software-review [Pre-submission Inquiry]: Athlytics

Submitting Author Name: Zhiang HE Submitting Author Github Handle: HzaCode Other Package Authors Github handles: Repository: https://github.com/HzaCode/Athlytics Submission type: Pre-submission Language: en

Paste the full DESCRIPTION file inside a code block below:

Package: Athlytics
Title: Advanced Sports Performance Analysis for 'Strava' Data
Version: 0.1.0
Author: Zhiang HE [aut, cre]
Maintainer: Zhiang HE <[email protected]>
Authors@R: 
    person(given = "Zhiang", family = "HE", email = "[email protected]", role = c("aut", "cre"))
Description: Provides a suite of tools for advanced sports performance analysis and modeling, designed to work with activity data retrieved from 'Strava'. It focuses on applying established sports science models and statistical methods to gain deeper insights into training load, performance prediction, recovery status, and identifying key performance factors, extending basic data analysis capabilities.
License: MIT + file LICENSE
URL: https://github.com/HzaCode/Athlytics 
BugReports: https://github.com/HzaCode/Athlytics/issues 
Encoding: UTF-8
Depends: 
    R (>= 3.6.0) 
Imports:
    dplyr (>= 1.0.0),
    ggplot2,            
    httr,
    jsonlite,
    lubridate,          
    purrr,              
    rlang (>= 0.4.0),   
    rStrava,            
    tidyr,              
    viridis,
    zoo,
    mockery
Suggests:
    devtools,
    knitr,
    pkgdown,
    rmarkdown,
    testthat (>= 3.0.0),
    mockery
VignetteBuilder: knitr
RoxygenNote: 7.3.2
NeedsCompilation: no

Scope

Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check one or more appropriate boxes below):

Data Lifecycle Packages
- [x] data retrieval
- [ ] data extraction
- [x] data munging
- [ ] data deposition
- [ ] data validation and testing
- [x] workflow automation
- [ ] version control
- [ ] citation management and bibliometrics
- [ ] scientific software wrappers
- [ ] field and lab reproducibility tools
- [ ] database software bindings
- [ ] geospatial data
- [ ] text analysis
Statistical Packages
- [ ] Bayesian and Monte Carlo Routines
- [ ] Dimensionality Reduction, Clustering, and Unsupervised Learning
- [ ] Machine Learning
- [ ] Regression and Supervised Learning
- [x] Exploratory Data Analysis (EDA) and Summary Statistics
- [ ] Spatial Analyses
- [x] Time Series Analyses
- [ ] Probability Distributions
Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of: Athlytics performs data retrieval from the Strava API and subsequent data munging to prepare activity data. Its core function is applying established sports science models for Time Series Analyses of performance trends and physiological responses. It facilitates Exploratory Data Analysis by computing and visualizing these specialized metrics, providing an integrated workflow automation from raw data to advanced insights, going beyond basic descriptive statistics.
If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package? No, not yet.
Who is the target audience and what are scientific applications of this package?
The target audience includes athletes, coaches, sports scientists, and researchers analyzing Strava data. Scientific applications involve studying training effectiveness, modeling performance, and investigating physiological responses to exercise reproducibly.
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category? Yes, related packages exist, but Athlytics offers distinct value:
- rStrava mainly gets data from the Strava API. Athlytics uses this data (or similar methods) but focuses on analyzing it with sports science models (like training load).
- trackeR is great for analyzing activity files (GPX, TCX) and calculating general stats. Athlytics focuses on API data from Strava and applies specific sports science models not found in trackeR.
Athlytics fills a specific niche by integrating Strava API data directly with these advanced, validated sports science modeling techniques within R, offering analytical capabilities not directly provided by pure API wrappers or general file analyzers, thus making sophisticated performance analysis more accessible and reproducible for the R community.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research? The package processes personal activity data retrieved via user authentication from Strava, relying on user authorization and Strava's platform for privacy controls.
Any other questions or issues we should be aware of?: No. I'm submitting this as a pre-submission inquiry mainly to ensure Athlytics aligns well with rOpenSci's goals and scope before investing further effort into preparing for the full review process. Perhaps the automated package checks (check package) might not be necessary.Any initial thoughts on its fit would be very helpful at this point.Thank you!

May 05 '25 07:05 HzaCode

Thanks for you inquiry @HzaCode and apologies for taking a while to get back to you. I'll send a request for automated package checks now, and proceed to more specific issues following that. I anticipate that we'll have some conceptual issues regarding scientific application of this package, so maybe you could help in the meantime with a brief answer to the following:

I can see the immediate utility of the functionality provided by {Athlytics} in helping me (as a passionate cyclist 🚴 ) in my personal training regime, but have a harder time conceiving of broader utility for scientific research. Could you please help me, and potential editors and reviewers to come, to understand in more detail how the outputs of {Athlytics} help to advance the scientific processes of sports analytics?

Part of a potential problem that I foresee in that question is that the Strava API only offers access to my personal data, and only to others with their explicit approval. The package therefore seems inherently restricted to only being able to analyse my data in all their fullness, while being unable to position the outputs within any broader analytic context of data from others, or even any kind of general or average data. Do you have any ideas on how I might contextualize output from your package based on my personal data within more general scientific understandings of the metrics you provide?

May 20 '25 08:05 mpadge

@ropensci-review-bot check package

May 20 '25 09:05 mpadge

Thanks, about to send the query.

May 20 '25 09:05 ropensci-review-bot

:rocket:

Error: Issue template has no 'repourl'

:wave:

May 20 '25 09:05 ropensci-review-bot

@HzaCode Can you please edit your initial comment by starting a new pre-submission inquiry issue, pasting the initial section of 6 lines at the top, and filling out the details again? Note in particular that the top of the issue template says,

Below, please enter values for (1) submitting author GitHub handle (replacing "@github_handle"); and (2) Repository URL (replacing "https://repourl"). Values for additional package authors may also be specified, replacing "@github_handle1", "@github_handle2" - delete these if not needed. DO NOT DELETE HTML SYMBOLS (everything between "<!" and ">"). Replace only "@github_handle" and "https://repourl". This comment may be deleted once it has been read and understood.

Where the important bit is:

DO NOT DELETE HTML SYMBOLS (everything between "<!" and ">")

That's why the bot reported the error above. Once you're re-inserted all HTML symbols in the first 6 lines, feel free to repeat the check package command as I did above, and it should work. Thanks!

May 20 '25 09:05 mpadge

Hi @mpadge, thanks for your feedback.

In terms of broader data use: group-level analysis is possible when multiple users authorize access, which is how team-based or collaborative athlete studies can be done.As for benchmarking features and broader context, those are definitely on my list and something I plan to explore when I have more time.

May 22 '25 09:05 HzaCode

@HzaCode Can you please address the previous comment so we can proceed here? Can also provide a bit more detail on your comment that,

group-level analysis is possible when multiple users authorize access, which is how team-based or collaborative athlete studies can be done

? Are there any published studies which have used Strava data? Can you link to those, and frame your package in a more general context that way? Or provide any other clear demonstrations of how the package could be used in directly scientific contexts? Thanks

Jun 23 '25 08:06 mpadge

Hi @mpadge,

Thank you very much for your time and the detailed, thoughtful feedback on my pre-submission inquiry. I sincerely appreciate the guidance on both fixing the issue template and, more importantly, on framing the package's scientific utility. Your points are well-taken and will be very helpful for the project's future.

Due to a shift in priorities and because the work associated with this package has since been submitted for review at another journal, I've concluded that I won't have the capacity to move forward with the rOpenSci review process right now. For this reason, I've decided it's best not to move forward with a formal submission at this time.

I apologize for any inconvenience this may cause and thank you again for your consideration and the valuable work you do for the community.

Jun 23 '25 17:06 HzaCode

Thank you for the kind words @HzaCode. I'll close this issue for now, but please feel very welcome to re-open at any stage in future to continue discussions. Good luck in the meantime with the package.

Jun 24 '25 07:06 mpadge

Hi @mpadge,

Hope you are doing well.

Following up on our previous discussion about my Athlytics package, I've taken your feedback on its scientific utility to heart. I've since found published research using multi-user Strava data and am now drafting a new vignette to demonstrate a group-level analysis.

My main question is whether these additions would help align the package with rOpenSci's scope, and if you anticipate that suitable reviewers with expertise in this domain could be found for a potential resubmission.

To be transparent, the package is also submitted to JOSS. I greatly respect their process, though it appears their reviewers' availability may be limited at this time. I believe rOpenSci is the ideal home for this package and would be willing to pause the JOSS submission if you think the direction I'm taking here is promising.

Any brief guidance would be greatly appreciated.

Oct 18 '25 06:10 HzaCode

@HzaCode Thank you very much for the effort you've put into re-framing the whole package. That now looks much more like a package primarily intended for scientific analysis! I'll ask the review team what they think of the scope of the package. It definitely seems like it'll need reviewers familiar with sports analytics, and ideally an editor with some knowledge as well. Although that area is a bit beyond our usual scope, we'll do our best and get back to you asap with a decision.

If we do proceed - which hopefully will happen! - note that concurrent submission to JOSS presents no problems, but the two processes are designed to work together and generally with an rOpenSci review first, following which JOSS accepts our reviews as sufficient, and subsequent submissions there will be automatically accepted. Given that, I'd suggest that if we do notify you (hopefully soon) that we'll proceed with your submission here, you can simply inform JOSS that you'd like to pause the review there until we're done here. That will save excessive burden on reviewers too, as both JOSS and us only need two reviews in total, with ours designed to take precedence.

Our current Editor-in-Chief @ldecicco-USGS will get back to you soon with a decision.

Oct 18 '25 12:10 mpadge

Hi @mpadge,

Thank you so much for your quick and very encouraging response.

I'm delighted to hear that the new framing for Athlytics aligns well with rOpenSci's scientific goals. I truly appreciate you taking the time to discuss this with the review team and for your willingness to find reviewers with expertise in sports analytics.

The information regarding the concurrent review process with JOSS is also incredibly helpful and provides a clear path forward. I will follow your suggestion and contact JOSS to pause the review there as soon as I hear about your team's decision to proceed.

I'll look forward to hearing from you or the Editor-in-Chief.

Thanks again for all your guidance.

Oct 18 '25 22:10 HzaCode

Hi @mpadge,

Just wanted to follow up gently on our discussion from last week regarding the "Athlytics" package. I was very encouraged by your positive feedback on the new direction.

I understand you and the review team are busy, so no rush at all. I was just wondering if there might be any preliminary thoughts or updates from your discussion.

Thank you again for your time and consideration.

Oct 25 '25 09:10 HzaCode

Thanks @HzaCode for your always considerate responses and questions. We should have further information this coming week.

Oct 25 '25 11:10 mpadge

Hi @mpadge,

Thank you for the update, that's very kind of you. I'll gladly wait to hear from the team next week, so please don't feel any rush.

The conversation has been very helpful for me. It's prompted me to think more deeply about where Athlytics might fit within the broader R ecosystem.

My hope is that the package can offer a useful tool for researchers wanting to use Strava data in their scientific work. I felt that a key challenge is often contextualizing an individual's data, so the package focuses on benchmarking as a potential way to help with this.

By making it more straightforward to compare an individual against a reference cohort, I hope Athlytics can help bridge the gap from personal tracking to group-level scientific analysis. This was the main goal I had in mind when developing it.

Thank you again for your thoughtful guidance.

Oct 26 '25 03:10 HzaCode

@HzaCode Good news: Our editorial team fully supports this package being submitted for a full review, and even had a couple of suggestions for reviewers. Please open a new "Full submission" issue, making sure you link back to both this pre-submission issue here, and the useful discussions you've already started over in the JOSS issue, and we'll proceed from there. Please also ask the JOSS folk to put their review on hold. Once we've finished here, the JOSS process will then use our reviews instead of separate ones over there, and should be processed very quickly.

Thank you very much for all of your positive and highly engaged responses, and for the updates you've already implemented in preparation for review :+1:

Oct 29 '25 09:10 mpadge

Hi @mpadge,

Thank you so much for the wonderful news. I'm thrilled to hear that the editorial team supports Athlytics for a full review. I truly appreciate all your guidance and engagement throughout this process.

I will follow your instructions and open a new "Full submission" issue right away, making sure to link back to this pre-submission thread.

Thank you again for your support. I'm very much looking forward to the review process.

Oct 29 '25 13:10 HzaCode