Trim out artifacts and other outliers from the extremes of the tunnel
Source:R/utility_functions.R
trim_tunnel_outliers.Rd
The user provides estimates of min and max values of data. This function then trims out anything beyond these estimates.
Usage
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 throughrelabel_viewr_axes()
andgather_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.
See also
Other data cleaning functions:
gather_tunnel_data()
,
get_full_trajectories()
,
quick_separate_trajectories()
,
redefine_tunnel_center()
,
relabel_viewr_axes()
,
rename_viewr_characters()
,
rotate_tunnel()
,
select_x_percent()
,
separate_trajectories()
,
standardize_tunnel()
,
visualize_frame_gap_choice()
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)