U18AII5202_23BAD119_EX1

Implementation of Data Charts

Name: Swetha P

Roll Number: 23BAD119

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Project Overview

This repository demonstrates the implementation of data visualization charts using R to analyze student performance data. The project focuses on computing internal assessment averages and visualizing subject-wise performance, test-wise trends, and grade distribution using ggplot2.

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Dataset Description

The dataset (1.student_performance.csv) contains student academic performance details, including:

• Roll Number
• Subject names
• Internal Test 1 marks
• Internal Test 2 marks
• Final grades

The data is used to compute average internal marks and generate meaningful visual insights.

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Software and Tools Used

• R Programming Language

• R version 4.4.1

• RStudio (IDE)

• Libraries:

  • ggplot2 – for data visualization
  • dplyr – for data manipulation
  • tidyr – for data reshaping

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Steps Performed

1. Loaded the required R libraries (ggplot2, dplyr, tidyr).
2. Imported the student performance dataset using read.csv().
3. Explored the dataset structure and summary statistics.
4. Calculated the average of Internal Test 1 and Internal Test 2 for each student.
5. Computed subject-wise mean internal marks.
6. Created a bar chart to visualize subject-wise average internal marks.
7. Generated a line chart to analyze performance trends across Internal Test 1 and Internal Test 2.
8. Calculated grade proportions and visualized them using a pie chart.

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Charts Implemented

• Bar Chart: Subject-wise Average Internal Marks
• Line Chart: Performance Trend Across Internal Tests
• Pie Chart: Final Grade Distribution

(The implemented charts are included seperately)

Conclusion

This project demonstrates how R can be effectively used for exploratory data analysis and visualization. The charts provide clear insights into subject-wise performance, test-wise trends, and grade distribution, making it easier to interpret academic data and support data-driven academic evaluation.

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