Marathon Analysis

• author: Kexin (Coco) Shi

Summary

Over the last ten years, marathon running has become a popular physical activity around the world. This study aims to investigate the relationship between marathon runners' maximum distance ran per week during race training and their race time. We modeled a simple linear regression to predict marathon times based on their training patterns and tested on the model with the test dataset. By having this analysis, it helps marathon lovers to gain insights into how training volume influences race performance and to better prepare the race. Our final model performed moderate on the unseen test data set, with the RMSPE of 0.5504829, This means that, our simple linear regression model's predictions deviate from the actual race times by approximately 0.55 hours on average.However, there are several limitations of the model that there is only one predictor in the model, which may be not sufficient to make prediction.

This study uses the dataset from a a public dataset on GitHub, containing 13 variables about runners, such as age, bmi, maximum training distance per week (max, in miles) and actual marathon race time (time_hrs, in hours) and so on.

Report

The final report can be found here.

Usage

Setup

If you are using Windows or Mac, make sure Docker Desktop is running.

1. Clone this GitHub repository.

Running the analysis

1. Navigate to the root of this project on your computer using the command line and enter the following command:

docker compose up

2. In the terminal, look for a URL that starts with http://127.0.0.1:8888/lab?token= (for an example, see the highlighted text in the terminal below). Copy and paste that URL into your browser.

3. Connect to the instance of RStudio that is running in the container by visiting http://localhost:8787/.

4. Open a terminal (from the docker rstudio) and run the following commands:

Rscript scripts/download_data.R "https://raw.githubusercontent.com/UBC-DSCI/dsci-100-student/refs/heads/master/materials/R/worksheet_regression2/data/marathon.csv" "data/marathon.csv"

Rscript scripts/validate_data.R "data/marathon.csv"

Rscript scripts/eda.R "data/marathon.csv" "results/subset_scatterplot_maxDistance_racetime.png" "results/full_scatterplot_maxDistance_racetime.png"

Rscript scripts/analysis.R "data/marathon.csv" "results/training_scatterplot.png" "results/training_prediction_plot.png" "results/test_prediction_plot.png"

quarto render report/marathon_analysis.qmd --to pdf

Clean up

1. To shut down the container and clean up the resources, type Cntrl + C in the terminal where you launched the container, and then type docker compose rm

Dependencies

The following dependencies are required to run the analysis:

• R version 4.2 or later

• tidyverse: for data manipulation and visualization

• fs: for working with the filesystem

• here: for easy path management

• tidymodels: for modeling and machine learning

• broom: for summarizing model outputs

• yardstick: for evaluating model performance

• Docker

• VS Code

• VS Code Jupyter Extension

Developer notes

Developer dependencies

• conda (version 23.9.0 or higher)
• conda-lock (version 2.5.7 or higher)

Adding a new dependency

1. Add the dependency to the environment.yml file on a new branch.

2. Run conda-lock -k explicit --file environment.yml -p linux-64 to update the conda-linux-64.lock file.

3. Re-build the Docker image locally to ensure it builds and runs properly by running docker build --platform linux/amd64 --tag shikexi6/marathon-analysis .

4. Push the changes to GitHub. A new Docker image will be built and pushed to Docker Hub automatically. It will be tagged with the SHA for the commit that changed the file.

5. Update the docker-compose.yml file on your branch to use the new container image (make sure to update the tag specifically).

6. Send a pull request to merge the changes into the main branch.

License

This project is licensed under the following:

Code License: MIT License

Report License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

See the license file for more information. . If re-using/re-mixing please provide attribution and link to this webpage. The software code contained within this repository is licensed under the MIT license. See the license file for more information.

Reference

• Zoladz, J. A., & Nieckarz, Z. (2021). Marathon race performance increases the amount of particulate matter deposited in the respiratory system of runners: an incentive for "clean air marathon runs". PeerJ, 9, e11562. https://doi.org/10.7717/peerj.11562

• Kaufmann, C. C., Wegberger, C., Tscharre, M., Haller, P. M., Piackova, E., Vujasin, I., Kassem, M., Tentzeris, I., & Freynhofer, M. K. (2020). Effect of marathon and ultra-marathon on inflammation and iron homeostasis. Scandinavian Journal of Medicine & Science in Sports. https://doi.org/10.1111/sms.13869

• Taraji, M., Haddad, P. R., Amos, R. I. J., Talebi, M., Szucs, R., Dolan, J. W., & Pohl, C. A. (2017). Error measures in quantitative structure-retention relationships studies. Journal of Chromatography A, 1524, 298-302. https://doi.org/10.1016/j.chroma.2017.09.050

• Kolhatkar, V., & Östblom, J. (2023). Regression metrics [Lecture notes]. UBC GitHub Pages. https://pages.github.ubc.ca/mds-2024-25/DSCI_573_feat-model-select_students/lectures/02_regression-metrics.html