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Introduction to Athlytics

library(Athlytics)library(rStrava)# Required for authentication

Introduction

TheAthlytics R package simplifies advanced analysis ofathletic performance and training load using data sourced directly fromthe Strava API. It provides functions to fetch Strava data (via therStrava package), calculate key metrics based on sportsscience principles, and generate insightful visualizations for trainingmonitoring.

This vignette covers:

Installation

You can install the released version of Athlytics from CRAN (onceavailable) with:

# install.packages("Athlytics")

Alternatively, install the development version from GitHub:

# install.packages('remotes') # If needed# remotes::install_github('HzaCode/Athlytics')

Authentication with Strava

Athlytics requires a valid Strava API token to fetchdata. This is handled using therStrava package.

  1. Create a Strava API Application:
    • Go tohttps://developers.strava.com/ and navigate to “My APIApplication” under your settings, or follow their “Getting Started”guide to create an app.
    • Create an application (e.g., “My Athlytics App”).
    • Set “Authorization Callback Domain” tolocalhost.
    • Note yourClient ID andClientSecret.
  2. Authenticate in R:
    • UserStrava::strava_oauth().
    • Usingcache = TRUE is highly recommended to securelystore the token and avoid re-authenticating every session.
# --- Example Authentication Code ---# (Do not run this block directly in the vignette build)# Replace with your actual credentials or use environment variables# app_name <- 'MyAthlyticsApp'# client_id <- "YOUR_CLIENT_ID"# client_secret <- "YOUR_SECRET"# This function performs the OAuth handshake (may open browser)# stoken <- rStrava::strava_oauth(#   app_name,#   client_id = client_id,#   client_secret = client_secret,#   app_scope = "activity:read_all", # Ensure necessary scopes#   cache = TRUE # IMPORTANT: caches the token# )# For the examples below, we assume you have obtained a valid token# object named 'stoken' in your interactive R session.# Replace this placeholder line if running interactively:stoken_placeholder<-"<Replace this string with your actual stoken object>"# You would use the real 'stoken' object in function calls, e.g.:# some_data <- calculate_acwr(stoken = stoken, ...)

Important: The following examples usestoken = stoken_placeholder and haveeval=FALSE. You need to replacestoken_placeholder with your actual token object(stoken) and potentially seteval=TRUE ifrunning interactively with a valid, cached token.

Example Analysis Visualizations

These functions generate plots to analyze trends and performance.

1. Load Exposure (Acute vs. Chronic)

Visualizes the relationship between short-term (acute) and long-term(chronic) training load to assess readiness and potential injury risk.Note that TSS/HRSS calculations based on activity summaries areapproximations.

# Calculate using approximate TSS for Rides (Requires FTP)exposure_data_tss<-calculate_exposure(stoken = stoken_placeholder,activity_type ="Ride",load_metric ="tss",user_ftp =280,# Example FTP, replace with yoursacute_period =7,chronic_period =28)# Plot the resultplot_exposure(exposure_data = exposure_data_tss,risk_zones =TRUE)# Calculate using approximate HRSS for Runs (Requires Max & Resting HR)hrss_data<-calculate_exposure(stoken = stoken_placeholder,activity_type ="Run",load_metric ="hrss",user_max_hr =190,# Example Max HRuser_resting_hr =50,# Example Resting HRacute_period =7,chronic_period =42)plot_exposure(exposure_data = hrrss_data,risk_zones =TRUE)

2. ACWR Trend

Tracks the Acute:Chronic Workload Ratio over time, highlightingperiods of potentially risky load increases.

# Calculate ACWR using duration for Runsacwr_data_run<-calculate_acwr(stoken = stoken_placeholder,activity_type ="Run",load_metric ="duration_mins",acute_period =7,chronic_period =28)# Plot the trendplot_acwr(acwr_data = acwr_data_run,highlight_zones =TRUE)

3. Efficiency Factor (EF) Trend

Monitors aerobic efficiency, typically calculated as output (Pace orPower) divided by input (Heart Rate). Requires activities with bothrelevant metrics.

# Calculate EF (Pace/HR) for Runs and Ridesef_data_pacehr<-calculate_ef(stoken = stoken_placeholder,activity_type =c("Run","Ride"),ef_metric ="Pace_HR")# Plot the trendplot_ef(ef_data = ef_data_pacehr,add_trend_line =TRUE)

4. Personal Bests (PBs)

Visualizes the progression of estimated best efforts over variousdistances (currently primarily for running).

Note: Fetching PB data can be slow andAPI-intensive.

# Calculate PBs for 1k, 5k, 10k Runs# Limit activities checked for speedpb_data_run<-calculate_pbs(stoken = stoken_placeholder,distance_meters =c(1000,5000,10000),activity_type ="Run",max_activities =50# Limit for example)# Plot the progression, highlighting new PBsplot_pbs(pb_data = pb_data_run)

5. Decoupling Trend

Assesses cardiovascular drift during activities by comparingperformance (Pace or Power) in the first half vs. the second halfrelative to Heart Rate.

Note: Fetching decoupling data now uses directStrava API calls viahttr (instead ofrStrava::get_activity_streams) to obtain detailed activitystreams. This can be very slow and may hit API rate limits more easily,especially for many activities. Consider using themax_activities parameter.

# Calculate Pace/HR decoupling for Runs# Limit activities checked for speeddecoupling_data_run<-calculate_decoupling(stoken = stoken_placeholder,activity_type ="Run",decouple_metric ="Pace_HR",max_activities =20# Limit for example)# Plot the trendplot_decoupling(decoupling_data = decoupling_data_run,add_trend_line =TRUE)

2. Strava API Setup

Further Information

This vignette provides a basic overview. For details on specificfunctions, their parameters, and underlying calculations, please consultthe function help pages (e.g.,?calculate_acwr,?plot_acwr) in R.

For bug reports or feature requests, please use the GitHub repositoryissues tracker:https://github.com/HzaCode/Athlytics/issues

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