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Enhanced ACWR Plot with Confidence Bands and Reference

Source: R/plot_acwr_enhanced.R
plot_acwr_enhanced.Rd

Creates a comprehensive ACWR visualization with optional confidence bands and cohort reference percentiles.

Usage

plot_acwr_enhanced(
  acwr_data,
  reference_data = NULL,
  show_ci = TRUE,
  show_reference = TRUE,
  reference_bands = c("p25_p75", "p05_p95", "p50"),
  highlight_zones = TRUE,
  title = NULL,
  subtitle = NULL,
  method_label = NULL
)

Arguments

acwr_data

A data frame from calculate_acwr_ewma() containing ACWR values.

reference_data

Optional. A data frame from cohort_reference() for adding cohort reference bands.

show_ci

Logical. Whether to show confidence bands (if available in data). Default TRUE.

show_reference

Logical. Whether to show cohort reference bands (if provided). Default TRUE.

reference_bands

Which reference bands to show. Default c("p25_p75", "p05_p95", "p50").

highlight_zones

Logical. Whether to highlight ACWR risk zones. Default TRUE.

title

Plot title. Default NULL (auto-generated).

subtitle

Plot subtitle. Default NULL (auto-generated).

method_label

Optional label for the method used (e.g., "RA", "EWMA"). Default NULL.

Value

A ggplot object.

Details

This enhanced plot function combines multiple visualization layers:

  • Risk zone shading (sweet spot: 0.8-1.3, caution: 1.3-1.5, high risk: >1.5)

  • Cohort reference percentile bands (if provided)

  • Bootstrap confidence bands (if available in data)

  • Individual ACWR trend line

The layering order (bottom to top):

  1. Risk zones (background)

  2. Cohort reference bands (P5-P95, then P25-P75)

  3. Confidence intervals (individual uncertainty)

  4. ACWR line (individual trend)

Examples

# Example using sample data
data("athlytics_sample_acwr", package = "Athlytics")
if (!is.null(athlytics_sample_acwr) && nrow(athlytics_sample_acwr) > 0) {
  p <- plot_acwr_enhanced(athlytics_sample_acwr, show_ci = FALSE)
  print(p)
}
#> No reference data provided. Setting show_reference = FALSE.
#> Warning: Removed 18 rows containing missing values or values outside the scale range
#> (`geom_line()`).


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

# Calculate ACWR with EWMA and confidence bands
acwr <- calculate_acwr_ewma(
  activities,
  method = "ewma",
  ci = TRUE,
  B = 200
)

# Basic enhanced plot
plot_acwr_enhanced(acwr)

# With cohort reference
reference <- cohort_reference(cohort_data, metric = "acwr_smooth")
plot_acwr_enhanced(acwr, reference_data = reference)
} # }