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Summarise mechanical loading variables

Source: R/summarise_loading.R
summarise_loading.Rd

Creates a summary table of the selected mechanical loading variables including the number of peaks, the minimum, maximum, mean and standard deviation values of these peaks and also the number of peaks inside a given magnitude range. The summaries can be displayed by day or as a daily average.

Usage

summarise_loading(
  data,
  variable,
  vector,
  daily_average = TRUE,
  ranges_acc = NULL,
  ranges_grf = NULL,
  ranges_lr = NULL,
  save_summary = FALSE
)

Arguments

data

An impactr_peaks object, as obtained with find_peaks() and/or predict_loading().

variable

A character vector indicating the variable to summarise. Can be either "acc" (for the acceleration peaks), "grf" (for the ground reaction force peaks), "lr" (for the loading rate peaks) or "all" (for all variables).

vector

A character string indicating which vector to use to create the summaries. Can be "resultant", "vertical" or "all".

daily_average

Create a daily average summary? Can be TRUE (default) or FALSE.

ranges_acc, ranges_grf, ranges_lr

A numeric vector to specify ranges in which to count the peaks. E.g., If ranges_acc = c(1, 2, 3), it will summarise the number of acceleration peaks from 1 to 2g, from 2 to 3g and above 3g. Set to NULL (default) if no summary by range will be provided.

save_summary

Indicates whether or not to save the summary to a csv file(s). Defaults to FALSE. Provide a valid path to a directory as a character string to save all generated summaries.

Value

A tibble (or a list of tibbles) with the requested summaries.

Examples

# \donttest{
# Ensure that {accdata} package is available before running the example.
# If it is not, run install_accdata() to install the required package.
if (requireNamespace("accdata", quietly = TRUE)) {
  data <- import_dataset("daily_acc_3d")
  data <- remove_nonwear(data)
  data <- filter_acc(data)
  data <- find_peaks(data, vector = "vertical")
  summarise_loading(
    data,
    variable = "acc", vector = "vertical",
    ranges_acc = 1:5
  )
}
#> $`Summary per day`
#> # A tibble: 3 × 15
#>   filename    date       weekday measu…¹ varia…² n_peaks min_p…³ max_p…⁴ mean_…⁵
#>   <chr>       <date>     <chr>     <int> <chr>     <dbl>   <dbl>   <dbl>   <dbl>
#> 1 daily_acc_… 2016-01-20 Wednes…       1 vertic…    2844     1.3    4.92    1.63
#> 2 daily_acc_… 2016-01-21 Thursd…       2 vertic…    2420     1.3    6.29    1.59
#> 3 daily_acc_… 2016-01-22 Friday        3 vertic…    2366     1.3    6.08    1.55
#> # … with 6 more variables: sd_peaks <dbl>, n_peaks_1_to_2_g <dbl>,
#> #   n_peaks_2_to_3_g <dbl>, n_peaks_3_to_4_g <dbl>, n_peaks_4_to_5_g <dbl>,
#> #   n_peaks_above_5_g <dbl>, and abbreviated variable names ¹​measurement_day,
#> #   ²​variable, ³​min_peaks, ⁴​max_peaks, ⁵​mean_peaks
#> # ℹ Use `colnames()` to see all variable names
#> 
#> $`Daily average`
#> # A tibble: 1 × 12
#>   filename       varia…¹ n_peaks min_p…² max_p…³ mean_…⁴ sd_pe…⁵ n_pea…⁶ n_pea…⁷
#>   <chr>          <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>   <chr>  
#> 1 daily_acc_3d.… vertic… 2543.33 1.3     5.76    1.59    0.35    2332.67 188.33 
#> # … with 3 more variables: n_peaks_3_to_4_g <chr>, n_peaks_4_to_5_g <chr>,
#> #   n_peaks_above_5_g <chr>, and abbreviated variable names ¹​variable,
#> #   ²​min_peaks, ³​max_peaks, ⁴​mean_peaks, ⁵​sd_peaks, ⁶​n_peaks_1_to_2_g,
#> #   ⁷​n_peaks_2_to_3_g
#> # ℹ Use `colnames()` to see all variable names
#> 
# }