Calculate Training Load Exposure (ATL, CTL, ACWR) — calculate

Calculates training load metrics like ATL, CTL, and ACWR from local Strava data.

Usage

calculate_exposure(
  activities_data,
  activity_type = c("Run", "Ride", "VirtualRide", "VirtualRun"),
  load_metric = "duration_mins",
  acute_period = 7,
  chronic_period = 42,
  user_ftp = NULL,
  user_max_hr = NULL,
  user_resting_hr = NULL,
  end_date = Sys.Date(),
  verbose = FALSE
)

Arguments

activities_data: A data frame of activities from load_local_activities(). Must contain columns: date, distance, moving_time, elapsed_time, average_heartrate, average_watts, type, elevation_gain.
activity_type: Type(s) of activities to include (e.g., "Run", "Ride"). Default includes common run/ride types.
load_metric: Method for calculating daily load (e.g., "duration_mins", "distance_km", "tss", "hrss"). Default "duration_mins".
acute_period: Days for the acute load window (e.g., 7).
chronic_period: Days for the chronic load window (e.g., 42). Must be greater than acute_period.
user_ftp: Required if load_metric = "tss". Your Functional Threshold Power.
user_max_hr: Required if load_metric = "hrss". Your maximum heart rate.
user_resting_hr: Required if load_metric = "hrss". Your resting heart rate.
end_date: Optional. Analysis end date (YYYY-MM-DD string or Date). Defaults to today. The analysis period covers the chronic_period days ending on this date.
verbose: Logical. If TRUE, prints progress messages. Default FALSE.

Value

A data frame with columns: date, daily_load, atl (Acute Load), ctl (Chronic Load), and acwr (Acute:Chronic Ratio) for the analysis period.

Details

Calculates daily load, ATL, CTL, and ACWR from Strava activities based on the chosen metric and periods.

Provides data for plot_exposure. Requires extra prior data for accurate initial CTL. Requires FTP/HR parameters for TSS/HRSS metrics.

Examples

# Example using simulated data
data(sample_exposure)
print(head(sample_exposure))
#> # A tibble: 6 × 5
#>   date       daily_load   ctl   atl  acwr
#>   <date>          <dbl> <dbl> <dbl> <dbl>
#> 1 2023-01-01        8.8    NA    NA    NA
#> 2 2023-01-02       23.7    NA    NA    NA
#> 3 2023-01-03       20.2    NA    NA    NA
#> 4 2023-01-04       35.1    NA    NA    NA
#> 5 2023-01-05       14.6    NA    NA    NA
#> 6 2023-01-06       31.9    NA    NA    NA

# 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
)
print(head(exposure_result))
#>         date daily_load atl ctl acwr
#> 1 2026-01-24         10  10  10    1
#> 2 2026-01-25         10  10  10    1
#> 3 2026-01-26         10  10  10    1
#> 4 2026-01-27         10  10  10    1
#> 5 2026-01-28         10  10  10    1
#> 6 2026-01-29         10  10  10    1

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

# Calculate training load for Rides using TSS
ride_exposure_tss <- calculate_exposure(
  activities_data = activities,
  activity_type = "Ride",
  load_metric = "tss",
  user_ftp = 280,
  acute_period = 7,
  chronic_period = 28
)
print(head(ride_exposure_tss))

# Calculate training load for Runs using HRSS
run_exposure_hrss <- calculate_exposure(
  activities_data = activities,
  activity_type = "Run",
  load_metric = "hrss",
  user_max_hr = 190,
  user_resting_hr = 50
)
print(tail(run_exposure_hrss))
} # }