Submission: instaeda (R)

Submitting Author:

Justin Fu (@jufu)
Selma Duric (@d-sel)
Zeliha Ural Merpez (@zmerpez)
Siqi Zhou (@roycezhou)

Repository: instaeda_R Version submitted: v0.2.0 Editor: Tiffany Timbers (@ttimbers) Reviewers: TBD Archive: TBD Version accepted: TBD

Paste the full DESCRIPTION file inside a code block below:

Package: instaeda
Title: InstaEDA: Quick and Easy Way to Clean Data and Build Exploratory Data Analysis Plots
Version: 0.0.0.9000
Authors@R: 
    person(given = "Justin",
           family = "Fu",
           email = "jufu11@gmail.com",
           role = c("aut", "cre"))
    person(given = "Royce",
           family = "Zhou",
           email = "royce.siqizhou@gmail.com",
           role = c("aut", "cre"))
    person(given = "Selma",
           family = "Duric",
           email = "selma@duric.ca",
           role = c("aut", "cre"))
    person(given = "Zeliha",
           family = "Ural Merpez",
           email = "zmerpez@gmail.com",
           role = c("aut", "cre"))
Description: This idea came up as we have been building data projects for quite some time now in the UBC MDS program. We noticed that there are some repetitive activities that occur when we first explore the data. This project will help you take a given raw data set an conduct some data cleansing and plotting with a minimal amount of code.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.1
Config/testthat/edition: 2
Imports: 
    ggplot2,
    dplyr,
    magrittr,
    testthat,
    palmerpenguins,
    stringr,
    ggthemes,
    utils,
    covr,
    imputeR,
    scales,
    tidyr,
    tidyselect,
    stats,
    rlang
URL: https://github.com/UBC-MDS/instaeda_R
BugReports: https://github.com/UBC-MDS/instaeda_R/issues

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
- [ ] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [ ] geospatial data
- [ ] text analysis
- [x] data visualization
- [x] exploratory data analysis
Explain how and why the package falls under these categories (briefly, 1-2 sentences):

The package falls under data visualization and exploratory data analysis since it performs to explore the raw data with data checking, data cleaning, exploratory visualization including numerical correlation plotting and basic distribution plotting by datatype with a minimal amount of code.

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

The target audience of this package includes anyone who has the requirements to clean data and build exploratory data analysis plots. For instance, students with computer science and data science background might be the target audience. Besides, data scientists, data engineers, statisticians are possible target users as well.

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

There are some R packages to conduct exploratory data analysis (EDA) such as "SmartEDA" and "dlookr". However, our R package provides a different working flow and functionality. There are four main components of our R package and each of the functions has its innovation points. For example, our data checking function gives users a sense of the whole distribution summary of the raw data. Although there are some built-in functions to summarize the missing data, mean, standard deviation, etc, our function implements straightforward plotting including several bar charts to describe the data summary with metrics considering the numeric columns, factor columns, complete rows and missing observations.

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

NA

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.

NA

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.

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[ ] 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*)

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

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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).

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

The package contains a paper.md matching JOSS's requirements with:

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[ ] Authors: A list of authors with their affiliations

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[ ] 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: about 3h

[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 idea of generating the quick info of a given data frame by plot_info, but find this output is somehow confusing at the first glance. The user might wonder why there is "Complete Rows" and "Missing Values" in column subplot and how to interpret them. I understand it could be easier to just facet_wrap() into 3 subplots. If possible, try to have only 4 bars to show those 4 metrics, instead of in total (seems) 12 bars.
For the last function plot_basic_distribution, it outputs nothing if some columns are ordered factor. I would suggest to use is.factor and is.character in this case.
I'm able to find usage examples in the vignettes page, but it would be great to also include in README page if possible.
All functions are well formatted and mostly followed DRY well. It would be great to have some narratives that the imputed "mean" etc are based on the "mean" of the column. (even though it may sound obvious 😃 )
The color_config argument in plot_info does not work when I try to input 3 color codes (eg, c("#999999", "#E69F00", "#56B4E9")). Then I looked into the source code and found that scale_color_manual() may not be the proper one. I have tested the version by scale_fill_discrete("Measurements", type = color_config) and delete scale_color_manual(values = color_config), which aligns well with the function purpose I think.

I like the whole package of helping eda more accessible and investigating swiftly! Nice job instaeda team👍🏻

Package Review

Briefly describe any working relationship you have (had) with the package authors. The package authors and I are classmates in the UBC MDS program.
[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: 2h 30min

[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

DOCUMENTATION Overall your documentation is very good! The examples using palmerpenguins in the Vignette's 'Get Started' section were very useful when first getting to know the package, great job with those. However, the examples in the actual function documentation could be more detailed to help out the user when searching for documentation within RStudio for example. I would recommend adding very simple examples similar to how you have done in the Vignette.
DOCUMENTATION I wanted to mention a caution with one of the claims in the README file, which states: "This project will help you take a given raw data set an conduct some data cleansing and plotting with a minimal amount of code". However, I tried loading the penguins_raw dataframe and applying all of the functions. I found that three out of four ran successfully on this "messy" dataframe, however I ran into issues with the plot_basic_distributions function. I think it would be helpful to a user of the function to mention that the input dataframe must meet some basic criteria in order to work with all of the functions.
TESTS Overall tests were very well written and many were included for each function (as seen by the high amount of code coverage)! However, it may be a good idea to add some tests in the test-divide_and_fill.R file to check the output values using an expect_equal expression. I noticed that all of the tests for this function cover either expect_error or expect_true and the expect_true tests are all checking if the returned object is a dataframe. I would suggest taking a simple dataframe (e.g. penguins) and manually calculating the mean or median values to see if the outuput of the imputation equal these values.
INSTALLATION I encountered a very minor setback during installation using RStudio console. After I installed the instaeda package from github and installed/updated required supporting packages, I couldn't immediately call library(instaeda). RStudio console threw an error and it took me a minute to realize that it was because I had to restart my R session in order for the installations to take effect. This could be something useful to mention in the documentation for the installation instructions.
CODE/STYLE I ran spell check on your package using the spelling package and found no spelling mistakes. I also ran goodpractice::gp() and found a few recommendations that could improve the style and robustness of your code. I've included a few from the output just for you to see, but I'd recommend running the command from your project's main directory in order to see all of them.
- In instaeda.R: Line 140 - avoid long code lines, it is bad for readability. Also, many people prefer editor windows that are about 80 characters wide. Try make your lines shorter than 80 characters
- In instaeda.R: Line 369, 370 - avoid sapply(), it is not type safe. It might return a vector, or a list, depending on the input data. Consider using vapply() instead.
- In instaeda.R: Line 249 - avoid 1:length(...), 1:nrow(...), 1:ncol(...) expressions. They are error prone and result 1:0 if the expression on the right hand side is zero. Use seq_len() or seq_along() instead.

Overall very useful package and a serious time saver when it comes to conducting EDA on your data. Nice work team!

@camharris22 Thanks for your comments, I am glad to hear the suggestion, you have made for 1:length(), I could use those in future. Luckly, I am checking for the case lenght() < 1 before to have a meaningful number of columns given as input. I agree for the tests that divide_and_fill might cover more, I would look into this.

UBC-MDS / software-review-2021