trim_tunnel_outliers {pathviewr} | R Documentation |
Trim out artifacts and other outliers from the extremes of the tunnel
Description
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
|
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 |
Details
Anything supplied to _min or _max arguments should be single numeric values.
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.
Author(s)
Vikram B. Baliga
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)