[x] Repository: Is the source code for this data analysis available? Is the repository well organized and easy to navigate?
[x] License: Does the repository contain a plain-text LICENSE file with the contents of an OSI approved software license?
Documentation
[x] Installation instructions: Is there a clearly stated list of dependencies?
[x] Example usage: Do the authors include examples of how to use the software to reproduce the data analysis?
[x] Functionality documentation: Is the core functionality of the data analysis software documented to a satisfactory level?
[ ] Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support
Code quality
[x] Readability: Are scripts, functions, objects, etc., well named? Is it relatively easy to understand the code?
[x] Style guidelides: Does the code adhere to well known language style guides?
[x] Modularity: Is the code suitably abstracted into scripts and functions?
[ ] Tests: Are there automated tests or manual steps described so that the function of the software can be verified? Are they of sufficient quality to ensure software robsutness?
Reproducibility
[x] Data: Is the raw data archived somewhere? Is it accessible?
[x] Computational methods: Is all the source code required for the data analysis available?
[x] Conditions: Is there a record of the necessary conditions (software dependencies) needed to reproduce the analysis? Does there exist an easy way to obtain the computational environment needed to reproduce the analysis?
[x] Automation: Can someone other than the authors easily reproduce the entire data analysis?
Analysis report
[x] Authors: Does the report include a list of authors with their affiliations?
[x] What is the question: Do the authors clearly state the research question being asked?
[x] Importance: Do the authors clearly state the importance for this research question?
[x] Background: Do the authors provide sufficient background information so that readers can understand the report?
[x] Methods: Do the authors clearly describe and justify the methodology used in the data analysis? Do the authors communicate any assumptions or limitations of their methodologies?
[x] Results: Do the authors clearly communicate their findings through writing, tables and figures?
[x] Conclusions: Are the conclusions presented by the authors correct?
[ ] References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?
[x] Writing quality: Is the writing of good quality, concise, engaging?
Estimated hours spent reviewing: 3
Review Comments:
Please provide more detailed feedback here on what was done particularly well, and what could be improved. It is especially important to elaborate on items that you were not able to check off in the list above.
First and foremost, you did a fantastic job executing your project. The classification of each file is done very well, allowing me to understand what you have done at a glance when I first look through it. In addition, I wholeheartedly agree with the recommendations made by the two reviewers. Specifically, I agree with the feedback about code redundancy. like 'add_price_category' is duplicated in both the src/ and scripts/ folders and should be imported instead. Here are a few more suggestions:
The inclusion of visualization(from results, I noticed you have a lots of vizs in the figures folder) in the README is a great approach as it improves readers' readability and understanding, enabling them to immediately understand the research content and findings. But it's crucial to make sure the images are pertinent and have descriptive or captioned text.
Detailed instructions are essential to improve documentation for reproducibility and clarity, especially when it comes to setting up and using the Docker environment(if someone never use Docker before, it would be very helpful). The project documentation is extensive in certain places, but it is missing important elements that make it difficult to understand and duplicate. More information about configuring and using the Docker environment should be included, and the README should give a more thorough explanation of the analysis process. Detailed Docker instructions will greatly help users in efficiently reproducing the study and comprehending the project workflow. These instructions should include how to access the JupyterLab instance within the container and troubleshoot common Docker issues.
Overall, your project is very outstanding! I respect your planning, execution, and meticulousness. The amazing work that your team has produced is incredibly motivating! I might use it as a reference in the future when I'm looking for lodging in New York City:)
Reviewer: Chenyi0309
Conflict of interest
Code of Conduct
General checks
Documentation
Code quality
Reproducibility
Analysis report
Estimated hours spent reviewing: 3
Review Comments:
Please provide more detailed feedback here on what was done particularly well, and what could be improved. It is especially important to elaborate on items that you were not able to check off in the list above.
First and foremost, you did a fantastic job executing your project. The classification of each file is done very well, allowing me to understand what you have done at a glance when I first look through it. In addition, I wholeheartedly agree with the recommendations made by the two reviewers. Specifically, I agree with the feedback about code redundancy. like 'add_price_category' is duplicated in both the src/ and scripts/ folders and should be imported instead. Here are a few more suggestions:
The inclusion of visualization(from results, I noticed you have a lots of vizs in the figures folder) in the README is a great approach as it improves readers' readability and understanding, enabling them to immediately understand the research content and findings. But it's crucial to make sure the images are pertinent and have descriptive or captioned text.
Detailed instructions are essential to improve documentation for reproducibility and clarity, especially when it comes to setting up and using the Docker environment(if someone never use Docker before, it would be very helpful). The project documentation is extensive in certain places, but it is missing important elements that make it difficult to understand and duplicate. More information about configuring and using the Docker environment should be included, and the README should give a more thorough explanation of the analysis process. Detailed Docker instructions will greatly help users in efficiently reproducing the study and comprehending the project workflow. These instructions should include how to access the JupyterLab instance within the container and troubleshoot common Docker issues.
Overall, your project is very outstanding! I respect your planning, execution, and meticulousness. The amazing work that your team has produced is incredibly motivating! I might use it as a reference in the future when I'm looking for lodging in New York City:)
Attribution
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Originally posted by @Chenyi0309 in https://github.com/DSCI-310-2024/data-analysis-review-2024/issues/9#issuecomment-2041920813