Submission: Group 12: Egg-Production-Inferential-Test

[Kierst01]

Submitting authors: @Kierst01@paolocodina@carinaya

Repository: https://github.com/UBC-MDS/Egg-Production-Inferential-Test Report link: https://ubc-mds.github.io/Egg-Production-Inferential-Test/egg_production_inferential_report.html Abstract/executive summary: Here we attempt to use inference to determine if there is a difference in the number of eggs produced by each hen between hatching and table eggs (fertilized vs unfertilized). We did this using a permutation method on our sample to compare the observed statistics. We got a p-value of 0 which is very rare and is below our chosen 0.05 alpha so we reject the null hypothesis that the number of eggs produced by each hen between hatching and table eggs are the same.

Editor: @ttimbers Reviewer: Andy Zhang, Hayley Han, Katherine Chen, Jerry Yu

• [ ] I agree to abide by MDS's Code of Conduct during the review process and in maintaining my package should it be accepted.

[jy1909]

Data analysis review checklist

Reviewer: @jy1909

Conflict of interest

• [x] As the reviewer I confirm that I have no conflicts of interest for me to review this work.

Code of Conduct

• [x] I confirm that I read and will adhere to the MDS code of conduct.

General checks

• [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?
• [ ] Functionality documentation: Is the core functionality of the data analysis software documented to a satisfactory level?
• [x] 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?
• [ ] Automation: Can someone other than the authors easily reproduce the entire data analysis?

Analysis report

• [ ] 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?
• [ ] 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: 2

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.

• Overall, the research topic is interesting and quite unique. I also find the report easy to follow. I am putting forward a few recommendations in the following points.
• In docker-compose.yml, the port should be changed to 8787:8787 to match with the description in README.md.
• README.md should tell the user to cd to /home/jovyan/work in the RStudio web app after launching it. By default, the user is in the directory /home/rstudio.
• Please add documentation on how to run the tests.
• Please update link to your issues page on your CONTRIBUTING.md.
• Author names and affiliations should be added to the report.
• The methods were clearly described, but the assumptions and limitations with the methodology should also be stated.
• The team may want to check the images in the report again. For some reason, the figures were showing up on the local html file, but not showing up on the URL link.
• A DOI link should be added to the second reference.

Attribution

This was derived from the JOSE review checklist and the ROpenSci review checklist.

[andyzhangstat]

Data analysis review checklist

Reviewer:

Conflict of interest

• [x] As the reviewer I confirm that I have no conflicts of interest for me to review this work.

Code of Conduct

• [x] I confirm that I read and will adhere to the MDS code of conduct.

General checks

• [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?
• [ ] Functionality documentation: Is the core functionality of the data analysis software documented to a satisfactory level?
• [x] 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?
• [ ] Automation: Can someone other than the authors easily reproduce the entire data analysis?

Analysis report

• [ ] 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?
• [ ] 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: 2 hours.

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.

The research topic is intriguing and the report is well-structured and easy to comprehend. Here are some recommendations based on my own understanding.

1. The report needs to incorporate the names and affiliations of the authors.
2. Include documentation on how to execute the tests.
3. Add a DOI link to the second reference.
4. The port configuration in docker-compose.yml should be updated.
5. There is an absence of figures in the report.
6. Ensure the link to the issues page in CONTRIBUTING.md is updated.
7. It is better to explicitly outline the assumptions and limitations associated with the methodology.

Attribution

This was derived from the JOSE review checklist and the ROpenSci review checklist.

[yhan178]

Data analysis review checklist

Reviewer: yhan178

Conflict of interest

• [x] As the reviewer I confirm that I have no conflicts of interest for me to review this work.

Code of Conduct

• [x] I confirm that I read and will adhere to the MDS code of conduct.

General checks

• [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?
• [x] 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?
• [x] 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?
• comment and fix: add a 'Running the tests' section in readme.

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?
• comment and fix: I git clone the repo and I ran docker compose up, however I failed to get to the Rstudio Sign in page by following the instruction. As Jerry mentioned above, one has to manually edit the author's docker-compose.yml file to get things work.

Analysis report

• [x] Authors: Does the report include a list of authors with their affiliations?
• Comment and fix: authors are listed, however affiliations are missing.
• [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?
• [x] References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?
• comment and fix: DOI need to be added for some papers.
• [x] Writing quality: Is the writing of good quality, concise, engaging?

Estimated hours spent reviewing:

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.

Interesting topic and looking good overall, yet missing figures from the final report decrease the report's readability. Besides other peers' comments and my fix suggestions listed above, I have some general suggestions:

1. Normally, p-value cannot be zero but a very very small number, e.g., 1.23e-123. I did not find the original data output of the permutation analysis. It would be helpful to provide the output of the original analysis instead of a single p-value.
2. I found the result plot in the folder since figures are missing from the report. From the plot, it seems that p is likely to be a small value rather than 0. Therefore, p can be reported as p < 0.001. If I was wrong, it would be better for the authors to provide an explanation why p is 0 in this case.
3. As a report for general readers, providing a description of the dataset might be more helpful for your reader to capture key information of your data and analyses. For instance, instead of printing the first few rows, last few rows, and number of rows as separated table, providing a description of each column in a table might be more informative.

Attribution

This was derived from the JOSE review checklist and the ROpenSci review checklist.

[czwcandy]

Data analysis review checklist

Reviewer:

Conflict of interest

• [X] As the reviewer I confirm that I have no conflicts of interest for me to review this work.

Code of Conduct

• [X] I confirm that I read and will adhere to the MDS code of conduct.

General checks

• [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?
• [ ] Functionality documentation: Is the core functionality of the data analysis software documented to a satisfactory level?
• [X] 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?
• [ ] Automation: Can someone other than the authors easily reproduce the entire data analysis?

Analysis report

• [ ] 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?
• [ ] 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:

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.

This topic is very interesting and I am attracted with the permutation test in this report, which is very different from other groups. The report is well organized with a clear description.

1. Need add authors and affiliations to the report.
2. Need add DOI link.
3. I failed to enter RStudio Sign In page.
4. No test instruction.
5. All figures are missing in the report, maybe it is the render problem.

Attribution

This was derived from the JOSE review checklist and the ROpenSci review checklist.