1 Introduction

Programming requires clear syntax and logical structures to control how instructions are executed. Two important components in this process are conditional statements and loops, which allow programs to make decisions based on specific conditions and execute code repeatedly to process data efficiently. In the context of data science, these concepts are commonly used to manipulate datasets, automate calculations, and filter relevant information. This report demonstrates the application of syntax and control flow in R using a simple employee dataset to calculate bonuses based on performance and process data using different types of loops and loop control.

2 Dataset

The dataset used in this exercise represents employee information including salary, performance level, and job position. This dataset will serve as the input for demonstrating conditional statements and loop operations in R.

# Membuat dataset karyawan dalam bentuk data frame
employees <- data.frame(
  ID = c(1,2,3,4,5), # ID unik setiap karyawan
  Name = c("Bagas","Joan","Alya","Dwi","Nabil"), # Nama karyawan
  Salary = c(5000,7000,6500,10000,12000), # Gaji masing-masing karyawan
  Performance = c("Good","Very Good","Average","Good","Very Good"), # Penilaian performa karyawan
  Position = c("Staff","Supervisor","Staff","Manager","Director") # Posisi/jabatan karyawan
)

# Menampilkan dataset yang telah dibuat
employees

The dataset contains five employee records with information about salary, performance level, and job position. This dataset will be used to demonstrate how programming logic can be applied to perform calculations, filtering, and conditional operations.

3 Conditional Statement

Conditional statements allow a program to execute different actions depending on whether certain conditions are satisfied. In this section, conditional logic is used to calculate employee bonuses based on their performance level.

The bonus policy is defined as follows:

\[ \begin{aligned} \text{Very Good} &\rightarrow 20\% \text{ of salary} \\ \text{Good} &\rightarrow 10\% \text{ of salary} \\ \text{Average} &\rightarrow 5\% \text{ of salary} \end{aligned} \]

# Membuat kolom Bonus dan mengisi nilai awal 0
employees$Bonus <- 0

# Melakukan perulangan untuk setiap baris pada dataset
for (i in 1:nrow(employees)) {

  # Jika performa karyawan "Very Good" maka bonus 20% dari gaji
  if (employees$Performance[i] == "Very Good") {
    employees$Bonus[i] <- employees$Salary[i] * 0.20
  }

  # Jika performa "Good" maka bonus 10% dari gaji
  else if (employees$Performance[i] == "Good") {
    employees$Bonus[i] <- employees$Salary[i] * 0.10
  }

  # Jika performa selain dua kondisi di atas (Average) maka bonus 5%
  else {
    employees$Bonus[i] <- employees$Salary[i] * 0.05
  }
  
  # Menampilkan nama karyawan dan bonus yang diperoleh
  cat(paste0("Name: ", employees$Name[i],
             ", Bonus: ", employees$Bonus[i], "\n"))
}

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

# Menghitung total gaji setelah ditambahkan bonus
employees$Total_Salary <- employees$Salary + employees$Bonus

# Menampilkan beberapa kolom penting saja
employees[,c("Name","Salary","Bonus","Total_Salary")]

4 Loop

Loops allow a block of code to be executed repeatedly when processing datasets. They are useful when the same operation must be applied to multiple data records.

# Perulangan untuk mengecek setiap karyawan dalam dataset
for (i in 1:nrow(employees)) {

  # Mengecek apakah gaji karyawan lebih dari 6000
  if (employees$Salary[i] > 6000) {
    
    # Jika kondisi terpenuhi maka tampilkan nama dan gaji
    cat(paste0("Name: ", employees$Name[i],
               ", Salary: ", employees$Salary[i], "\n"))
  }

}

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

# Inisialisasi indeks awal
i <- 1

# Variabel penanda apakah manager sudah ditemukan
manager_found <- FALSE

# Perulangan berjalan selama indeks masih dalam jumlah data
# dan manager belum ditemukan
while (i <= nrow(employees) && manager_found == FALSE) {

  # Mengecek apakah posisi karyawan adalah Manager
  if (employees$Position[i] == "Manager") {

    # Jika ditemukan manager maka tampilkan informasi
    cat("Name:", employees$Name[i],
        ", Position:", employees$Position[i],
        "(Stop here)\n")

    # Mengubah status menjadi TRUE agar loop berhenti
    manager_found <- TRUE

  } else {

    # Jika bukan manager maka tetap tampilkan informasi
    cat(paste0("Name: ", employees$Name[i],
               ", Position: ", employees$Position[i], "\n"))

  }

  # Menambah nilai indeks untuk berpindah ke baris berikutnya
  i <- i + 1
}

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

5 Loop Control

Loop control statements modify the normal execution of loops by stopping or skipping certain iterations.

# Perulangan untuk mengecek gaji setiap karyawan
for (i in 1:nrow(employees)) {

  # Jika ditemukan gaji lebih dari 10000
  if (employees$Salary[i] > 10000) {

    # Tampilkan pesan bahwa perulangan dihentikan
    cat("(Stopped because", employees$Name[i],
        "has a salary above 10,000)\n")

    # Menghentikan loop secara langsung
    break
  }

  # Jika kondisi belum terpenuhi maka tampilkan data
  cat(paste0("Name: ", employees$Name[i],
             ", Salary: ", employees$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)

# Perulangan untuk mengecek performa setiap karyawan
for (i in 1:nrow(employees)) {

  # Jika performa karyawan Average
  if (employees$Performance[i] == "Average") {

    # Tampilkan pesan bahwa data dilewati
    cat("(Skipped because", employees$Name[i],
        "has an Average performance)\n")

    # Melewati iterasi saat ini dan lanjut ke iterasi berikutnya
    next
  }

  # Jika bukan Average maka tampilkan nama dan performa
  cat(paste0("Name: ", employees$Name[i],
             ", Performance: ", employees$Performance[i], "\n"))
}

## Name: Bagas, Performance: Good
## Name: Joan, Performance: Very Good
## (Skipped because Alya has an Average performance)
## Name: Dwi, Performance: Good
## Name: Nabil, Performance: Very Good

6 Conclusion

This report demonstrates the practical implementation of syntax and control flow concepts in the R programming language. Through conditional statements and loop structures, programs can perform logical decision-making and repetitive operations when processing datasets.

The exercise illustrates how conditional statements determine employee bonuses based on performance, while loops allow the program to iterate through data records efficiently. Additionally, loop control statements such as break and next provide flexibility by allowing loops to terminate early or skip certain iterations.

Understanding syntax and control flow is essential for building structured and efficient programs, especially in data science where large datasets must be processed systematically.