[REVIEW]: EFAshiny: An User-Friendly Shiny Application for Exploratory Factor Analysis

[whedon]

Submitting author: @PsyChiLin (Chi-Lin Yu)
Repository: https://github.com/PsyChiLin/EFAshiny
Version: 1.0.0
Editor: @cMadan
Reviewer: @DominiqueMakowski
Archive: 10.5281/zenodo.1171998

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General checks

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• Version: Does the release version given match the GitHub release (1.0.0)?
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Functionality

• Installation: Does installation proceed as outlined in the documentation?
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Documentation

• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
• Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
• Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
• Automated tests: Are there automated tests or manual steps described so that the function of the software can be verified?
• Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support

Software paper

• Authors: Does the paper.md file include a list of authors with their affiliations?
• A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
• References: Do all archival references that should have a DOI list one (e.g., papers, datasets, software)?

[whedon]

Hello human, I'm @whedon. I'm here to help you with some common editorial tasks. @DominiqueMakowski it looks like you're currently assigned as the reviewer for this paper 🎉.

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[whedon]

Attempting PDF compilation. Reticulating splines etc...

[whedon]

https://github.com/openjournals/joss-papers/blob/joss.00567/joss.00567/10.21105.joss.00567.pdf

[DominiqueMakowski]

@cMadan @PsyChiLin

EFAshiny is both an R package and an online app designed to provide a clear, straightforward pipeline to exploratory factor analysis, a widely used statistical method. It is a nice package, very well done, that will be very useful to many people in social science and related fields. Installation and launch work as expected, and everything looks in order within the paper and the documentation.

Below are some observations:

General

1. The documentation on how to use the package is ok (with example data).
2. There is no CRAN depot
3. There is no github release, which will be necessary for further publication
4. There is no automated test coverage check, which could be a useful addition
5. Dependencies are stated clearly

Specific

1. In "numeric statistics" (data summary): enabling the display of Median & MAD would be useful
2. For the "distribution" tab in data summary, it would be nice to 1) have the possibility to have density plots rather than histograms and 2) to render these plots dynamically using ggplotly. I believe this wouldn't be complicated to implement.
3. Correlation plot (data summary) could possibly benefit from being generated with ggcorrplot instead of corrplot, allowing for increased flexibility and visual coherence. (altough the aesthetic gain is indeed relative).
4. Factor retention: why not allow the use of different methods for factor retention, or methods summary (see the psycho's function and this unsolved issue)
5. Exploratory graph analysis, It would be a small yet useful addition to be able to plot the graph with different layouts (tree, circle, ..., see qgraph documentation)
6. It would be great to be able to extract figures in R (in ggplot format or so), to further use them in rmarkdown reports or customize them.
7. In the same vein, a button to get the R code used to generate the results would be very useful for learning R and implement automated processing scripts.
8. I understand this is a package for exploraty factor analysis (hence its name), however, I'm curious on the possibility of adding a CFA tab (at the end, refitting the model using CFA (and, maybe at the beginning, an option to split the data into a training and a test dataset))... Your tool would be then sufficient for the entire, end-to-end, factor analysis.
9. In general, the feature that lacks the most is the easy possibility to interface your app with a programming R usage. In other words, the possibility to use your package within a more general script pipeline. That includes the possibility to extract the data (for example, the loadings with their confidence intervals), the code used at each step to generate the results etc., to be able to further reproduce the entire analysis without launching the app and clicking on the buttons.

These are minor suggestions that do not hinder the general quality of the package. Therefore, I'm very positive about its publication.

Best,

[cMadan]

Thank you for the prompt and thorough review, @DominiqueMakowski!

[PsyChiLin]

@DominiqueMakowski Thanks for these useful suggestions!
I have opened several issues for all of them to track their progress. Will improve this project in the next couple of days.
@cMadan Will let you know the progress after I have advanced this submission.
Thanks again !

[PsyChiLin]

@whedon generate pdf

[whedon]

Attempting PDF compilation. Reticulating splines etc...

[whedon]

--> Check article proof 📄 <--

[PsyChiLin]

@cMadan I have tackled all the points except for the CFA one, which is not directly addressed. The latest version is 1.0.1 with github release. All the information/references in the tutorial and paper are updated (thus I re-generate the pdf file) according to the useful suggestions from the review process. Again, many thanks for all these great suggestions, @DominiqueMakowski !!

[DominiqueMakowski]

@cMadan @PsyChiLin I'm happy with these changes 🎉

[cMadan]

@PsyChiLin, I think we're almost all set then! Can you archive the current code on Zenodo or FigShare and tell me the DOI?

[PsyChiLin]

@cMadan The DOI : 10.5281/zenodo.1171998

Thanks a lot !

[arfon]

@whedon set 10.5281/zenodo.1171998 as archive

[arfon]

@whedon set 10.5281/zenodo.1171998 as archive

[whedon]

OK. 10.5281/zenodo.1171998 is the archive.

[arfon]

@DominiqueMakowski - many thanks for your review here and to @cMadan for editing this submission ✨

@PsyChiLin - your submission is now accepted into JOSS and your DOI is https://doi.org/10.21105/joss.00567 ⚡️ 🚀 💥

[whedon]

🎉🎉🎉 Congratulations on your paper acceptance! 🎉🎉🎉

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[PsyChiLin]

@arfon @cMadan @DominiqueMakowski
Thanks a lot for all the processes !