STA 9750 Mini-Project #04: Exploring Recent US Political Shifts
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Due Dates
- Released to Students: 2025-04-24
- Initial Submission: 2025-05-07 11:45pm ET on GitHub and Brightspace
-
Peer Feedback:
- Peer Feedback Assigned: 2025-05-08 on GitHub
- Peer Feedback Due: 2025-05-14 11:45pm ET on GitHub
Estimated Time to Complete: 5 Hours
Estimated Time for Peer Feedback: 1 Hour
Introduction
Welcome to Mini-Project #04! Following the 2024 US Presidential election, the New York Times published a highly influential map depicting a national political shift to the right. This map, and associated commentary, portended a national “vibe shift”, which has already (arguably) been reflected throughout corporate, educational, and government sectors. In this mini-project, you will explore this national political shift, determining the extent (if any) it may have been over- or under-exaggerated and developing appropriate visualizations.
For this mini-project, you will pay the roll of a partisan “Talking Head” aligned with one of the two major US political parties. You will find facts supporting your party’s narrative and prepare related visualizations. For this project, you are encouraged (but not required) to adopt the opposite side of your own political beliefs.1 For example, if you adopt a Republican perspective, you may choose to highlight the fact that 89% of counties nationwide voted more Republican than in 2020; if you adopt a Democratic perspective, you may retort that “land doesn’t vote” and that a population weighted measure of national shift shows a far smaller effect.
Note that, compared to previous mini-projects, the scope of this project is relatively smaller: in light of this, and the more advanced skills you have spent the past 3 months developing, this mini-project should be the least difficult of the course. At this point in the course, you should be spending the majority of your out-of-class hours on your Course Project.
This mini-project completes our whirlwind tour of several different forms of data-driven writing:
- White-Papers and Policy Briefs (MP#01)
- Quantifying Successful Public Policy Endeavors (MP#02)
- Using Data to Supplement the Creative Process (“assistive intelligence”, MP#03)
- Using Data to Support a Pre-Ordained Conclusion (this mini-project)
There are, of course, many other ways that data can be used to generate and communicate insights, but hopefully this “hit parade” has exposed you to many of the ways that you can use data to evaluate complex qualitative and quantitative claims outside of a binary classroom “correct/incorrect” structure. The tools of quantitative analysis and communication you have developed in this course can be used in essentially infinite contexts– we have only scratched the surface–and I’m excited to see what you do in the remainder of this course, in your remaining time at Baruch, and in your future careers.
Student Responsbilities
Recall our basic analytic workflow and table of student responsibilities:
- Data Ingest and Cleaning: Given a single data source, read it into
Rand transform it to a reasonably useful standardized format. - Data Combination and Alignment: Combine multiple data sources to enable insights not possible from a single source.
- Descriptive Statistical Analysis: Take a data table and compute informative summary statistics from both the entire population and relevant subgroups
- Data Visualization: Generate insightful data visualizations to spur insights not attainable from point statistics
- Inferential Statistical Analysis and Modeling: Develop relevant predictive models and statistical analyses to generate insights about the underlying population and not simply the data at hand.
| Ingest and Cleaning | Combination and Alignment | Descriptive Statistical Analysis | Visualization | |
|---|---|---|---|---|
| Mini-Project #01 | ✓ | |||
| Mini-Project #02 | ✓ | ✓ | ½ | |
| Mini-Project #03 | ½ | ✓ | ✓ | ✓ |
| Mini-Project #04 | ✓ | ✓ | ✓ | ✓ |
In this mini-project, you are in charge of the whole pipeline, from TBD to TBD. The rubric below evaluates your work on all aspects of this project.
Rubric
STA 9750 Mini-Projects are evaluated using peer grading with meta-review by the course GTAs. Specifically, variants of the following rubric will be used for the mini-projects:
| Course Element | Excellent (9-10) | Great (7-8) | Good (5-6) | Adequate (3-4) | Needs Improvement (1-2) | Extra Credit |
|---|---|---|---|---|---|---|
| Written Communication | Report is well-written and flows naturally. Motivation for key steps is clearly explained to reader without excessive detail. Key findings are highlighted and appropriately given context. | Report has no grammatical or writing issues. Writing is accessible and flows naturally. Key findings are highlighted, but lack suitable motivation and context. | Report has no grammatical or writing issues. Key findings are present but insufficiently highlighted. | Writing is intelligible, but has some grammatical errors. Key findings are obscured. | Report exhibits significant weakness in written communication. Key points are difficult to discern. | Report includes extra context beyond instructor provided information. |
| Project Skeleton | Code completes all instructor-provided tasks correctly. Responses to open-ended tasks are particularly insightful and creative. | Code completes all instructor-provided tasks satisfactorially. | Response to one instructor provided task is skipped, incorrect, or otherwise incomplete. | Responses to two instructor provided tasks are skipped, incorrect, or otherwise incomplete. | Response to three or ore instructor provided tasks are skipped, incorrect, or otherwise incomplete. | Report exhibits particularly creative insights drawn from thorough student-initiated analyses. |
| Formatting & Display |
Tables and figures are full ‘publication-quality’. Report includes at least one animated visualization designed to effectively communicate findings. |
Tables have well-formatted column names, suitable numbers of digits, and attractive presentation. Figures are ‘publication-quality’, with suitable axis labels, well-chosen structure, attractive color schemes, titles, subtitles, and captions, etc. |
Tables are well-formatted, but still have room for improvement. Figures are above ‘exploratory-quality’, but do not reach full ‘publication-quality’. |
Tables lack significant ‘polish’ and need improvement in substance (filtering and down-selecting of presented data) or style. Figures are suitable to support claims made, but are ‘exploratory-quality’, reflecting minimal effort to customize and ‘polish’ beyond |
Unfiltered ‘data dump’ instead of curated table. Baseline figures that do not fully support claims made. |
Report includes interactive (not just animated) visual elements. |
| Code Quality |
Code is (near) flawless. Code passes all |
Comments give context of the analysis, not simply defining functions used in a particular line. | Code has well-chosen variable names and basic comments. | Code executes properly, but is difficult to read. | Code fails to execute properly. | Code takes advantage of advanced Quarto features to improve presentation of results. |
| Data Preparation | Data import is fully-automated and efficient, taking care to only download from web-sources if not available locally. | Data is imported and prepared effectively, in an automated fashion with minimal hard-coding of URLs and file paths. | Data is imported and prepared effectively, though source and destination file names are hard-coded. | Data is imported in a manner likely to have errors. | Data is hard-coded and not imported from an external source. | Report uses additional data sources in a way that creates novel insights. |
Note that this rubric is designed with copious opportunities for extra credit if students go above and beyond the instructor-provided scaffolding. Students pursuing careers in data analytics are strongly encouraged to go beyond the strict ambit of the mini-projects to i) further refine their skills; ii) learn additional techniques that can be used in the final course project; and iii) develop a more impressive professional portfolio.
Because students are encouraged to use STA 9750 mini-projects as the basis for a professional portfolio, the basic skeleton of each project will be released under a fairly permissive usage license. Take advantage of it!
Submission Instructions
After completing the analysis, write up your findings, showing all of your code, using a dynamic quarto document and post it to your course repository. The qmd file should be named mp04.qmd so the rendered document can be found at docs/mp04.html in the student’s repository and served at the URL:2
https://<GITHUB_ID>.github.io/STA9750-2025-SPRING/mp04.html
Once you confirm this website works (substituting <GITHUB_ID> for the actual GitHub username provided to the professor in MP#00 of course), open a new issue at
https://github.com/<GITHUB_ID>/STA9750-2025-SPRING/issues/new.
Title the issue STA 9750 <GITHUB_ID> MiniProject #04 and fill in the following text for the issue:
Hi @michaelweylandt!
I've uploaded my work for MiniProject #**04** - check it out!
https://<GITHUB_ID>.github.io/STA9750-2025-SPRING/mp04.htmlOnce the submission deadline passes, the instructor will tag classmates for peer feedback in this issue thread.
Additionally, a PDF export of this report should be submitted on Brightspace. To create a PDF from the uploaded report, simply use your browser’s ‘Print to PDF’ functionality.
NB: The analysis outline below specifies key tasks you need to perform within your write up. Your peer evaluators will check that you complete these. You are encouraged to do extra analysis, but the bolded Tasks are mandatory.
NB: Your final submission should look like a report, not simply a list of facts answering questions. Add introductions, conclusions, and your own commentary. You should be practicing both raw coding skills and written communication in all mini-projects. There is little value in data points stated without context or motivation.
Mini-Project #04: Exploring Recent US Political Shifts
For this project, we are going to need to collect election result data. As of the time I’m writing this project, it’s surprisingly hard to find free, clean county-level election data, so we are going to use our new web-scraping skills to extract this data from Wikipedia.
High-quality R packages exist to help work with some of the data providers used in this mini-project. Despite this, you may not use these packages: the learning goal of this mini-project is to practice scraping and importing data from various web based services. Using R packages instead of scraping the data “by hand” will not provide this practice. By the same token, do not use alternative data sources. (That said, if you find better county level records of the 2024 election, please let me know. I found this surprisingly difficult data to locate.)
County Shapes: US Census Bureau
Begin by downloading a shapefile containing US county (and equivalent) boundaries from the US Census Bureau. Note that this file is available in three resolutions. I recommend the finest resolution you can to produce the most accurate graphics, but if your compute is struggling with high levels of detail, you should consider falling back to the coarser resolutions.
Write code to download an appropriate US County shapefile from the Census Bureau website. To be responsible, your download code should:
- Only download the file if it is not already present
- Create a directory titled
data/mp04if one is not already present - Save the file in
data/mp04and, if necessary, decompress it. - Use built-in
Rfunctions likedownload.file
See previous mini-projects for guidance.
2024 County-Level Election Results
We will next obtain county-level election results for each of the 50 US states. While most states make this data available via the Secretary of State or an Election Board, these sites are non-uniform and tricky to use in an automated fashion. Instead, we will take our results from Wikipedia. For each state, there is a Wikipedia page describing the 2024 US Presidential Election results in that state; e.g., New York.
Each of these pages has a table of county-level results which we need to extract.
Using httr2 and rvest download county-level election results from Wikipedia for each of the 50 US States.
This is a moderately advanced web-scraping exercise, so I recommend you approach it by writing a function which does the following.
- Take a US state name as input
- Construct an appropriate request using
httr2::requestandreq_url_path()3 - Performs the request, saving the result locally to avoid unnecessary repeated downloads
- Extracts all tables into a list object
- Selects only the table with a column titled
County - Appropriately cleans and parses the table
- Adds a column with the state name
Note that you will have to deal with various additional minor irregularities not described above. For instance, at least one US State uses a term other than “county” to describe the relevant unit of government.
The code used to extract EIA State Energy Profiles in an early mini-project may be somewhat helpful here.
2020 County-Level Election Results
Next, modify your code to extract results for the 2020 election.
Modify your code from Task 2 to acquire 2020 US Presidential Election Results.
Combine Data and Perform Initial Analyses
At this point, we have all of the data needed to complete this mini-project (though you are, as always, welcome to download additional data you find helpful.) Combine the three data files (county shapes, 2020 results, 2024 results) and use them to answer the following questions.
Answer the following questions using the combined data sources. As always, as you do this initial analysis, use it as an opportunity to verify that your data import and cleaning was accurate.
- Which county or counties cast the most votes for Trump (in absolute terms) in 2024?
- Which county or counties cast the most votes for Biden (as a fraction of total votes cast) in 2020?
- Which county or counties had the largest shift towards Trump (in absolute terms) in 2024?
- Which state had the largest shift towards Harris (or smallest shift towards Trump) in 2024? (Note that the total votes for a state can be obtained by summing all counties in that state.)
- What is the largest county, by area, in this data set?
- Which county has the highest voter density (voters per unit of area) in 2020?
- Which county had the largest increase in voter turnout in 2024?
Reproduce NYT Figure
Having confirmed our data is mainly reliable, we are now ready to reproduce the NYT Figure that initially motivated this project.
To do so, you need to:
- Compute the shift (as a percentage of votes cast) rightwards for each county.
- Modify the geometry file to put Hawaii and Alaska in a more reasonable position for visualization. This StackOverflow Answer has useful suggestions.
- Draw a map of the US with the modified geometry. 4
- Compute the center point (“centroid”) of each county.
- Add an arrow for each county, located at its centroid. The length of the arrow should be proportional to the shift and the direction should indicate whether that shift was rightward or leftward.
You may need to play around with the parameters defining each arrow to create an attractive plot.
Reproduce the NYT County Shift figure using the Census shapefiles and Wikipedia-extracted election results.
Additional Data Analysis
Further analyze this data and come up with three partisan “talking points” (and associated figures) to support “your side”.
Your analysis should include at least two computationally-intensive statistical tests, e.g., “did the median county shift by more than 5%?”. You may implement these directly or with help of the infer package.
Deliverable: Partisan Talking Points
Taking the position of a “partisan hack”, analyze the 2024 election and argue that Trump’s victory was either a nationwide seismic shift portending a new era in American politics (if you adopt a Republican persona) or a narrow win made to look more meaningful than it actually was by accidents of geometry and political organization (if you adopt a Democratic persona).
You should write in the style of an “op-ed” or a television commercial script (your choice) attempting to influence national post-election discourse in support of your chosen side.
Have some fun with this! You can be as shameless and over-the-top as you want. After all, it was a politician who (anecdotally) gave us the phrase:
and a (rather controversial) journalist who gave us
Extra Credit Opportunities
There are no structured opportunities for extra credit on this mini-project beyond those stated in the rubric. As always, evaluators should assign appropriate extra credit (no more than 5 points total) for particularly creative insights, use of additional data sources, attractive figures, etc.
This work ©2025 by Michael Weylandt is licensed under a Creative Commons BY-NC-SA 4.0 license. 
Footnotes
It is well-documented that political partisans subject their opponents to far more rigorous critique than their co-partisans. By adopting a point-of-view opposite your own, you will not only (hopefully) become more sympathetic to and informed about alternative political views, but you will also perform a more rigorous analysis. Still, since I do not know and do not want to know your true political views, this is only a suggestion and cannot be enforced.↩︎
Throughout this section, replace
<GITHUB_ID>with your GitHub ID from Mini-Project #00, making sure to remove the angle brackets. Note that the automated course infrastructure will be looking for precise formatting, so follow these instructions closely.↩︎Note that Wikipedia allows spaces in URLs here, so you can use
https://en.wikipedia.org/wiki/2024 United States presidential election in New Yorkand it will be automatically corrected tohttps://en.wikipedia.org/wiki/2024_United_States_presidential_election_in_New_York.↩︎A map with lines for each county may be a bit too dense to read. You might find it more visually appealing to take the “union” of counties to get states for creating the base map layer.↩︎