## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  eval     = FALSE   # all chunks require live credentials; run interactively
)

## ----auth---------------------------------------------------------------------
# library(rCoros)
# 
# auth <- coros_login()
# auth
# #> <coros_auth>
# #>   region:    us
# #>   user_id:   123456789
# #>   logged in: 2026-06-07 08:00:00

## ----activities---------------------------------------------------------------
# library(dplyr)
# 
# acts <- coros_activities(auth)
# acts
# #> # A tibble: 47 × 16
# #>   activity_id name            sport_type sport_name date       start_time
# #>   <chr>       <chr>                <int> <chr>      <date>     <dttm>
# #> 1 4780…       Morning Trail R…       102 Trail Run… 2026-06-06 2026-06-06 06:12:00
# #> 2 4779…       Easy Run               100 Running    2026-06-04 2026-06-04 07:00:00
# #> …

## ----runs---------------------------------------------------------------------
# runs <- acts |>
#   filter(sport_type %in% c(100L, 102L)) |>
#   arrange(date)
# 
# library(ggplot2)
# 
# ggplot(runs, aes(date, distance_km)) +
#   geom_col(fill = "#3A7BD5") +
#   labs(title = "Weekly volume", x = NULL, y = "Distance (km)")

## ----detail-------------------------------------------------------------------
# detail <- coros_activity_detail(
#   auth,
#   activity_id = acts$activity_id[[1]],
#   sport_type  = acts$sport_type[[1]]
# )
# 
# # One-row summary
# detail$summary |> glimpse()
# 
# # Lap splits
# detail$laps
# 
# # Time in HR zones
# ggplot(detail$hr_zones, aes(factor(zone), minutes, fill = factor(zone))) +
#   geom_col(show.legend = FALSE) +
#   scale_fill_brewer(palette = "RdYlGn", direction = -1) +
#   labs(title = "Time in HR zones", x = "Zone", y = "Minutes")

## ----metrics------------------------------------------------------------------
# metrics <- coros_daily_metrics(auth, start_day = "20260101", end_day = "20260607")
# 
# # HRV trend with baseline
# ggplot(metrics, aes(date)) +
#   geom_ribbon(aes(ymin = hrv_baseline - 5, ymax = hrv_baseline + 5),
#               fill = "steelblue", alpha = 0.2) +
#   geom_line(aes(y = hrv_baseline), colour = "steelblue", linewidth = 0.8) +
#   geom_point(aes(y = hrv), size = 1.5) +
#   labs(title = "Overnight HRV vs. baseline", x = NULL, y = "HRV (ms)")

## ----hrv----------------------------------------------------------------------
# coros_hrv(auth)
# #> # A tibble: 7 × 4
# #>   date         hrv baseline hrv_sd
# #>   <date>     <dbl>    <dbl>  <dbl>
# #> 1 2026-06-01  61.2     58.4    4.1
# #> 2 2026-06-02  64.8     58.8    3.9
# #> …

## ----load---------------------------------------------------------------------
# metrics |>
#   filter(!is.na(load_ratio)) |>
#   ggplot(aes(date, load_ratio)) +
#   geom_hline(yintercept = c(0.8, 1.3), linetype = "dashed", colour = "grey60") +
#   geom_line(colour = "#E06C2C", linewidth = 1) +
#   annotate("text", x = min(metrics$date), y = 1.35,
#            label = "Caution zone", hjust = 0, size = 3, colour = "grey40") +
#   labs(title = "Acute:chronic training load ratio",
#        x = NULL, y = "Load ratio")

## ----workouts-----------------------------------------------------------------
# w <- coros_workouts(auth)
# 
# # All workouts
# w$workouts
# 
# # Steps for a specific workout
# w$steps |> filter(workout_id == w$workouts$id[[1]])

## ----schedule-----------------------------------------------------------------
# sched <- coros_schedule(auth)
# 
# sched
# #> # A tibble: 9 × 5
# #>   happen_day name              sport_name estimated_min completed
# #>   <date>     <chr>             <chr>               <dbl> <lgl>
# #> 1 2026-06-07 Long Run          Running              90   FALSE
# #> 2 2026-06-09 Recovery Run      Running              40   FALSE
# #> …

## ----join---------------------------------------------------------------------
# combined <- runs |>
#   left_join(
#     metrics |> select(date, hrv, hrv_baseline, rhr, load_ratio),
#     by = "date"
#   )
# 
# ggplot(combined, aes(hrv, avg_hr, colour = load_ratio)) +
#   geom_point(size = 2.5) +
#   geom_smooth(method = "lm", se = FALSE, colour = "grey40") +
#   scale_colour_viridis_c(name = "Load ratio") +
#   labs(
#     title  = "HRV vs. average run HR",
#     x      = "Overnight HRV (ms)",
#     y      = "Average HR (bpm)"
#   )