R Programming Case Study : Analyzing Student Performance
Introduction
R is a powerful programming language for statistical computing and data visualization. In this case study, we will explore a dataset containing student performance metrics, such as scores in math, reading, and writing. Our goal is to analyze the data to uncover insights, such as the relationship between study hours and exam scores, and visualize these findings using ggplot2.
By the end of this tutorial, you will learn how to:
- Load and explore a dataset in R using data frames.
- Perform basic data manipulation and summary statistics.
- Create insightful visualizations using ggplot2.
- Interpret the results to draw meaningful conclusions.
Prerequisites
Before we begin, ensure you have the following:
- R installed on your computer (download from CRAN).
- RStudio, an integrated development environment (IDE) for R (download from RStudio).
- Basic understanding of R syntax (e.g., variables, functions).
- The ggplot2 package installed. Install it by running install.packages(“ggplot2”) in R.
Step 1: Setting Up the Environment
First, let’s load the necessary libraries and create a sample dataset. For this case study, we’ll simulate a dataset since we don’t have an external file. If you have a real dataset, you can load it using read.csv() or similar functions.
# Load the ggplot2 library
library(ggplot2)
# Create a sample dataset
set.seed(123) # For reproducibility
students_data <- data.frame(
Student_ID = 1:50,
Gender = sample(c("Male", "Female"), 50, replace = TRUE),
Study_Hours = round(runif(50, 1, 10), 1),
Math_Score = round(rnorm(50, mean = 75, sd = 10)),
Reading_Score = round(rnorm(50, mean = 80, sd = 8)),
Writing_Score = round(rnorm(50, mean = 78, sd = 9))
)
# View the first few rows
head(students_data)Explanation:
- We load ggplot2 for visualizations.
- We create a data frame students_data with 50 rows, including columns for student ID, gender, study hours, and scores in math, reading, and writing.
- set.seed(123) ensures reproducibility of random numbers.
- head(students_data) displays the first six rows to verify the data structure.
Output (sample):
Student_ID Gender Study_Hours Math_Score Reading_Score Writing_Score
1 1 Male 6.3 76 82 79
2 2 Female 3.9 68 78 74
...Step 2: Exploring the Dataset
Before analyzing the data, let’s understand its structure and contents.
# Check the structure of the dataset
str(students_data)
# Summary statistics
summary(students_data)Explanation:
- str() shows the data types and a preview of each column (e.g., numeric, factor).
- summary() provides descriptive statistics like mean, median, min, and max for numeric columns and counts for categorical columns.
Output (sample):
'data.frame': 50 obs. of 6 variables:
$ Student_ID : int 1 2 3 ...
$ Gender : chr "Male" "Female" "Male" ...
$ Study_Hours : num 6.3 3.9 7.8 ...
$ Math_Score : num 76 68 82 ...
$ Reading_Score: num 82 78 85 ...
$ Writing_Score: num 79 74 81 ...
Student_ID Gender Study_Hours Math_Score Reading_Score Writing_Score
Min. : 1.00 Length:50 Min. :1.10 Min. :55.0 Min. :62.0 Min. :58.0
1st Qu.:13.25 Class :character 1st Qu.:3.33 1st Qu.:68.0 1st Qu.:75.0 1st Qu.:72.0
Median :25.50 Mode :character Median :5.45 Median :75.0 Median :80.0 Median :78.0
Mean :25.50 Mean :5.49 Mean :74.6 Mean :79.8 Mean :77.9
...Insights:
- The dataset has 50 students with a mix of male and female students.
- Study hours range from 1.1 to 9.9 hours, with an average of 5.49 hours.
- Math, reading, and writing scores are approximately normally distributed, with means around 75, 80, and 78, respectively.
Step 3: Data Manipulation
Let’s perform some basic data manipulation to prepare the data for analysis. We’ll calculate the average score for each student and categorize study hours into low, medium, and high.
# Calculate average score
students_data$Average_Score <- rowMeans(students_data[, c("Math_Score", "Reading_Score", "Writing_Score")])
# Categorize study hours
students_data$Study_Category <- cut(students_data$Study_Hours,
breaks = c(0, 3, 6, 10),
labels = c("Low", "Medium", "High"),
include.lowest = TRUE)
# View updated data
head(students_data)Explanation:
- rowMeans() computes the average of math, reading, and writing scores for each student.
- cut() categorizes Study_Hours into three groups: Low (0-3 hours), Medium (3-6 hours), and High (6-10 hours).
- The updated data frame now includes Average_Score and Study_Category.
Output (sample):
Student_ID Gender Study_Hours Math_Score Reading_Score Writing_Score Average_Score Study_Category
1 1 Male 6.3 76 82 79 79.00 High
2 2 Female 3.9 68 78 74 73.33 Medium
...Step 4: Data Analysis
Let’s answer some questions using the dataset:
- Does gender affect average scores?
- How do study hours relate to average scores?
Gender vs. Average Score
# Summary statistics by gender
aggregate(Average_Score ~ Gender, data = students_data, FUN = mean)Output (sample):
Gender Average_Score
1 Female 77.58333
2 Male 76.89286Insight: The average scores for female and male students are similar, with females slightly higher (77.58 vs. 76.89).
Study Hours vs. Average Score
# Summary statistics by study category
aggregate(Average_Score ~ Study_Category, data = students_data, FUN = mean)Output (sample):
Study_Category Average_Score
1 Low 75.58333
2 Medium 76.83333
3 High 78.41667Insight: Students who study more (High category) tend to have higher average scores (78.42) compared to those in the Low (75.58) and Medium (76.83) categories.
Step 5: Visualizations with ggplot2
Now, let’s create visualizations to make our findings more intuitive.
1. Boxplot: Average Score by Gender
ggplot(students_data, aes(x = Gender, y = Average_Score, fill = Gender)) +
geom_boxplot() +
labs(title = "Average Score by Gender",
x = "Gender",
y = "Average Score") +
theme_minimal()Explanation:
- ggplot() initializes the plot with the dataset and aesthetic mappings (aes).
- geom_boxplot() creates a boxplot to show the distribution of average scores by gender.
- labs() adds titles and labels.
- theme_minimal() applies a clean visual theme.
Output: A boxplot showing the spread of average scores for male and female students, confirming similar distributions with slight differences in medians.
2. Scatter Plot: Study Hours vs. Average Score
ggplot(students_data, aes(x = Study_Hours, y = Average_Score, color = Study_Category)) +
geom_point(size = 3) +
labs(title = "Study Hours vs. Average Score",
x = "Study Hours",
y = "Average Score") +
theme_minimal()Explanation:
- geom_point() creates a scatter plot with study hours on the x-axis and average score on the y-axis.
- Points are colored by Study_Category to highlight the categories.
- size = 3 makes points more visible.
Output: A scatter plot showing a positive trend: higher study hours are associated with higher average scores, especially in the High category.
3. Bar Plot: Average Score by Study Category
ggplot(students_data, aes(x = Study_Category, y = Average_Score, fill = Study_Category)) +
geom_bar(stat = "summary", fun = "mean") +
labs(title = "Average Score by Study Category",
x = "Study Category",
y = "Average Score") +
theme_minimal()Explanation:
- geom_bar(stat = “summary”, fun = “mean”) computes the mean average score for each study category and displays it as a bar.
- fill colors the bars by study category.
Output: A bar plot confirming that the High study category has the highest average score, followed by Medium and Low.
Step 6: Saving the Dataset
To share or reuse the dataset, let’s save it as a CSV file.
write.csv(students_data, "student_performance.csv", row.names = FALSE)Explanation:
- write.csv() saves the data frame as a CSV file named student_performance.csv.
- row.names = FALSE excludes row numbers from the output file.
Step 7: Conclusion
In this case study, we analyzed a student performance dataset using R’s data frames and ggplot2. We learned that:
- Gender has a minimal impact on average scores, with females slightly outperforming males.
- Study hours have a positive correlation with average scores, with students in the High study category (6-10 hours) achieving the highest scores.
- Visualizations like boxplots, scatter plots, and bar plots helped us communicate these findings effectively.