Practicum DS Programming Week 4

Independent Practice: Conditional Statements and Loops in Python & R

1 Employee Dataset

The following dataset represents a simple employee record used for this exercise. It includes basic information such as employee ID, name, age, salary, position, and performance evaluation. This dataset will be used to demonstrate conditional statements and loop operations in R.

2 Conditional Statements

Conditional statements allow a program to execute different actions depending on specific conditions. In programming, this logic is commonly implemented using structures such as if, else if, and else statements.

2.1 Bonus Rules

Establish employee bonus rules based on performance

In this exercise, conditional logic is applied to determine the bonus percentage for each employee based on their performance evaluation. The bonus rules used in this analysis are presented in the following table:

Performance	Bonus
Very Good	20% of salary
Good	10% of salary
Average	5% of salary

2.2 Determine Bonus Levels

Determine bonus employee's based on employee performance

The bonus calculation is implemented in R using the case_when() function from the dplyr package, which works similarly to multiple if–else conditions. Bonus for each employee is calculated by multiplying the bonus percentage by the employee's salary.

# menghitung bonus berdasarkan performance
bonus_results <- employees %>%
  mutate(
    Bonus_Percent = case_when(
      Performance == "Very Good" ~ "20%",
      Performance == "Good" ~ "10%",
      TRUE ~ "5%"
    ),
    Bonus = case_when(
      Performance == "Very Good" ~ Salary * 0.20,
      Performance == "Good" ~ Salary * 0.10,
      TRUE ~ Salary * 0.05
    )
  )

The resulting bonus values for each employee are presented in the table below:

Interpretation:

The table shows the calculated bonus for each employee based on their performance evaluation. Employees with higher performance ratings receive larger bonus amounts, with Nabil receiving the highest bonus due to his high salary and “Very Good” performance.

2.3 Visualization

Interpretation:

The bar chart illustrates the distribution of bonus values among employees. It shows that employees with higher salaries and better performance ratings receive larger bonus amounts.

3 Loops (For and While)

Loops are used to repeat a block of code multiple times based on a specified condition. In this section, different looping techniques are applied to analyze the employee dataset. These examples demonstrate how loops can be used to filter data, stop execution based on certain conditions, and skip specific records.

3.1 For Loop

Task: Use a for loop to list employees with a salary greater than 6000.

A for loop in R is implemented using the for syntax to iterate through a sequence of values. In this exercise, the for(i in 1:nrow(employees)) loop is used to iterate through the employee dataset and display employees whose salary is greater than 6000.

high_salary_employees <- data.frame()   # membuat list kosong untuk menyimpan hasil

for(i in 1:nrow(employees)){            # melakukan perulangan untuk setiap baris data
  salary <- employees$Salary[i]         # mengambil nilai salary
  if(salary > 6000){                    # mengecek apakah salary lebih besar dari 6000
    high_salary_employees <- rbind(     # jika benar, data karyawan dimasukkan ke hasil
      high_salary_employees,
      employees[i, c("Name","Salary")]
    )
  }
}

The employees who meet this condition are displayed in the table below:

Interpretation:

The table shows employees whose salary is greater than 6000. Bagas is not included in the result because his salary (5000) does not satisfy the condition defined in the loop.

3.2 While Loop

Task: Use a while loop to display employees until a "Manager" is found.

A while loop in R uses the while syntax to repeatedly execute a block of code as long as a condition remains true. In this example, while(index <= nrow(employees)) is used to iterate through the dataset until an employee with the position "Manager" is found.

employees_until_manager <- data.frame()   # membuat tabel kosong untuk menyimpan hasil
index <- 1                                # membuat index awal
while(index <= nrow(employees)){          # menjalankan while loop selama index masih dalam jumlah data
  
  employee <- employees[index,]           # mengambil data karyawan berdasarkan index
  employees_until_manager <- rbind(       # menyimpan nama dan posisi ke tabel hasil
    employees_until_manager,
    employee[,c("Name","Position")]
  )

  if(employee$Position == "Manager"){     # mengecek apakah posisi karyawan adalah Manager
    break                                 # jika Manager ditemukan, loop berhenti
  }

  index <- index + 1                      # menambah index agar membaca data berikutnya
}

The employees listed during the iteration until a “Manager” is found are shown in the table below:

Interpretation:

The loop stops once an employee with the position “Manager” is found. Therefore, only employees from the beginning of the dataset until Dwi are displayed, while Nabil does not appear because the loop terminates when the Manager position is encountered.

3.3 Break Statement

Task: Use break to stop the loop when an employee with a salary above 10,000 is found.

The break statement in R is used to immediately terminate a loop when a specific condition is met. In this example, break stops the iteration once an employee with a salary greater than 10,000 is encountered.

employees_before_high_salary <- data.frame()  # membuat tabel kosong untuk menyimpan hasil

for(i in 1:nrow(employees)){                  # melakukan perulangan membaca setiap karyawan
  salary <- employees$Salary[i]               # mengambil nilai salary
  
  if(salary > 10000){                         # mengecek apakah salary lebih dari 10000
    break                                     # jika ditemukan salary > 10000, loop berhenti
  }

  employees_before_high_salary <- rbind(      # jika salary belum > 10000, data disimpan
    employees_before_high_salary,
    employees[i, c("Name","Salary")]
  )

}

The employees processed before the loop stops are shown in the table below.

Interpretation:

The loop stops when an employee with a salary greater than 10,000 is encountered. Since Nabil has a salary of 12,000, the loop terminates at that point, and therefore Nabil is not included in the result table.

3.4 Next (Continue)

Task: Use continue to skip employees with "Average" performance.

The next statement in R is used to skip the current iteration and continue with the next cycle of the loop. In this example, next is used to skip employees whose performance is classified as "Average".

filtered_employees <- data.frame()          # membuat tabel kosong untuk menyimpan hasil

for(i in 1:nrow(employees)){                # melakukan perulangan untuk setiap karyawan
  performance <- employees$Performance[i]   # mengambil nilai performance
  if(performance == "Average"){             # mengecek apakah performance adalah "Average"
    next                                    # jika Average, maka data dilewati
  }

  filtered_employees <- rbind(              # jika bukan Average, data disimpan
    filtered_employees,
    employees[i, c("Name","Performance")]
  )
}

The remaining employees after skipping those with “Average” performance are displayed in the table below:

Interpretation:

Employees with “Average” performance are skipped during the iteration using the next statement. As a result, Alya does not appear in the table because her performance rating is classified as “Average”.

Conclusion

This exercise demonstrates the use of conditional statements and looping structures in R for basic data processing tasks. By applying functions such as case_when(), for, and while, the program can evaluate conditions, perform repeated operations, and control the flow of execution using statements like break and next. These fundamental programming concepts are essential for automating data manipulation and analysis in R.

Practicum DS Programming Week 4