Student Depression Analysis

Contents

• Project Overview
• Main Files
• Software Used
• Dataset
• Requirements
• Installation
• Usage

Project Overview

In this project, I conducted a comprehensive data analysis of depression among students using a large-scale survey dataset. The analysis was performed in R within a Jupyter Notebook as part of the Google Data Analytics Capstone project.

Main Files

• student_depression_analysis.ipynb – Jupyter Notebook containing the complete analysis, visualizations, and interpretations.
• student_depression_dataset.csv – The dataset containing the student depression data.
• LICENSE – MIT License governing use and distribution of this project.

Software Used

• Programming Language: R 4.4.3
• Environment: Jupyter Notebook

Dataset

The dataset used in this project is the Student Depression Dataset, sourced from Kaggle. It contains anonymized, self-reported survey responses from students across diverse academic and demographic backgrounds.

Requirements

Ensure the following libraries are installed:

• autofileIO (A custom package I created for automatically reading and writing files)
• dplyr
• janitor
• tidyr
• skimr
• ggplot2
• stringr
• forcats

Installation

1. Clone or Download the Repository

To get started, first clone or download this repository to your local machine.

To clone the repository using Git, run the following command in your terminal or CLI:

git clone https://github.com/ankito090/Student-Depression-Analysis.git

or

[Download the Repository]https://codeload.github.com/ankito090/Student-Depression-Analysis/zip/refs/heads/main)

2. Install R and Jupyter Notebook

Ensure that R and Jupyter Notebook are installed on your machine. If not:

• Download and install R from CRAN.

• To use R within Jupyter Notebook, install Anaconda, which includes Jupyter Notebook.

• After installing Anaconda, open Anaconda Navigator or launch a terminal and type:

  jupyter notebook
  

  This will open the Jupyter interface in your browser.

3. Add R Kernel to Jupyter

To enable R in Jupyter, follow these steps:

• Open R or RStudio

• Run the following in the R console:

  install.packages("IRkernel")
  IRkernel::installspec(user = FALSE)
  

  You should now see R as a kernel option when creating a new notebook in Jupyter.

4. Install Required R Packages

• In R or RStudio, install the required packages (if not already installed) by running:

  install.packages(c("dplyr", "janitor", "tidyr", "skimr", "ggplot2", "stringr", "forcats"))
  

• Also, install the custom autofileIO package used in this project by running:

  install.packages("devtools")
  devtools::install_github("ankito090/autofileIO")
  

Usage

Open your terminal or CLI and navigate to the root directory of the cloned or downloaded project using this command:

cd path/to/project-directory

Replace path/to/project-directory with the actual path where the project is saved.

Open and run the notebook using Jupyter:

jupyter notebook student_depression_analysis.ipynb