Running

Motivation Since I started my own running data dashboard, I’ve been gradually adding my own custom features that I find useful for my training objectives. One bit of analysis I’ve always wanted to do is model my fitness with the overarching objective of generating accurate race time predictions (or at least more accurate than Garmin’s which I’m wildly sceptical of). Modelling fitness isn’t too hard - you can simply look at your pace per heart rate for a rough guide - however, predicting race times is a lot more involved.

Motivation I recently needed to implement some time-series clustering at work on a high-dimensional dataset. Having not tackled this specific problem before, I wanted to practice on a smaller, univariate dataset where I already had a strong intuition about the underlying structure. I turned to my exported Strava running data as a testbed to get familiar with clustering using Dynamic Time Warping (DTW). Heartrate data Show the code library(tidyverse) library(duckdb) library(plotly) library(dtw) library(parallel) library(tidytext) library(ggwordcloud) library(patchwork) library(dendextend) library(ggridges) library(broom) library(cluster) I started by pulling data from my existing database, restricting the scope to runs that contain both heart rate and GPS location data.

I’ve finally managed to combine my two main hobbies of data science and running into one project: a Shiny web-app that allows you to explore your Strava fitness data, as well as providing a local database of all your activities that you can analyse with R. My initial motivation was that I wanted quick access to certain visualisations and metrics that either Strava doesn’t provide, or are awkward to get so I wrote a basic app for my own use.