Plot Personal Best (PB) Trends — plot

Visualizes the trend of personal best times for specific running distances.

Usage

plot_pbs(
  data,
  add_trend_line = TRUE,
  caption = NULL,
  facet_ncol = 2,
  title = NULL,
  subtitle = NULL,
  ...
)

Arguments

data: A data frame from calculate_pbs(). Must contain activity_date, distance, time_seconds.
add_trend_line: Logical. Whether to add a trend line to the plot. Default TRUE.
caption: Plot caption. Default NULL (no caption).
facet_ncol: Integer. Number of columns for faceted plots when multiple distances are shown. Default 2 for better aspect ratio. Set to 1 for vertical stacking.
title: Optional. Custom title for the plot.
subtitle: Optional. Custom subtitle for the plot.
...: Additional arguments. Arguments activity_type, distance_meters, max_activities, date_range, pbs_df are deprecated and ignored.

Value

A ggplot object showing PB trends, faceted by distance if multiple are plotted.

Details

Visualizes data from calculate_pbs. Points show best efforts; solid points mark new PBs. Y-axis is MM:SS. Best practice: Use calculate_pbs() first, then pass the result to this function.

Examples

# Example using the built-in sample data
data("sample_pbs", package = "Athlytics")

if (!is.null(sample_pbs) && nrow(sample_pbs) > 0) {
  # Plot PBs using the package sample data directly
  p <- plot_pbs(sample_pbs)
  print(p)
}
#> `geom_smooth()` using formula = 'y ~ x'


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

# Calculate PBs first
pb_data_run <- calculate_pbs(
  activities_data = activities,
  activity_type = "Run",
  distances_m = c(1000, 5000, 10000)
)

if (nrow(pb_data_run) > 0) {
  plot_pbs(pb_data_run)
}
} # }