STA/OPR 9750 CristelKZuniga- MiniProject #03

[CristelKZuniga]

Hi @michaelweylandt!

Please check out my MP#03!

https://cristelkzuniga.github.io/STA9750-2024-FALL/mp03.html

Thank you!

[michaelweylandt]

Thanks @CristelKZuniga !

Peer Feedback:

• @ayrama
• @whaleron
• @Timbila614
• @katariasiddhi

It is now time for the peer feedback round for Mini-Project 03. Please review @CristelKZuniga's submission for this mini-project and provide peer feedback.

Using the rubric at https://michael-weylandt.com/STA9750/miniprojects/mini03.html#rubric, please grade their submission out of a total of 50 points. Note that this rubric is slightly different than that used for Mini-Projects 01 and 02.

For each of the five categories, please give them a separate score and provide a total (sum) score across the entire assignment. Feel free to assign extra credit if you feel it is warranted (following the rubric).

If you give a score of less than 5 for any category, please provide a suggestion for improvement. (You can also give suggestions for any element they did well - more feedback is always great!)

As you go through this peer feedback exercise, think about what you particularly like about this submission and how you can incorporate that approach in your future work. If something is particularly insightful or creative, give some kudos!

Evaluators: This should take you around 15 minutes per peer feedback. You are not required to engage in substantial back-and-forth with @CristelKZuniga, but you are of course welcome to initiate a discussion.

@CristelKZuniga: please engage fully with your peers. They are here to help you!

Submission URL should be: https://CristelKZuniga.github.io/STA9750-2024-FALL/mp03.html

Feel free to link to other repos, the course documentation, or other useful examples.

Thanks! @michaelweylandt

[ayrama]

Hey, CristelKZuniga. Here is my feedback.

Written Communication (9/10) The report is well-written, and the language is clear and accessible. The explanations accompanying the plots and tables are helpful and add context to the findings. However, the lack of detailed comments in some code chinks makes it challenging for a reader unfamiliar with the methods to follow the analysis. Additionally, the absence of a result for the New York State Fusion Parties Analysis in Section 3.2 leaves a gap in the narrative.

Project Skeleton (8/10) The project addresses most of the tasks outlined in the instructions. However, Task 5 is incomplete as the desired map is not fully achieved, and Task 6 does not meet the requirements for an animated chloropleth visualization showing results from 1976 to 2020. The analysis in the “Evaluating Fairness of ECV Allocation Schemes” section only considers two schemes (Winner-Takes-All and Proportional Representation) instead of all four as required.

Formatting & Display (8/10) The tables are minimalistic and titled, making them easy to read. However, some results in the tables are confusing, such as the 90.91% percentage difference in the 1984 Arkansas row of the “Comparison Analysis by State” table (though the voting numbers are around 50% different). Pie charts in the "Distribution of Votes Over Time" section are too small and awkwardly laid out. Enlarging and grouping the pie charts into rows of 3-4 charts would improve readability. Percentages in tables could be rounded to one decimal place for clarity. There are slight inaccuracies in the map visualization task, such as the unnecessary inclusion of degree markers and a confusing vote scale.

Code Quality (9/10) The code runs efficiently and is well-structured, but it lacks comments in some code chunks, making the code harder to understand for someone reviewing the project. Code could be made more concise by avoiding the creation of intermediate tables. Additionally, folding code blocks would make the analysis report cleaner and easier to navigate. There is some disorder in Section 3.3's code for trend analysis.

Data Preparation (10/10) The data is well-prepared, with clear evidence of effective data manipulation and cleaning. The process of downloading and extracting shapefiles is automated and efficient.

Overall Score: 44/50 This mini-project is well-executed and showcases thoughtful analysis and visualization of voting trends and allocation schemes. The black theme and choice of fonts make the report visually appealing. Improvements could be made in providing complete results, better code organization, and clarity in visualizations and tables. While the project demonstrates strong analytical skills and effective use of R, addressing the noted gaps in tasks and refining visualizations would enhance the overall quality.

[Timbila614]

Hi @CristelKZuniga!

My feedback on your report is as follows:

Written Communication (9/10) The introduction and analysis provide a clear overview of the project’s goals and context. While the sections are well-organized, they could benefit from smoother transitions and additional explanation of key findings to enhance accessibility for non-expert readers.

Project Skeleton (8.5/10) The project structure is logical, with each task building on the previous one, but some steps are underdeveloped:

• Open-ended questions (e.g., "evaluating fairness of ECV allocation schemes") are answered but not fully explained.
• The 2000 Presidential Election Electoral College Results heat map has an ambiguous legend and does not clearly show the winning party. Suggested improvement: Add more detailed justifications for each task and include original insights or creative solutions.

Formatting & Display (8.5/10) Header level: Current section titles are formatted inconsistently and could benefit from hierarchical structuring using Level 2 headers (##) (e.g. ## Seat Changes in the US House of Representatives (1976–2022) Table formatting: Use functions like kableExtra to add styling, such as bold headers, alternating row colors, and 'thousands' separators for large numbers.

Code Quality (9/10) Codes execute properly and is well-commented, making it accessible to other analysts. However, variable names and overall structure could adhere more strictly to coding style guides for clarity and reusability.

Data Preparation (10/10) Data import and preparation processes are fully automated, with attention to avoiding redundant downloads and systematic file naming. External data sources are well-documented and cited, demonstrating professionalism and attention to reproducibility

Overall Score: 45/50