| import_actigraph_csv_chunked | R Documentation |
import_actigraph_csv_chunked imports the raw multi-channel accelerometer data
stored in Actigraph raw csv format. It supports files from the following
devices: GT3X, GT3X+, GT3X+BT, GT9X, and GT9X-IMU.
import_actigraph_csv_chunked( filepath, in_voltage = FALSE, header = TRUE, has_ts = TRUE, chunk_samples = 180000 )
filepath |
string. The filepath of the input data.The first column of the input data should always include timestamps. |
in_voltage |
set as TRUE only when the input Actigraph csv file is in analog quantized format and need to be converted into g value |
header |
boolean. If TRUE, the input csv file will have column names in the first row. |
has_ts |
boolean. If TRUE, the input csv file should have a timestamp column at first. |
chunk_samples |
number. The number of samples in each chunk. Default is 180000. |
For old device (GT3X) that stores accelerometer values as digital voltage. The function will convert the values to g unit using the following equation.
x_g = \frac{x_{voltage}r}{(2 ^ r) - \frac{v}{2}}
Where v is the max voltage corresponding to the max accelerometer value that can be found in the meta section in the csv file; r is the resolution level which is the number of bits used to store the voltage values. r can also be found in the meta section in the csv file.
list. The list contains two items. The first item is a generator
function that each time it is called, it will return a data.frame of the
imported chunk. The second item is a close function which you can
call at any moment to close the file loading.
This function is a File IO function that is used to import data from Actigraph devices during algorithm validation.
Other File I/O functions:
export_to_actilife(),
import_actigraph_count_csv(),
import_actigraph_csv(),
import_actigraph_meta(),
import_activpal3_csv(),
import_enmo_csv(),
import_mhealth_csv_chunked(),
import_mhealth_csv()
default_ops = options()
options(digits.secs=3)
# Use the actigraph csv file shipped with the package
filepath = system.file('extdata', 'actigraph_timestamped.csv', package='MIMSunit')
# Check original file format
readLines(filepath)[1:15]
# Example 1: Load chunks every 2000 samples
results = import_actigraph_csv_chunked(filepath, chunk_samples=2000)
next_chunk = results[[1]]
close_connection = results[[2]]
# Check data as chunks, you can see chunks are shifted at each iteration.
n = 1
repeat {
df = next_chunk()
if (nrow(df) > 0) {
print(paste('chunk', n))
print(paste("df:", df[1, 1], '-', df[nrow(df),1]))
n = n + 1
}
else {
break
}
}
# Close connection after reading all the data
close_connection()
# Example 2: Close loading early
results = import_actigraph_csv_chunked(filepath, chunk_samples=2000)
next_chunk = results[[1]]
close_connection = results[[2]]
# Check data as chunks, you can see chunk time is shifting forward at each iteration.
n = 1
repeat {
df = next_chunk()
if (nrow(df) > 0) {
print(paste('chunk', n))
print(paste("df:", df[1, 1], '-', df[nrow(df),1]))
n = n + 1
close_connection()
}
else {
break
}
}
# Restore default options
options(default_ops)
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