; PSY 1903
PSY 1903 Programming for Psychologists

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Control Structures and Built-in Functions in R

In this section, we’ll learn how R makes decisions, repeats tasks, and performs common operations automatically.
These concepts — control structures and built-in functions — are essential for writing efficient and flexible R code.

1. What Are Control Structures?

Control structures determine the flow of your program — what happens, when, and under what conditions.

They include:

  1. Conditional statements (if, else if, else)
  2. Loops (for, while, repeat)
  3. Vectorized alternatives (e.g., ifelse())

2. Conditional Statements

Conditional statements let your code make decisions based on logical tests.

Basic if Example

x <- 10

if (x > 5) {
  print("x is greater than 5")
}

If the condition inside the parentheses is TRUE, the code inside the braces runs.

You may notice this is similar to some JavaScript code we have already seen, namely:

let x = 10;

if (x > 5) {
  console.log("x is greater than 5");
}
Concept R JavaScript
Variable assignment x <- 10 let x = 10;
Conditional statement r<br>if (x > 5) {<br> print("x is greater than 5")<br>} javascript<br>if (x > 5) {<br> console.log("x is greater than 5");<br>}
Output function print() prints to the R console console.log() prints to the browser console or Node terminal
Result x is greater than 5 (appears in RStudio console) x is greater than 5 (appears in browser dev tools / terminal)

if…else

x <- 3

if (x > 5) {
  print("x is greater than 5")
} else {
  print("x is 5 or less")
}

if…else if…else

x <- 7

if (x > 10) {
  print("x is greater than 10")
} else if (x > 5) {
  print("x is between 5 and 10")
} else {
  print("x is 5 or less")
}

JavaScript Comparison

let x = 7;

if (x > 10) {
  console.log("x is greater than 10");
} else if (x > 5) {
  console.log("x is between 5 and 10");
} else {
  console.log("x is 5 or less");
}

3. The ifelse() Function

ifelse() is a vectorized version of if that applies to each element of a vector.

scores <- c(95, 82, 67, 74)
grades <- ifelse(scores >= 90, "A", "Not A")
grades

Output:

[1] "A" "Not A" "Not A" "Not A"

This is much faster than looping through each element manually.

4. Loops

Loops repeat actions. They are useful for automation, simulations, or processing data step-by-step.

for Loop

for (i in 1:5) {
  print(paste("Iteration:", i))
}

In this example, i takes on the values 1 through 5.

JavaScript Comparison

for (let i = 1; i <= 5; i++) {
  console.log("Iteration: " + i);
}
Concept R JavaScript
Loop variable for (i in 1:5) automatically iterates i over the sequence 1, 2, 3, 4, 5 for (let i = 1; i <= 5; i++) explicitly defines a counter (start; condition; increment)
Printing output print(paste("Iteration:", i)) uses paste() to combine strings console.log("Iteration: " + i) uses + for string concatenation
Loop control for iterates over elements of a vector for executes a block repeatedly while the condition is true
Output Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5		 Iteration: 1
Iteration: 2
Iteration: 3
Iteration: 4
Iteration: 5

Looping Over a Vector

animals <- c("cat", "dog", "bird")

for (a in animals) {
  print(paste("I love", a, "s!"))
}

JavaScript Comparison

let animals = ["cat", "dog", "bird"];
for (let a of animals) {
  console.log(`I love ${a}s!`);
}

5. While Loops

A while loop repeats as long as a condition remains TRUE.

count <- 1

while (count <= 5) {
  print(paste("Count is", count))
  count <- count + 1
}

If you forget to update the counter, the loop will run forever (infinite loop), so use with care!

6. Repeat Loops

A repeat loop runs until explicitly stopped with break.

x <- 1

repeat {
  print(x)
  x <- x + 1
  if (x > 3) break
}

This is less common but useful when the number of iterations isn’t known in advance.

7. Next and Break

You can modify loops with next and break.

  • next: Skip to the next iteration.
  • break: Exit the loop entirely.

Example:

for (i in 1:5) {
  if (i == 3) next
  if (i == 5) break
  print(i)
}

Output:

[1] 1
[1] 2
[1] 4

8. Built-in Functions in R

R has hundreds of built-in functions. These are predefined operations that simplify data analysis.

Mathematical Functions

x <- c(1, 2, 3, 4, 5)

sum(x)      # 15
mean(x)     # 3
median(x)   # 3
sd(x)       # standard deviation
max(x)      # 5
min(x)      # 1

String Functions

names <- c("Alice", "Bob", "Carmen")
nchar(names)              # number of characters
toupper(names)            # convert to uppercase
paste(names, "is great!") # combine text

Logical and Test Functions

x <- c(1, 2, 3, NA)
is.na(x)       # check for missing values
any(is.na(x))  # TRUE if any are missing
all(x > 0)     # TRUE if all elements are positive

9. Combining Loops and Functions

You can use loops and functions together to perform complex operations.

Example: Calculate the mean of each column in a data frame.

data <- data.frame(
  a = c(1, 2, 3),
  b = c(4, 5, 6),
  c = c(7, 8, 9)
)

for (col in names(data)) {
  m <- mean(data[[col]])
  print(paste("Mean of", col, "is", m))
}

Output:

[1] "Mean of a is 2"
[1] "Mean of b is 5"
[1] "Mean of c is 8"

10. Vectorized Alternatives to Loops

Loops can be replaced by vectorized functions, which are faster and simpler.

Example using apply():

apply(data, 2, mean)

This applies the mean function to each column (2 indicates columns, 1 would indicate rows).

Other apply-family functions include:

  • lapply() — returns a list
  • sapply() — simplifies result to a vector
  • tapply() — applies a function by group

Example:

tapply(iris$Sepal.Length, iris$Species, mean)

11. Practical Example: Reaction Time Categorization

rt <- c(520, 410, 615, 450, 395)

# Categorize reaction times
category <- ifelse(rt < 500, "Fast", "Slow")
category

# Use a loop to display messages
for (i in 1:length(rt)) {
  print(paste("Trial", i, "was", category[i]))
}

Example output:

[1] "Trial 1 was Slow"
[1] "Trial 2 was Fast"
[1] "Trial 3 was Slow"
[1] "Trial 4 was Fast"
[1] "Trial 5 was Fast"

12. Summary

  • Use if, else if, and else for conditional logic.
  • Use loops (for, while, repeat) for repetition, or vectorized alternatives like ifelse() and apply().
  • Use built-in functions (sum, mean, sd, paste, etc.) to simplify computations.
  • Prefer vectorized operations whenever possible — they are faster and cleaner than explicit loops.