Lecture 5: Introduction to Functions in R

Overview

• In this lecture, we’ll explore:
  • What functions are and how to use them.
  • How to write your own functions in R.
  • The importance of comments in code to improve readability.

1. What are Functions in R?

• Functions in R are blocks of code designed to perform specific tasks.

• They can take input, process the data, and return a result.

• R includes many built-in functions that you can use right away, such as mean(), sum(), and sqrt().

• Example: Using the mean() function:

numbers <- c(1, 2, 3, 4, 5)  # Create a vector of numbers
mean(numbers)  # Calculate the mean of the numbers

[1] 3

• Functions can take arguments (input values), perform an operation, and then return a result.

2. Writing Your Own Functions in R

• Why write your own functions?
  • Custom functions allow you to encapsulate a series of commands into a reusable block of code.
  • They make your code modular, efficient, and easier to maintain.
• Structure of a function in R:
  • Functions in R follow this structure:

function_name <- function(argument1, argument2, ...) {
    # Code that performs some task
    result <- argument1 + argument2  # Example of an operation
    return(result)  # Return the result
}

• Example: Creating a function to add two numbers:

add_numbers <- function(a, b) {
    sum <- a + b  # Add the two numbers
    return(sum)  # Return the result
}

• Using the function:

add_numbers(3, 5)  # Call the function with two arguments

[1] 8

• Explanation:
  • The function add_numbers() takes two inputs, a and b.
  • Inside the function, the numbers are added together, and the result is returned.
  • The function can now be reused whenever you need to add two numbers.

3. Adding Comments to Your Code

• Why are comments important?
  • Comments are notes you add to your code to explain what it’s doing.
  • They make your code easier to understand, especially when sharing with others or revisiting it after some time.
• How to add comments in R:
  • Use the # symbol to add a comment. Everything after # on the same line is ignored by R.
• Example: Adding comments to a function:

# This function adds two numbers together
add_numbers <- function(a, b) {
    sum <- a + b  # Add the two input numbers
    return(sum)  # Return the result
}

# Call the function with arguments 3 and 5
add_numbers(3, 5)

[1] 8

• Best practices for commenting:
  • Add comments to describe the purpose of your function.
  • Use comments to explain complex logic or calculations.
  • Don’t over-comment; focus on clarity.

4. Returning Values from Functions

• Functions in R often return a value after performing their operations.

• The return() statement is used to output the result from a function.

• If no return() is specified, R automatically returns the last expression evaluated.

• Example: A function that multiplies two numbers:

multiply_numbers <- function(x, y) {
    product <- x * y  # Multiply the inputs
    return(product)  # Return the result
}

• Using the function:

multiply_numbers(4, 6)

[1] 24

Key Takeaways

• You’ve learned how to use built-in functions like mean() and how to create your own custom functions in R.
• Writing custom functions helps make your code modular and reusable.
• Comments in R are essential for making your code easier to understand and maintain.

Looking Forward

• In the next lecture, we’ll explore working with vectors and variables in more detail, including how to manipulate and index data in R.