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Simulated Fitbit‑Style Heart Rate Data

Source: R/data.R
raw_hr.Rd

`raw_hr` contains *synthetic Fitbit‑style heart rate (HR) data* for two simulated participants (`P01` and `P02`). The dataset spans **two weeks** at **1‑minute resolution**, with **realistic physiological patterns**, **exercise sessions**, **sleep‑related HR depressions**, and **Fitbit‑like missingness** (periodic NA values).

Usage

raw_hr

Format

A tibble with three columns:

id

Character participant ID ("P01", "P02").

hr_timestamp

POSIXct timestamp at 1‑minute resolution (UTC).

heart_rate

Heart rate in beats per minute (integer).

Details

This dataset is designed for demonstration and testing of HR‑related processing and visualisation tools within the `hypometrics` package.

The dataset includes:

Participants

  • "P01" — HR start: "2026‑01‑01 07:17:00" (UTC)

  • "P02" — HR start: "2026‑01‑02 15:52:00" (UTC)

Simulation features

  • 1‑minute HR sampling for 14 days per participant

  • Circadian rhythm (afternoon HR peak, overnight depression)

  • Sleep HR lowering (smaller noise, lower baseline)

  • Stochastic noise resembling optical HR sensors

  • Exercise bouts with HR rise → plateau → recovery

  • Fitbit‑style missingness: 1–2 minute random gaps, occasional long dropouts with NA heart rate

  • HR values are rounded to whole‑number bpm

Relation to CGM data

The HR timeline is aligned with the simulated CGM dataset [`raw_cgm`]: for each participant, HR starts exactly when CGM monitoring begins and continues for the same two‑week duration.

Generation script

The data are reproducibly generated using:


  data-raw/simulate_raw_hr.R

Examples

data(raw_hr, package = "hypometrics")

# Inspect structure
dplyr::glimpse(raw_hr)
#> Rows: 40,320
#> Columns: 3
#> $ id           <chr> "P01", "P01", "P01", "P01", "P01", "P01", "P01", "P01", "…
#> $ hr_timestamp <dttm> 2026-01-01 07:17:00, 2026-01-01 07:18:00, 2026-01-01 07:…
#> $ heart_rate   <dbl> 63, 65, 72, 68, 55, 69, 68, 67, 64, 65, 61, 70, 64, 64, 6…

# Check first few HR values for P01
dplyr::filter(raw_hr, id == "P01") %>% head()
#> # A tibble: 6 × 3
#>   id    hr_timestamp        heart_rate
#>   <chr> <dttm>                   <dbl>
#> 1 P01   2026-01-01 07:17:00         63
#> 2 P01   2026-01-01 07:18:00         65
#> 3 P01   2026-01-01 07:19:00         72
#> 4 P01   2026-01-01 07:20:00         68
#> 5 P01   2026-01-01 07:21:00         55
#> 6 P01   2026-01-01 07:22:00         69

# Distribution of HR
hist(raw_hr$heart_rate, breaks = 40, main = "Simulated HR Distribution")


# Missingness rate per participant
raw_hr %>%
  dplyr::group_by(id) %>%
  dplyr::summarise(
    pct_missing = mean(is.na(heart_rate)) * 100
  )
#> # A tibble: 2 × 2
#>   id    pct_missing
#>   <chr>       <dbl>
#> 1 P01          4.39
#> 2 P02          7.58