚠️ R has poor performance in loops

6. while Loop: Conditional Loop

(1) while Syntax

(2) Classic Example: The "Guess the Number" Game

(3) Differences from for

7. repeat and break: Infinite Loops

(1) repeat Syntax

(2) Hands-On: Menu Selection

8. next and break: Loop Control

9. Common Pitfalls

(1) Pitfalls of Floating-Point Comparisons

(2) The else statement must be on a new line or on the same line

(3) if does not print automatically

10. Complete Example: Customer Tier Management

▶ Example: Customer Segmentation and Marketing Strategies

❓ FAQ

📖 Summary

📝 Exercises

🙏 帮我们做得更好

(1) Basic Syntax of the `if` Statement

(3) Differences from `for`

(1) `repeat` Syntax

(2) The `else` statement must be on a new line or on the same line