This repository contains two exploratory data analysis projects completed on Kaggle, focusing on financial markets and student mental health patterns using R and its key data analysis libraries. Both projects emphasize data cleaning, categorical and numerical analysis, and clear visual communication of insights through ggplot2 and the wider tidyverse ecosystem.
Tools used: dplyr, ggplot2.
This project explores mental health survey data from students to uncover patterns related to stress, academic pressure, and family background. Using dplyr, the data is cleaned and transformed, and categorical variables such as gender, academic pressure level, and family history are summarized and visualized. ggplot2 charts present distributions and relationships that make it easier to interpret how different factors may be associated with reported mental health issues.
The analysis highlights differences in reported mental health concerns across gender, levels of academic pressure, and presence of family history, supporting data‑driven discussions about where awareness campaigns and institutional support may be most needed.
Tools used: tidyverse, ggplot2
This project analyses historical stock price data for Google to understand overall trends, volatility, and potential investment signals. The workflow includes importing and cleaning the dataset, calculating daily returns, and generating moving averages to smooth price behaviour over time. Visualizations created in ggplot2 highlight how price and volatility evolved, helping to identify periods of rapid change and more stable phases in the stock’s history.
The analysis reveals clear uptrends and downtrends over specific periods, with moving averages and return distributions showing when the stock experienced higher volatility versus more stable performance, providing a concise view of historical risk and momentum.