Getting started with rCoros

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rCoros gives you tidy access to your COROS Training Hub data from R. Every function returns a tibble, so you can pipe results straight into dplyr and ggplot2.

Setup

Store your credentials in ~/.Renviron so they are never hard-coded in a script:

COROS_EMAIL=you@example.com
COROS_PASSWORD=secret

Then restart R and authenticate:

library(rCoros)

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

Pass region = "eu" if your account was created in Europe. The auth object is just a lightweight list — create it once per session and pass it to every subsequent call.

Activities

coros_activities() returns one row per activity. By default it fetches the last 30 days and auto-paginates until all matching activities are returned:

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
#> …

Filter to runs only, then look at distance and training load over time:

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)")

Activity detail

Drill into a single activity to get lap splits and heart-rate zones:

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")

Daily wellness metrics

coros_daily_metrics() pulls per-day HRV, resting heart rate, VO2max, training load, and more for up to 90 days at a time:

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)")

For a quick view of just the last 7 days, coros_hrv() hits a lighter dashboard endpoint:

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
#> …

Training load

Training load and its acute:chronic ratio are in coros_daily_metrics(). A ratio above ~1.5 is a useful proxy for injury risk:

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")

Workout programmes

coros_workouts() returns your saved workout library as two linked tibbles — the workout header and its individual steps:

w <- coros_workouts(auth)

# All workouts
w$workouts

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

Training calendar

coros_schedule() shows what’s planned for the next two weeks (or any window you specify):

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
#> …

Putting it together

A common pattern is to join activities back to daily metrics to explore how recovery scores relate to performance:

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)"
  )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.