Open spencergerlach opened 1 year ago
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
The package includes all the following forms of documentation:
URL
, BugReports
and Maintainer
(which may be autogenerated via Authors@R
).Estimated hours spent reviewing:
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.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.description
file from the default "Version: 0.0.0.9000" to your version "1.0.0".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!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
The package includes all the following forms of documentation:
URL
, BugReports
and Maintainer
(which may be autogenerated via Authors@R
).Estimated hours spent reviewing: 1
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!
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
The package includes all the following forms of documentation:
URL
, BugReports
and Maintainer
(which may be autogenerated via Authors@R
).Estimated hours spent reviewing: 1hr
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
The package includes all the following forms of documentation:
URL
, BugReports
and Maintainer
(which may be autogenerated via Authors@R
).Estimated hours spent reviewing: 1
year = 2015
instead of just 2015
).
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
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.):
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.
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