The goal of this project is to write a data story on philosophy using the dataset for the Philosophy Data Project. Applying data mining, statistical analysis and visualization, students should derive interesting findings in this collection of philosophy texts and write a "data story" that can be shared with a general audience.
• The data sets can be found at https://www.kaggle.com/kouroshalizadeh/history-of-philosophy.
In this project I carried out an exploratory data analysis (EDA) of philosophy texts and write a blog on interesting findings about theology topic in philosophy from my analysis.
I also explored the text corpus using tools from data mining, statistical analysis and visualization, etc, and write a blog post using R Notebook.
Related documents can also be found in Text Mining Project folder
In this project, working in teams, I created a R shiny app for exploring and visualizing a previously under-explored topic about COVID-19 in New York City or United States.
The COVID-19 pandemic has had a drastic impact on all of us whether it’s losing a job, getting behind on rent or mortgage payments, or struggling to bring food to the table. It has been even harder for low income families to have access to the resources they need to stay safe and healthy. In an effort to help low income families, specifically the youth, as they might find it challenging to navigate these unprecedented times, we created this app. We’re hopeful that our aggregated resources to covid and flu vaccination sites, food, shelter, after school centers, job/internship opportunities, and crime rates will play a small but crucial role in supporting the youth of New York City.
In this app, We mainly developed 3 functions, the interactive map, statistical analysis and about.
Interactive Map:
Locations for Covid vaccination, flu shots, wifi, food centers, drop in centers, youth shelters, job/internship centers Heatmap distribution of covid cases and crime Statistical Analysis:
Visualization of relationships between crime status and Covid and housing status and Covid Plausible explanations and conclusions for trends observed About:
Links to data sources, data disclaimer, app contributors