DATA SCIENCE PROGRAMMING

Practicum Week 4

NIM: 52250007

SEMESTER 2

Yosef Teofani Tamba

🎓 Active students in the 2025/2026 academic year

Data Science Undergraduate at Institut Teknologi dan Sains Bandung (ITSB)

👨‍🏫 Lecturer

Mr. Bakti Siregar, M.Sc., CDS

📊 R Programming

🤖 Data Science

📚 Currently in the Semester 2 (2026) - Focus on Data Science Programming

1 Dataset

Create a Dummy Dataset with using dataframe and library kableExtra to create a table

# Create a data frame
data_emp <- data.frame(
  ID = c(1, 2, 3, 4, 5),
  Name = c("Bagas", "Joan", "Alya", "Dwi", "Nabil"),
  Age = c(25, 30, 27, 35, 40),
  Salary = c(5000, 7000, 6500, 10000, 12000),
  Position = c("Staff", "Supervisor", "Staff", "Manager", "Director"),
  Performance = c("Good", "Very Good", "Average", "Good", "Very Good"),
  stringsAsFactors = FALSE 
)

# Add kableExtra for create a table
library(kableExtra)

kable(data_emp, caption = "Dummy Dataset Employee") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "bordered")) %>%
  row_spec(0, background = "purple", color = "bisque", bold = TRUE) %>%
  row_spec(1:nrow(data_emp), background = "darkorchid3")

Dummy Dataset Employee
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

2 Conditional Statement

Determine bonus level based on employee performance:

Very Good -> 20% of salary
Good -> 10% of salary
Average -> 5% of salary

for (i in 1:nrow(data_emp)) {
  
  name_emp <- data_emp$Name[i]
  salary_emp <- data_emp$Salary[i]
  performance_emp <- data_emp$Performance[i]
  
  if (performance_emp == "Very Good") {
    percentage_bonus <- 0.20
  } else if (performance_emp == "Good") {
    percentage_bonus <- 0.10
  } else if (performance_emp == "Average") {
    percentage_bonus <- 0.05
  } else {
    percentage_bonus <- 0
  }
  
  # Calculate Bonus 
  total_calculate <- salary_emp * percentage_bonus
  
  cat(sprintf("Name: %s, Bonus: %g\n", name_emp, total_calculate))
}

## Name: Bagas, Bonus: 500
## Name: Joan, Bonus: 1400
## Name: Alya, Bonus: 325
## Name: Dwi, Bonus: 1000
## Name: Nabil, Bonus: 2400

3 Loops (For & While)

Loops allow programs to repeat actions multiple times.

3.1 For Loop

Use For Loop to list employees with a salary greater than 6000.

for (i in 1:nrow(data_emp)) {
  if (data_emp$Salary[i] > 6000) {
    cat("Name:", data_emp$Name[i], ", Salary:", data_emp$Salary[i], "\n")
  }
}

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

3.2 While Loop

Use a While Loop to display employees until a “Manager” is found.

for (i in 1:nrow(data_emp)) {
  if (data_emp$Salary[i] > 6000) {
    cat("Name:", data_emp$Name[i], ", Salary:", data_emp$Salary[i], "\n")
  }
}

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

3.3 Break

Use break to stop the loop when a employees with a salary above 10,000 is found.

for (i in 1:nrow(data_emp)) {
  if (data_emp$Salary[i] > 10000) {
    cat("(Stopped because", data_emp$Name[i], "has a salary above 10,000)\n")
    break
  }
  cat("Name:", data_emp$Name[i], ", Salary:", data_emp$Salary[i], "\n")
}

## Name: Bagas , Salary: 5000 
## Name: Joan , Salary: 7000 
## Name: Alya , Salary: 6500 
## Name: Dwi , Salary: 10000 
## (Stopped because Nabil has a salary above 10,000)

3.4 Continue

Use Continue to skip employees with “Average” performance.

for (i in 1:nrow(data_emp)) {
  if (data_emp$Performance[i] == "Average") {
    next 
  }
  cat("Name:", data_emp$Name[i], ", Performance:", data_emp$Performance[i], "\n")
}

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