This repository contains an exploratory data analysis (EDA) project focused on understanding unemployment trends in the United States during the year 2021, a period marked by the ongoing recovery from the COVID-19 pandemic. Using microdata from IPUMS USA, we investigate how unemployment distributions vary across states and demographic groups, as well as the influence of factors such as age, race, gender, and education level on employment outcomes.
Identify geographical disparities in unemployment rates across different U.S. states and discuss potential factors contributing to these differences.
Examine how race, gender, and age correlate with unemployment outcomes, highlighting groups that are disproportionately affected.
Investigate the relationship between educational attainment and unemployment, quantifying the protective effect of education during economic instability.
Provide insights that can inform policymakers, educators, and employers on strategies to address unemployment disparities and strengthen labor market resilience.
Orginial dataset we get from IPUMS USA, and the cleaned data set we generated for our analysis
Jupyter/R Markdown notebooks containing the exploratory analysis, visualization, and interpretation of results.
Renv files that can help users recontruct our library, which is located in "Renv Files" folder.
Clone the Repository:
git clone https://github.com/yourusername/your-repo-name.git
Set Up Environment: Install the required R packages before running the analysis notebooks.
A sample list of required packages includes:
tidyverse, maps, ggplot2, readr, viridis, dplyr, to import these libraries in R, use:
install.packages(c("tidyverse", "maps", "ggplot2", "readr", "viridis", "dplyr"))
Run the EDA Notebook: Open the .Rmd or .ipynb files in RStudio or Jupyter Notebook to reproduce the analysis. Execute code cells in sequence to replicate data preprocessing, visualization, and statistical modeling steps.
Review plots, charts, and summary tables to understand unemployment distributions by state and demographic factors. Check correlation outputs and regression summaries to see how strongly education correlates with unemployment rates. Read the conclusion section to gain insights into policy implications and potential areas for further research. Contributing Contributions to improve the code, add new analyses, or extend the dataset are welcome. Please submit a pull request or open an issue if you encounter bugs, have suggestions, or wish to contribute new features.
For questions, comments, or collaboration inquiries, please reach out to XiPu Wang at xipu.wang@emory.edu. This project is for QTM302W at Emory University, the authors include: Nora Zhou, Mandy Zhou, Ruihan Zhang, Xipu Wang.