For & While Loops

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

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1. For Loop

A For Loop is used when you know exactly how many times you want to repeat a task. You are “iterating” over a sequence (like a vector or a list).

The Logic (Pseudo-code):

Note

Think of a for loop like an assembly line. You have a box of items (a vector or list), and you pick them up one by one, do something to them, and set them aside until the box is empty.

for (EACH_ITEM in UR_COLLECTION) {
    # 1. Take the current ITEM
    # 2. Perform the ACTION (e.g., calculate its square root)
    # 3. Move to the next ITEM automatically
}

# STOP: The loop ends automatically when the COLLECTION is empty

Example: Counting

# Iterate through the numbers 1 to 10
for (i in 1:10) {
  print(i)
}

[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
[1] 10

Example: Processing Strings

# Iterate through a character vector
x <- c('a', 'ab', 'cab', 'taxi')

for (word in x) {
  print(word)
}

[1] "a"
[1] "ab"
[1] "cab"
[1] "taxi"

Tip

The i or word in the code above is just a placeholder (an “iterator”). You can name it anything you want, but using descriptive names like gene or sample makes your code much easier to read!

2. While Loop

A While Loop is more flexible but also more “dangerous.” It repeats a task as long as a certain condition remains TRUE.

You use this when you don’t know exactly how many iterations you need, but you know when you want to stop.

The Logic (Pseudo-code):

Note

Think of a while loop like a security guard at a gate. The guard checks the condition before letting the code inside. If the condition is still true, the code runs, and then it goes back to the guard to check again.

WHILE (condition is TRUE) {
    1. Perform the ACTION (e.g., print a value or do math)
    2. UPDATE the variables involved in the condition 
       (If you don't update them, the loop runs forever!)
}
# STOP: The loop ends automatically when the condition becomes FALSE

Example:

x <- 5

# While x is greater than 0, keep running
while (x > 0) {
  print(x)
  # Crucial: you must change x, or the loop will run forever!
  x <- x - 1
}

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

CautionA Cautionary Note on While Loops

If your condition in a while loop never becomes FALSE (for example, if you forgot to write x <- x - 1), you create an Infinite Loop.

This will cause R to hang or crash. If this happens, look for the red “Stop” icon in the top-right of your RStudio Console to break the loop!

3. Comparison of Iteration Methods

In R, we often prefer Vectorized functions (the “Apply” family) over for loops because they are usually faster and require less code.

Method	Best Use Case	Output Type
for loop	Complex logic with many steps.	Whatever you define.
while loop	Simulations or tasks with unknown end-points.	Whatever you define.
lapply	Applying a function to every element of a list/vector.	List
sapply	Same as lapply, but tries to “simplify” the result.	Vector or Matrix

4. The “Apply” Family (lapply and sapply)

In modern R, lapply (List Apply) and sapply (Simple Apply) are the gold standard for iteration. They are cleaner and less prone to “off-by-one” errors than standard loops.

4.1 lapply

Always returns a list. This is great for bioinformatics because it can store complex results (like model outputs) for each gene.

genes <- c("BRCA1", "TP53", "EGFR")

# Convert every gene name to lowercase
results_list <- lapply(genes, tolower)
results_list

[[1]]
[1] "brca1"

[[2]]
[1] "tp53"

[[3]]
[1] "egfr"

4.2 sapply

Does the same thing as lapply, but it tries to “simplify” the result into a vector if possible.

# Convert every gene name to lowercase and return a vector
results_vector <- sapply(genes, tolower)
results_vector

  BRCA1    TP53    EGFR 
"brca1"  "tp53"  "egfr" 

5. Complicated Case: sapply with a Custom Function

In bioinformatics, we often need to do more than just simple math. We might want to “Score” a list of genes based on several criteria at once.

In this case, we will write a Custom Function and then use sapply to “fly” that function over a vector of gene expression values.

The Scenario:

We have a vector of Gene Expression values. We want to label them:

• “High” if the value is >100.

• “Low” if the value is <10.

• “Medium” for everything else.

# 1. Create our data (Sample Gene Expression)
expression_values <- c(150, 5, 45, 200, 8, 55)
names(expression_values) <- c("Gene_A", "Gene_B", "Gene_C", "Gene_D", "Gene_E", "Gene_F")

# 2. Write a Custom Function to handle the logic
# This function takes ONE number and returns ONE label
classify_gene <- function(x) {
  if (x > 100) {
    return("High")
  } else if (x < 10) {
    return("Low")
  } else {
    return("Medium")
  }
}

# 3. Use sapply to apply this function to every value in our vector
# sapply handles the "looping" for us!
gene_status <- sapply(expression_values, classify_gene)

# 4. Look at the result
gene_status

  Gene_A   Gene_B   Gene_C   Gene_D   Gene_E   Gene_F 
  "High"    "Low" "Medium"   "High"    "Low" "Medium"