Group 13 - mercedestrenzr

[spencergerlach]

---

name: mercedestrenzr
about: Show various information about used Mercedes-Benz vehicles, and provide useful functions for comparison of different vehicles and prediction of vehicle price based on various attributes.

---

Submitting Author Name: Ty Andrews, Spencer Gerlach, Kelly Wu, Morris Zhao
Submitting Author Github Handle: @tieandrews, @spencergerlach, @kellywujy, @mozhao0331
Other Package Authors Github handles: (comma separated, delete if none) @github_handle1, @github_handle2
Repository: https://github.com/UBC-MDS/mercedestrenzr
Version submitted: v1.0.0
Submission type: Standard
Editor: Ty Andrews, Spencer Gerlach, Kelly Wu, Morris Zhao
Reviewers: Eyre Hong, Dhruvi Nishar, Caroline Tang, Jonah Hamilton

Archive: TBD
Version accepted: TBD
Language: en

• Paste the full DESCRIPTION file inside a code block below:

Package: mercedestrenzr
Title: Inspect And Analyze Used Mercedez Benz Car Prices
Version: 0.0.0.9000
Authors@R: 
    c(person("Spencer", "Gerlach", , "[email protected]", role = c("aut", "cre"),
           comment = c(ORCID = "YOUR-ORCID-ID")),
      person("Ty", "Andrews", , "[email protected]", role = c("aut"),
           comment = c(ORCID = "YOUR-ORCID-ID")),
      person("Kelly", "Wu", , "[email protected]", role = c("aut"),
           comment = c(ORCID = "YOUR-ORCID-ID")),
      person("Morris", "Zhao", , "[email protected]", role = c("aut"),
           comment = c(ORCID = "YOUR-ORCID-ID")))
Description: The package helps users to get simple answers on how to choose the used Mercedes Benz car in the market. The package also includes useful visualization tool and trained model to serve buyers and sellers.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
Suggests: 
    covr,
    testthat (>= 3.0.0)
Config/testthat/edition: 3
Depends: 
    R (>= 2.10)
LazyData: true
LazyDataCompression: xz
Imports: 
    ggplot2,
    rlang,
    tidyverse,
    dplyr,
    bundle,
    here,
    tidymodels,
    xgboost
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
  • data munging
  • data deposition
  • data validation and testing
  • 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):

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

• The target audience for this package is people in the market for buying a used vehicle (Mercedes-Benz), that are looking to understand the current market, easily see a list of available cars that suit their needs, and to predict the price of a vehicle with certain desired traits. This can also be used in a similar fashion for people looking to sell their vehicle, as they may also want to know the current market, and predict how much they should sell their vehicle for.

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

• Our package is specific to Mercedes-Benz enthusiasts. It is completely unique in that sense, as the data used to train the prediction model and show market summaries is specifically used Mercedes-Benz vehicles.

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

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[eyrexh]

Package Review

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The package includes all the following forms of documentation:

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• Installation instructions: for the development version of package and any non-standard dependencies in README
• Vignette(s): demonstrating major functionality that runs successfully locally
• Function Documentation: for all exported functions
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Functionality

• Installation: Installation succeeds as documented.
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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

1. The package as a whole is well-designed and organized. All the functions are working great and smoothly. The documentation in the R repo is much easier to follow compared to the python repo. Great work!
2. The vignette GitHub pages suppose to show the README on the homepage and the Example usage on the article page. Only the Example usage has been shown on the pages. I recommend adding the README part which can show the detailed contributors, licenses, and badges.
3. I suggest the test-datasummary.R and test-listing_search.R can also have the comments describing what the test is about for each case as the other two test functions do.
4. I suggest updating the version part in the description file from the default "Version: 0.0.0.9000" to your version "1.0.0".
5. I suggest changing the tag milestone3 to a better name to follow the pattern of other tags and match the version rule.
   Overall the package is well done and practical. It is smart to call a pre-trained machine learning model in R rather than build it from scratch. Nice job!

[xXJohamXx]

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.
• 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:

• A statement of need: clearly stating problems the software is designed to solve and its target audience in README
• Installation instructions: for the development version of package and any non-standard dependencies in README
• Vignette(s): demonstrating major functionality that runs successfully locally
• Function Documentation: for all exported functions
• 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.
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Estimated hours spent reviewing: 1

• 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

Nice work team! Very impressed that you were able to match the functionality of the Python package here so closely!

• I am not sure if this was done on purpose but under the reference tab of the github pages site, mercedes_data and mercedes_price_model are listed under the "all functions" heading. This obviously does not affect the functionality of the package but just wanted to bring it to your attention.

• I agree that the Vignette for this package is much more clear compared the the example usage section for the python version. I found the detailed explanations and calling the functions with different input values very helpful in navigating how the package works. The only suggestion I have is to fix a few grammatical errors that are present.

For example: "The results are also sorted by ascending price, and another the specified feature in the sort_feature parameter"

could be changed to: "The results are also sorted by ascending price and the specified feature in the sort_feature parameter"

• Again I think for the 'predict_mercedes_price' function adding some simple formatting to the output would increase the "user friendliness" of the function. Currently the only output is tibble with one number, but you could add string the includes some more pertinent information like "For the you entered with the current predicted price is BLANK.

• It could be useful to link to the Python version of this package in the README (and vice versa for the Python package) to let users know there is an equivalent package in another language that they could use.

• For the prediction function as you are loading a pre-trained model I think it would be helpful to include a quick description of what it is and how it was built in the README.

Overall this is a fantastic project and I think there are a lot of options for adding functions to this package moving forward. Great job everyone, I really enjoyed reviewing this package!

[dhruvinishar]

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.
• 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:

• A statement of need: clearly stating problems the software is designed to solve and its target audience in README
• Installation instructions: for the development version of package and any non-standard dependencies in README
• Vignette(s): demonstrating major functionality that runs successfully locally
• Function Documentation: for all exported functions
• 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.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• 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.

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 documentation and the README file are designed very well, easy to follow and run the functions
• All functions run very well as suggested on the Usage.
• The package has 2 licenses - Unknown and MIT: could not find the discussion for why both were used.
• Maybe report the predictions with accuracy metrics and probabilities/confidence intervals so that the user is confident enough to use the predicted values.
• The function unit tests and the testing framework look great and test the functions really well. Great package!

[carolinetang77]

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.
• 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:

• A statement of need: clearly stating problems the software is designed to solve and its target audience in README
• Installation instructions: for the development version of package and any non-standard dependencies in README
• Vignette(s): demonstrating major functionality that runs successfully locally
• Function Documentation: for all exported functions
• 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.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• 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.

Estimated hours spent reviewing: 1

• 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

1. Very nice package! The README and vignette examples offer a good overview of what functions exist within the package and how to use them, but there are a few grammar mistakes in the introduction to the package.
2. It may help to include some information in the README or other documentation about the model(s?) you're using to predict the car's value, for those who may be interested in how it works.
3. For the examples with many arguments, I would suggest adding the argument names so that it's clear what the different values represent (e.g. having year = 2015 instead of just 2015).
4. I really like the plot function! It's cool to see where the price of a theoretical car would fall within the range of market prices. However, when you're comparing the value to the average, it would be good to have the actual value of the average written somewhere on the plot, either as a line or with the number in the title.
5. The unit testing looks very comprehensive and offers great coverage of the functions!