Peer Evaluation - Jingchuan Xu

[Jonathan666Charlie]

Overall, the project is progressing well, with strong data simulation and visualization, but improvements in documentation, flexibility, and consistency could elevate its clarity and usability.

Strong Positive Points

1. Data Simulation in 00-simulate_data.R:
   o The script generates synthetic data effectively, providing a solid foundation for testing and validating models before applying real-world data. The modular structure makes it easier to maintain and extend for various use cases.
   o The usage of different functions to simulate variables and build datasets ensures that the code is flexible and can be adapted to different modeling tasks.
2. Data Workflow Structure:
   o The organization of scripts (e.g., 01-download_data.R, 02-data_cleaning.R, 03-test_data.R) clearly separates tasks such as data download, cleaning, testing, and modeling. This separation of concerns makes the workflow easier to follow and understand.
   o Each step in the data pipeline is logically organized and builds on the previous scripts, creating a cohesive progression from raw data to clean, analyzable data.
3. Paper Clarity and Analysis:
   o The paper includes a well-defined introduction that explains the context of the project, focusing on hate crimes in Toronto from 2018 to 2024. It effectively outlines the objectives and societal importance of the research.
   o Visualizations such as those in Figures 1 and 2 present the data in a clear and interpretable manner. They make it easy to identify trends and racial biases, aligning well with the goals of the analysis.

Critical Improvements Needed

1. README.md and Documentation:
   o The README.md file needs to provide more comprehensive documentation. It should explain the role of each script (e.g., 00-simulate_data.R, 02-data_cleaning.R, etc.) and give clear instructions on how to run each script, what parameters can be modified, and what outputs to expect.
   o Inline comments within the scripts are minimal. Adding more detailed explanations for key sections would make it easier for others (or future collaborators) to understand the rationale behind certain choices, such as the simulation method in 00-simulate_data.R.
2. Parameterization of Scripts:
   o The 00-simulate_data.R script and others could benefit from allowing more user-defined parameters. For example, allowing the user to specify the number of observations, the range of values for different variables, or the types of distributions used in data generation would make the script more versatile.
   o Similarly, the cleaning and testing scripts (02-data_cleaning.R and 03-test_data.R) should allow users to pass in different datasets or adjust model parameters, improving adaptability for future analyses.
3. Consistency Across Scripts:
   o Ensure consistent naming conventions for variables and functions across all scripts. This will improve readability and ease of use, especially for those unfamiliar with your codebase. For example, if variable names are structured differently between scripts, it can lead to confusion and hinder collaboration.

Suggestions for Improvement

1. Error Handling and Robustness:
   o Introduce error handling in the scripts to catch potential issues like incorrect input formats, missing data, or failed downloads. This would enhance the robustness of your scripts and prevent unexpected crashes during execution.
2. Testing and Validation:
   o Implement tests to validate the outputs of your simulations and data cleaning processes. For example, simple checks to ensure the cleaned data matches expected formats or that simulated data follows the desired statistical properties would add reliability to the project.
3. Reproducibility:
   o To ensure reproducibility, it would be beneficial to add a random seed to your simulation code in 00-simulate_data.R. This would make it easier for others to replicate your results precisely.
4. Abstract and Conclusion in the Paper:
   o The abstract could be expanded to include more details about the methodology and key findings. While it currently addresses what was done and why it matters, adding a bit more depth regarding the findings and their implications would strengthen the introduction to the research.
   o Similarly, the conclusion should highlight the practical implications of the research, particularly in informing policy or community-based initiatives aimed at combating hate crimes.

Evaluation:
The project is on track with a clear structure and solid data simulation, but by addressing the documentation gaps, increasing script flexibility, and ensuring consistent naming conventions, it will be more accessible and easier to maintain. Additionally, the final paper could benefit from a clearer and more detailed discussion of the results and their broader societal implications.

Estimated Mark: 56/64

Rubric:
• R is appropriately cited: 1
• LLM usage: 1
• Title: 1
• Author, date, repo: 2
• Abstract: 4
• Introduction: 4
• Data: 8
• Measurement: 2.5
• Prose: 4
• Cross-references: 1
• Graphs: 2
• Referencing: 4
• Commits: 2
• Sketches: 2
• Simulation: 4
• Tests: 4
• Reproducibility: 2
• Code Style: 1

Incorporating these improvements will make the project more robust.