import_actigraph_csv_chunked() R function from [MIMSunit]

Import large raw multi-channel accelerometer data stored in Actigraph raw csv format in chunks

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
)

Arguments

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.

Returns

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.

Details

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.

How is it used in MIMS-unit algorithm?

This function is a File IO function that is used to import data from Actigraph devices during algorithm validation.

Examples


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)

import_actigraph_csv_chunked function