README.md
In catcrumpet/accelR: Accelerometer Data Analysis in R (experimental)
The goal of accelR is to provide a simple and easy interface to
processing accelerometer data for physical activity analyses.
Currently, only Actigraph data files are supported. These data files can
be *.agd or *.csv files and they must be generated by Actigraph
software.
Everything is still being developed so anything can change at anytime.
In fact, things are changing all the time.
Installation
Installation requires the devtools package, install it with:
install.packages("devtools")
Then install accelR:
devtools::install_github("catcrumpet/accelR")
A short and painful tutorial
Load the package.
library(accelR)
Read in accelerometer data from an ActiGraph (*.agd) file
In this case, the data was collected in Los Angeles, so the
locale/timezone is set appropriately.
good_data <- read_agd("./inst/extdata/catcrumpet_data.agd", tz = "America/Los_Angeles")
good_data
## # A tsibble: 34,554 x 10 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 steps lux inclineoff inclinestanding
## <dttm> <int> <int> <int> <int> <int> <int> <int>
## 1 2017-11-29 16:00:00 0 0 0 0 0 0 10
## 2 2017-11-29 16:00:10 0 0 0 0 0 0 10
## 3 2017-11-29 16:00:20 0 0 0 0 0 0 10
## 4 2017-11-29 16:00:30 0 0 0 0 0 0 10
## 5 2017-11-29 16:00:40 0 0 0 0 0 0 10
## 6 2017-11-29 16:00:50 0 0 0 0 0 0 10
## 7 2017-11-29 16:01:00 0 0 0 0 0 0 10
## 8 2017-11-29 16:01:10 0 0 0 0 0 0 10
## 9 2017-11-29 16:01:20 0 0 0 0 0 0 10
## 10 2017-11-29 16:01:30 0 0 0 0 0 0 10
## # … with 34,544 more rows, and 2 more variables: inclinesitting <int>,
## # inclinelying <int>
The print out displays how many observations/epochs there are (34554)
and the counts for the three axes (axis1, axis2, and axis3). Not
all ActiGraph accelerometers collect data on three axes. Also, we can
see the timezone/locale (America/Los_Angeles) and the epoch length (10
seconds).
Data need to pass several checks:
- The data is ordered by timestamp (earliest to latest).
- Each observation is regularly spaced by a constant time period
(i.e., the epoch length).
- No observations are duplicates.
- The data spans the entire specified data collection time period.
- The data contains no gaps in observations.
- The epoch length of the data must an exact divisor of multiple of
60.
For better display of the following operations, we will remove the
unnecessary columns, preserving only the accelerometer counts columns.
# method 1, using base R
good_data <- good_data[, c("timestamp", "axis1", "axis2", "axis3")]
# method 2, using dplyr
good_data <- dplyr::select(good_data, timestamp, axis1, axis2, axis3)
# result
good_data
## # A tsibble: 34,554 x 4 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3
## <dttm> <int> <int> <int>
## 1 2017-11-29 16:00:00 0 0 0
## 2 2017-11-29 16:00:10 0 0 0
## 3 2017-11-29 16:00:20 0 0 0
## 4 2017-11-29 16:00:30 0 0 0
## 5 2017-11-29 16:00:40 0 0 0
## 6 2017-11-29 16:00:50 0 0 0
## 7 2017-11-29 16:01:00 0 0 0
## 8 2017-11-29 16:01:10 0 0 0
## 9 2017-11-29 16:01:20 0 0 0
## 10 2017-11-29 16:01:30 0 0 0
## # … with 34,544 more rows
Accelerometer-based calculations
All accelerometer-based calculations or methods do not assume which
column of data contains the count data to be processed. Most Actigraph
accelerometers capture count data in three axes: axis1, axis2, and
axis3. In order for most, if not all, of the following calculations to
work, the proper axis (or magnitude column) must be provided.
Add a column for nonwear
The default approach for nonwear detection is the Troiano approach. We
will use a modified Troiano approach that is more strict with no spikes.
This can be done by setting the argument spike_tolerance = 0.
better_data <- add_nonwear(good_data, counts = axis1, algorithm = nonwear_troiano, spike_tolerance = 0)
better_data
## # A tsibble: 34,554 x 5 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 nonwear
## <dttm> <int> <int> <int> <lgl>
## 1 2017-11-29 16:00:00 0 0 0 TRUE
## 2 2017-11-29 16:00:10 0 0 0 TRUE
## 3 2017-11-29 16:00:20 0 0 0 TRUE
## 4 2017-11-29 16:00:30 0 0 0 TRUE
## 5 2017-11-29 16:00:40 0 0 0 TRUE
## 6 2017-11-29 16:00:50 0 0 0 TRUE
## 7 2017-11-29 16:01:00 0 0 0 TRUE
## 8 2017-11-29 16:01:10 0 0 0 TRUE
## 9 2017-11-29 16:01:20 0 0 0 TRUE
## 10 2017-11-29 16:01:30 0 0 0 TRUE
## # … with 34,544 more rows
The first argument is the data that is being worked on, good_data. The
counts argument is the column in good_data to calculate nonwear on.
In this case it is set to axis1 and is unquoted (i.e., it is axis1
and not "axis1"). The algorithm used is nonwear_troiano and is
unquoted as it is a nonwear function in the accelR package. The last
argument is an additional nonwear algorithm specification. In this case,
spike_tolerance is set to 0 so that the algorithm looks for spans of
0s with no breaks. By default, the new column for nonwear values is
named “nonwear” but this can be changed by setting the nonwear
argument to some other string like nonwear = "hello".
This approach, which we (the lab I work in) refer to as the “modified
Troiano” approach is so commonly used that there is a separate algorithm
for it included in the package. The following should provide the same
results.
better_data_alternate <- add_nonwear(good_data, counts = axis1, algorithm = nonwear_troiano_modified)
# test for equality (should be TRUE)
all(better_data$nonwear == better_data_alternate$nonwear)
## [1] TRUE
The name for the new nonwear column can be customized by setting the
nonwear argument.
Add a column for physical activity category
The default is to use Troiano age-based cutpoints. In this case, the
person is 12 years old and the appropriate age-based cutpoints are
chosen automatically. Again, note that the counts argument is the
unquoted name of the column we want to categorize physical activity.
By default, the new physical activity column is named “pa”. This can be
changed using the pa argument.
best_data <- add_pa(better_data, counts = axis1, age = 12)
best_data
## # A tsibble: 34,554 x 6 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 nonwear pa
## <dttm> <int> <int> <int> <lgl> <ord>
## 1 2017-11-29 16:00:00 0 0 0 TRUE sed
## 2 2017-11-29 16:00:10 0 0 0 TRUE sed
## 3 2017-11-29 16:00:20 0 0 0 TRUE sed
## 4 2017-11-29 16:00:30 0 0 0 TRUE sed
## 5 2017-11-29 16:00:40 0 0 0 TRUE sed
## 6 2017-11-29 16:00:50 0 0 0 TRUE sed
## 7 2017-11-29 16:01:00 0 0 0 TRUE sed
## 8 2017-11-29 16:01:10 0 0 0 TRUE sed
## 9 2017-11-29 16:01:20 0 0 0 TRUE sed
## 10 2017-11-29 16:01:30 0 0 0 TRUE sed
## # … with 34,544 more rows
Accelerometer summaries
Summarizing accelerometer data is such an important feature that it
deserves its own section. The accelR package contains two functions
for summarizing accelerometer data: summarise_day and
summarise_window.
Note that these functions use the British/European spelling
(“summarise”) and not the American spelling (“summarize”). This is
to somewhat follow the naming convention of dplyr functions (e.g.,
summarise).
Add a column data validity
Before data can be summarized, it must have a column for physical
activity (generated by add_pa) and a column of logical values for
whether the data is valid. For this example, we will define validity as
fulfilling all of the following criteria:
- The participant must be wearing the device (
!nonwear).
- The physical activity values must be within valid ranges (for the
included Troiano cutpoints, this means
pa != "ext").
These criteria are used to create a new column, valid.
# method 1, using base R
bestest_data <- best_data
bestest_data$valid <- !bestest_data$nonwear & bestest_data$pa != "ext"
# method 2, using base R
bestest_data <- best_data
bestest_data$valid <- with(bestest_data, !nonwear & pa != "ext")
# method 3, using dplyr
bestest_data <- dplyr::mutate(best_data, valid = !nonwear & pa != "ext")
# result
bestest_data
## # A tsibble: 34,554 x 7 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 nonwear pa valid
## <dttm> <int> <int> <int> <lgl> <ord> <lgl>
## 1 2017-11-29 16:00:00 0 0 0 TRUE sed FALSE
## 2 2017-11-29 16:00:10 0 0 0 TRUE sed FALSE
## 3 2017-11-29 16:00:20 0 0 0 TRUE sed FALSE
## 4 2017-11-29 16:00:30 0 0 0 TRUE sed FALSE
## 5 2017-11-29 16:00:40 0 0 0 TRUE sed FALSE
## 6 2017-11-29 16:00:50 0 0 0 TRUE sed FALSE
## 7 2017-11-29 16:01:00 0 0 0 TRUE sed FALSE
## 8 2017-11-29 16:01:10 0 0 0 TRUE sed FALSE
## 9 2017-11-29 16:01:20 0 0 0 TRUE sed FALSE
## 10 2017-11-29 16:01:30 0 0 0 TRUE sed FALSE
## # … with 34,544 more rows
Summarize by day
The simplest summary to perform is on the day level.
day_summary <- summarise_day(bestest_data, counts = axis1, pa = pa, valid = valid)
day_summary
## # A tibble: 5 x 20
## # Groups: date [5]
## date timestamp_start timestamp_stop epochlength total_e total_m
## <date> <dttm> <dttm> <int> <int> <dbl>
## 1 2017-11-29 2017-11-29 16:00:00 2017-11-30 00:00:00 10 2880 480
## 2 2017-11-30 2017-11-30 00:00:00 2017-12-01 00:00:00 10 8640 1440
## 3 2017-12-01 2017-12-01 00:00:00 2017-12-02 00:00:00 10 8640 1440
## 4 2017-12-02 2017-12-02 00:00:00 2017-12-03 00:00:00 10 8640 1440
## 5 2017-12-03 2017-12-03 00:00:00 2017-12-03 15:59:00 10 5754 959
## # … with 14 more variables: total_c <int>, valid_e <int>, valid_m <dbl>,
## # valid_c <int>, sed_m <dbl>, lig_m <dbl>, mod_m <dbl>, vig_m <dbl>,
## # ext_m <dbl>, sed_c <dbl>, lig_c <dbl>, mod_c <dbl>, vig_c <dbl>,
## # ext_c <dbl>
This summary provides total minutes for each category (*_m) of
physical activity as well as an aggregate sum of activity counts by
category (*_c) of physical activity.
Summarize by time window
Summarizing around a given time is more advanced but is useful when
trying to integrate accelerometer data with data from ecological
momentary assessment (EMA) studies. Data in EMA studies is typically
organized around a timestamp for when a survey is administered.
Summarizing accelerometer data in a specified window around this
timestamp can provide a metric of activity that occurred around the time
at survey.
The summarise_window uses two parameters to determine the window:
- An anchor time, which acts as the zero point (position 0).
- One or two numeric values to signify the relative window position
(more details following).
The window values can take one or two positive or negative numeric
values. If one value is provided, then the window assumes that one of
the boundaries is the zero point. The following shows different window
parameters using different input values (input_value).
## # A tibble: 6 x 4
## input_value window_left window_right window_length
## <dbl> <dbl> <dbl> <int>
## 1 -30 -30 0 30
## 2 -20 -20 0 20
## 3 -10 -10 0 10
## 4 10 0 10 10
## 5 20 0 20 20
## 6 30 0 30 30
Using two values allow specification of both left and right window
positions.
## # A tibble: 5 x 4
## input_value window_left window_right window_length
## <chr> <dbl> <dbl> <int>
## 1 c(-10, 10) -10 10 20
## 2 c(-20, 20) -20 20 40
## 3 c(-30, 30) -30 30 60
## 4 c(-60, -30) -60 -30 30
## 5 c(30, 60) 30 60 30
Typically many timestamps are processed, so the summarise_window
function can process multiple anchor times and windows at once. The same
set of windows is reused for each anchor time.
# anchor time will use lubridate to convert a timestamp string to a datetime object
anchor_timestamps <-
lubridate::ymd_hms(c("2017-11-30 10:00:00",
"2017-11-30 13:00:00"),
tz = "America/Los_Angeles")
windows_boundaries <- list(-30, -10, 10, 30, c(-15, 15), c(30, 60))
summarise_window(bestest_data,
anchor_times = anchor_timestamps,
windows = windows_boundaries,
counts = axis1,
pa = pa,
valid = valid)
## # A tibble: 12 x 25
## anchor_time window_left window_right window_length
## <dttm> <dbl> <dbl> <int>
## 1 2017-11-30 10:00:00 -30 0 30
## 2 2017-11-30 10:00:00 -10 0 10
## 3 2017-11-30 10:00:00 0 10 10
## 4 2017-11-30 10:00:00 0 30 30
## 5 2017-11-30 10:00:00 -15 15 30
## 6 2017-11-30 10:00:00 30 60 30
## 7 2017-11-30 13:00:00 -30 0 30
## 8 2017-11-30 13:00:00 -10 0 10
## 9 2017-11-30 13:00:00 0 10 10
## 10 2017-11-30 13:00:00 0 30 30
## 11 2017-11-30 13:00:00 -15 15 30
## 12 2017-11-30 13:00:00 30 60 30
## # … with 21 more variables: window_start <dttm>, window_stop <dttm>,
## # timestamp_start <dttm>, timestamp_stop <dttm>, epochlength <int>,
## # total_e <int>, total_m <dbl>, total_c <int>, valid_e <int>, valid_m <dbl>,
## # valid_c <int>, sed_m <dbl>, lig_m <dbl>, mod_m <dbl>, vig_m <dbl>,
## # ext_m <dbl>, sed_c <dbl>, lig_c <dbl>, mod_c <dbl>, vig_c <dbl>,
## # ext_c <dbl>
catcrumpet/accelR documentation built on June 28, 2020, 11:48 p.m.
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The goal of accelR is to provide a simple and easy interface to
processing accelerometer data for physical activity analyses.
Currently, only Actigraph data files are supported. These data files can be *.agd or *.csv files and they must be generated by Actigraph software.
Everything is still being developed so anything can change at anytime. In fact, things are changing all the time.
Installation
Installation requires the devtools package, install it with:
install.packages("devtools")
Then install accelR:
devtools::install_github("catcrumpet/accelR")
A short and painful tutorial
Load the package.
library(accelR)
Read in accelerometer data from an ActiGraph (*.agd) file
In this case, the data was collected in Los Angeles, so the locale/timezone is set appropriately.
good_data <- read_agd("./inst/extdata/catcrumpet_data.agd", tz = "America/Los_Angeles")
good_data
## # A tsibble: 34,554 x 10 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 steps lux inclineoff inclinestanding
## <dttm> <int> <int> <int> <int> <int> <int> <int>
## 1 2017-11-29 16:00:00 0 0 0 0 0 0 10
## 2 2017-11-29 16:00:10 0 0 0 0 0 0 10
## 3 2017-11-29 16:00:20 0 0 0 0 0 0 10
## 4 2017-11-29 16:00:30 0 0 0 0 0 0 10
## 5 2017-11-29 16:00:40 0 0 0 0 0 0 10
## 6 2017-11-29 16:00:50 0 0 0 0 0 0 10
## 7 2017-11-29 16:01:00 0 0 0 0 0 0 10
## 8 2017-11-29 16:01:10 0 0 0 0 0 0 10
## 9 2017-11-29 16:01:20 0 0 0 0 0 0 10
## 10 2017-11-29 16:01:30 0 0 0 0 0 0 10
## # … with 34,544 more rows, and 2 more variables: inclinesitting <int>,
## # inclinelying <int>
The print out displays how many observations/epochs there are (34554)
and the counts for the three axes (axis1, axis2, and axis3). Not
all ActiGraph accelerometers collect data on three axes. Also, we can
see the timezone/locale (America/Los_Angeles) and the epoch length (10
seconds).
Data need to pass several checks:
- The data is ordered by timestamp (earliest to latest).
- Each observation is regularly spaced by a constant time period (i.e., the epoch length).
- No observations are duplicates.
- The data spans the entire specified data collection time period.
- The data contains no gaps in observations.
- The epoch length of the data must an exact divisor of multiple of 60.
For better display of the following operations, we will remove the unnecessary columns, preserving only the accelerometer counts columns.
# method 1, using base R
good_data <- good_data[, c("timestamp", "axis1", "axis2", "axis3")]
# method 2, using dplyr
good_data <- dplyr::select(good_data, timestamp, axis1, axis2, axis3)
# result
good_data
## # A tsibble: 34,554 x 4 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3
## <dttm> <int> <int> <int>
## 1 2017-11-29 16:00:00 0 0 0
## 2 2017-11-29 16:00:10 0 0 0
## 3 2017-11-29 16:00:20 0 0 0
## 4 2017-11-29 16:00:30 0 0 0
## 5 2017-11-29 16:00:40 0 0 0
## 6 2017-11-29 16:00:50 0 0 0
## 7 2017-11-29 16:01:00 0 0 0
## 8 2017-11-29 16:01:10 0 0 0
## 9 2017-11-29 16:01:20 0 0 0
## 10 2017-11-29 16:01:30 0 0 0
## # … with 34,544 more rows
Accelerometer-based calculations
All accelerometer-based calculations or methods do not assume which
column of data contains the count data to be processed. Most Actigraph
accelerometers capture count data in three axes: axis1, axis2, and
axis3. In order for most, if not all, of the following calculations to
work, the proper axis (or magnitude column) must be provided.
Add a column for nonwear
The default approach for nonwear detection is the Troiano approach. We
will use a modified Troiano approach that is more strict with no spikes.
This can be done by setting the argument spike_tolerance = 0.
better_data <- add_nonwear(good_data, counts = axis1, algorithm = nonwear_troiano, spike_tolerance = 0)
better_data
## # A tsibble: 34,554 x 5 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 nonwear
## <dttm> <int> <int> <int> <lgl>
## 1 2017-11-29 16:00:00 0 0 0 TRUE
## 2 2017-11-29 16:00:10 0 0 0 TRUE
## 3 2017-11-29 16:00:20 0 0 0 TRUE
## 4 2017-11-29 16:00:30 0 0 0 TRUE
## 5 2017-11-29 16:00:40 0 0 0 TRUE
## 6 2017-11-29 16:00:50 0 0 0 TRUE
## 7 2017-11-29 16:01:00 0 0 0 TRUE
## 8 2017-11-29 16:01:10 0 0 0 TRUE
## 9 2017-11-29 16:01:20 0 0 0 TRUE
## 10 2017-11-29 16:01:30 0 0 0 TRUE
## # … with 34,544 more rows
The first argument is the data that is being worked on, good_data. The
counts argument is the column in good_data to calculate nonwear on.
In this case it is set to axis1 and is unquoted (i.e., it is axis1
and not "axis1"). The algorithm used is nonwear_troiano and is
unquoted as it is a nonwear function in the accelR package. The last
argument is an additional nonwear algorithm specification. In this case,
spike_tolerance is set to 0 so that the algorithm looks for spans of
0s with no breaks. By default, the new column for nonwear values is
named “nonwear” but this can be changed by setting the nonwear
argument to some other string like nonwear = "hello".
This approach, which we (the lab I work in) refer to as the “modified Troiano” approach is so commonly used that there is a separate algorithm for it included in the package. The following should provide the same results.
better_data_alternate <- add_nonwear(good_data, counts = axis1, algorithm = nonwear_troiano_modified)
# test for equality (should be TRUE)
all(better_data$nonwear == better_data_alternate$nonwear)
## [1] TRUE
The name for the new nonwear column can be customized by setting the
nonwear argument.
Add a column for physical activity category
The default is to use Troiano age-based cutpoints. In this case, the
person is 12 years old and the appropriate age-based cutpoints are
chosen automatically. Again, note that the counts argument is the
unquoted name of the column we want to categorize physical activity.
By default, the new physical activity column is named “pa”. This can be
changed using the pa argument.
best_data <- add_pa(better_data, counts = axis1, age = 12)
best_data
## # A tsibble: 34,554 x 6 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 nonwear pa
## <dttm> <int> <int> <int> <lgl> <ord>
## 1 2017-11-29 16:00:00 0 0 0 TRUE sed
## 2 2017-11-29 16:00:10 0 0 0 TRUE sed
## 3 2017-11-29 16:00:20 0 0 0 TRUE sed
## 4 2017-11-29 16:00:30 0 0 0 TRUE sed
## 5 2017-11-29 16:00:40 0 0 0 TRUE sed
## 6 2017-11-29 16:00:50 0 0 0 TRUE sed
## 7 2017-11-29 16:01:00 0 0 0 TRUE sed
## 8 2017-11-29 16:01:10 0 0 0 TRUE sed
## 9 2017-11-29 16:01:20 0 0 0 TRUE sed
## 10 2017-11-29 16:01:30 0 0 0 TRUE sed
## # … with 34,544 more rows
Accelerometer summaries
Summarizing accelerometer data is such an important feature that it
deserves its own section. The accelR package contains two functions
for summarizing accelerometer data: summarise_day and
summarise_window.
Note that these functions use the British/European spelling
(“summarise”) and not the American spelling (“summarize”). This is
to somewhat follow the naming convention of dplyr functions (e.g.,
summarise).
Add a column data validity
Before data can be summarized, it must have a column for physical
activity (generated by add_pa) and a column of logical values for
whether the data is valid. For this example, we will define validity as
fulfilling all of the following criteria:
- The participant must be wearing the device (
!nonwear). - The physical activity values must be within valid ranges (for the
included Troiano cutpoints, this means
pa != "ext").
These criteria are used to create a new column, valid.
# method 1, using base R
bestest_data <- best_data
bestest_data$valid <- !bestest_data$nonwear & bestest_data$pa != "ext"
# method 2, using base R
bestest_data <- best_data
bestest_data$valid <- with(bestest_data, !nonwear & pa != "ext")
# method 3, using dplyr
bestest_data <- dplyr::mutate(best_data, valid = !nonwear & pa != "ext")
# result
bestest_data
## # A tsibble: 34,554 x 7 [10s] <America/Los_Angeles>
## timestamp axis1 axis2 axis3 nonwear pa valid
## <dttm> <int> <int> <int> <lgl> <ord> <lgl>
## 1 2017-11-29 16:00:00 0 0 0 TRUE sed FALSE
## 2 2017-11-29 16:00:10 0 0 0 TRUE sed FALSE
## 3 2017-11-29 16:00:20 0 0 0 TRUE sed FALSE
## 4 2017-11-29 16:00:30 0 0 0 TRUE sed FALSE
## 5 2017-11-29 16:00:40 0 0 0 TRUE sed FALSE
## 6 2017-11-29 16:00:50 0 0 0 TRUE sed FALSE
## 7 2017-11-29 16:01:00 0 0 0 TRUE sed FALSE
## 8 2017-11-29 16:01:10 0 0 0 TRUE sed FALSE
## 9 2017-11-29 16:01:20 0 0 0 TRUE sed FALSE
## 10 2017-11-29 16:01:30 0 0 0 TRUE sed FALSE
## # … with 34,544 more rows
Summarize by day
The simplest summary to perform is on the day level.
day_summary <- summarise_day(bestest_data, counts = axis1, pa = pa, valid = valid)
day_summary
## # A tibble: 5 x 20
## # Groups: date [5]
## date timestamp_start timestamp_stop epochlength total_e total_m
## <date> <dttm> <dttm> <int> <int> <dbl>
## 1 2017-11-29 2017-11-29 16:00:00 2017-11-30 00:00:00 10 2880 480
## 2 2017-11-30 2017-11-30 00:00:00 2017-12-01 00:00:00 10 8640 1440
## 3 2017-12-01 2017-12-01 00:00:00 2017-12-02 00:00:00 10 8640 1440
## 4 2017-12-02 2017-12-02 00:00:00 2017-12-03 00:00:00 10 8640 1440
## 5 2017-12-03 2017-12-03 00:00:00 2017-12-03 15:59:00 10 5754 959
## # … with 14 more variables: total_c <int>, valid_e <int>, valid_m <dbl>,
## # valid_c <int>, sed_m <dbl>, lig_m <dbl>, mod_m <dbl>, vig_m <dbl>,
## # ext_m <dbl>, sed_c <dbl>, lig_c <dbl>, mod_c <dbl>, vig_c <dbl>,
## # ext_c <dbl>
This summary provides total minutes for each category (*_m) of
physical activity as well as an aggregate sum of activity counts by
category (*_c) of physical activity.
Summarize by time window
Summarizing around a given time is more advanced but is useful when trying to integrate accelerometer data with data from ecological momentary assessment (EMA) studies. Data in EMA studies is typically organized around a timestamp for when a survey is administered. Summarizing accelerometer data in a specified window around this timestamp can provide a metric of activity that occurred around the time at survey.
The summarise_window uses two parameters to determine the window:
- An anchor time, which acts as the zero point (position 0).
- One or two numeric values to signify the relative window position (more details following).
The window values can take one or two positive or negative numeric
values. If one value is provided, then the window assumes that one of
the boundaries is the zero point. The following shows different window
parameters using different input values (input_value).
## # A tibble: 6 x 4
## input_value window_left window_right window_length
## <dbl> <dbl> <dbl> <int>
## 1 -30 -30 0 30
## 2 -20 -20 0 20
## 3 -10 -10 0 10
## 4 10 0 10 10
## 5 20 0 20 20
## 6 30 0 30 30
Using two values allow specification of both left and right window positions.
## # A tibble: 5 x 4
## input_value window_left window_right window_length
## <chr> <dbl> <dbl> <int>
## 1 c(-10, 10) -10 10 20
## 2 c(-20, 20) -20 20 40
## 3 c(-30, 30) -30 30 60
## 4 c(-60, -30) -60 -30 30
## 5 c(30, 60) 30 60 30
Typically many timestamps are processed, so the summarise_window
function can process multiple anchor times and windows at once. The same
set of windows is reused for each anchor time.
# anchor time will use lubridate to convert a timestamp string to a datetime object
anchor_timestamps <-
lubridate::ymd_hms(c("2017-11-30 10:00:00",
"2017-11-30 13:00:00"),
tz = "America/Los_Angeles")
windows_boundaries <- list(-30, -10, 10, 30, c(-15, 15), c(30, 60))
summarise_window(bestest_data,
anchor_times = anchor_timestamps,
windows = windows_boundaries,
counts = axis1,
pa = pa,
valid = valid)
## # A tibble: 12 x 25
## anchor_time window_left window_right window_length
## <dttm> <dbl> <dbl> <int>
## 1 2017-11-30 10:00:00 -30 0 30
## 2 2017-11-30 10:00:00 -10 0 10
## 3 2017-11-30 10:00:00 0 10 10
## 4 2017-11-30 10:00:00 0 30 30
## 5 2017-11-30 10:00:00 -15 15 30
## 6 2017-11-30 10:00:00 30 60 30
## 7 2017-11-30 13:00:00 -30 0 30
## 8 2017-11-30 13:00:00 -10 0 10
## 9 2017-11-30 13:00:00 0 10 10
## 10 2017-11-30 13:00:00 0 30 30
## 11 2017-11-30 13:00:00 -15 15 30
## 12 2017-11-30 13:00:00 30 60 30
## # … with 21 more variables: window_start <dttm>, window_stop <dttm>,
## # timestamp_start <dttm>, timestamp_stop <dttm>, epochlength <int>,
## # total_e <int>, total_m <dbl>, total_c <int>, valid_e <int>, valid_m <dbl>,
## # valid_c <int>, sed_m <dbl>, lig_m <dbl>, mod_m <dbl>, vig_m <dbl>,
## # ext_m <dbl>, sed_c <dbl>, lig_c <dbl>, mod_c <dbl>, vig_c <dbl>,
## # ext_c <dbl>
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