Analyzing My 2019 Cycling Data

Project Status: Completed

Project Intro/Objective

The purpose of this project was to conduct an exploratory data analysis on my 2019 cycling data.

Methods Used

• Statistics
• Data Visualization
• Time Series

Technologies

• Python
• Altair
• The Strava API
• Stravalib
• Pandas
• Jupyter Notebooks

Credits

• David Yang's Download Running Data From Strava to Analyze Yourself
• Mark Koester's qs_ledger

Project Description

I have been using Strava to house all of my cycling data since 2013. Using the Strava API, Python, Altair, and Jupyter notebooks, I extracted and analyzed my cycling data from 2019. In doing so, I answered the following questions:

• What percentage of days in 2019 did I ride?
• How many miles did I ride throughout the year?
• How many hours did I ride throughout the year?
• What were the number of rides by 30 minute durations?
• What were the number of rides by distances?
• What was my variation in wattage like?
• What was my variation in heart rate like?
• On average, how many hours did I ride per day of the week?
• Can I find any correlations between my different cycling data points?
• etc.

Getting Started

1. Clone this repo (for help see this tutorial).

2. Modify credentials.py with your own client ID and client Secret.

3. pip install requirements.txt

4. Run 2020-01-29-analysis.ipynb

5. Click on the link in the second cell, replace the code variable with the string you see in the URL, then execute the rest of the notebook.

6. Consult David Yang's Download Running Data From Strava to Analyze Yourself if you have any issues or contact me.

Deliverables

• Notebook
• README.md

Contact

• chasekregor.com