Control Statements: If - Conditional Block

Why Learn Control Statements in Data Analytics?

Imagine you are developing a credit approval system for a bank. You have a dataset of customer credit scores. You need to categorize each applicant automatically based on their score:

• Score of 700 or above: Status is "Approved".
• Score between 600 and 699: Status is "Under Review".
• Score below 600: Status is "Denied".

If you write a simple sequential script, it will execute every line of code without stopping to think. To make decisions, you must deviate from this straight path. You need Control Statements (if, else if, and else) to guide the program down different paths depending on each applicant's credit score.

1. The if Statement Syntax

In R, conditions must be enclosed in parentheses ( ), and the block of code to run must be enclosed in curly braces { }.

score <- 750

if (score >= 700) {
  print("Status: Approved")
}

2. Short-Circuiting Logical Operators (&& and ||)

In the Vectors chapter, you learned about element-wise logical operators (&, |, and !) for filtering data. However, inside if statements, you should use their scalar counterparts: && (AND) and || (OR).

Short-Circuit Evaluation

These double operators feature Short-Circuit Evaluation, which means R evaluates expressions from left to right and stops as soon as the final result is determined:

• && stops at the first FALSE: If the left side is FALSE, R knows the entire expression must be FALSE and skips evaluating the right side.
• || stops at the first TRUE: If the left side is TRUE, R knows the entire expression must be TRUE and skips evaluating the right side.

This is extremely useful for writing safe conditions. For instance:

x <- NULL

# This is safe because !is.null(x) evaluates to FALSE.
# R "short-circuits" and never evaluates x > 0, preventing a crash!
if (!is.null(x) && x > 0) {
  print("x is a positive number")
}

3. Multi-Way Conditionals: else if and else

To handle multiple branches, chain them together using else if and else:

score <- 650

if (score >= 700) {
  print("Status: Approved")
} else if (score >= 600) {
  print("Status: Under Review")
} else {
  print("Status: Denied")
}

if (score >= 700) {
  print("Approved")
}
else { # This will throw a syntax error in R!
  print("Denied")
}

4. Inline Conditionals: ifelse() vs. if_else()

For simple conditional assignments, writing a full multi-line if/else block can be verbose. R offers vectorized inline functions for this.

Base R: ifelse()

The built-in ifelse(test, yes, no) evaluates a test condition, returning the yes value if TRUE and the no value if FALSE:

score <- 550
status <- ifelse(score >= 600, "Pass", "Fail")
print(status) # "Fail"

Base R's ifelse() is flexible and will automatically coerce data types if they differ between the yes and no values (e.g., mixing characters and numbers).

Tidyverse: if_else()

The dplyr package (part of the tidyverse) provides a stricter alternative called if_else(condition, true, false, missing = NULL):

library(dplyr)
score <- 650
# Returns "large" if >600, else "small"
status <- if_else(score > 600, "large", "small")
print(status)

Hands-on Exercises

Exercise 1: Risk Profiling

Analyst Question: How can we dynamically classify a client's investment risk profile as conservative, moderate, or aggressive based on their numerical age?

Analytical Guidance: Develop a decision rule inside a custom logical script. Your code should:

1. Assign a test variable age <- 62.
2. Construct a control flow statement:
   • If age is strictly under 35, print "Risk Profile: Aggressive".
   • If age is between 35 and 60 (inclusive), print "Risk Profile: Moderate".
   • If age is above 60, print "Risk Profile: Conservative".

# Write your code below and click Run Code

age <- 62

if (age < 35) {
  print("Risk Profile: Aggressive")
} else if (age <= 60) {
  print("Risk Profile: Moderate")
} else {
  print("Risk Profile: Conservative")
}

Exercise 2: Premium Pricing Logic

Analyst Question: How can we calculate the subscription price for a client based on their student status and numerical age?

Analytical Guidance: Create a logical evaluation branch using conditional logic. Your code should:

1. Define variables is_student <- TRUE and age <- 22.
2. Write a conditional control statement that checks if the client is a student OR is under 18 years of age:
   • If true, assign a price of $10.
   • Otherwise, assign a price of $15.
3. Print the final price outcome.

# Write your code below and click Run Code

is_student <- TRUE
age <- 22

if (is_student || age < 18) {
  price <- 10
} else {
  price <- 15
}

print(paste("Subscription Price: $", price))