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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