Package 'actimetrics'

Apply Tudor–Locke sleep period detection

Description

Delegates to actigraph.sleepr::apply_tudor_locke(). Input must be 60-second epoch data with activity counts and a sleep state. Data from acti_apply_cole_kripke() is accepted directly. Data labeled by acti_apply_tudor_locke_diary() is also accepted: its logical sleep column is converted to the S/W states expected downstream.

Usage

acti_apply_tudor_locke(data, ...)

Arguments

data	Epoch-level data containing activity counts and sleep state.
...	Arguments passed to actigraph.sleepr::apply_tudor_locke().

Value

A data frame summarizing detected sleep periods.

---

Apply a sleep diary to epoch data for Tudor–Locke

Description

Adds a logical sleep column to epoch data from diary onset and wakeup times. The result can be passed to acti_apply_tudor_locke() when it also has activity counts and represents 60-second epochs. The wrapper converts the logical diary labels to the S/W states required downstream.

Usage

acti_apply_tudor_locke_diary(
  data,
  sleep_diary,
  time_col = "time",
  diary_onset_col = "onset",
  diary_wakeup_col = "wakeup",
  sleep_col = "sleep"
)

Arguments

data	Epoch-level data containing a POSIXct timestamp column.
sleep_diary	A data frame with POSIXct sleep onset and wakeup columns. Each interval may span midnight; multiple days are supported.
time_col	Name of the POSIXct timestamp column in data.
diary_onset_col	Name of the diary onset column.
diary_wakeup_col	Name of the diary wakeup column.
sleep_col	Name of the logical sleep column to add or replace.

Value

data with a logical sleep_col, TRUE from onset (inclusive) to wakeup (exclusive).

---

Process Count Data

Description

Process Count Data

Process Count Data

Usage

acti_calculate_counts(
  data,
  epoch = 60L,
  resample = TRUE,
  lfe_select = FALSE,
  verbose = TRUE,
  fix_first_second = TRUE
)

acti_calculate_wear(
  data,
  method = c("choi", "troiano"),
  use_magnitude = TRUE,
  ...
)

acti_calculate_nonwear(
  data,
  method = c("choi", "troiano"),
  use_magnitude = TRUE,
  ...
)

acti_apply_cole_kripke(data)

acti_apply_sadeh(data, ...)

Arguments

data	A data.frame from acti_calculate_counts that has columns axis1-3 and counts
epoch	epoch length in seconds. Default is 60 seconds. See agcounts::calculate_counts()
resample	(recommended) resample the data to 30Hz using actibase::acti_resample vs. using the resampling method from agcounts::calculate_counts.
lfe_select	Apply the Actigraph Low Frequency Extension filter. See agcounts::calculate_counts() higher values are higher levels of verbosity.
verbose	print diagnostic messages. Either logical or integer, where
fix_first_second	Fix the first second bug in agcounts. Appends replicated data of the first record if the first record is not an "even" epoch (e.g. 60 second epoch and data does not start at a 00 second).
method	Method for detecting non-wear, either "choi" or "troiano", corresponding to actigraph.sleepr::apply_choi() or actigraph.sleepr::apply_troiano()
use_magnitude	If TRUE, the magnitude of the vector (axis1, axis2, axis3) is used to measure activity; otherwise the axis1 value is used.
...	additional arguments to pass to actigraph.sleepr function

Value

A data.frame of transformed data

A data.frame of transformed data with columns axis1-3, counts, and counts_log10.

A data.frame of transformed data

Note

This calls the downstream wear-processing helpers used by actigraph.sleepr

Examples

path = actiread::acti_example_gt3x()
ac = actiread::acti_read_gt3x(path)
out = acti_calculate_counts(ac)

data = actimetrics::acti_count_data
wear = actimetrics::acti_calculate_wear(data)
tro_wear = actimetrics::acti_calculate_wear(data, method = "troiano")
ck = actimetrics::acti_apply_cole_kripke(data)
tl = actimetrics::acti_apply_tudor_locke(ck)
sadeh = actimetrics::acti_apply_sadeh(ck)

---

Calculate Step Counts via Oak/Forest

Description

Calculate Step Counts via Oak/Forest

Usage

acti_calculate_forest(data, ...)

Arguments

data	A data.frame, AccData object, or GT3X file with X, Y, Z, and time
...	Additional arguments passed to walking::find_walking()

Value

A tibble with minute-level time, steps columns.

Examples

Sys.setenv("SSQ_PARALLEL" = 0)
  if (reticulate::py_module_available("forest")) {
    data = actiread::acti_read_gt3x(actiread::acti_example_gt3x())
    steps = acti_calculate_forest(data, sample_rate = 100)
  }

---

Calculate Summary Measures from Raw Accelerometer Data

Description

Calculate Summary Measures from Raw Accelerometer Data

Usage

acti_calculate_measures(
  data,
  unit = "1 min",
  fix_zeros = TRUE,
  dynamic_range = NULL,
  calculate_mims = TRUE,
  calculate_ac = TRUE,
  flag_data = TRUE,
  flags = NULL,
  ensure_all_time = TRUE,
  verbose = TRUE,
  sample_rate = NULL,
  ...
)

acti_calculate_ai(
  data,
  unit = "1 min",
  ensure_all_time = TRUE,
  verbose = FALSE
)

acti_calculate_activity_index(
  data,
  unit = "1 min",
  ensure_all_time = TRUE,
  verbose = FALSE
)

acti_calculate_flags(data, unit = "1 min", ensure_all_time = TRUE)

acti_calculate_n_idle(data, unit = "1 min", ensure_all_time = TRUE)

acti_calculate_enmo(...)

acti_calculate_ai_defined(...)

acti_calculate_mad(
  data,
  unit = "1 min",
  ensure_all_time = TRUE,
  verbose = FALSE
)

acti_calculate_auc(
  data,
  unit = "1 min",
  sample_rate = NULL,
  allow_truncation = FALSE,
  ensure_all_time = TRUE,
  verbose = TRUE
)

acti_calculate_fast_mims(
  data,
  unit = "1 min",
  dynamic_range = NULL,
  sample_rate = NULL,
  allow_truncation = TRUE,
  ensure_all_time = TRUE,
  verbose = TRUE,
  ...
)

acti_calculate_mims(
  data,
  unit = "1 min",
  dynamic_range = c(-6, 6),
  ensure_all_time = TRUE,
  ...
)

Arguments

data	An object with columns X, Y, and Z or an object of class AccData
unit	length of time to calculate measures over. a character string specifying a time unit or a multiple of a unit to be rounded to. Valid base units are second, minute, hour, day, week, month, bimonth, quarter, season, halfyear, and year. Arbitrary unique English abbreviations as in the lubridate::period() constructor are allowed.
fix_zeros	Should actibase::acti_fill_zeros() be run before calculating the measures?
dynamic_range	Dynamic range of the device, in gravity units
calculate_mims	Should MIMS units be calculated?
calculate_ac	Should Activity Counts from the agcounts package be calculated?
flag_data	Should the downstream overlay flag_qc() be run? It will be executed after fix_zeros before any measure calculation
flags	the flags to calculate, passed to the downstream overlay flag_qc()
ensure_all_time	if TRUE, then all times from the first to last times will be in the output, even if data during that time was not in the input
verbose	print diagnostic messages
sample_rate	sample rate of data, if not specified in header of object
...	additional arguments to pass to MIMSunit::mims_unit()
allow_truncation	truncate small values

Value

A data set with the calculated features

Examples

file = actiread::acti_example_gt3x()
res = actiread::acti_read_gt3x(file, verbose = FALSE)
res = res[1:12000, ]
measures = acti_calculate_measures(
  res,
  dynamic_range = NULL,
  calculate_mims = FALSE,
  calculate_ac = FALSE,
  flag_data = FALSE
)
auc = acti_calculate_auc(res)

mims = acti_calculate_mims(res, dynamic_range = NULL)

if (requireNamespace("data.table", quietly = TRUE)) {
   dt = data.table::as.data.table(res)
   out = acti_calculate_measures(dt, calculate_mims = FALSE, flag_data = FALSE,
     calculate_ac = FALSE)
}

---

Calculate Step Counts via Oak/Forest

Description

Calculate Step Counts via Oak/Forest

Usage

acti_calculate_sdt(data, sample_rate = NULL, ...)

Arguments

data	A data.frame, AccData object, or GT3X file with X, Y, Z, and time
sample_rate	Sample rate in Hz. If omitted, it is taken from the input object when available.
...	Additional arguments passed to walking::sdt_count_steps()

Value

A tibble with minute-level time, steps columns.

Examples

data = actiread::acti_read_gt3x(actiread::acti_example_gt3x())
steps = acti_calculate_sdt(data)

---

Calculate Step Counts via stepcount

Description

Use the stepcount package to estimate steps from raw accelerometer data and summarize them to minute-level epochs (as opposed to 10s default)

Usage

acti_calculate_stepcount(data, sample_rate = NULL, ..., epoch = "1 minute")

Arguments

data	A data.frame, AccData object, or GT3X file with X, Y, Z, and time
sample_rate	Sample rate in Hz. If omitted, it is taken from the input object when available.
...	Additional arguments passed to stepcount::stepcount()
epoch	epoch unit to aggregate the data to, passed to lubridate::floor_date(), original output is at 10-seconds

Value

A tibble with minute-level time, steps, and walking columns.

Examples

# reticulate::py_require("stepcount==3.11.0", python_version = "3.10", action = "add")
  # sc = try({ reticulate::import("stepcount") })
  if (stepcount::have_stepcount()) {
    data = actiread::acti_read_gt3x(actiread::acti_example_gt3x())
    steps = acti_calculate_stepcount(data, sample_rate = 100)
    steps = acti_calculate_stepcount(data, model_type = "rf")
  }

---

Calculate Step Counts via Verisense

Description

Calculate Step Counts via Verisense

Usage

acti_calculate_verisense(
  data,
  resample_to_15hz = TRUE,
  method = c("original", "revised"),
  ...
)

Arguments

data	A data.frame, AccData object, or GT3X file with X, Y, Z, and time
resample_to_15hz	resample data to 15Hz, passed to walking::estimate_steps_verisense()
method	parameters to estimate walking, either original or revised, passed to walking::estimate_steps_verisense()
...	Additional arguments passed to walking::estimate_steps_verisense()

Value

A tibble with minute-level time, steps columns.

Examples

data = actiread::acti_read_gt3x(actiread::acti_example_gt3x())
steps = acti_calculate_verisense(data)

---

Calibrate Accelerometer Data using agcounts

Description

This uses the van Hees calibration method typically exposed through GGIR, implemented here via agcounts::agcalibrate().

Usage

acti_calibrate(
  data,
  verbose = TRUE,
  fill_zeroes = TRUE,
  round_after_calibration = TRUE,
  ...
)

Arguments

data	Either a GT3X file, AccData object, or data.frame with X/Y/Z and time
verbose	print diagnostic messages, higher number result in higher verbosity
fill_zeroes	Should actibase::acti_fill_zeros() be run before calculating the measures? This trims zero values from the beginning and the end of the time course using last observation carried forward behavior.
round_after_calibration	Should the data be rounded after calibration? Will round to 3 digits
...	Additional arguments to pass to agcounts::agcalibrate()

Value

A calibrated dataset with the same columns as the input data, but with the X/Y/Z values calibrated using the van Hees method.

Examples

res = acti_calibrate(data = actiread::acti_example_gt3x())

---

Example Actigraphy/Activity Count Data

Description

Example Actigraphy/Activity Count Data

Usage

acti_count_data

Format

A data.frame with the columns

time

time at the minute level

axis1

axis1 (Y) counts

axis2

axis2 (X) counts

axis3

axis3 (Z) counts

counts

vector magnitude of all 3 axes column

This data was taken from running agcounts::calculate_counts via acti_calculate_counts on actibase::acti_raw_data.

---

Process Raw data to Counts

Description

Process Raw data to Counts

Usage

acti_process(
  data,
  lfe_select = FALSE,
  method = c("choi", "troiano"),
  use_magnitude = TRUE,
  calibrate = FALSE,
  verbose = TRUE,
  ...,
  min_required = 1368L
)

Arguments

data	A data.frame from acti_calculate_counts that has columns axis1-3 and counts
lfe_select	Apply the Actigraph Low Frequency Extension filter. See agcounts::calculate_counts() higher values are higher levels of verbosity.
method	Method for detecting non-wear, either "choi" or "troiano", corresponding to actigraph.sleepr::apply_choi() or actigraph.sleepr::apply_troiano()
use_magnitude	If TRUE, the magnitude of the vector (axis1, axis2, axis3) is used to measure activity; otherwise the axis1 value is used.
calibrate	Logical. If TRUE, the data will be calibrated using acti_calibrate()
verbose	print diagnostic messages. Either logical or integer, where
...	additional arguments to pass to actigraph.sleepr function
min_required	Number of minutes required in a day to be called included. No day inclusion is run if NULL.

Value

A data frame containing activity counts and wear-time indicators.

Note

For acti_process_gt3x, the ... argument are passed to actiread::acti_read_gt3x()

---

Default MIMS worker functions

Description

Default MIMS worker functions

Usage

mims_default_extrapolation(data, dynamic_range = NULL)

mims_default_interpolation(data)

mims_default_filtering(data)

Arguments

data	data set of data, usually time and X/Y/Z. Usually from actiread::acti_read_gt3x()
dynamic_range	dynamic range of the data. Will be passed to actibase::get_dynamic_range()

Value

A data set of data

---

Default MIMS Pre-processing

Description

Default MIMS Pre-processing

Usage

mims_default_processing(
  data,
  use_extrapolation = TRUE,
  use_filtering = TRUE,
  verbose = TRUE,
  dynamic_range = NULL,
  round_after_processing = FALSE
)

Arguments

data	Data set of raw accelerometry values, usually time and X/Y/Z. Usually from actiread::acti_read_gt3x()
use_extrapolation	If TRUE the function will apply extrapolation algorithm to the input signal, otherwise it will skip extrapolation but only linearly interpolate the signal to 100Hz.
use_filtering	If TRUE the function will apply bandpass filtering to the input signal, otherwise it will skip the filtering.
verbose	print diagnostic messages
dynamic_range	the dynamic ranges of the input signal. Passed to actimetrics::mims_default_extrapolation(). Only needed if use_extrapolation = TRUE
round_after_processing	Should the result be rounded to 3 decimal values after processing, to make similar to standard accelerometry?

Value

A processed data set

---

Perform step count calculation in a separate Python environment

Description

Perform step count calculation in a separate Python environment

Usage

py_acti_calculate_forest(
  ...,
  pyenv_function = function() {
     reticulate::import("forest")
 },
  show = FALSE
)

Arguments

...	arguments passed to acti_calculate_forest()
pyenv_function	function that loads the forest Python package. By default, it uses reticulate::py_import("forest") to import the package. If this function has an args argument, the output of pyenv_function will be re-assigned to args.
show	Logical, whether to show the standard output on the screen while the child process is running, passed to callr::r()

Value

The output from acti_calculate_forest().

Examples

Sys.setenv("SSQ_PARALLEL" = 0)
  data = actiread::acti_read_gt3x(actiread::acti_example_gt3x())
  steps = py_acti_calculate_forest(data, sample_rate = 100)

---

Perform step count calculation in a separate Python environment

Description

Perform step count calculation in a separate Python environment

Usage

py_acti_calculate_stepcount(
  ...,
  pyenv_function = function() {
     stepcount::py_require_stepcount()
 },
  show = FALSE
)

Arguments

...	arguments passed to acti_calculate_stepcount()
pyenv_function	function that loads the stepcount Python package. By default, it uses reticulate::py_import("stepcount") to import the package. If this function has an args argument, the output of pyenv_function will be re-assigned to args.
show	Logical, whether to show the standard output on the screen while the child process is running, passed to callr::r()

Value

The output from acti_calculate_stepcount(). A tibble with minute-level time, steps, and walking columns.

Examples

if (stepcount::have_stepcount()) {
    data = actiread::acti_read_gt3x(actiread::acti_example_gt3x())
    steps = py_acti_calculate_stepcount(data, sample_rate = 100)
  }

---