Part 1 Of Your Data Visualization Project Last Updated May 2

Part 1 Of Your Data Visualization Projectlast Updated May 2021in The R

This assignment involves developing a data visualization project focused on analyzing consumer complaint data from the CFPB database, specifically examining whether consumers in big cities or higher-income communities receive preferential treatment. Students are to formulate a clear, testable research question based on provided data parameters and prepare a project brief addressing key aspects such as importance, clarification needs, potential pitfalls, audience expectations, project constraints, and the scope of visualizations. The process includes working with data fields such as date_received, product, issue, company, state, zip_code, submitted_via, date_sent_to_company, company_response_to_consumer, timely_response, complaint_id, delay, population, and median_household_income. While the initial focus is on understanding, cleaning, and exploring the data, students will ultimately create at least 10 visualizations from different perspectives and document their interpretations, all while updating and refining their project plan based on data investigation.

Paper For Above instruction

The investigation of whether consumers in large cities or higher-income communities experience preferential treatment by financial institutions is a pertinent question in consumer rights and financial fairness research. This analysis leverages data from the CFPB complaint database, which contains consumer reports related to various financial products and issues. The core objective is to identify potential disparities or biases in service delivery, using specific geographical and economic indicators such as population and median household income associated with consumer ZIP codes.

Introduction

Understanding how geographic and economic factors influence consumer experiences requires a thorough examination of complaint data. By analyzing delays between complaint receipt and resolution, alongside indicators like population size and income levels, we aim to uncover patterns that suggest whether certain communities face longer wait times or receive different levels of attention from financial service providers. Given the sensitivity of this topic, the analysis must be rigorous, transparent, and multifaceted, incorporating various visualization techniques to reveal nuanced relationships.

Significance of the Study

This project holds importance because it addresses potential systemic inequalities in financial service delivery, which can inform policy-making and promote fair lending practices. Unearthing disparities may lead to targeted interventions, consumer protections, or reforms that mitigate bias based on geographic or income status. Additionally, the study contributes to the broader discourse on social equity and access to financial resources, aligning with consumer rights advocacy and financial inclusion goals.

Clarification and Scope

Clarification is necessary around defining what constitutes a “big city” versus a “higher-income community.” For this analysis, community size could be approximated through population data linked to ZIP codes, while income levels are derived from median household income figures. The focus is confined to complaints received in 2020 from consumers in Vermont, based on the sample data and the specified state, focusing on delays in complaint resolution as the dependent variable. Fields like zip_code are acknowledged but deemed outside the immediate scope unless used for validation or detailed geographic breakdowns.

Potential Pitfalls

Challenges include data inconsistencies such as incorrect data types, missing values, or anomalies like delays of zero or excessively long durations (>50 days). Misclassification of geographic or income data, as well as potential biases in complaint submission or reporting, could skew results. Also, the choice of variables and the scope of visualizations should avoid oversimplification or overgeneralization; multiple perspectives are necessary to provide a comprehensive analysis.

Audience Expectations and Tools

The primary audience comprises policy analysts, consumer advocates, and financial regulators expecting evidence-based insights into disparities. Visualizations should be clear, well-labeled, and supported by interpretations explaining what each figure depicts and its relevance. Software limitations (such as restrictions on tools) are acknowledged, with R and RStudio being the presumed platforms. The final presentation will include a minimum of 10 visualizations, each examining different factors or relationships, culminating in a narrative that synthesizes findings across views.

Visualization Strategy

The visualizations will explore relationships between population size, median household income, and complaint delays at different levels—overall, by complaint issue, or by company response. Multiple perspectives include scatterplots, boxplots, bar charts, and grouped summaries, providing insights into distribution, correlation, and potential disparities. The intention is to produce visual evidence supporting or refuting the hypothesis of preferential treatment, with interpretations highlighting whether higher-income or larger communities experience shorter delays or different complaint handling patterns.

Conclusion

This project emphasizes a holistic approach, prioritizing data understandings and observations over mere visualization production. By developing detailed, interpretative visualizations from diverse perspectives, it seeks to uncover meaningful patterns tied to geographic and socioeconomic variables. The culmination will be a comprehensive report that synthesizes findings, considerations for data limitations, and implications for consumer protection policies.

References

• Consumer Financial Protection Bureau. (n.d.). Consumer complaint database API docs [Data set and code book]. Office of Civil Rights. Retrieved April 28, 2021, from https://www.consumerfinance.gov/data-research/consumer-complaints/data-browser/
• Rozzi, G. (2021). Data & functions for working with US zip codes. GitHub. https://github.com/rozzi/us-zip-codes
• Kirk, A. (2019). Data analysis and visualisation: A handbook for data driven design (2nd ed.). Sage.
• Jee, K. (2020, April 3). Data science project from scratch - part 1 (project planning) [Video]. YouTube.
• Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
• R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/
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