impactr_data_printing.md
In impactr: Mechanical Loading Prediction Through Accelerometer Data

printing from read_acc() works

Code
  read_acc(test_path("test-data-hip-imu.csv"))
Output
  # Start time:              2017-12-09 15:00:00
  # Sampling frequency:      100Hz
  # Accelerometer placement: Non-specified
  # Subject body mass:       Non-specified
  # Filter:                  No filter applied
  # Data dimensions:         100 x 4
     timestamp             acc_X   acc_Y acc_Z
     <dttm>                <dbl>   <dbl> <dbl>
   1 2017-12-09 15:00:00 -0.0122 0.00830 0.967
   2 2017-12-09 15:00:00 -0.0151 0.00732 0.967
   3 2017-12-09 15:00:00 -0.0137 0.00635 0.964
   4 2017-12-09 15:00:00 -0.0132 0.00684 0.969
   5 2017-12-09 15:00:00 -0.0137 0.00586 0.965
   6 2017-12-09 15:00:00 -0.0137 0.00586 0.965
   7 2017-12-09 15:00:00 -0.0151 0.00586 0.967
   8 2017-12-09 15:00:00 -0.0146 0.00635 0.964
   9 2017-12-09 15:00:00 -0.0156 0.00586 0.966
  10 2017-12-09 15:00:00 -0.0151 0.00635 0.963
  # i 90 more rows

printing from specify_parameter() works

Code
  specify_parameters(data, "hip", 80)
Output
  # Start time:              2017-12-09 15:00:00
  # Sampling frequency:      100Hz
  # Accelerometer placement: Hip
  # Subject body mass:       80kg
  # Filter:                  No filter applied
  # Data dimensions:         100 x 4
     timestamp             acc_X   acc_Y acc_Z
     <dttm>                <dbl>   <dbl> <dbl>
   1 2017-12-09 15:00:00 -0.0122 0.00830 0.967
   2 2017-12-09 15:00:00 -0.0151 0.00732 0.967
   3 2017-12-09 15:00:00 -0.0137 0.00635 0.964
   4 2017-12-09 15:00:00 -0.0132 0.00684 0.969
   5 2017-12-09 15:00:00 -0.0137 0.00586 0.965
   6 2017-12-09 15:00:00 -0.0137 0.00586 0.965
   7 2017-12-09 15:00:00 -0.0151 0.00586 0.967
   8 2017-12-09 15:00:00 -0.0146 0.00635 0.964
   9 2017-12-09 15:00:00 -0.0156 0.00586 0.966
  10 2017-12-09 15:00:00 -0.0151 0.00635 0.963
  # i 90 more rows

printing from filter_acc() works

Code
  filter_acc(data)
Output
  # Start time:              2017-12-09 15:00:00
  # Sampling frequency:      100Hz
  # Accelerometer placement: Non-specified
  # Subject body mass:       Non-specified
  # Filter:                  Butterworth (4th-ord, low-pass, 20Hz)
  # Data dimensions:         100 x 4
     timestamp              acc_X   acc_Y acc_Z
     <dttm>                 <dbl>   <dbl> <dbl>
   1 2017-12-09 15:00:00 -0.00948 0.00552 0.678
   2 2017-12-09 15:00:00 -0.0138  0.00738 0.964
   3 2017-12-09 15:00:00 -0.0150  0.00744 1.05 
   4 2017-12-09 15:00:00 -0.0140  0.00656 0.995
   5 2017-12-09 15:00:00 -0.0131  0.00590 0.943
   6 2017-12-09 15:00:00 -0.0135  0.00578 0.943
   7 2017-12-09 15:00:00 -0.0147  0.00585 0.966
   8 2017-12-09 15:00:00 -0.0155  0.00592 0.976
   9 2017-12-09 15:00:00 -0.0154  0.00623 0.970
  10 2017-12-09 15:00:00 -0.0146  0.00691 0.962
  # i 90 more rows

printing from find_peaks() works

Code
  find_peaks(data, "vertical")
Output
  # Start time:              2021-04-06 15:43:00
  # Sampling frequency:      100Hz
  # Accelerometer placement: Non-specified
  # Subject body mass:       Non-specified
  # Filter:                  No filter applied
  # Data dimensions:         251 x 2
     timestamp           vertical_peak_acc
     <dttm>                          <dbl>
   1 2021-04-06 15:43:00              1.83
   2 2021-04-06 15:43:03              1.41
   3 2021-04-06 15:43:04              1.59
   4 2021-04-06 15:43:06              1.35
   5 2021-04-06 15:43:09              2.61
   6 2021-04-06 15:43:11              1.38
   7 2021-04-06 15:43:14              1.42
   8 2021-04-06 15:43:16              1.36
   9 2021-04-06 15:43:16              1.46
  10 2021-04-06 15:43:17              1.32
  # i 241 more rows

Code
  find_peaks(data, "resultant")
Output
  # Start time:              2021-04-06 15:43:00
  # Sampling frequency:      100Hz
  # Accelerometer placement: Non-specified
  # Subject body mass:       Non-specified
  # Filter:                  No filter applied
  # Data dimensions:         303 x 2
     timestamp           resultant_peak_acc
     <dttm>                           <dbl>
   1 2021-04-06 15:43:00               2.24
   2 2021-04-06 15:43:00               1.43
   3 2021-04-06 15:43:02               1.49
   4 2021-04-06 15:43:03               1.68
   5 2021-04-06 15:43:04               1.49
   6 2021-04-06 15:43:04               1.30
   7 2021-04-06 15:43:05               2.13
   8 2021-04-06 15:43:05               1.34
   9 2021-04-06 15:43:06               1.39
  10 2021-04-06 15:43:07               1.46
  # i 293 more rows

Code
  find_peaks(data, "all")
Output
  # Start time:              2021-04-06 15:43:00
  # Sampling frequency:      100Hz
  # Accelerometer placement: Non-specified
  # Subject body mass:       Non-specified
  # Filter:                  No filter applied
  # Data dimensions:         331 x 3
     timestamp           vertical_peak_acc resultant_peak_acc
     <dttm>                          <dbl>              <dbl>
   1 2021-04-06 15:43:00              1.83               2.24
   2 2021-04-06 15:43:00             NA                  1.43
   3 2021-04-06 15:43:02             NA                  1.49
   4 2021-04-06 15:43:03              1.41              NA   
   5 2021-04-06 15:43:03             NA                  1.68
   6 2021-04-06 15:43:04             NA                  1.49
   7 2021-04-06 15:43:04             NA                  1.30
   8 2021-04-06 15:43:04              1.59              NA   
   9 2021-04-06 15:43:05             NA                  2.13
  10 2021-04-06 15:43:05             NA                  1.34
  # i 321 more rows