Calculates the time between getting into bed and falling asleep for each night.
This is the duration from the first SleepInBedBinary to the first SleepStateBinary.
Usage
sleep_onset_latency(
.data,
start = "start_time",
end = "end_time",
variable = "variable",
tz_offset = "tz_offset"
)Arguments
- .data
A data frame containing the wearable data, typically from
clean_dynamic_data().- start
The name of the column containing start timestamps. Defaults to
"start_time".- end
The name of the column containing end timestamps. Defaults to
"end_time".- variable
The name of the column containing variable names. Defaults to
"variable".- tz_offset
The name of the column containing timezone offsets. Defaults to
"tz_offset".
Examples
# Calculate the sleep onset latency from
# intraday (dynamic) data.
sleep_onset_latency(dynamic_data)
#> # A tibble: 15 × 3
#> connectionId day SleepOnSetLatency
#> <chr> <date> <int>
#> 1 123456 2025-11-12 0
#> 2 123456 2025-11-13 0
#> 3 123456 2025-11-14 0
#> 4 123456 2025-11-15 0
#> 5 123456 2025-11-16 0
#> 6 123456 2025-11-17 0
#> 7 123456 2025-11-18 0
#> 8 123456 2025-11-19 0
#> 9 123456 2025-11-20 0
#> 10 123456 2025-11-21 0
#> 11 123456 2025-11-22 0
#> 12 123456 2025-11-23 0
#> 13 123456 2025-11-24 0
#> 14 123456 2025-11-25 0
#> 15 123456 2025-11-26 0
# We can compare this to the sleep onset latency from the
# daily data.
# Note that in the daily data, the sleep onset latency is shown
# in minutes instead of seconds.
daily_data[daily_data$variable == "SleepLatency", c("day", "value")]
#> # A tibble: 0 × 2
#> # ℹ 2 variables: day <date>, value <chr>
