Intro to Coding with R – Property Tax Analysis

Overview

This repository offers a look into my early coding journey as a data professional. In 2022, I volunteered on this project, defined by Cape Fear Collective, to explore property tax changes and housing cost burdens in New Hanover County, NC, using R for data analysis and visualization.

Important Notice

This repository is not intended to be duplicated, as some external files (such as datasets and credentials) have been excluded. Instead, it serves as a reference for my early experience with R and data analysis.

Key Topics Covered

• Data wrangling with tidyverse
• Creating interactive visualizations with plotly
• Working with geospatial data (sf, ggsflabel)
• Analyzing income, property taxes, and cost-burdened households

Contents

• eda_and_graphs.R – Code for data exploration and visualization
• outline_and_notes.md – Initial project planning and findings
• cfc_property_tax.pdf – Reference material related to the analysis

Background

This project stemmed from a broader initiative to examine housing affordability and tax burdens. The goal was to identify trends in property tax increases and their disproportionate impact on different neighborhoods.

Findings

The analysis found that from 2010 to 2019, property taxes in New Hanover County rose faster than incomes, with some neighborhoods feeling the impact much more than others. It highlights the connection between rising taxes and housing cost burden, which happens when more than 30% of income goes to housing. To put this into perspective, the one-pager translates tax increases into real-life costs by comparing them to essential expenses like groceries, healthcare, and transportation.