Submission: eda.utilsR (R)

Submitting Author: Micah Kwok (@micahkwok), Fatime Selimi (@fatse), Chuang Wang (@chuangw46), Jiacheng Wang (@wangjc640)

Repository: https://github.com/UBC-MDS/eda.utilsR Version submitted: v0.3.0 Editor: Tiffany Timbers (@ttimbers) Reviewer 1: TBD Reviewer 2: TBD

Archive: TBD Version accepted: TBD

Description

Package: eda.utilsR
Title: R package containing utility functions for EDA in machine learning
Version: 0.0.0.9000
Authors@R: c(person("Chuang", "Wang", role = c("aut", "cre"),
                     email = "chuangw.sde@gmai.com"),
              person("Fatime", "Selimi", role = "aut", 
                     email = "fatimeseelimi@gmail.com"),
              person("Jiacheng", "Wang", role = "aut",
                     email = "jiachengw@gmail.com"),
              person("Micah", "Kwok", role = "ctb",
                     email = "micahk@gmail.com"))
Description: The package contains basic functions for imputation, scaling, 
    dealing with outliers and plotting correlation heatmap.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Imports: 
    tibble,
    dplyr,
    ggplot2,
    reshape2,
    stats,
    rlang
Suggests: 
    testthat (>= 3.0.0),
    knitr,
    rmarkdown,
    covr
Config/testthat/edition: 3
VignetteBuilder: knitr

Scope

Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
- [ ] data retrieval
- [ ] data extraction
- [x] data munging
- [ ] data deposition
- [x] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [ ] geospatial data
- [ ] text analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences):

This package includes functions to help with data manipulation asks such as scaling, imputation and plotting in the exploratory data analysis (EDA) portion of tasks, while keeping the code short and simple to use.

Who is the target audience and what are scientific applications of this package?

This package is targeted towards those in the data science field who have to do EDA as part of their project. This is ideally intended for datasets to be used in machine learning as it has functions that help with the preprocessing stage of work including scaling, imputation and identifying outliers. The scientific applications of this package is that users can easily call on these functions with one simple line of code, helpful for users that are unfamiliar with R and saving time overall.

Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

Yes, there are other R packages that accomplish similar tasks such as scaler, ggcorr, mice, and OutlierDetection. However, these all exist in different packages while all these functions would probably be required during EDA. Furthermore, some of the functions in this package offer higher levels of customizability such as color scheme in the plot that is usually not easily available otherwise.

(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
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

Confirm each of the following by checking the box.

[x] I have read the guide for authors and rOpenSci packaging guide.

This package:

[x] does not violate the Terms of Service of any service it interacts with.
[ ] has a CRAN and OSI accepted license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions, created with roxygen2.
[x] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, including reporting of test coverage using services such as Travis CI, Coveralls and/or CodeCov.

Publication options

[ ] Do you intend for this package to go on CRAN?
[ ] Do you intend for this package to go on Bioconductor?
[ ] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:

MEE Options

- [ ] The package is novel and will be of interest to the broad readership of the journal. - [ ] The manuscript describing the package is no longer than 3000 words. - [ ] You intend to archive the code for the package in a long-term repository which meets the requirements of the journal (see [MEE's Policy on Publishing Code](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/journal-resources/policy-on-publishing-code.html)) - (*Scope: Do consider MEE's [Aims and Scope](http://besjournals.onlinelibrary.wiley.com/hub/journal/10.1111/(ISSN)2041-210X/aims-and-scope/read-full-aims-and-scope.html) for your manuscript. We make no guarantee that your manuscript will be within MEE scope.*) - (*Although not required, we strongly recommend having a full manuscript prepared when you submit here.*) - (*Please do not submit your package separately to Methods in Ecology and Evolution*)

Code of conduct

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

Assigning @williamxu7 & @tdkhanhvu as reviewers.

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

Briefly describe any working relationship you have (had) with the package authors.
[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 package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all exported functions
[x] Examples (that run successfully locally) for all exported functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING, and DESCRIPTION with URL, BugReports and Maintainer (which may be autogenerated via Authors@R).

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition

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).

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: Unit tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Packaging guidelines: The package conforms to the rOpenSci packaging guidelines

Estimated hours spent reviewing: 2

[x] Should the author(s) deem it appropriate, I agree to be acknowledged as a package reviewer ("rev" role) in the package DESCRIPTION file.

Review Comments

Hi Chuang, Fatime, Jiacheng, and Micah, the eda.utilsR package looks great, I am impressed by the work you have done! I included some general comments and specific comments below you may consider to make the package even greater.

General Comments

Overall the package is easy to use, I tested your R packages on my computer and there is no issue with install. There is no error or warning which is amazing. Great job on 100% code coverage! You have done a great job on continuous integration services (GitHub Actions, Codecov, etc.). Your Readme and vignette documents are clear and provide great guidance for target users to understand and use your functions.

Specific Comments

Code

I tested functions and codes, they work well, and I like that your code format is clean/tidy which makes it easy to read.

Documentation

I enjoyed reading your readme file, the examples and correlation heatmap provided are well designed.
"Our Place in the R Ecosystem" section in the README is well written, it is useful to see the documentation links for several R packages with similar functionalities
I ran goodpractice::gp() to identify likely sources of errors and style issues. I noticed that in your .R files, there are many '=', it may be better to use '<-' for assignment instead of '='. R users and developers may find it easier to read code if you use '<-'.
I ran spelling::spell_check_package() as well as spelling::spell_check_files("README.Rmd"). Minor spelling errors in the readme file (such as words 'anslysis', 'preperation' in README.md can be corrected).

GitHub Repo Homepage

It may be a good idea to include some description, website link, or topics provided in the About section on your repo homepage. For example, you could consider adding an URL of your vignette homepage there (although I see you have linked the vignette in the readme file which is great)

I enjoyed reviewing your package and you have done a great job. I think your target audience/users would enjoy using your package too.

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

Briefly describe any working relationship you have (had) with the package authors.
[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 package and any non-standard dependencies in README
[X] Vignette(s) demonstrating major functionality that runs successfully locally
[X] Function Documentation: for all exported functions
[X] Examples (that run successfully locally) for all exported functions
[X] Community guidelines including contribution guidelines in the README or CONTRIBUTING, and DESCRIPTION with URL, BugReports and Maintainer (which may be autogenerated via Authors@R).

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition

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).

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: Unit tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[X] Packaging guidelines: The package conforms to the rOpenSci packaging guidelines

Estimated hours spent reviewing: 2h

[X] Should the author(s) deem it appropriate, I agree to be acknowledged as a package reviewer ("rev" role) in the package DESCRIPTION file.

Review Comments

General comments

Hi team, I am impressed by the amount of work put into the projects by all team members that helps create a lot of useful features in this package.

The features are well divided into their separate functions, so users can know which function they need to use for their specific needs. As all of the necessary components such as Vignette website, documentation are also present, users can refer to them whenever they have any doubts.

However, I think your project can be even greater if you can address some of my feedback below:

Specific comments

Vignette

Introduction

This package contains four functions: - cor_map: A function to plot a correlation matrix of numeric columns in the dataframe - outlier_identifier: A function to identify and deal with outliers - scale A function to scale numerical values in the dataset - imputer: A function to impute missing values

It is better to split this paragraph into bullet points in their own lines for readability. I'm not sure if Vignette causes this issue or not.

imputer example

It is better to explain how you did the imputation in this example, and probably list down the available options for users to choose from. I think users may be interested in understanding how the imputation is performed as it will impact their analysis.

outlier_identifier

This function could help you by eliminate ourlier points or impute ourlier values with mean or median

I think it is better to let the function focus on 1 functionality: eliminate / impute outliers. It is also not clear from the example that you how deal with the value 30 to make it become 3.57. I think users may be interested in understanding how the imputation is performed on outliers as it will impact their analysis.

Sentence structure

Scaling is important for multiple machine learning tasks. Since imbalanced numeric scale in data frame will cause several features being ignored or amplified. Thus diminish the effectiveness of the machine learning model. This is where scaling function comes in handy. This function can help to re-scale all the numeric columns within a dataframe to balance the magnitude of each column.

We can improve further on the documentation to make it sound more formal by writing more connected sentences.

Coding files

Correlation map

Maybe an option to let the user customize the font size and the map size? As when I run the code in my RStudio, the correlation map is quite big and occupies a lot of space, so I guess a smaller version may fit the screen better.

Consistent test file names

We have 2 group of names: (test-cor_map.R & test-scale.R) vs (test.imputer.R & test.outlier.R). It is better to choose one style over the other to make it consistent.

Some typos / wrong grammar

outlier.R: "Defualt" in the Roxygen2 string, "has already process"...

Nested if-else logic

outlier.R: When the condition fails, the function will throw the exception and stop. So it is better to remove the nested if-else to make it easier to follow the logic. It seems that my suggestion is applied in cor_map.R, so probably outlier.R author can try to follow suit?

Overall, I believe this is a great project and probably that due to the time constraint, there are a few minor mistakes made and it lacks some consistency. However, I think you all can easily address these drawbacks and can make it become even greater!

UBC-MDS / software-review-2021