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Plot Training Load Exposure (ATL vs CTL)

Source: R/plot_exposure.R
plot_exposure.Rd

Visualizes the relationship between Acute and Chronic Training Load.

Usage

plot_exposure(
  data,
  activity_type = c("Run", "Ride", "VirtualRide", "VirtualRun"),
  load_metric = "duration_mins",
  acute_period = 7,
  chronic_period = 42,
  user_ftp = NULL,
  user_max_hr = NULL,
  user_resting_hr = NULL,
  end_date = NULL,
  risk_zones = TRUE,
  exposure_df = NULL
)

Arguments

data

A data frame from load_local_activities(). Required unless exposure_df is provided.

activity_type

Type(s) of activities to include (e.g., "Run", "Ride"). Default uses common types.

load_metric

Method for calculating daily load (e.g., "duration_mins", "tss", "hrss"). Default "duration_mins". See calculate_exposure for details on approximate TSS/HRSS calculations.

acute_period

Days for acute load window (e.g., 7).

chronic_period

Days for chronic load window (e.g., 42). Must be > acute_period.

user_ftp

Required if load_metric = "tss". Your FTP.

user_max_hr

Required if load_metric = "hrss". Your max HR.

user_resting_hr

Required if load_metric = "hrss". Your resting HR.

end_date

Optional. Analysis end date (YYYY-MM-DD string or Date). Defaults to today.

risk_zones

Add background shading for typical ACWR risk zones? Default TRUE.

exposure_df

Optional. A pre-calculated data frame from calculate_exposure. If provided, data and other calculation parameters are ignored. Must contain date, atl, ctl (and acwr if risk_zones = TRUE).

Value

A ggplot object showing ATL vs CTL.

Details

Plots ATL vs CTL, optionally showing risk zones based on ACWR. Uses pre-calculated data or calls calculate_exposure.

Visualizes training state by plotting ATL vs CTL (related to PMC charts). Points are colored by date, latest point is highlighted (red triangle). Optional risk zones (based on ACWR thresholds ~0.8, 1.3, 1.5) can be shaded. If exposure_df is not provided, it calls calculate_exposure first.

Examples

# Example using simulated data
data(sample_exposure)
# Ensure exposure_df is named and other necessary parameters like activity_type are provided
p <- plot_exposure(exposure_df = sample_exposure, activity_type = "Run")
#> Generating plot...
print(p)
#> Warning: Removed 27 rows containing missing values or values outside the scale range
#> (`geom_point()`).


if (FALSE) { # \dontrun{
# Example using local Strava export data
activities <- load_local_activities("strava_export_data/activities.csv")

# Plot Exposure trend for Runs (last 6 months)
plot_exposure(
  data = activities,
  activity_type = "Run",
  end_date = Sys.Date(),
  user_ftp = 280
) # Example, if load_metric = "tss"

# Plot Exposure trend for Rides
plot_exposure(
  data = activities,
  activity_type = "Ride",
  user_ftp = 280
) # Example, provide if load_metric = "tss"

# Plot Exposure trend for multiple Run types (risk_zones = FALSE for this example)
plot_exposure(
  data = activities,
  activity_type = c("Run", "VirtualRun"),
  risk_zones = FALSE,
  user_ftp = 280
) # Example, provide if load_metric = "tss"
} # }