Introduction

This report discusses the processing of an employee dataset using the R programming language in RStudio. The dataset contains various employee information, such as name, age, salary, job title, and work performance. The data is then analyzed using basic programming concepts such as conditional statements and looping structures. The objective of this assignment is to understand and apply conditional statements and looping techniques (for loops, while loops, break and continue) to analyze a dataset.

Dataset

# Membuat dataset

df <- data.frame(
  Id = c(1,2,3,4,5),
  Name = c("Bagus","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")
)

# Menampilkan data

df

1. Conditional Statements

# Menentukan bonus berdasarkan kinerja karyawan

df$Bonus <- ifelse(df$Performance == "Very Good", df$Salary * 0.20,
            ifelse(df$Performance == "Good", df$Salary * 0.10,
            ifelse(df$Performance == "Average", df$Salary * 0.05, 0)))

# Menampilkan data

df[, c("Id", "Name", "Salary", "Performance", "Bonus")]

2. For Loop

# Salary > 6000

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

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

3. While Loop

i <- 1

while(i <= nrow(df)){
  
  if(df$Position[i] == "Manager"){
    cat("Name:", df$Name[i], ", Position:", df$Position[i], "(Stop here)\n")
    break
  } else {
    cat("Name:", df$Name[i], ", Position:", df$Position[i], "\n")
  }
  
  i <- i + 1
}

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

4. Break

# Break saat salary > 10000

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

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

5. Continue atau Next

# Continue untuk skip average

for(i in 1:nrow(df)){
  
  if(df$Performance[i] == "Average"){
    cat('Alya is skipped because the performance is "Average"\n')
    next
  }
  
  cat("Name:", df$Name[i], ", Performance:", df$Performance[i], "\n")
}

## Name: Bagus , Performance: Good 
## Name: Joan , Performance: Very Good 
## Alya is skipped because the performance is "Average"
## Name: Dwi , Performance: Good 
## Name: Nabil , Performance: Very Good

Conclusion

Based on the analysis, the use of conditional statements and looping structures in R can assist in processing and displaying data according to desired conditions. By utilizing for loops, while loops, and controls such as break and next, the dataset analysis process can be performed more systematically and efficiently.

References

R for Data Science - Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.

R Programming for Data Science - Peng, R. D. (2016). R Programming for Data Science. Leanpub.

https://bookdown.org/dsciencelabs/data_science_programming/02-Syntax-and-Control-Flow.html

https://chatgpt.com/