Submitting Author: Andrea Lee (@andrealee011 ), Jaekeun Lee (@agdal1125), Sakariya Aynashe (@eyrakas), Xinwen Wang (@xiw315 ) Repository: rb4model Version submitted: v1.1.0 Editor: Varada Kolhatkar (@kvarada) Reviewer 1: Yi Liu (@v5y8)
Reviewer 2: Victor Cuspinera-Contreras (@vcuspinera) Archive: TBD
Version accepted: TBD

Paste the full DESCRIPTION file inside a code block below:

Package: rb4model
Title: rb4model
Version: 0.0.0.9000
Authors@R: 
    c(person(given = "Sakariya",
           family = "Aynashe",
           role = c("aut")),
       person(given = "Andrea",
           family = "Lee",
           role = c("aut", "cre"),
           email = "andrea.lee.011@gmail.com"),
        person(given = "Jaekeun",
           family = "Lee",
           role = c("aut")),
        person(given = "Xinwen",
           family = "Wang",
           role = c("aut")))
Description: An R package that preprocesses data and performs machine learning!
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.0.2
Suggests: 
    testthat (>= 2.1.0),
    covr,
    knitr,
    rmarkdown
Imports: 
    mice,
    stats,
    caret,
    datasets,
    mlbench,
    randomForest,
    e1071
URL: https://github.com/UBC-MDS/rb4model
BugReports: https://github.com/UBC-MDS/rb4model/issues
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 automataion
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] database software bindings
- [ ] geospatial data
- [ ] text analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences):

Our package smoothly pre-processes data for machine learning and provides wrapper functions of pre-existing machine learning libraries to reduce lines of code and promote efficiency.
Who is the target audience and what are scientific applications of this package?

The target audience is data scientists. They can use our package to smoothly pre-process their data and conduct machine learning using their model of choice.
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

caret is probably the most widely used package for supervised learning problems in R. Although the library provides various model fitting and preprocessing features, programmers end up with writing the same line of code over and over again. Our rb4model library provides a simple solution to this pain point: wrapper functions of caret and other primary libraries used for supervised learning to reduce lines of code and promote efficiency.
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.
[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.
[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 automatically submit to the Journal of Open Source Software? If so:

JOSS Options

- [ ] The package has an **obvious research application** according to [JOSS's definition](https://joss.readthedocs.io/en/latest/submitting.html#submission-requirements). - [ ] The package contains a `paper.md` matching [JOSS's requirements](https://joss.readthedocs.io/en/latest/submitting.html#what-should-my-paper-contain) with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: - (*Do not submit your package separately to JOSS*)

[ ] 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.

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 package and any non-standard dependencies in README
[ ] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all exported functions in R help
[x] Examples for all exported functions in R Help that run successfully locally
[ ] 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.
[ ] Packaging guidelines: The package conforms to the rOpenSci packaging guidelines

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:

[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

I like the code style; it's very simple and easy-to-read!

However, there are a few problems with your project, structure wise.

there are a few edge-cases that are covered in rb4model.R, but no corresponding tests.
- lines 64, 75, 114, 117, 123, 132 in rb4model.R
testthat shouldn't be in the list of dependencies in README.md; it should be a dev dependency instead.
rb4model.Rproj shouldn't be in the repo
there are no instructions on how to use the package, other than the documentation of the functions; I was able to run your code after copy+pasting your vignette.Rmd, but that wasn't obvious (see below)

- Overall, I think you need to upgrade your CI / automated build process; there were quite a few things I noticed were missing and/or were odd:

not sure where the Vignette is? I see the .Rmd file, but not sure where it's knitted to. There's also a docs folder, but no docs are linked in README.md, and your *.yml files don't mention any sort of build either.
build.yml doesn't actually do anything; it looks like you might want to replace it with R-CMD-Check.yml
from R-CMD-Check.yml's latest build:

        'datasets' 'e1071' 'mlbench' 'randomForest'
        All declared Imports should be used.

These libraries need to be removed from DESCRIPTIONS.

Hi rb4model team, I was assigned as a reviewer of your R package and I am pleased to share the following review and recommendations:

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 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 in R help
[X] Examples for all exported functions in R Help that run successfully locally
[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:

[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.

[ ] 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.
[ ] Packaging guidelines: The package conforms to the rOpenSci packaging guidelines

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:

[ ] 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

I found your package very complete in the sense that have complementary functions that help you to perform an appropriate data pre-processing. I believe that the idea is very good and you could improve your package mainly sharing more information for the user.

Vignette: Before the link to the Vignette you could add a briefly description (e.g. build an R package that elegantly performs data pre-processing in a fast and easy manner).
Function documentation: I think it would be helpful to have more complete documentation of the functions in the Readme file and/or the Vignette; I didn’t see the description or an example of how to use them, but I find out how with the description and in RStudio help.
Examples: I really like the comments of your functions when I ask for help in RStudio; maybe you can use that information in the Usage section located in the Readme file of the Package, to show examples of how to use each function and which would be the output. I found a good example that could be useful: look at the example section of Jenny’s foofators3 example.
Feature Splitter function: The feature_splitter function splits the features into two categories features; however, it doesn’t say which of those are numerical and which are categorical. For example, I made a data frame with 3 numerical an 2 categorical variables, and the function separate them well but it doesn’t tell me which belomg to each cathegory:
```
x <- data.frame('X1'=c(1,2,3,4), 'X2'=c('a','b','c','d'), 'X3'=c(11,12,13,14),'X4'=c(90,99,120,421), 'X5'=c('ax','bx','cx','dx'))
feature_splitter(x)
```

output:

[[1]] [1] "X2" "X5"

[[2]] [1] "X1" "X3" "X4"

- Forward Selection function: When I run the `ForwardSelection`, if I change the minimum number of features it doesn’t return an appropriate output of what I am asking. In the next example I ask at least for three features and I get only one as a result:
```{r}
y <- iris$Species
x <- iris[c(1,2,3,4)]
ffs <- ForwardSelection(feature=x, label=y, my_mod="rf", min_f = 2)
ffs
[1] 2

print(head(x[ffs], 10))

[output]:
   Sepal.Width
1          3.5
2          3.0
3          3.2
4          3.1
5          3.6
6          3.9
7          3.4
8          3.4
9          2.9
10         3.1

I hope you find this review useful!

Thank you for the feedback! It was extremely hepful! Because of time constraints, we were unable to address all your feedback. Here is a list of the changes we did make:

added a link to our vignette in the README
removed .Rproj file from repo
added examples for all our functions in the usage section of the README
added edge case tests for our forward selection function
improved our feature splitter function by indicating which of the output is numerical and which is categorical
fixed our forward selection function to remove the minimum number of features argument

Please find our new relase here: https://github.com/UBC-MDS/rb4model/tree/v1.1.1

Thank you @v5y8 and @vcuspinera for your valuable feedback. We have made the changes outlined above by @andrealee011. I just wanted to add one more comment on the feature_splitter for the use of OHE. The intended use of the function was to help users visualize what kind of data they are dealing with, i.e. which ones are numerical and which ones are categorical. Kind of quick EDA for them. Not necessarily to do full data preprocessing. But we have changed its output into a data frame which is more presentable to users.

UBC-MDS / software-review

Submission: rb4model (R) #27

Scope

Technical checks

Publication options

Code of conduct

Package Review

Documentation

For packages co-submitting to JOSS

Functionality

Final approval (post-review)

Review Comments

- Overall, I think you need to upgrade your CI / automated build process; there were quite a few things I noticed were missing and/or were odd:

Package Review

Documentation

For packages co-submitting to JOSS

Functionality

Final approval (post-review)

Review Comments

output: