The user provides estimates of min and max values of data. This function then trims out anything beyond these estimates.

trim_tunnel_outliers(
  obj_name,
  lengths_min = 0,
  lengths_max = 3,
  widths_min = -0.4,
  widths_max = 0.8,
  heights_min = -0.2,
  heights_max = 0.5,
  ...
)

Arguments

obj_name

The input viewr object; a tibble or data.frame with attribute pathviewR_steps that includes "viewr" that has been passed through relabel_viewr_axes() and gather_tunnel_data() (or is structured as though it has been passed through those functions).

lengths_min

Minimum length

lengths_max

Maximum length

widths_min

Minimum width

widths_max

Maximum width

heights_min

Minimum height

heights_max

Maximum height

...

Additional arguments passed to/from other pathviewR functions

Value

A viewr object (tibble or data.frame with attribute pathviewR_steps that includes "viewr") in which data outside the specified ranges has been excluded.

Details

Anything supplied to _min or _max arguments should be single numeric values.

See also

Author

Vikram B. Baliga

Examples

## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewR_motive_example_data.csv", package = 'pathviewR')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running trim_tunnel_outliers(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## Now trim outliers using default values motive_trimmed <- motive_gathered %>% trim_tunnel_outliers(lengths_min = 0, lengths_max = 3, widths_min = -0.4, widths_max = 0.8, heights_min = -0.2, heights_max = 0.5)