Submission group 29: r_outliers_utils (R)

Submitting Author Name: Qingqing Song Submitting Author Github Handle: @scarlqq Other Package Authors Github handles: @karanpreetkaur @lipcai

Repository: https://github.com/UBC-MDS/r_outliers_utils Version submitted: 1.0.0 Submission type: Standard

Editor: Karanpreet Kaur (@karanpreetkaur) Linhan Cai (@lipcai) Qingqing Song (@scarlqq)

Reviewers: Wanying Ye (@GloriaWYY ) Amir Shojakhani (@Amirshoja ) Adam MORPHY (@adammorphy) Jacqueline Chong (@Jacq4nn)

Archive: TBD Version accepted: TBD Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: routliersutils
Title: Clean and Perform Initial Exploratory Data Analysis specific to Outliers
Version: 0.0.0.9000
Authors@R: 
    person("Karanpreet", "Kaur", , "karanpreet088@gmail.com", role = c("aut", "cre"))
    person("Linhan", "Cai", , "1209560233@qq.com", role = c("aut", "cre"))
    person("Qingqing", "Song", , "song.qingqing1001@gmail.com", role = c("aut", "cre"))
Description: As data rarely comes ready to be used and analyzed for machine learning right away, this package aims to help speed up the process of cleaning and doing initial exploratory data analysis specific to outliers. The package focuses on the tasks of identifying univariate outliers, providing summary of outliers like count, range of outliers, visualize them and giving functionality to remove them from data.
Imports:  tidyverse, dplyr, ggplot2, tidyr,  tibble
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Suggests: 
    testthat (>= 3.0.0)
Config/testthat/edition: 3

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
- [ ] 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 aims to help speed up the process of cleaning and doing initial exploratory data analysis specific to outliers. It focuses on the tasks of identifying univariate outliers, providing outlier summary, visualize them and giving functionality to remove or impute them.
Who is the target audience and what are scientific applications of this package?

Researchers or data analysts who need to deal with outliers.
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. While R packages with similar functionalities exist, r_outliers_utils aims to provide summary, visualization of outliers in a single package with an additional functionality to generate outlier-free dataset.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
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Explain reasons for any pkgcheck items which your package is unable to pass.

Technical checks

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This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] 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.
[ ] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[ ] has continuous integration, including reporting of test coverage using services such as Travis CI, Coveralls and/or CodeCov.

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Code of conduct

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Package Review

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Documentation

The package includes all the following forms of documentation:

[] 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
[ ] 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
[ ] Community guidelines: including contribution guidelines in the README or CONTRIBUTING, and DESCRIPTION with URL, BugReports and Maintainer (which may be autogenerated via Authors@R).

Functionality

[ ] 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: 1hr

[ ] 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

The CRAN package is not available for download. I had a similar issue when I was doing the project, thinking that it is automatically uploaded to CRAN.
The documentations of your functions is great, they were helpful when using the key '?.../ . Perhaps you can improve on some parts of the documentation. When I was testing out the functions, I did not know that I had to pass the columns in a list for instance. I am also not sure why I get an error when I was testing out the trim_outlier function on the palmerpenguins::penguins_raw dataset. Error in[.data.frame(df_cp, col) : undefined columns selected when I keyed in trim_outliers(penguins2, columns = c('Culmen Length (mm)', 'Culmen Depth (mm)')) [penguins2 <- palmerpenguins::penguins_raw] . The other two functions worked great.
I believe that you will include the vignettes later this week, so that is not a concern.
I think you team has forgotten to upload the 'Contributing.md' file

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).

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: 1 hour
[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

Congrats everyone on creating a very useful package! It is obvious you have put a lot of work onto it with great results. The documentation was also easy to follow and made it simply to install and use your package. I have noted a few things below:

The vignettes could contain more detail, particularly in the descriptions of the arguments and return values. For example, the type argument in the visualization function could be farther described with the options users have for its value (violin, boxplot etc.)
Similarly, a more thorough description of the functions utility could be beneficial, as the current descriptions are sparse. The function descriptions are also repeated twice in the headers and sub headers, appearing cumbersome.
Not all authors are listed in the vignette, only Qingqing Song is listed on the right hand side of the home page. This section should contain all contributors.
Their are some minor grammar issues which could be corrected, for example, under How to use routlierutils, library is spelled wrong.
Similar to your Python package, more comments could be helpful to improve readability of your code, as your current scripts contain very few comments.
Additionally, the style of your code contains inconsistent spacing between lines, with some scripts using no spaces, and others using many, which is difficult to read. For example, trim_outliers contains a series of if-else conditions which have awkward spacing between them, making it difficult to identify which statements are sequential, while visualize_outliers contains no spacing at all. Keeping this formatting consistent across scripts would be very helpful!

In general, this is great work and I enjoyed using your 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

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).

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: 1 hour

[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

Good Job Team on developing this nice and neat package! A few points that I think would help you improve it further:

Right now, the code of conduct is just a template generated from the package. It would be a good idea to customize it a bit and add items that the team agree on. (At least it should contain your contact method.)
While inspecting the project structure, I noticed there's a figure living in the following path r_outliers_utils/man/figures/README-pressure-1.png. I am not sure what it is, but it seems that it is not related to your package. Would you consider removing it?
This is mentioned in my review for your Python package, but it is still one of my concerns for your R package - the source code of outlier_identifier is rather complicated, and so including more in-line comments explaining the job of some code and the overall flow will definitely make it easier to read and understand.
I like your outlier_identifier function because it is neat and provides many customization. When I tested its functionality - if return_df=FALSE, it outputs a dataframe with the 6th and 7th row being lower_range and upper_range, respectively. At first these two rows did not make sense to me by themselves, and you do not provide explanation on what these are in your docstring. Although I finally understood what these two rows meant, I still think that explaining these in documentation will help to avoid confusion.
trim_outliers takes in a parameter identifier=Z_score; however, it does not actually use this parameter. No matter what random value I give to identifier, it always outputs the same dataframe and does not throw an error. Therefore, I suggest to add exception handling and corresponding test to this parameter. Moreover, I wonder if you will be working on this function in the future to implement more features (such as what you have in your Python package).
visualize_outliers outputs either a violin plot or a boxplot. It maps variable names to x-axis, but it also facets the graph by variable names. It might not be an issue when we only want to visualize the data distribution of less than 3 variables, but when the number goes up to 4 or 5, the generated plot has many unnecessary spaces in between and the actual boxes or violins are too narrow. Hence, for better visualization, my suggestion is that you may consider only doing facetting and leaving x-axis untouched (or vice versa).

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).

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: 1 Hour

[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

A very clean and practical package. As mentioned in the review of the python package, I find your group perfect in describing the package, examples and docstrings. These make using the package very easy and guide me through the process of working with the package. Just a few points to make this package even better are:

Similar to the python package, the outlier_identifier() function could be better with more comments. This would help developers and users better understand your process.
In the trim_outliers.py, as mentioned in the python review, adding a method that would allow the user to input any single value they want to replace all the outliers with, could be useful.
Adding the option to store the new outlier free data as a .csv file would be helpful to the user.
Adding more customizable options for the plots could be helpful.
I can see in the visualize_outliers() function, ncol is hardcoded. This could create issues when the number of variables goes higher.

Overall, I once again congratulate your group on a very well implemented package. This package could definitely help in finding outliers, saving a lot of time.

UBC-MDS / software-review-2022