Plot Aerobic Decoupling Trend — plot

Visualizes the trend of aerobic decoupling over time.

Usage

plot_decoupling(
  data,
  add_trend_line = TRUE,
  smoothing_method = "loess",
  caption = NULL,
  title = NULL,
  subtitle = NULL,
  ...
)

Arguments

data: A data frame from calculate_decoupling(). Must contain 'date' and 'decoupling' columns.
add_trend_line: Add a smoothed trend line (geom_smooth)? Default TRUE.
smoothing_method: Smoothing method for trend line (e.g., "loess", "lm"). Default "loess".
caption: Plot caption. Default NULL (no caption).
title: Optional. Custom title for the plot.
subtitle: Optional. Custom subtitle for the plot.
...: Additional arguments. Arguments activity_type, decouple_metric, start_date, end_date, min_duration_mins, decoupling_df are deprecated and ignored.

Value

A ggplot object showing the decoupling trend.

Details

Plots decoupling percentage ((EF_1st_half - EF_2nd_half) / EF_1st_half * 100). Positive values mean HR drifted relative to output. A 5\% threshold line is often used as reference. Best practice: Use calculate_decoupling() first, then pass the result to this function.

Examples

# Example using pre-calculated sample data
data("sample_decoupling", package = "Athlytics")
p <- plot_decoupling(sample_decoupling)
print(p)
#> `geom_smooth()` using formula = 'y ~ x'


# Runnable example with a manually created decoupling data frame:
decoupling_df <- data.frame(
  date = seq(Sys.Date() - 29, Sys.Date(), by = "day"),
  decoupling = rnorm(30, mean = 5, sd = 2)
)
plot_decoupling(data = decoupling_df)
#> `geom_smooth()` using formula = 'y ~ x'