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 = NULL,
distances_m = c(1000, 5000, 10000, 21097.5, 42195)
)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). Defaults to NULL (all dates).
- end_date
Optional end date for analysis (YYYY-MM-DD). Defaults to NULL (all dates).
- distances_m
Target distances in meters to track. Default: c(1000, 5000, 10000, 21097.5, 42195) for 1k, 5k, 10k, half, full marathon.
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.
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(athlytics_sample_pbs)
print(head(athlytics_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… 2023-03-12 00:00:00 1000 240 235. 240
#> 2 activity_1… 2023-03-19 00:00:00 1000 237. 233. 237.
#> 3 activity_1… 2023-03-27 00:00:00 1000 235. 230. 235.
#> 4 activity_1… 2023-04-20 00:00:00 1000 232. 227. 232.
#> 5 activity_1… 2023-04-27 00:00:00 1000 229. 225. 229.
#> 6 activity_1… 2023-04-30 00:00:00 1000 226. 222. 226.
#> # 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))
} # }