Grade Classification Based on Multiple Conditions in R

Grade classification based on multiple conditions is a common task in data analysis, often performed using programming languages like R. This process involves assigning grades or labels to data points based on predefined criteria or conditions. In this context, we’ll explore how to classify data into different grades using R, considering various conditions.

Concepts Related to the Topic:

Conditional Statements:

Conditional statements such as if, else if, and else, are fundamental in R and used to define the conditions for grade classification.

• if: It checks a condition and executes a block of code if the condition is TRUE.
• else if: Used in conjunction with if to specify additional conditions to be checked if the initial if condition is FALSE.
• else: Executed if none of the preceding conditions are TRUE.

score <- 85

if (score >= 90) {

grade <- “A”

} else if (score >= 80) {

grade <- “B”

} else {

grade <- “C”

}

Vectors and Data Frames:

You’ll often work with vectors or data frames in R to store and manipulate data. Understanding how to extract, manipulate, and update data is essential.

Vectors: Vectors are one-dimensional arrays that can store data of the same type. You can create vectors using functions like c() or by using sequences.

numbers <- c(1, 2, 3, 4, 5)

Data Frames: Data frames are two-dimensional tabular structures used to store data in rows and columns, similar to a spreadsheet. Each column in a data frame can be a different data type. You can create data frames using the data.frame() function.

data <- data.frame(Name = c(“Alice”, “Bob”, “Charlie”), Age = c(25, 30, 22))

Functions:

You might create functions to encapsulate the grade classification logic, making your code more modular and reusable.

Functions in R allow you to encapsulate a set of instructions into a reusable unit. They typically take inputs (arguments) and produce outputs. You can define your own functions using the function() keyword.

calculate_grade <- function(score) {

if (score >= 90) {

return(“A”)

} else if (score >= 80) {

return(“B”)

} else {

return(“C”)

}

}

Factor Levels:

If you’re working with categorical data, understanding factor levels can be important for grade assignments.

Factor levels are crucial when dealing with categorical data in R. A factor is a data structure used to represent categorical variables. Each level of a factor represents a unique category, and R assigns numerical values to each level internally. You can convert character vectors to factors using the factor() function.

colors <- c(“Red”, “Green”, “Blue”, “Red”, “Green”)

color_factor <- factor(colors)

You can access the levels and their associated numeric values using levels() and as.numeric() functions, respectively.

Steps Needed:

• Data Import: Start by importing your dataset into R. This could be in the form of a CSV, Excel, or other file formats.
• Data Exploration: Explore your data to understand its structure and identify the variables you want to use for grade classification.
• Define Grading Criteria: Determine the criteria for assigning grades. These criteria could be numeric thresholds, string matching, or a combination of conditions.
• Create a Function: Write a function that takes your data and applies the grading criteria to classify each data point into the appropriate grade.
• Apply the Function: Apply the function to your dataset, adding a new column that contains the assigned grades.
• Review and Verify: Double-check the results to ensure the grade classification is accurate and meets your expectations.

Grading Exam Scores

Criteria: A (90-100), B (80-89), C (70-79), D (60-69), F (0-59)

R

# Define the grading function for exam scores
grade_exam <- function(score) {
    if (score >= 90) {
        return("A")
    } else if (score >= 80) {
        return("B")
    } else if (score >= 70) {
        return("C")
    } else if (score >= 60) {
        return("D")
    } else {
        return("F")
    }
}

# Load the dataset
student_data <- data.frame(
  StudentName = c("Alice", "Bob", "Charlie", "David", "Eve"),
  ExamScore = c(85, 72, 93, 60, 78)
)

# Apply the grade_exam function to classify students based on exam scores
student_data$Grade <- sapply(student_data$ExamScore, grade_exam)

# Print the updated dataset with grades
print(student_data)

Output:

  StudentName ExamScore Grade
1       Alice        85     B
2         Bob        72     C
3     Charlie        93     A
4       David        60     D
5         Eve        78     C

Grading Customer Satisfaction

Criteria: Excellent (5), Good (4), Satisfactory (3), Poor (2), Very Poor (1)

R

# Define the grading function for customer ratings
grade_satisfaction <- function(rating) {
    if (rating == 5) {
        return("Excellent")
    } else if (rating == 4) {
        return("Good")
    } else if (rating == 3) {
        return("Satisfactory")
    } else if (rating == 2) {
        return("Poor")
    } else if (rating == 1) {
        return("Very Poor")
    } else {
        return("Invalid Rating")
    }
}

# Load the dataset
customer_ratings <- data.frame(
  CustomerName = c("John", "Mary", "Tom", "Emily", "Michael"),
  Rating = c(5, 4, 3, 2, 1)
)

# Apply the grade_satisfaction function to classify customers based on ratings
customer_ratings$RatingLabel <- sapply(customer_ratings$Rating, grade_satisfaction)

# Print the updated dataset with rating labels
print(customer_ratings)

Output:

  CustomerName Rating  RatingLabel
1         John      5    Excellent
2         Mary      4         Good
3          Tom      3 Satisfactory
4        Emily      2         Poor
5      Michael      1    Very Poor

Grade Calculation for Student

Here’s a more complex example of grade classification in R using nested conditional statements based on multiple conditions:

Suppose you are a teacher and want to classify student grades based on their performance in three subjects: Math, Science, and English. The grading criteria are as follows:

• To pass, a student must score at least 40 in each subject.
• If a student fails in any one subject, they automatically fail the entire course.
• If a student scores 90 or above in all three subjects, they receive an “A+.”
• If a student scores between 80 and 89 (inclusive) in all three subjects, they receive an “A.”
• If a student scores between 70 and 79 (inclusive) in all three subjects, they receive a “B.”
• If a student scores between 60 and 69 (inclusive) in all three subjects, they receive a “C.”
• If a student scores between 40 and 59 (inclusive) in all three subjects, they receive a “D.”
• If a student scores below 40 in any subject, they fail and receive an “F.”

Here’s how you can implement this complex grade classification in R:

R

# Define the student's scores in each subject
math_score <- 85
science_score <- 78
english_score <- 92

# Check if the student passed all subjects
if (math_score >= 40 && science_score >= 40 && english_score >= 40) {
  # Check for grade based on overall performance
  if (math_score >= 90 && science_score >= 90 && english_score >= 90) {
    final_grade <- "A+"
  } else if (math_score >= 80 && science_score >= 80 && english_score >= 80) {
    final_grade <- "A"
  } else if (math_score >= 70 && science_score >= 70 && english_score >= 70) {
    final_grade <- "B"
  } else if (math_score >= 60 && science_score >= 60 && english_score >= 60) {
    final_grade <- "C"
  } else if (math_score >= 40 && science_score >= 40 && english_score >= 40) {
    final_grade <- "D"
  } else {
    final_grade <- "F"
  }
} else {
  final_grade <- "F"  # Student failed in at least one subject
}

# Print the final grade
cat("Final Grade:", final_grade, "\n")

Output:

Final Grade: B 

In this example, we first check if the student has passed all subjects (scores >= 40 in each). If they pass, we then use nested conditional statements to determine the final grade based on their performance in all three subjects. If they fail in any subject, they receive an “F” for the entire course. Otherwise, they receive a grade based on their overall performance.

Conclusion

Using conditional statements, vectors, data frames, and functions, this inquiry explores grade categorization in R. It highlights the value of investigating data and developing evaluation standards. We develop a reusable function to classify data points, as shown by examples including test results and client happiness. Analysts and researchers can systematically classify and understand data by learning these approaches, enabling reasoned decision-making across a range of areas.