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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,
  risk_zones = TRUE,
  show_date_color = TRUE,
  caption = NULL,
  axis_limit = NULL,
  title = NULL,
  subtitle = NULL,
  ...
)

Arguments

data

A data frame from calculate_exposure(). Must contain date, atl, and ctl columns.

risk_zones

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

show_date_color

Logical. Whether to color points by date (gradient). Default TRUE. The date gradient helps visualize the training trajectory over time: lighter colors represent earlier dates and darker colors represent more recent dates, so you can trace how training state has evolved across a season. Set to FALSE for a simpler single-color plot (useful when the temporal ordering is less important than the overall distribution).

caption

Plot caption. Default NULL (no caption).

axis_limit

Optional. Numeric value to set both x and y axis limits (0 to axis_limit). Useful when plotting risk zones without data or with sparse data. Default NULL (auto-scale).

title

Optional. Custom title for the plot.

subtitle

Optional. Custom subtitle for the plot.

...

Additional arguments. Arguments activity_type, load_metric, acute_period, chronic_period, user_ftp, user_max_hr, user_resting_hr, end_date, exposure_df are deprecated and ignored.

Value

A ggplot object showing ATL vs CTL.

Details

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. Best practice: Use calculate_exposure() first, then pass the result to this function.

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(sample_exposure)
print(p)
#> Warning: Removed 27 rows containing missing values or values outside the scale range
#> (`geom_point()`).


# Runnable example with dummy data:
end <- Sys.Date()
dates <- seq(end - 59, end, by = "day")
dummy_activities <- data.frame(
  date = dates,
  type = "Run",
  moving_time = rep(3600, length(dates)), # 1 hour
  distance = rep(10000, length(dates)), # 10 km
  average_heartrate = rep(140, length(dates)),
  suffer_score = rep(50, length(dates)),
  tss = rep(50, length(dates)),
  stringsAsFactors = FALSE
)

# Calculate Exposure (ATL/CTL)
exposure_result <- calculate_exposure(
  activities_data = dummy_activities,
  activity_type = "Run",
  load_metric = "distance_km",
  acute_period = 7,
  chronic_period = 28,
  end_date = end
)
plot_exposure(exposure_result)