Why functions organize code in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to define a function named greet that prints a message.

R Programming

greet <- function() {
  print([1])
}

Drag options to blanks, or click blank then click option'

AHello, friend!

B"Hello, friend!"

Cprint("Hello, friend!")

Dmessage("Hello, friend!")

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the message

Putting print inside the string

The print function needs a string inside quotes to display the message.

2fill in blank

medium

Complete the code to call the greet function.

R Programming

[1]()

Drag options to blanks, or click blank then click option'

Agreet

Bprint

Cmessage

Dsay

Attempts:

3 left

💡 Hint

Common Mistakes

Using print instead of the function name

Forgetting parentheses

To run the function, use its name followed by parentheses.

3fill in blank

hard

Fix the error in the function definition to accept a name parameter.

R Programming

greet <- function([1]) {
  print(paste("Hello," , name))
}

Drag options to blanks, or click blank then click option'

Atext

Bmessage

Cinput

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using a different parameter name than inside the function

Forgetting to add a parameter

The function needs a parameter named 'name' to use it inside.

4fill in blank

hard

Fill both blanks to create a function that adds two numbers and returns the result.

R Programming

add_numbers <- function(a, b) {
  result <- a [1] b
  return([2])
}

Drag options to blanks, or click blank then click option'

A+

Bresult

C-

Da

Attempts:

3 left

💡 Hint

Common Mistakes

Using subtraction instead of addition

Returning the wrong variable

The function adds a and b, then returns the result variable.

5fill in blank

hard

Fill all three blanks to create a function that checks if a number is positive, negative, or zero.

R Programming

check_number <- function(num) {
  if (num [1] 0) {
    return("Positive")
  } else if (num [2] 0) {
    return("Negative")
  } else {
    return([3])
  }
}

Drag options to blanks, or click blank then click option'

A>

B<

C"Zero"

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators

Returning a variable instead of a string for zero

The function uses > and < to check positive or negative, and returns "Zero" if neither.

Practice

1. Why do we use functions to organize code in R?

easy

A. To make the program run faster

B. To group related code and make it reusable

C. To avoid using variables

D. To write code without comments

Solution

1. Step 1: Understand the purpose of functions

   Functions group related lines of code into one block that can be reused multiple times.

2. Step 2: Identify the main benefit

   This grouping helps keep code organized and avoids repeating the same code again and again.

3. Final Answer:

   To group related code and make it reusable -> Option B

4. Quick Check:

   Functions organize code = A [OK]

Hint: Functions group and reuse code easily [OK]

Common Mistakes:

• Thinking functions make code run faster
• Believing functions remove the need for variables
• Assuming functions replace comments

2. Which of the following is the correct way to define a function named greet in R?

easy

A. def greet() print("Hello")

B. function greet() { print("Hello") }

C. greet <- function() { print("Hello") }

D. greet() = { print("Hello") }

Solution

1. Step 1: Recall R function syntax

   In R, functions are defined using the syntax: name <- function() { ... }

2. Step 2: Match the correct syntax

   greet <- function() { print("Hello") } matches this syntax exactly, defining greet as a function that prints "Hello".

3. Final Answer:

   greet <- function() { print("Hello") } -> Option C

4. Quick Check:

   R function syntax = A [OK]

Hint: Use <- function() { } to define functions in R [OK]

Common Mistakes:

• Using Python or JavaScript syntax in R
• Missing the <- assignment operator
• Using parentheses incorrectly

3. What will be the output of this R code?

add <- function(x, y) {
  return(x + y)
}
result <- add(3, 4)
print(result)

medium

A. 7

B. 34

C. Error: object 'add' not found

D. NULL

Solution

1. Step 1: Understand the function behavior

   The function add takes two inputs and returns their sum.

2. Step 2: Calculate the function call

   Calling add(3, 4) returns 3 + 4 = 7, which is stored in result and printed.

3. Final Answer:

   7 -> Option A

4. Quick Check:

   3 + 4 = 7 [OK]

Hint: Functions return values; print shows the result [OK]

Common Mistakes:

• Thinking the function concatenates numbers as strings
• Expecting an error due to missing parentheses
• Assuming the function prints automatically without print()

4. Identify the error in this R function definition:

multiply <- function(x, y) {
  product = x * y
  return product
}

medium

A. Function name cannot be multiply

B. Using = instead of <- for assignment

C. No error, code is correct

D. Missing parentheses in return statement

Solution

1. Step 1: Check the return statement syntax

   In R, return must be followed by parentheses enclosing the value to return.

2. Step 2: Identify the error

   The code uses return product without parentheses, which causes a syntax error.

3. Final Answer:

   Missing parentheses in return statement -> Option D

4. Quick Check:

   return() needs parentheses [OK]

Hint: Always use return(value) with parentheses in R [OK]

Common Mistakes:

• Omitting parentheses in return
• Confusing assignment operators
• Thinking function names are restricted

5. You want to write a function in R that calculates the average of a numeric vector and returns it. Which of these function definitions correctly organizes the code to do this?

hard

A. average <- function(nums) {
return(sum(nums) / length(nums))
}

B. average <- function(nums) {
for (n in nums) {
sum <- sum + n
}
return(sum / length(nums))
}

C. average <- function(nums) {
mean <- sum(nums) * length(nums)
return(mean)
}

D. average <- function(nums) {
for (n in nums) {
sum <- sum + n
}
return(sum)
}

Solution

1. Step 1: Analyze each function's logic

   average <- function(nums) { for (n in nums) { sum <- sum + n } return(sum / length(nums)) } attempts to manually sum numbers but does not initialize sum, causing an error on undefined sum. average <- function(nums) { return(sum(nums) / length(nums)) } uses built-in functions sum() and length() correctly. average <- function(nums) { mean <- sum(nums) * length(nums) return(mean) } multiplies sum by length, which is wrong. average <- function(nums) { for (n in nums) { sum <- sum + n } return(sum) } sums but never initializes sum and returns sum without averaging.

2. Step 2: Choose the correct function

   average <- function(nums) { return(sum(nums) / length(nums)) } is concise, uses built-in functions, and returns the correct average.

3. Final Answer:

   average <- function(nums) { return(sum(nums) / length(nums)) } -> Option A

4. Quick Check:

   Use built-in sum and length for average [OK]

Hint: Use sum(nums)/length(nums) for average in one line [OK]

Common Mistakes:

• Forgetting to initialize sum before loop
• Multiplying sum by length instead of dividing
• Returning sum without dividing by length