plot_acwr: Plot ACWR Trend

In Athlytics: Advanced Sports Performance Analysis for 'Strava' Data

Plot ACWR Trend

Description

Visualizes the Acute:Chronic Workload Ratio (ACWR) trend over time.

Usage

plot_acwr(
  stoken,
  activity_type = NULL,
  load_metric = "duration_mins",
  acute_period = 7,
  chronic_period = 28,
  start_date = NULL,
  end_date = NULL,
  user_ftp = NULL,
  user_max_hr = NULL,
  user_resting_hr = NULL,
  smoothing_period = 7,
  highlight_zones = TRUE,
  acwr_df = NULL
)

Arguments

stoken	A valid Strava token from 'rStrava::strava_oauth()'. Required unless 'acwr_df' is provided.
activity_type	Type(s) of activities to analyze (e.g., "Run", "Ride").
load_metric	Method for calculating daily load (e.g., "duration_mins", "distance_km", "tss", "hrss").
acute_period	Days for the acute load window (e.g., 7).
chronic_period	Days for the chronic load window (e.g., 28). Must be greater than 'acute_period'.
start_date	Optional. Analysis start date (YYYY-MM-DD string or Date). Defaults to ~1 year ago.
end_date	Optional. Analysis end date (YYYY-MM-DD string or Date). Defaults to today.
user_ftp	Required if 'load_metric = "tss"' and 'acwr_df' is not provided. Your Functional Threshold Power.
user_max_hr	Required if 'load_metric = "hrss"' and 'acwr_df' is not provided. Your maximum heart rate.
user_resting_hr	Required if 'load_metric = "hrss"' and 'acwr_df' is not provided. Your resting heart rate.
smoothing_period	Days for smoothing the ACWR using a rolling mean (e.g., 7). Default 7.
highlight_zones	Logical, whether to highlight different ACWR zones (e.g., sweet spot, high risk) on the plot. Default 'TRUE'.
acwr_df	Optional. A pre-calculated data frame from 'calculate_acwr'. If provided, 'stoken' and other calculation parameters are ignored.

Details

Plots the ACWR trend over time. Uses pre-calculated data or calls 'calculate_acwr' (can be slow). ACWR is calculated as acute load / chronic load. A ratio of 0.8-1.3 is often considered the "sweet spot". If 'acwr_df' is not provided, calls 'calculate_acwr' first (can be slow and hit API limits).

Value

A ggplot object showing the ACWR trend.

Examples

# Example using simulated data
data(Athlytics_sample_data)
if (!is.null(athlytics_sample_acwr)) {
  # Ensure acwr_df is named and other necessary parameters are provided if plot_acwr expects them
  p <- plot_acwr(acwr_df = athlytics_sample_acwr)
  print(p)
}

## Not run: 
# Example using real data (requires authentication)
# Please replace with your actual Strava application details for this to work.
# stoken_example <- rStrava::strava_oauth(
#   app_name = "YOUR_APP_NAME_PLACEHOLDER",
#   client_id = "YOUR_CLIENT_ID_PLACEHOLDER",
#   client_secret = "YOUR_CLIENT_SECRET_PLACEHOLDER",
#   cache = TRUE
# )

# If you have a valid stoken_example, you can then use it:
# Plot ACWR trend for Runs (using duration as load metric)
# if (exists("stoken_example") && inherits(stoken_example, "Token2.0")) {
#   plot_acwr(stoken = stoken_example,
#             activity_type = "Run",
#             load_metric = "duration_mins",
#             acute_period = 7,
#             chronic_period = 28)
#
#   # Plot ACWR trend for Rides (using TSS as load metric)
#   plot_acwr(stoken = stoken_example,
#             activity_type = "Ride",
#             load_metric = "tss",
#             user_ftp = 280)  # FTP value is required
# } else {
#  message("stoken_example not created or invalid. Skipping real data example for plot_acwr.")
# }

## End(Not run)

Athlytics documentation built on June 8, 2025, 1:11 p.m.