Summary:
The project US Election Forecast seeks to predict the results of the 2024 U.S. Presidential Election through data modelling using R. The repository includes initial data processing, regression modelling, and simulation attempts. Despite these foundational elements, many crucial sections are incomplete. The project lacks the final paper, model validation, references, and documentation of LLM usage. Additionally, the README file was updated partially, and no like sketches or graphs were provided.
Strong positive points:
The folder structure is clear, with a distinction between scripts, raw data, and analysis files, making it easy to navigate.
The use of both real and simulated data sets demonstrates an understanding of diverse data sources, which is beneficial for analysis. The Rproj name was updated. The authors are updated. Scripts are partially done. EDA was conducted.
Critical improvements needed:
Significant portions of the project are missing, including the final paper, references, and a detailed discussion of model assumptions and validation. The README file needs to be expanded to outline the project's goals, the analysis process, and how to run the scripts.
LLM usage is mentioned but not explained or documented, making it unclear how it contributes to the analysis. There are no visual elements like sketches or graphs that illustrate the analysis or model approach. R code in scripts needs to be finished and delete unused R files. The model needs to be fixed since unable to open.
Suggestions for improvement:
Focus on completing the missing parts: draft the paper, include an abstract, introduction, data section, result discussion, and appendix; add proper citations for both data and tools. Update the README to give potential users a clear understanding of the project's purpose and how to reproduce the results. Include visual representations like flowcharts or sketches to outline the data analysis process or model structure. Use more descriptive commit messages to better document the changes made throughout the development process. Make sure to have R references and LLM updated.
Evaluation:
R is appropriately cited: 0/1 (No citations for R or other software tools).
Data are appropriately cited: 0/1 (Data sources are not cited).
Class paper: 0/1 (The final paper is missing).
LLM usage is documented: 0/1 (LLM usage is mentioned but not explained).
Title: 0/2 (No title or subtitle summarizing key findings).
Author, date, and repo: 1/2 (Some files are missing).
Abstract: 0/4 (An abstract is not provided).
Introduction: 0/4 (No introduction or background on the election and analysis).
Estimand: 0/1 (The estimand is not described).
Data: 0/10 (Lacks data section).
Measurement: 0/4 (No discussion of the measurement strategy).
Model: 0/10 (No validation or discussion of the model).
Results: 0/10 (Results section is missing).
Discussion: 0/10 (No discussion of findings).
Prose: 0/6 (Prose sections remain as provided in the starter files).
Cross-references: 0/1 (No cross-references within the paper).
Captions: 0/2 (No tables or graphs included).
Graphs/tables/etc: 0/4 (No visual aids provided).
Idealized methodology: 0/10 (Methodology section is absent).
Idealized survey: 0/4 (No discussion of surveys or data sources).
Pollster methodology overview and evaluation: 0/10 (missing).
Referencing: 0/4 (References are missing).
Commits: 1/2 (Commit messages need more detail).
Sketches: 0/2 (No sketches or diagrams included).
Simulation: 2/4 (Simulation files are incomplete).
Tests – simulation: 0/4 (No tests for the simulation).
Tests – actual: 0/4 (incomplete).
Parquet: 0/1 (Data is not in .parquet format).
Reproducible workflow: 1/4 (Workflow is partially reproducible but could be enhanced).
Miscellaneous: 0/3 (No extra elements or notes).
Estimated overall mark:
0/126
Due to the absence of core components like the paper, references, and LLM usages, the project currently scores a 0 overall.
Summary: The project US Election Forecast seeks to predict the results of the 2024 U.S. Presidential Election through data modelling using R. The repository includes initial data processing, regression modelling, and simulation attempts. Despite these foundational elements, many crucial sections are incomplete. The project lacks the final paper, model validation, references, and documentation of LLM usage. Additionally, the README file was updated partially, and no like sketches or graphs were provided.
Strong positive points: The folder structure is clear, with a distinction between scripts, raw data, and analysis files, making it easy to navigate. The use of both real and simulated data sets demonstrates an understanding of diverse data sources, which is beneficial for analysis. The Rproj name was updated. The authors are updated. Scripts are partially done. EDA was conducted.
Critical improvements needed: Significant portions of the project are missing, including the final paper, references, and a detailed discussion of model assumptions and validation. The README file needs to be expanded to outline the project's goals, the analysis process, and how to run the scripts. LLM usage is mentioned but not explained or documented, making it unclear how it contributes to the analysis. There are no visual elements like sketches or graphs that illustrate the analysis or model approach. R code in scripts needs to be finished and delete unused R files. The model needs to be fixed since unable to open.
Suggestions for improvement: Focus on completing the missing parts: draft the paper, include an abstract, introduction, data section, result discussion, and appendix; add proper citations for both data and tools. Update the README to give potential users a clear understanding of the project's purpose and how to reproduce the results. Include visual representations like flowcharts or sketches to outline the data analysis process or model structure. Use more descriptive commit messages to better document the changes made throughout the development process. Make sure to have R references and LLM updated.
Evaluation: R is appropriately cited: 0/1 (No citations for R or other software tools). Data are appropriately cited: 0/1 (Data sources are not cited). Class paper: 0/1 (The final paper is missing). LLM usage is documented: 0/1 (LLM usage is mentioned but not explained). Title: 0/2 (No title or subtitle summarizing key findings). Author, date, and repo: 1/2 (Some files are missing). Abstract: 0/4 (An abstract is not provided). Introduction: 0/4 (No introduction or background on the election and analysis). Estimand: 0/1 (The estimand is not described). Data: 0/10 (Lacks data section). Measurement: 0/4 (No discussion of the measurement strategy). Model: 0/10 (No validation or discussion of the model). Results: 0/10 (Results section is missing). Discussion: 0/10 (No discussion of findings). Prose: 0/6 (Prose sections remain as provided in the starter files). Cross-references: 0/1 (No cross-references within the paper). Captions: 0/2 (No tables or graphs included). Graphs/tables/etc: 0/4 (No visual aids provided). Idealized methodology: 0/10 (Methodology section is absent). Idealized survey: 0/4 (No discussion of surveys or data sources). Pollster methodology overview and evaluation: 0/10 (missing). Referencing: 0/4 (References are missing). Commits: 1/2 (Commit messages need more detail). Sketches: 0/2 (No sketches or diagrams included). Simulation: 2/4 (Simulation files are incomplete). Tests – simulation: 0/4 (No tests for the simulation). Tests – actual: 0/4 (incomplete). Parquet: 0/1 (Data is not in .parquet format). Reproducible workflow: 1/4 (Workflow is partially reproducible but could be enhanced). Miscellaneous: 0/3 (No extra elements or notes). Estimated overall mark: 0/126
Due to the absence of core components like the paper, references, and LLM usages, the project currently scores a 0 overall.