📊 Grade Analysis Lab

Author: Sami Kader-Yettefti
Repository: r-101-grade-analysis

The goal of this lab is to analyze a real-world collection of grades obtained by students during a semester. The analysis explores performance patterns, attendance issues, and correlations using R and data visualization.

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📁 About the Project

This lab is part of a course assignment focused on data exploration and visualization in R. It answers a series of structured questions using a dataset of student grades and groups.

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📂 Repository Contents

• grades.csv — Dataset containing student IDs, group info, and grades.
• lab.qmd or lab.Rmd — Source document with all code and analysis.
• output.html — Rendered version of the lab with plots and tables.
• README.md — Project overview and instructions.

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🧰 Tools & Packages

• R and RStudio
• tidyverse: For data manipulation and plotting
• vroom: Fast CSV import
• here: File path management
• knitr: Nicely formatted tables
• stringr: String utilities
• quarto or rmarkdown: Reproducible report generation

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▶️ Getting Started

1. Clone the Repository

git clone https://github.com/kaderrsami/r-101-grade-analysis
cd r-101-grade-analysis

2. Open in RStudio Open the .Rproj file for a project-scoped environment.

3. Install Dependencies install.packages(c("vroom", "ggplot2", "dplyr", "tidyr", "knitr", "stringr", "here"))

4. Run or Render the Lab Report quarto::quarto_render("lab.qmd") # for Quarto

or

rmarkdown::render("lab.Rmd") # for R Markdown

🔍 Analysis Overview

Question	Focus
Q1	Load data
Q2–3	Summary stats + missing exam grades
Q4	Distribution of exam grades
Q5–6	Student count per group
Q7	Grade distribution by group (bar and boxplot)
Q8–9	Exam attendance per group
Q10–12	Missing grades per student
Q13–17	Missing MCQ analysis
Q18	Correlation between MCQ attendance and exam performance

📊 Sample Visualizations Includes:

Bar plots

Histograms

Box plots

Faceted charts

Dual-axis comparisons

All charts are made with ggplot2 and dynamically generated from the dataset.

📝 License & Contributions This lab is a personal academic project. If you find ways to improve the analysis, feel free to fork the repo and submit a pull request.

📌 Note You can view the rendered HTML file locally or host it on GitHub Pages if desired.