Control structures

Topics: loop, if, for, while, functions

Control structures determine the order in which your code executes, allowing you to create decision-making and repetition logic that drives your program’s behavior in R.

Control structures can automate workflows such as checking patient eligibility, iterating over lab results, or categorizing patients based on diagnostic criteria.

If Statements

The if statement is used for conditional execution, allowing a block of code to run only if a specified condition is true. This is done to create decision-making logic in your programs.

age <- 72
bp  <- 145

if (age >= 60 & bp > 140) {
  print("High-risk: Schedule follow-up consultation.")
} else if (age >= 60 & bp <= 140) {
  print("Monitor regularly.")
} else {
  print("Low-risk: Maintain routine check-ups.")
}

# Output
# "High-risk: Schedule follow-up consultation."

The code above helps automatically classify patients based on age and blood pressure (bp) readings, similar to decision rules used in clinical triage systems.

For Loops

for loops repeat a block of code for each item in a collection. They are crucial for automating repetitive tasks.

Loop through a vector

patients <- c("Alice", "Bob", "Carlos")
for (name in patients) {
  cat("Processing lab report for", name, "\n")
}

# Output:
# Processing lab report for Alice
# Processing lab report for Bob
# Processing lab report for Carlos

This loop ensures that all patient reports are processed without manual repetition — useful when automating data cleaning or report generation for large datasets.

Loop through a range of numbers

for (i in 0:4) {
  cat("Count:", i, "\n")
}

# Output:
# Count: 0
# Count: 1
# Count: 2
# Count: 3
# Count: 4

Loop through a named list

student <- list(name = "Alice", age = 20, is_diabetic = TRUE)

for (key in names(student)) {
  cat(key, ":", as.character(student[[key]]), "\n")
}

# Output:
# name : Alice
# age : 20
# is_diabetic : TRUE
While Loops

while loops repeat a block of code as long as a condition is TRUE.

Basic while loop

count <- 0
while (count < 5) {
  cat("Count is", count, "\n")
  count <- count + 1
}

# Output:
# Count is 0
# Count is 1
# Count is 2
# Count is 3

while loop with a break condition

user_input <- ""
while (user_input != "quit") {
  user_input <- readline(prompt = "Enter a command (or 'quit' to exit): ")
  if (user_input != "quit") {
    cat("You entered:", user_input, "\n")
  }
}

# Output
# Enter a command (or 'quit' to exit): 10
# You entered: 10 
# Enter a command (or 'quit' to exit): 5
# You entered: 5 
# Enter a command (or 'quit' to exit): 1
# You entered: 1 
# Enter a command (or 'quit' to exit): 0
# You entered: 0 
# Enter a command (or 'quit' to exit): quit

After the user inputs the word “quit”, the loop ends.

Note: readline() is used for interactive console input in R. In scripts or R Markdown, user input is typically handled differently.

Functions

Functions are reusable blocks of code designed to perform a specific task. They are used to break down complex problems into smaller, manageable pieces, promote code reusability, and improve the organization and readability of your programs.

Basic function

greet <- function(name) {
  return(paste("Hello,", name, "!"))
}

Function with default parameters

introduce <- function(name, age = 25) {
  return(paste0("Hi, I'm ", name, " and I'm ", age, " years old."))
}

Function with multiple parameters

calculate_area <- function(length, width) {
  area <- length * width
  return(area)
}

Using the functions

message   <- greet("Alice")
print(message)                                # Hello, Alice !

intro <- introduce("Bob")                     # Uses default age
print(intro)                                  # Hi, I'm Bob and I'm 25 years old.

room_area <- calculate_area(10, 12)
cat("Room area:", room_area, "square feet\n") # Room area: 120 square feet
Reusable Function for BMI Classification

This function encapsulates a clinical rule (BMI categories), demonstrating how logic and computation combine to yield meaningful interpretations in healthcare analytics.

classify_bmi <- function(weight_in_kg, height_in_meters) {
  bmi <- weight_in_kg / (height_in_meters ^ 2)
  if (bmi < 18.5) {
    return("Underweight")
  } else if (bmi < 25) {
    return("Normal")
  } else if (bmi < 30) {
    return("Overweight")
  } else {
    return("Obese")
  }
}

print(classify_bmi(70, 1.75))       # "Normal"
String Formatting Practice Exercises