R/plotting_functions.R
visualize_frame_gap_choice.Rd
Run separate_trajectories() with many different frame gaps to help determine what value to use
visualize_frame_gap_choice(obj_name, loops = 20, ...)
obj_name | The input viewr object; a tibble or data.frame with attribute
|
---|---|
loops | How many total frame gap entries to consider |
... | Additional arguments |
A plot and a tibble, each of which shows the total number of
trajectories that result from using the specified range of
max_frame_gap
values.
The input viewr object (obj_name
) should likely be an object
that has passed through the select_x_percent()
step.
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()
,
trim_tunnel_outliers()
Other plotting functions:
plot_by_subject()
,
plot_viewr_trajectories()
Other functions that define or clean trajectories:
get_full_trajectories()
,
quick_separate_trajectories()
,
separate_trajectories()
Melissa S. Armstrong and Vikram B. Baliga
library(pathviewR) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewR_motive_example_data.csv", package = 'pathviewR')) motive_selected <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% get_velocity() %>% select_x_percent(desired_percent = 50) visualize_frame_gap_choice(motive_selected, loops = 10)#> [[1]] #> # A tibble: 10 x 3 #> frame_gap_allowed trajectory_count file_id #> <dbl> <dbl> <chr> #> 1 1 15 motive_selected #> 2 2 13 motive_selected #> 3 3 13 motive_selected #> 4 4 13 motive_selected #> 5 5 13 motive_selected #> 6 6 13 motive_selected #> 7 7 13 motive_selected #> 8 8 13 motive_selected #> 9 9 13 motive_selected #> 10 10 13 motive_selected #> #> [[2]] #> NULL #>