Apply family vs loops in R Programming - Practice Questions

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of lapply vs for loop

What is the output of the following R code snippet?

R Programming

x <- list(a = 1:3, b = 4:6)
result_lapply <- lapply(x, sum)
result_loop <- list()
for (name in names(x)) {
  result_loop[[name]] <- sum(x[[name]])
}
result_lapply
result_loop

A result_lapply and result_loop are identical lists with elements 6 and 15

B result_lapply is a numeric vector, result_loop is a list with sums

C result_lapply is a list with sums, result_loop is a numeric vector

D Both result_lapply and result_loop are numeric vectors

💡 Hint

Remember that lapply returns a list and for loop can build a list similarly.

Both lapply and the for loop create lists where each element is the sum of the corresponding vector in x. So both results are identical lists with sums 6 and 15.

❓ Predict Output

intermediate

Output difference between sapply and for loop

What is the output of this R code?

R Programming

x <- list(a = 1:3, b = 4:6)
result_sapply <- sapply(x, sum)
result_loop <- numeric(length(x))
names(result_loop) <- names(x)
for (i in seq_along(x)) {
  result_loop[i] <- sum(x[[i]])
}
result_sapply
result_loop

A result_sapply is a numeric vector, result_loop is a list

B Both result_sapply and result_loop are lists

C result_sapply is a list, result_loop is a numeric vector

D Both result_sapply and result_loop are named numeric vectors with values 6 and 15

💡 Hint

sapply tries to simplify the result to a vector if possible.

sapply returns a named numeric vector with sums 6 and 15. The for loop builds a numeric vector with the same values and names, so both are identical.

🧠 Conceptual

advanced

Why use apply family over loops?

Which of the following is the best reason to prefer apply functions over for loops in R?

A For loops cannot handle lists or data frames

B Apply functions are always faster than for loops in every case

C Apply functions provide concise code and often better readability

D Apply functions automatically parallelize code without extra setup

💡 Hint

Think about code clarity and style rather than speed alone.

Apply functions help write shorter and clearer code, improving readability. They are not always faster and do not automatically parallelize code.

❓ Predict Output

advanced

Output of mapply with multiple arguments

What is the output of this R code?

R Programming

x <- 1:3
y <- 4:6
result <- mapply(function(a, b) a * b, x, y)
result

A An error because mapply requires a list input

B A numeric vector: 4, 10, 18

C A numeric vector: 5, 7, 9

D A list: list(4, 10, 18)

💡 Hint

mapply applies the function element-wise over multiple vectors.

mapply multiplies corresponding elements: 1*4=4, 2*5=10, 3*6=18, returning a numeric vector.

🔧 Debug

expert

Identify the error in this apply usage

What error does this R code produce and why?

R Programming

mat <- matrix(1:9, nrow=3)
result <- apply(mat, 2, function(x) x + y)
result

A Error: object 'y' not found because y is not defined

B Error: incorrect margin argument in apply

C No error, returns matrix with y added to each column

D Error: function must return a scalar value

💡 Hint

Check if all variables used inside the function are defined.

The function uses variable y which is not defined anywhere, causing an error.

Practice

1. Which of the following is a key advantage of using the apply family of functions over traditional loops in R?

easy

A. They make code shorter and easier to read

B. They always run faster than loops

C. They require less memory than loops

D. They can only be used with numeric data

Solution

1. Step 1: Understand the purpose of apply functions

   Apply functions are designed to perform repetitive tasks on data structures more concisely than loops.

2. Step 2: Compare advantages

   While apply functions often improve readability and reduce code length, they do not always guarantee faster execution or less memory use, and they work with various data types.

3. Final Answer:

   They make code shorter and easier to read -> Option A

4. Quick Check:

   Apply functions improve code clarity [OK]

Hint: Apply functions simplify repetitive tasks in fewer lines [OK]

Common Mistakes:

• Assuming apply functions always run faster
• Thinking apply functions only work with numbers
• Believing apply functions use less memory always

2. Which of the following is the correct syntax to use lapply on a list named my_list applying the function sqrt?

easy

A. lapply(my_list, sqrt)

B. lapply(my_list; sqrt)

C. lapply(my_list sqrt)

D. lapply(sqrt, my_list)

Solution

1. Step 1: Recall lapply syntax

   The correct syntax is lapply(X, FUN) where X is the list and FUN is the function to apply.

2. Step 2: Match syntax to options

   lapply(my_list, sqrt) correctly places the list first and the function second, separated by a comma.

3. Final Answer:

   lapply(my_list, sqrt) -> Option A

4. Quick Check:

   lapply(list, function) syntax [OK]

Hint: Remember lapply(list, function) order [OK]

Common Mistakes:

• Swapping the order of arguments
• Missing commas between arguments
• Using incorrect separators like semicolons

3. What is the output of the following R code?

mat <- matrix(1:6, nrow=2)
result <- apply(mat, 2, sum)
print(result)

medium

A. [1] 4 6 8

B. [1] 5 7 9

C. [1] 3 7 11

D. [1] 2 4 6

Solution

1. Step 1: Understand the matrix and apply parameters

   The matrix has 2 rows and 3 columns: columns are (1,3,5) and (2,4,6) if filled by column, but since nrow=2, the matrix is filled column-wise as: first column (1,2), second (3,4), third (5,6). Using apply with margin 2 applies the function to columns.

2. Step 2: Calculate column sums

   Sum of columns: 1+2=3, 3+4=7, 5+6=11.

3. Final Answer:

   [1] 3 7 11 -> Option C

4. Quick Check:

   apply with margin 2 sums columns [OK]

Hint: Margin 2 means apply function to columns [OK]

Common Mistakes:

• Confusing margin 1 (rows) with margin 2 (columns)
• Adding rows instead of columns
• Misreading matrix layout

4. Consider this R code snippet:

vec <- 1:5
result <- sapply(vec, function(x) x^2)
print(result)

What is the error in this code?

medium

A. Function syntax is incorrect

B. Missing parentheses after function

C. sapply cannot be used on vectors

D. No error; code runs correctly

Solution

1. Step 1: Check sapply usage on vector

   sapply works on vectors and applies the function to each element.

2. Step 2: Verify function syntax and parentheses

   The anonymous function syntax is correct and parentheses are properly placed.

3. Final Answer:

   No error; code runs correctly -> Option D

4. Quick Check:

   sapply with anonymous function on vector is valid [OK]

Hint: Anonymous functions need parentheses and braces [OK]

Common Mistakes:

• Thinking sapply can't handle vectors
• Misreading function syntax
• Assuming missing parentheses

5. You have a list of numeric vectors: my_list <- list(a = 1:3, b = 4:6, c = 7:9). You want to create a named vector with the mean of each element but only include those means greater than 5. Which R code correctly does this using apply family functions?

hard

A. means <- lapply(my_list, mean); means[means > 5]

B. means <- sapply(my_list, mean); means[means > 5]

C. means <- apply(my_list, 1, mean); means[means > 5]

D. means <- sapply(my_list, mean > 5)

Solution

1. Step 1: Choose correct apply function for list to vector

   sapply returns a vector with names, suitable for filtering.

2. Step 2: Calculate means and filter

   Calculate means with sapply, then subset with condition means > 5.

3. Final Answer:

   means <- sapply(my_list, mean); means[means > 5] -> Option B

4. Quick Check:

   sapply returns named vector, filter with logical index [OK]

Hint: Use sapply for named vector, then filter by condition [OK]

Common Mistakes:

• Using lapply returns list, not vector for filtering
• apply does not work on lists directly
• Passing condition inside mean function