Open jasmineortega opened 2 years ago
Nice work. I liked this analysis and it's unfortunate that the quality of the available data is not conducive to getting the desired results. I can think of the following as improvements on the work done so far:
Error: pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available).
Execution halted
As per stackoverflow, this error ccould be solved by inserting the below code in the R Script prior to the render command. You can investigate this further and incorporate any changes in the installation or data analysis pipeline running instructions.
Sys.setenv(RSTUDIO_PANDOC="--- insert directory here ---")
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Good job! The structure of the repo is clean and organized. The research topic is interesting! the scripts and the report are very well-structured. Please find my comments in the following.
Again, great work! It's really hard for me to pick out other points that need to be improved and I got some great ideas for our project after reading yours! Thank you! Linhan
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Well done guys! Thanks for the impressive project, I really enjoyed reviewing it:) Here are some detailed suggestions, and hope they can be helpful for any improvements!
make: Nothing to be done for `all'.$ make all
make: Nothing to be done for `all'.
when I run make all
from the terminal. I had the same error before and it was fixed after I correct the indentation. Just a reminder to double-check this.
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Very well done guys, the research topic is well introduced and your report had a clear structure to follow along. I was very engaged reading your project overall. Please find my comments below.
Error: pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available).
Execution halted.
make: *** [src/crime_vancouver_eda.md] Error 1
npm install -g vega vega-cli vega-lite canvas
to environment instruction. Because different user may encounter JSON decoder error. My group has this problem for one of reviewer in our group.Overall, very well done! Such an interesting project and you clearly delivered well. Minor suggestions and lots of good things to learn from.
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Thank you for your comments!
We really appreciate your feedback. We made the following changes regarding your comments:
Regarding Arijeet 's feedback on issue , we added test.py
that contains tests for functions used in the script files. Here is the commit c4d25b3
We added instructions to the README.md
file on how to solve the pandoc version issue. The changes are here: cf275a3
As per Li Cai's suggestion we reorganized the /src
folder: f2c116e
Regarding the issue, we moved all the notebooks to /raw
folder: e8fd231
All the files are in the repository now, regarding the issue, the Makefile was added: 71dcef7
Data analysis review checklist
Reviewer: @arijc76
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?
- [ ] 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
- [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?
- [x] 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:
Nice work. I liked this analysis and it's unfortunate that the quality of the available data is not conducive to getting the desired results. I can think of the following as improvements on the work done so far:
- Add tests for the script files.
- Specify the version numbers for tidyverser and knitr (Based on personal experience, the version number will be useful for any user with a older version to diagnose any potential issues in running the analysis).
- I was unable to run the automation script to reproduce the analysis. Got the below error after following the instructions to install the conda environment and run the script to execute the data analysis pipeline.
Error: pandoc version 1.12.3 or higher is required and was not found (see the help page ?rmarkdown::pandoc_available). Execution halted
As per stackoverflow, this error ccould be solved by inserting the below code in the R Script prior to the render command. You can investigate this further and incorporate any changes in the installation or data analysis pipeline running instructions.
Sys.setenv(RSTUDIO_PANDOC="--- insert directory here ---")
Attribution
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Hi Arijeet,
Thank you for your review! We improved our project by the following
If you have more questions or concerns, please kindly let us know.
Reviewer: @lipcai
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?
- [ ] 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?
- [x] 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: 1.5
Review Comments:
Good job! The structure of the repo is clean and organized. The research topic is interesting! the scripts and the report are very well-structured. Please find my comments in the following. #Points being done well:
- The model could be very useful for predicting the size of the forest fires and could potentially guide the rescue forces in the realistic context as needed.
- The data visualizations from the EDA part is formatted beautifully. The scripts, EDA, and coding are well designed and fully described.
- The source code was well separated into meaningful functions / modules.
Points could be improved:
- I think you make the README file pretty clear so I can reproduce the entire data analysis. I wolud suggest to add a flow chart / workflow diagram in README so the readers can have a better understanding of the overall data flow with adequate graphical representation.
- You guys have a very detailed data EDA, but for those interested in the raw data, I would hope in the final report, Data Section can be added to give a summary about the dataset (e.g. the number of columns, the type or description of the columns and total observations) that is used for the analysis of this project.
- I think you guys still need to add tests for the script files. it is one of the metrics standard.
- In src folder, there are too many file in just the root, it would be better to seperate and arrange those file into more appropriate ones.
- As what we have been taught in 531 (data visuaiazation), there could add a narrative so that it will be easy for others to follow along with what you have done here .I am sorry, this one is picky(I really can't find another one but it demands at least 5-point constructive feedback)
Again, great work! It's really hard for me to pick out other points that need to be improved and I got some great ideas for our project after reading yours! Thank you! Linhan
Attribution
This was derived from the JOSE review checklist and the ROpenSci review checklist.
Hi @lipcai,
Thank you for your review! To your comments
Thanks again for your comment. Please let us know if you have more questions or concerns.
Submitting authors: @thomassiu, @sy25wang, @RamiroMejia, @jasmineortega
Repository: https://github.com/UBC-MDS/DSCI_522_Crime_Prediction_Vancouver Report link: https://github.com/UBC-MDS/DSCI_522_Crime_Prediction_Vancouver/blob/main/doc/vancouver_crime_predict_report.md
Abstract/executive summary: In this project, we attempted to create a classification prediction model to predict the types of crimes that happens in Vancouver, BC based on neighborhood location and time of the crime. Based on our EDA results and model tuning, including necessary data cleaning tasks, we identified that the Logistic Regression model performed the best among all the models tested based on f1 score. The performance of predicting the results of the unseen data was not satisfied, that we believed was due to the lack of associations between the features (Time and Location) and crime type. We proposed further improvements in the future iterations of model optimisations, such as including adding relevant data from outside score (i.e. Vancouver weather, Vancouver housing etc).
Editor: @thomassiu, @sy25wang, @RamiroMejia, @jasmineortega Reviewer: @lipcai, @arijc76, @zzhzoe, @junrongz