Lecture 10: Programming in R – Scripts, Loops, and Conditional Statements

Overview

• In this lecture, we’ll cover:
  • How to write and run R scripts.
  • Using loops to automate repetitive tasks.
  • Applying conditional statements to control the flow of your programs.
  • Writing custom functions for reusable code.

1. Writing and Running R Scripts

• What is an R script?
  • An R script is a file containing a series of R commands that you can run together. Scripts are useful for automating tasks and organizing your code.
• How to create a new script:
  • In RStudio, navigate to File -> New File -> R Script. This opens a new script file where you can write and save your code.
• Running a script:
  • You can run individual lines of code in a script by selecting the line and pressing Ctrl + Enter (Windows) or Cmd + Enter (Mac).
  • To run the entire script, click on the Source button or use Ctrl + Shift + Enter.
• Example: A simple R script:

# This is a simple script that calculates the square of a number
number <- 5
square <- number^2
print(square)  # Output the result

[1] 25

• Saving the script:
  • Save your script by going to File -> Save As and choosing a file name with the .R extension (e.g., my_script.R).

2. Using Loops in R

• What is a loop?
  • A loop allows you to repeat a block of code multiple times. Loops are useful for automating repetitive tasks.
• For loop:
  • The for loop iterates over a sequence of values, running the code inside the loop for each value.
• Example: A simple for loop:

# A for loop that prints numbers from 1 to 5
for (i in 1:5) {
  print(i)
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

• While loop:
  • The while loop repeats a block of code as long as a specified condition is TRUE.
• Example: A simple while loop:

# A while loop that prints numbers from 1 to 5
i <- 1
while (i <= 5) {
  print(i)
  i <- i + 1  # Increment the value of i
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5

3. Using Conditional Statements in R

• What is a conditional statement?
  • A conditional statement allows you to execute certain blocks of code based on whether a condition is TRUE or FALSE.
• If-else statements:
  • The if-else statement runs code when a condition is true and optionally runs different code when the condition is false.
• Example: Using an if-else statement:

x <- 10

if (x > 5) {
  print("x is greater than 5")
} else {
  print("x is less than or equal to 5")
}

[1] "x is greater than 5"

• Nested if-else statements:
  • You can nest multiple if-else statements to check for multiple conditions.
• Example: Checking multiple conditions:

x <- 10

if (x > 15) {
  print("x is greater than 15")
} else if (x > 5) {
  print("x is greater than 5 but less than or equal to 15")
} else {
  print("x is 5 or less")
}

[1] "x is greater than 5 but less than or equal to 15"

4. Writing Custom Functions

• Why write custom functions?
  • Writing your own functions allows you to organize and reuse code. A function can accept input, process the data, and return a result.
• Structure of a function:
  • The general structure of a function in R is:

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

• Example: Writing a custom function:

# A function to calculate the square of a number
square <- function(x) {
  return(x^2)  # Return the square of the input
}

• Calling the function:

square(4)  # Call the function with input 4

[1] 16

• Using multiple arguments:
  • Functions can take multiple arguments, and you can define default values for these arguments.
• Example: A function with multiple arguments:

add_numbers <- function(a = 1, b = 2) {
  return(a + b)
}

add_numbers(3, 5)  # Call the function with specific values for a and b

[1] 8

Key Takeaways

• You can write and run R scripts to organize and automate tasks.
• Loops and conditional statements allow you to control the flow of your code and automate repetitive tasks.
• Writing custom functions helps make your code more modular and reusable.

Looking Forward

• In the next lecture, we’ll dive into more advanced programming techniques in R, including working with apply functions and exploring vectorized operations for efficient coding.