doeasyeda - Group 12 peer review

Submitting Author: Riya Eliza (@riyaeliza123) All current maintainers: (@MDSFusionist, @wqxxzd, @hbandukw) Package Name: Pdoeasyeda One-Line Description of Package: doeasyeda offers user-friendly functions for creating standard EDA plots for your data. Repository Link: https://github.com/UBC-MDS/doeasyeda?tab=readme-ov-file Version submitted: 2.1.0 (milestone 3) Editor: @ttimbers Reviewer 1: Alan Powichrowski
Reviewer 2: Yili Tang Reviewer 3: Yiwei Zhang Reviewer 4: Thomas Jian Archive: TBD JOSS DOI: TBD Version accepted: TBD Date accepted (month/day/year): TBD

Code of Conduct & Commitment to Maintain Package

[x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
[x] I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

doeasyeda is a Python package designed to streamline the process of Exploratory Data Analysis (EDA) by providing a suite of functions specifically tailored for creating standard EDA plots. This package aims to simplify the visualization aspect of data analysis, making it more accessible and efficient for users.

Scope

Please indicate which category or categories. Check out our package scope page to learn more about our scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):
- [ ] Data retrieval
- [ ] Data extraction
- [ ] Data processing/munging
- [ ] Data deposition
- [ ] Data validation and testing
- [x] Data visualization[^1]
- [x] Workflow automation
- [ ] Citation management and bibliometrics
- [ ] Scientific software wrappers
- [ ] Database interoperability

Domain Specific & Community Partnerships

[ ] Geospatial
[ ] Education
[ ] Pangeo

Community Partnerships

If your package is associated with an existing community please check below:

[ ] Pangeo
- [ ] My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

[^1]: Please fill out a pre-submission inquiry before submitting a data visualization package.

For all submissions, explain how the and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
- Who is the target audience and what are scientific applications of this package?
- Are there other Python packages that accomplish the same thing? If so, how does yours differ?
- If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] uses an OSI approved license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions.
[x] contains a tutorial with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

[ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.*

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

[x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Confirm each of the following by checking the box.

[x] I have read the author guide.
[ ] I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

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Editor and Review Templates

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Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

[X] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[X] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
[X] Installation instructions: for the development version of the package and any non-standard dependencies in README.
[X] Vignette(s) demonstrating major functionality that runs successfully locally.
[X] Function Documentation: for all user-facing functions.
[X] Examples for all user-facing functions.
[X] Community guidelines including contribution guidelines in the README or CONTRIBUTING. - Yes it's there but it would be good to also add mention to it in the readme, not just on ReadtheDocs
[ ] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere. They are not in the pyproject.toml file

Readme file requirements The package meets the readme requirements below:

[X] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[X] The package name
[X] Badges for:
- [X] Continuous integration and test coverage,
- [X] Docs building (if you have a documentation website),
- [X] A repostatus.org badge,
- [X] Python versions supported,
- [X] Current package version (on PyPI / Conda).

NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)

[X] Short description of package goals.
[X] Package installation instructions
[X] Any additional setup required to use the package (authentication tokens, etc.)
[X] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
- [X] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
[X] Link to your documentation website.
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem. Yes the package does mention other packages such as pandas-profiling and Dtale however it could help to make a specific Python Ecosystem section rather than "Why doeasyeda" to follow the typical setup of a ReadMe
[x] Citation information

Usability

Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:

[X] Package documentation is clear and easy to find and use.
[X] The need for the package is clear It is stated however the reasoning beyond streamlining and it's application can be touched upon - would mention reducing repeated code for commonly used EDA plots, because at the moment it feels like a repackaging of Altair plots
[X] All functions have documentation and associated examples for use
[X] The package is easy to install Very~

Functionality

[X] Installation: Installation succeeds as documented.
[X] Functionality: Any functional claims of the software been confirmed.
[X] Performance: Any performance claims of the software been confirmed.
[ ] Automated tests:
- [ ] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
- [x] Tests cover essential functions of the package and a reasonable range of inputs and conditions. There are no test instructions, the test folder however does seem to cover all edge cases where the function may error out
[ ] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
- [X] Package supports modern versions of Python and not End of life versions.
- [X] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

[X] The package has an obvious research application according to JOSS's definition in their submission requirements. To some extent yes, but other than stream lining EDA it doesn't exactly have an obvious research application

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

[X] A short summary describing the high-level functionality of the software
[X] Authors: A list of authors with their affiliations
[X] A statement of need clearly stating problems the software is designed to solve and its target audience.
[X] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

Review Comments

I would make the Python Ecosystem section separate, and not mixed into the "Why Doeasyeda" section, as if someone wants to glance the ReadMe quickly they may miss the reference to the two packages that have similarity.
Make sure to add testing instructions in our ReadME and ReadtheDocs, there is no mention on how to access or run the test suite on your package and the functions. This would be helpful so new users can seamlessly test the functions without having to set up the code themselves.
Maybe make direct reference to the Community guidelines and where to access the Code of Conduct from the ReadMe, right now there is just a mention that if you want to contribute you should check it out... but it's not clear where unless you look through ReadtheDocs - so maybe a quick mention as well as a link could help clear this up.
Maybe develop on the scope of the package a little more, it mentions streamlining is the main goal, however, it would be beneficial to build on the idea that it helps reduce the need for repetitions in the use of complex code - the main idea that the package helps in the creation of complex Altair plots, but the idea of 'repetition' isn't mention which is the main reason to make functions and streamlining.
One edit to the code and functionality could be to include or back in the group by function used before the create_hist_plot() example in the usage instructions. Yes the function does help big time with streamlining the creation of the histograms, however a large part of the process of making histograms is organizing your data correctly even once data wrangling has already been done. The added functionality of having the groupby be done in the create_hist_plot() could be an awesome addition to the function, and could broaden the application of the function beyond the repurposing of the Altair package.

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
[x] Installation instructions: for the development version of the package and any non-standard dependencies in README.
[x] Vignette(s) demonstrating major functionality that runs successfully locally.
[x] Function Documentation: for all user-facing functions.
[x] Examples for all user-facing functions.
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[ ] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for:
- [x] Continuous integration and test coverage,
- [x] Docs building (if you have a documentation website),
- [x] A repostatus.org badge,
- [x] Python versions supported,
- [x] Current package version (on PyPI / Conda).

[x] Short description of package goals.
[x] Package installation instructions
[x] Any additional setup required to use the package (authentication tokens, etc.)
[x] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
- [x] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
[x] Link to your documentation website.
[ ] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
[x] Citation information

Usability

[x] Package documentation is clear and easy to find and use.
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use
[x] The package is easy to install

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests:
- [x] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
- [x] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
[x] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
- [x] Package supports modern versions of Python and not End of life versions.
- [x] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

[x] A short summary describing the high-level functionality of the software
[x] Authors: A list of authors with their affiliations
[x] A statement of need clearly stating problems the software is designed to solve and its target audience.
[x] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1 hour

Review Comments

The team did a great job and demonstrate a robust knowledge foundation. It's a great opportunity for our team to learn, especially how to corporate and communicate with each other.
I noticed that the input variable 'interactive' was initially configured with a default value of 'False.' It would be intriguing to provide an illustrative example to engage the audience (set the value to 'True').
It might be beneficial to consider including some guidance regarding how one can effectively test the function.
It might be beneficial to change the label of x-axis to 'year' in line plot and area plot. The data type now is '1,970', it will be more user friendly to change it to time stamp.
It could enhance the presentation by providing a bit more context on how these four plots contribute to a comprehensive understanding of the data

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

[X] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[X] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
[X] Installation instructions: for the development version of the package and any non-standard dependencies in README.
[X] Vignette(s) demonstrating major functionality that runs successfully locally.
[X] Function Documentation: for all user-facing functions.
[X] Examples for all user-facing functions.
[X] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[X] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

[X] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[X] The package name
[X] Badges for:
- [X] Continuous integration and test coverage,
- [X] Docs building (if you have a documentation website),
- [X] A repostatus.org badge,
- [X] Python versions supported,
- [X] Current package version (on PyPI / Conda).

[X] Short description of package goals.
[X] Package installation instructions
[X] Any additional setup required to use the package (authentication tokens, etc.)
[X] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
- [X] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
[X] Link to your documentation website.
[X] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
[X] Citation information

Usability

[X] Package documentation is clear and easy to find and use.
[X] The need for the package is clear
[X] All functions have documentation and associated examples for use
[X] The package is easy to install

Functionality

[X] Installation: Installation succeeds as documented.
[X] Functionality: Any functional claims of the software been confirmed.
[X] Performance: Any performance claims of the software been confirmed.
[X] Automated tests:
- [X] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
- [X] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
[X] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
[X] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
- [X] Package supports modern versions of Python and not End of life versions.
- [X] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

[x] A short summary describing the high-level functionality of the software
[X] Authors: A list of authors with their affiliations
[X] A statement of need clearly stating problems the software is designed to solve and its target audience.
[X] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

1.5 hours

Review Comments

Excellent work on the EDA tool package. I found the package very interesting and enjoyable while testing it.
Since the project is heavily rely on Altair, it maybe a good idea to include a link to the documentation of Altair in Readme.
There are lots of optional arguments for each of the create plot functions, recommend to include more example on using optional arguments, i.e. tooltip=None, interactive=False.
The section order of the readme can be improve a bit, recommend to group Function section and Usage section together and move Developer Note section to the back.
The package import structure is a bit confusing, from doeasyeda.create_scatter_plot import create_scatter_plot can be change to from doeasyeda.plots import create_scatter_plot, but don't recommend to change the structure at this point.

Package Review

Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README.
[x] Installation instructions: for the development version of the package and any non-standard dependencies in README.
[x] Vignette(s) demonstrating major functionality that runs successfully locally.
[x] Function Documentation: for all user-facing functions.
[x] Examples for all user-facing functions.
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a pyproject.toml file or elsewhere.

Readme file requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for:
- [x] Continuous integration and test coverage,
- [x] Docs building (if you have a documentation website),
- [x] A repostatus.org badge,
- [x] Python versions supported,
- [x] Current package version (on PyPI / Conda).

[x] Short description of package goals.
[x] Package installation instructions
[x] Any additional setup required to use the package (authentication tokens, etc.)
[x] Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
- [x] Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
[x] Link to your documentation website.
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
[x] Citation information

Usability

[x] Package documentation is clear and easy to find and use.
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use
[x] The package is easy to install

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests:
- [x] All tests pass on the reviewer's local machine for the package version submitted by the author. Ideally this should be a tagged version making it easy for reviewers to install.
- [x] Tests cover essential functions of the package and a reasonable range of inputs and conditions.
[x] Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines. A few notable highlights to look at:
- [x] Package supports modern versions of Python and not End of life versions.
- [x] Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.

The package contains a paper.md matching JOSS's requirements with:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: With DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

1 hour

Review Comments

The Readme.md is stunning and fancy. However, the font size of the subtitle doeasyeda offers user-friendly functions for creating standard EDA plots for your data. could be smaller, as I thought it was a long package name.
In the function description of the Package Content session, the function signature and one-line statement could be broken into new separated lines, to make them easier to follow
Is this badge indicating the code coverage is not 100%? I am not sure if we need to make it to 100%
In the Readme.md, I didn't find a section covering instructions about how to run test cases. I suppose it is pytest?
In the Readme.md, I didn't find a section covering instructions about how to generate a detailed test code coverage report.
Overall, it is an awesome package. All I have mentioned were some minor documentation issues.

UBC-MDS / software-review-2024