Why advanced features enable complex work in R Programming - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of nested function calls with advanced features

What is the output of this R code using advanced features like closures and environments?

R Programming

make_counter <- function() {
  count <- 0
  function() {
    count <<- count + 1
    count
  }
}
counter <- make_counter()
c(counter(), counter(), counter())

A Error: object 'count' not found

B [1] 0 1 2

C [1] 1 2 3

D [1] 1 1 1

💡 Hint

Think about how the inner function changes the variable 'count' in its environment.

The inner function increments 'count' stored in the parent environment each time it is called, so the outputs are 1, 2, and 3.

❓ Predict Output

intermediate

Output of vectorized operations with recycling

What is the output of this R code that uses vector recycling in arithmetic?

R Programming

x <- c(1, 2, 3, 4)
y <- c(10, 20)
x + y

A [1] 11 22 13 24

B [1] 11 22 33 44

C [1] 11 12 13 14

D Error: non-conformable arrays

💡 Hint

Remember how R recycles shorter vectors to match the length of longer ones.

The shorter vector y is recycled to match x: (10,20,10,20). Adding element-wise gives (11,22,13,24).

🔧 Debug

advanced

Identify the error in advanced list manipulation

What error does this R code produce when trying to access a nested list element?

R Programming

my_list <- list(a = list(b = 1:3))
my_list$a$b[4]

A Error: object 'b' not found

B Error: subscript out of bounds

C NULL

D NA (no error, returns NA because index 4 is out of bounds)

💡 Hint

Check what happens when you access an index beyond the vector length in R.

Accessing an out-of-bounds index in a vector returns NA without an error.

🧠 Conceptual

advanced

Why use environments for complex R programming?

Which statement best explains why environments enable complex work in R?

A Environments allow storing variables with different scopes, enabling modular and stateful programming.

B Environments automatically speed up all R code without programmer effort.

C Environments are only used for graphical output in R.

D Environments prevent any variable from being changed once created.

💡 Hint

Think about how environments manage variable visibility and lifetime.

Environments let you keep variables separate and control their scope, which helps build complex programs with state.

❓ Predict Output

expert

Output of advanced metaprogramming with substitute and eval

What is the output of this R code using substitute and eval for metaprogramming?

R Programming

x <- 10
expr <- substitute(x + y)
y <- 5
eval(expr)

A [1] 10 + y

B [1] 15

C Error: object 'y' not found

D [1] 5

💡 Hint

Consider when substitute captures the expression and when eval evaluates it.

substitute captures the expression 'x + y' unevaluated. Later, eval evaluates it with y=5, so output is 15.