Introduction

Control flow is an important concept in programming because it determines how a program executes instructions. By using control flow, a program can make decisions and repeat certain operations automatically.

In this practicum, several basic control flow structures are applied, including:

Conditional statements (if, elif, else)
Loop structures (for loop and while loop)
Loop control statements (break and continue)

A simple dataset about employees is used to demonstrate how these concepts work in programming.

1. Dataset

In this section, a simple dataset containing employee information is used. The dataset includes ID, name, age, salary, job position, and performance. This dataset will be used to practice conditional statements and loops in programming.

# read CSV dataset
employee_data <- read.csv("C:/Users/nulin/OneDrive/Lampiran/SEMESTER 2 PROGRAMING SAINS DATA/Assigment Week 4/DATASET WEEK 4.CSV")

# display dataset as table
knitr::kable(employee_data)

ID	Name	Age	Salary	Position	Performance
1	Bagas	25	5000	Staff	Good
2	Joan	30	7000	Supervisor	Very good
3	Alya	27	6500	Staff	Average
4	Dwi	35	10000	Manager	Good
5	Nabil	40	12000	Director	Very good

name <- employee_data$Name
salary <- employee_data$Salary
performance <- employee_data$Performance
position <- employee_data$Position

2. Conditional Statements

Conditional statements are used to make decisions in a program based on certain conditions. In this example, conditional statements are used to determine employee bonuses based on their performance.

Bonus rules:

Very Good → 20%
Good → 10%
Average → 5%

for(i in 1:length(name)){
  
  if(performance[i] == "Very Good"){
    bonus <- salary[i] * 0.20
  }
  
  else if(performance[i] == "Good"){
    bonus <- salary[i] * 0.10
  }
  
  else{
    bonus <- salary[i] * 0.05
  }
  
  cat("Name:", name[i], ", Bonus:", bonus,"\n")
}

Name: Bagas , Bonus: 500 
Name: Joan , Bonus: 350 
Name: Alya , Bonus: 325 
Name: Dwi , Bonus: 1000 
Name: Nabil , Bonus: 600

result_name <- c()
result_bonus <- c()

for(i in 1:length(name)){
  
  if(performance[i] == "Very Good"){
    bonus <- salary[i] * 0.20
  }
  else if(performance[i] == "Good"){
    bonus <- salary[i] * 0.10
  }
  else{
    bonus <- salary[i] * 0.05
  }
  
  result_name[i] <- name[i]
  result_bonus[i] <- bonus
}

# membuat tabel hasil
result_table <- data.frame(
  Name = result_name,
  Bonus = result_bonus
)

knitr::kable(result_table)

Name	Bonus
Bagas	500
Joan	350
Alya	325
Dwi	1000
Nabil	600

Interpretation:

The program checks each employee’s performance using if, else if, and else. Based on the performance category, the program calculates the bonus according to the specified percentage.

3. Loops

Loops are used to repeat a process automatically on a dataset.

3.1 For Loop

In this example, the program displays employees who have a salary greater than 6000.

for(i in 1:length(name)){
  
  if(salary[i] > 6000){
    cat("Name:", name[i], ", Salary:", salary[i],"\n")
  }
}

Name: Joan , Salary: 7000 
Name: Alya , Salary: 6500 
Name: Dwi , Salary: 10000 
Name: Nabil , Salary: 12000

# filter salary > 6000
salary_filter <- employee_data[employee_data$Salary > 6000, c("Name","Salary")]

knitr::kable(salary_filter, row.names = FALSE)

Name	Salary
Joan	7000
Alya	6500
Dwi	10000
Nabil	12000

Interpretation:

The for loop iterates through each employee’s data. The program checks whether the salary is greater than 6000 and displays only those employees who meet the condition.

3.2 While Loop

A while loop continues running as long as the condition remains true. In this example, the loop stops when an employee with the position Manager is found.

i <- 1

while(i <= length(name)){
  
  cat("Name:", name[i], ", Position:", position[i],"\n")
  
  if(position[i] == "Manager"){
    cat("Stop here\n")
    break
  }
  
  i <- i + 1
}

Name: Bagas , Position: Staff 
Name: Joan , Position: Supervisor 
Name: Alya , Position: Staff 
Name: Dwi , Position: Manager 
Stop here

i <- 1
result <- data.frame(Name=character(), Position=character())

while(i <= length(name)){
  
  result <- rbind(result, data.frame(
    Name = name[i],
    Position = position[i]
  ))
  
  if(position[i] == "Manager"){
    break
  }
  
  i <- i + 1
}

knitr::kable(result)

Name	Position
Bagas	Staff
Joan	Supervisor
Alya	Staff
Dwi	Manager

Interpretation:

The program reads the employee data sequentially using a while loop. When the program finds an employee whose position is Manager, the loop stops using the break statement.

4. Break Statement

The break statement is used to stop a loop when a specific condition is met.

for(i in 1:length(name)){
  
  if(salary[i] > 10000){
    cat("Stopped because salary above 10000\n")
    break
  }
  
  cat("Name:", name[i], ", Salary:", salary[i],"\n")
}

Name: Bagas , Salary: 5000 
Name: Joan , Salary: 7000 
Name: Alya , Salary: 6500 
Name: Dwi , Salary: 10000 
Stopped because salary above 10000

i <- 1
result_break <- data.frame(Name=character(), Salary=numeric())

for(i in 1:length(name)){
  
  if(salary[i] > 10000){
    break
  }
  
  result_break <- rbind(result_break, data.frame(
    Name = name[i],
    Salary = salary[i]
  ))
}

knitr::kable(result_break)

Name	Salary
Bagas	5000
Joan	7000
Alya	6500
Dwi	10000

Interpretation:

The program checks each employee’s salary. When it encounters a salary greater than 10000, the loop stops immediately using the break statement.

5. Continue Statement

The continue statement is used to skip a specific iteration of a loop. In R, this is done using the next command.

for(i in 1:length(name)){
  
  if(performance[i] == "Average"){
    next
  }
  
  cat("Name:", name[i], ", Performance:", performance[i],"\n")
}

Name: Bagas , Performance: Good 
Name: Joan , Performance: Very good 
Name: Dwi , Performance: Good 
Name: Nabil , Performance: Very good

cat("(Alya is skipped because the performance is 'Average')")

(Alya is skipped because the performance is 'Average')

result_continue <- data.frame(Name=character(), Performance=character())

for(i in 1:length(name)){
  
  if(performance[i] == "Average"){
    next
  }
  
  result_continue <- rbind(result_continue, data.frame(
    Name = name[i],
    Performance = performance[i]
  ))
}

knitr::kable(result_continue)

Name	Performance
Bagas	Good
Joan	Very good
Dwi	Good
Nabil	Very good

Interpretation:

If an employee has Average performance, the program skips that iteration using next. As a result, Alya’s data is not displayed in the output.

Conclusion

In this practicum, several fundamental programming concepts were practiced, including conditional statements and loops. Conditional statements help programs make decisions, while loops allow processes to repeat automatically on datasets. Additionally, break stops loops when a condition is met, and next skips specific iterations.

Reference

Siregar, B. (2025). Data Science Programming: Study Case Using R and Python. Online module. bookdown.org. Retrieved from https://bookdown.org/dsciencelabs/data_science_programming/02-Syntax-and-Control-Flow.html

Assigment Week 4 ~ Syntax and Control Flow

Introduction

1. Dataset

2. Conditional Statements

3. Loops

3.1 For Loop

3.2 While Loop

4. Break Statement

5. Continue Statement

Conclusion

Reference