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.