Conditional Statements

Materials adapted from Adrien Osakwe, Larisa M. Soto and Xiaoqi Xie.

1. The Logic of Decision Making

In programming, Conditional Statements allow your code to branch. Instead of running every line of code from top to bottom, R will only run specific sections if a condition is met.

1.1 Binary Conditions (if … else)

This is used for simple “Yes/No” or “Either/Or” logic.

The Structure (Pseudo-code):

if (condition) {
  # Perform this task if condition is TRUE
} else {
  # Perform this task if condition is FALSE
}

Example:

x <- 5

if (x > 5) {
  # If the number is greater than 5, get its square
  x^2
} else {
  # If not, multiply it by 2
  x * 2
}
[1] 10
Note

Because the condition x > 5 evaluated to FALSE (since 5 is equal to 5, not strictly greater than 5), R skipped the first block and jumped straight to the else block.

1.2 Multi-branch Conditions (else if)

When your logic is more complex than a simple binary choice, you can chain conditions together. You can have as many else if blocks as you need.

The Structure (Pseudo-code):

if (condition_1) {
  # Perform Task A
} else if (condition_2) {
  # Perform Task B
} else if (condition_3) {
  # Perform Task C
} else {
  # Perform Task D if NONE of the above were TRUE
}
x <- 7

if (x > 5 && x < 10) {
  # If x is between 5 AND 10
  x^2
} else if (x < 0 || x > 10) {
  # If x is negative OR greater than 10
  -x
} else {
  # For everything else (like 0, 1, 2, 3, 4, 5)
  x * 2
}
[1] 49

2. Pro-Tips for Conditionals

2.1 & vs && (and | vs ||)

You might have noticed the use of double symbols (&& and ||) in the example above. In R, there is a small but important difference:

  • Single (&, |): These are Vectorized. Use these when comparing entire columns of data (e.g., filtering a Data Frame).

  • Double (&&, ||): These are Short-circuiting. They only look at the first element of a vector. They are faster and preferred inside if statements where you are only checking one value at a time.

2.2 The “Missing” Else

The else block is optional. If you don’t provide one and the if condition is FALSE, R will simply skip the block and move to the next line of code.

2.3 Indentation Matters for Humans!

While R is “space-insensitive” (it doesn’t care if your code is a messy pile of text), humans are not. We use indentation to show the hierarchy of logic.

It is standard practice to indent the code inside the curly braces { }. This makes it immediately obvious which code belongs to which condition.

Tip

The RStudio Secret

Don’t worry about manually hitting the spacebar! RStudio is designed to help you.

  • Auto-indent: When you type a { and hit Enter, RStudio automatically adds the correct gap.

  • The “Re-format” Trick: If your code looks messy, you don’t even need to copy-paste. Simply highlight the code and press Ctrl + Shift + A (Windows) or Cmd + Shift + A (Mac). RStudio will instantly clean up the spacing and indentation for you.

Tip

The styler Package

In professional programming, we often use a package called styler.

It acts like a “grammar checker” for your code, ensuring it follows the tidyverse style guide used by data scientists at NASA, Google, and beyond.

2.4 Best Practices for Conditional Logic

The MECE Principle

When designing your logic, try to follow the MECE principle: Mutually Exclusive, Collectively Exhaustive.

  • Mutually Exclusive: Your conditions shouldn’t overlap. A number shouldn’t be able to trigger both the if and the else if block at the same time.

  • Collectively Exhaustive: You have considered all possibilities. This is why a final else block is so important—it acts as a “safety net” for any data you didn’t expect.

The Power of Comments

In bioinformatics, your “Conditions” are often based on scientific thresholds (e.g., p-value <0.05 or Log2FoldChange >2).

Always write a comment explaining why you chose that threshold. Future you will thank you when you’re writing the “Methods” section of your paper!