Standardize the Accelerometry Data

Usage

acti_standardize_data(
  data,
  subset_xyz = TRUE,
  colname_time = "time",
  lower_case = FALSE,
  check_xyz = TRUE
)

acti_standardise_data(
  data,
  subset_xyz = TRUE,
  colname_time = "time",
  lower_case = FALSE,
  check_xyz = TRUE
)

Arguments

data: A data.frame with an optional time column and three accelerometer axes. Axes can be named X/Y/Z, x/y/z, acc_X/acc_Y/acc_Z, acc_x/acc_y/acc_z, or acceleration_X/acceleration_Y/acceleration_Z (with lowercase axis letters also accepted).
subset_xyz: should only the time (if available) and XYZ be subset?
colname_time: column name of header for time
lower_case: flag to make sure all columns are lower case.
check_xyz: Check if X/Y/Z is in the data

Value

A data.frame with X/Y/Z and a time in time (if available).

Details

When a complete canonical triplet and a complete prefixed triplet (acc_* or acceleration_*) are both present, the canonical columns are used and a single warning is issued. A data frame containing both X and x (or the analogous pair for Y or Z) is rejected as ambiguous.

Examples


acti_standardize_data(acti_raw_data)
#> # A tibble: 240,500 × 4
#>    time                    X      Y     Z
#>    <dttm>              <dbl>  <dbl> <dbl>
#>  1 2019-09-17 18:40:00 0      0.008 0.996
#>  2 2019-09-17 18:40:00 0.016  0     1.01 
#>  3 2019-09-17 18:40:00 0.02  -0.008 1.00 
#>  4 2019-09-17 18:40:00 0.016 -0.012 1.01 
#>  5 2019-09-17 18:40:00 0.016 -0.008 1.01 
#>  6 2019-09-17 18:40:00 0.008 -0.008 1.01 
#>  7 2019-09-17 18:40:00 0.016 -0.008 1.02 
#>  8 2019-09-17 18:40:00 0.02  -0.004 1.02 
#>  9 2019-09-17 18:40:00 0.016  0     1.01 
#> 10 2019-09-17 18:40:00 0.012  0     1.02 
#> # ℹ 240,490 more rows
acti_standardise_data(acti_raw_data)
#> # A tibble: 240,500 × 4
#>    time                    X      Y     Z
#>    <dttm>              <dbl>  <dbl> <dbl>
#>  1 2019-09-17 18:40:00 0      0.008 0.996
#>  2 2019-09-17 18:40:00 0.016  0     1.01 
#>  3 2019-09-17 18:40:00 0.02  -0.008 1.00 
#>  4 2019-09-17 18:40:00 0.016 -0.012 1.01 
#>  5 2019-09-17 18:40:00 0.016 -0.008 1.01 
#>  6 2019-09-17 18:40:00 0.008 -0.008 1.01 
#>  7 2019-09-17 18:40:00 0.016 -0.008 1.02 
#>  8 2019-09-17 18:40:00 0.02  -0.004 1.02 
#>  9 2019-09-17 18:40:00 0.016  0     1.01 
#> 10 2019-09-17 18:40:00 0.012  0     1.02 
#> # ℹ 240,490 more rows