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.
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, ... )
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 |
A viewr object (tibble or data.frame with attribute
pathviewR_steps
that includes "viewr"
) in which data outside
the specified ranges has been excluded.
Anything supplied to _min or _max arguments should be single numeric values.
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()
Vikram B. Baliga
## 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)