Compare RA and EWMA Methods Side-by-Side

Creates a faceted plot comparing Rolling Average and EWMA ACWR calculations.

Usage

plot_acwr_comparison(
  acwr_ra,
  acwr_ewma,
  title = "ACWR Method Comparison: RA vs EWMA"
)

Arguments

acwr_ra

A data frame from calculate_acwr_ewma(..., method = "ra").

acwr_ewma

A data frame from calculate_acwr_ewma(..., method = "ewma").

title

Plot title. Default "ACWR Method Comparison: RA vs EWMA".

Value

A ggplot object with faceted comparison.

Examples

# Example using sample data
data("athlytics_sample_acwr", package = "Athlytics")
if (!is.null(athlytics_sample_acwr) && nrow(athlytics_sample_acwr) > 0) {
  # Create two versions for comparison (simulate RA vs EWMA)
  acwr_ra <- athlytics_sample_acwr
  acwr_ewma <- athlytics_sample_acwr
  acwr_ewma$acwr_smooth <- acwr_ewma$acwr_smooth * runif(nrow(acwr_ewma), 0.95, 1.05)
  
  p <- plot_acwr_comparison(acwr_ra, acwr_ewma)
  print(p)
}
#> Warning: Removed 36 rows containing missing values or values outside the scale range
#> (`geom_line()`).


if (FALSE) { # \dontrun{
activities <- load_local_activities("export.zip")

acwr_ra <- calculate_acwr_ewma(activities, method = "ra")
acwr_ewma <- calculate_acwr_ewma(activities, method = "ewma")

plot_acwr_comparison(acwr_ra, acwr_ewma)
} # }