Economic Inequality in the United States: A Data-Driven Exploration
R | Sentiment Analysis | Text Mining | Data Preprocessing | Machine Learning | K-means Clustering | API Integration | Quanteda | ggplot2 | Data Visualization | Economic Forecasting | Risk Management | Parallel Processing | NLP | Business Analytics
Project Description
This project provides a comprehensive analysis of income and wealth distribution in the United States by leveraging datasets and APIs from sources such as the Census Bureau and the Federal Reserve. Using advanced data analytics techniques and economic modeling, the project visualizes trends in wealth inequality across different population segments, focusing on the growing disparity between the wealthiest 0.1% and the bottom 50%.
Through interactive visualizations, animated plots, and geospatial data, this analysis highlights the relationship between economic policies, such as Quantitative Easing (QE), and their effects on income distribution. The integration of Census API calls, combined with real-time data access, enabled the retrieval of county-level Gini Index and median income figures over multiple years, allowing for a granular view of economic inequality.
Project Skills
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Data Collection & Integration: Called Census API datasets (e.g., Gini Index, Median Income) across multiple years to construct a longitudinal dataset. This required proficiency in API handling, especially with the U.S. Census data (ACS5), and the merging of multiple datasets into a cohesive analysis.
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Data Cleaning & Transformation: Cleaned and reshaped data using R’s tidyverse and dplyr libraries, converting raw datasets into actionable insights by applying transformation techniques such as pivoting, renaming columns, and filtering out non-relevant observations.
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Economic Analysis: Applied economic principles, including Gini ratios and wealth distribution metrics, to contextualize the impact of fiscal and monetary policies, providing insights into wealth growth among different economic quantiles.
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Business Analytics: Utilized time-series forecasting, wealth distribution, and regression analysis to predict trends in income inequality, combining business strategy with data-driven decision-making tools to showcase how policies influence wealth accumulation across different socioeconomic groups.
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Geospatial Analysis: Incorporated spatial datasets to create county-level maps of income and wealth distribution using ggplot2, sf, and leaflet. This analysis allowed for a geographic representation of wealth inequality across the U.S., with particular focus on high-inequality counties.
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Visualization & Reporting: Generated dynamic and animated plots using gganimate and plotly, offering an engaging and interactive way to explore complex datasets. These visualizations were essential for clearly communicating the economic trends over time.
Project Demostrates
This project demonstrates a high level of proficiency in data analytics, economic modeling, and strategic insights by integrating data from public sources and employing sophisticated methods to analyze wealth trends. His understanding of the intersection between economic policies and wealth distribution reflects his ability to use data-driven approaches to inform business strategy and economic decision-making. The project highlights Joseph's unique skill set in data manipulation, geospatial analysis, and economic forecasting, positioning him as a valuable asset in addressing complex challenges in the intersection of economics and data analytics.