Function definition in R Programming - Mini Project: Build & Apply

Function definition

📖 Scenario: You are helping a small bakery keep track of how many cookies they bake each day. They want to use a simple function to calculate the total cookies baked over several days.

🎯 Goal: Create a function in R that adds two numbers representing cookies baked on two different days, then use it to find the total cookies baked.

📋 What You'll Learn

Create a function called add_cookies that takes two parameters: day1 and day2.

The function should return the sum of day1 and day2.

Create two variables cookies_day1 and cookies_day2 with values 50 and 70 respectively.

Call the function add_cookies with cookies_day1 and cookies_day2 as arguments and store the result in total_cookies.

Print the value of total_cookies.

💡 Why This Matters

🌍 Real World

Bakeries and small businesses often need simple calculations to track daily production or sales. Functions help automate these calculations.

💼 Career

Understanding how to define and use functions is a key skill for data analysis, automation, and software development jobs.

1

Create variables for cookies baked on two days

Create two variables called cookies_day1 and cookies_day2 and set them to 50 and 70 respectively.

R Programming

# Create variables cookies_day1 and cookies_day2 with values 50 and 70
# Your code here

Hint

Use the assignment operator <- to assign values to variables.

2

Define the function to add cookies

Define a function called add_cookies that takes two parameters: day1 and day2. The function should return the sum of day1 and day2.

R Programming

cookies_day1 <- 50
cookies_day2 <- 70

# Define the function add_cookies(day1, day2) that returns day1 + day2
# Your code here

Hint

Use function keyword to define a function in R.

3

Call the function and store the result

Call the function add_cookies with cookies_day1 and cookies_day2 as arguments and store the result in a variable called total_cookies.

R Programming

cookies_day1 <- 50
cookies_day2 <- 70

add_cookies <- function(day1, day2) {
  return(day1 + day2)
}

# Call add_cookies with cookies_day1 and cookies_day2 and save to total_cookies
# Your code here

Hint

Call the function by writing its name followed by parentheses with arguments inside.

4

Print the total cookies baked

Write a line to print the value of total_cookies.

R Programming

cookies_day1 <- 50
cookies_day2 <- 70

add_cookies <- function(day1, day2) {
  return(day1 + day2)
}

total_cookies <- add_cookies(cookies_day1, cookies_day2)

# Print total_cookies
# Your code here

Hint

Use the print() function to show the value on the screen.

Practice

1. What is the correct way to start defining a function named greet in R?

easy

A. greet <- function() { }

B. function greet() { }

C. def greet() { }

D. function <- greet() { }

Solution

1. Step 1: Recognize R function syntax

   In R, functions are defined by assigning a function() to a name using <-.

2. Step 2: Match the correct syntax

   greet <- function() { } uses the correct syntax: name <- function() { }.

3. Final Answer:

   greet <- function() { } -> Option A

4. Quick Check:

   Function definition = name <- function() [OK]

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

Common Mistakes:

• Using def instead of function
• Writing function name() without <-
• Placing function keyword after name

2. Which of the following is the correct syntax to define a function add that takes two arguments x and y in R?

easy

A. function add(x, y) { x + y }

B. add(x, y) <- function { return x + y }

C. add <- function(x, y) { return(x + y) }

D. add <- function x, y { return(x + y) }

Solution

1. Step 1: Check function definition format

   In R, arguments go inside parentheses after function keyword, and the body is inside braces.

2. Step 2: Verify return statement and assignment

   add <- function(x, y) { return(x + y) } correctly assigns function with arguments and uses return() properly.

3. Final Answer:

   add <- function(x, y) { return(x + y) } -> Option C

4. Quick Check:

   Arguments inside function() and return() used [OK]

Hint: Put arguments inside function() parentheses [OK]

Common Mistakes:

• Placing arguments outside function()
• Missing braces {} around function body
• Incorrect assignment syntax

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

multiply <- function(a, b) {
  result <- a * b
  return(result)
}
multiply(3, 4)

medium

A. 12

B. 7

C. 34

D. Error

Solution

1. Step 1: Understand function behavior

   The function multiply takes two numbers and returns their product.

2. Step 2: Calculate the product of 3 and 4

   3 * 4 equals 12, so the function returns 12.

3. Final Answer:

   12 -> Option A

4. Quick Check:

   3 * 4 = 12 [OK]

Hint: Multiply inputs and return result to get output [OK]

Common Mistakes:

• Adding instead of multiplying
• Concatenating numbers as strings
• Forgetting to return the result

4. Identify the error in this R function definition:

sum_values <- function(x, y) 
  total <- x + y
  return(total)
}

medium

A. Function name is invalid

B. Missing opening brace { after function()

C. Incorrect use of return()

D. Arguments should be inside square brackets []

Solution

1. Step 1: Check function body braces

   The function body must be enclosed in braces { } immediately after function() declaration.

2. Step 2: Locate missing brace

   The code lacks an opening brace { after function(x, y), causing syntax error.

3. Final Answer:

   Missing opening brace { after function() -> Option B

4. Quick Check:

   Function body needs { } [OK]

Hint: Always put { after function() to start body [OK]

Common Mistakes:

• Omitting braces around function body
• Placing return outside function
• Using wrong brackets for arguments

5. You want to write a function filter_positive that takes a numeric vector and returns only the positive numbers. Which function definition correctly does this in R?

hard

A. filter_positive <- function(vec) { return(vec[vec == 0]) }

B. filter_positive <- function(vec) { return(vec[vec < 0]) }

C. filter_positive <- function(vec) { vec > 0 }

D. filter_positive <- function(vec) { return(vec[vec > 0]) }

Solution

1. Step 1: Understand filtering in R

   To get positive numbers, select elements where vec > 0.

2. Step 2: Check return statement

   filter_positive <- function(vec) { return(vec[vec > 0]) } returns vec elements where condition vec > 0 is TRUE, correctly filtering positives.

3. Final Answer:

   filter_positive <- function(vec) { return(vec[vec > 0]) } -> Option D

4. Quick Check:

   Filter with vec[vec > 0] returns positives [OK]

Hint: Use vec[vec > 0] to filter positive numbers [OK]

Common Mistakes:

• Using wrong comparison operator
• Not returning filtered vector
• Returning logical vector instead of values