Calculate Personal Bests (PBs) from Local Strava Data

Tracks personal best times for standard distances (1k, 5k, 10k, half marathon, marathon) by analyzing detailed activity files from Strava export data.

Usage

calculate_pbs(
  activities_data,
  export_dir = "strava_export_data",
  activity_type = "Run",
  start_date = NULL,
  end_date = Sys.Date(),
  distances_m = c(1000, 5000, 10000, 21097.5, 42195),
  verbose = FALSE
)

Arguments

activities_data

A data frame of activities from load_local_activities(). Must contain columns: date, type, filename, distance.

export_dir

Base directory of the Strava export containing the activities folder. Default is "strava_export_data".

activity_type

Type of activities to analyze (typically "Run"). Default "Run".

start_date

Optional start date for analysis (YYYY-MM-DD). If NULL, defaults to 365 days before end_date.

end_date

End date for analysis (YYYY-MM-DD). Default Sys.Date() (today).

distances_m

Target distances in meters to track. Default: c(1000, 5000, 10000, 21097.5, 42195) for 1k, 5k, 10k, half, full marathon.

verbose

Logical. If TRUE, prints progress messages. Default FALSE.

Value

A data frame with columns: activity_id, activity_date, distance, elapsed_time, moving_time, time_seconds, cumulative_pb_seconds, is_pb, distance_label, time_period

Details

This function analyzes detailed activity files (FIT/TCX/GPX) to find the fastest efforts at specified distances. It tracks cumulative personal bests over time, showing when new PBs are set.

Personal best tracking is a standard approach in endurance sport performance monitoring. Systematic PB analysis over multiple distances helps identify fitness improvements, training phase effectiveness, and performance peaks (Matveyev, 1981). The multi-distance approach enables athletes to assess both speed (shorter distances) and endurance (longer distances) progression simultaneously.

References

• Matveyev, L. P. (1981). Fundamentals of Sports Training. Moscow: Progress Publishers.

Note: Requires detailed activity files from your Strava export. Activities must be long enough to contain the target distance segments.

Examples

# Example using simulated data
data(sample_pbs)
print(head(sample_pbs))
#> # A tibble: 6 × 10
#>   activity_id activity_date       distance elapsed_time moving_time time_seconds
#>   <chr>       <dttm>                 <dbl>        <dbl>       <dbl>        <dbl>
#> 1 activity_1  2023-01-01 00:00:00    1000           256         256          256
#> 2 activity_2  2023-01-01 00:00:00    5000          1373        1373         1373
#> 3 activity_3  2023-01-01 00:00:00   10000          2877        2877         2877
#> 4 activity_4  2023-01-01 00:00:00   21098.         6582        6582         6582
#> 5 activity_5  2023-02-01 00:00:00    1000           255         255          255
#> 6 activity_6  2023-03-01 00:00:00    1000           246         246          246
#> # ℹ 4 more variables: cumulative_pb_seconds <dbl>, is_pb <lgl>,
#> #   distance_label <fct>, time_period <Period>

if (FALSE) { # \dontrun{
# Load local activities
activities <- load_local_activities("strava_export_data/activities.csv")

# Calculate PBs for standard running distances
pbs_data <- calculate_pbs(
  activities_data = activities,
  export_dir = "strava_export_data",
  activity_type = "Run"
)
print(head(pbs_data))

# Calculate PBs for custom distances (e.g., 400m, 800m, 1500m for track)
track_pbs <- calculate_pbs(
  activities_data = activities,
  export_dir = "strava_export_data",
  activity_type = "Run",
  distances_m = c(400, 800, 1500, 3000)  # Custom distances in meters
)
} # }