View source: R/utility_functions.R
trim_tunnel_outliers | R Documentation |
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 |
Anything supplied to _min or _max arguments should be single numeric values.
A viewr object (tibble or data.frame with attribute
pathviewr_steps
that includes "viewr"
) in which data outside
the specified ranges has been excluded.
Vikram B. Baliga
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()
## 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)
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