View source: R/utility_functions.R
| separate_trajectories | R Documentation |
Separate rows of data into separately labeled trajectories.
separate_trajectories(
obj_name,
max_frame_gap = 1,
frame_rate_proportion = 0.1,
frame_gap_messaging = FALSE,
frame_gap_plotting = FALSE,
...
)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
max_frame_gap |
Default 1; defines the largest permissible gap in data before a new trajectory is forced to be defined. Can be either a numeric value or "autodetect". See Details for more. |
frame_rate_proportion |
Default 0.10; if |
frame_gap_messaging |
Default FALSE; should frame gaps be reported in the console? |
frame_gap_plotting |
Default FALSE; should frame gap diagnostic plots be shown? |
... |
Additional arguments passed to/from other pathviewr functions |
This function is designed to separate rows of data into separately labeled trajectories.
The max_frame_gap parameter determines how trajectories will be
separated. If numeric, the function uses the supplied value as the largest
permissible gap in frames before a new trajectory is defined.
If max_frame_gap = "autodetect" is used, the function
attempts to guesstimate the best value(s) of max_frame_gap. This is
performed separately for each subject in the data set, i.e. as many
max_frame_gap values will be estimated as there are unique subjects.
The highest possible value of any max_frame_gap is first set as a
proportion of the capture frame rate, as defined by the
frame_rate_proportion parameter (default 0.10). For each subject, a
plot of total trajectory counts vs. max frame gap values is created
internally (but can be plotted via setting
frame_gap_plotting = TRUE). As larger max frame gaps are allowed,
trajectory count will necessarily decrease but may reach a value that
likely represents the "best" option. The "elbow" of that plot is then used
to find an estimate of the best max frame gap value to use.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which a new column
file_sub_traj is added, which labels trajectories within the data by
concatenating file name, subject name, and a trajectory number (all
separated by underscores).
Vikram B. Baliga and Melissa S. Armstrong
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(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other functions that define or clean trajectories:
get_full_trajectories(),
quick_separate_trajectories(),
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
## "select" step before running select_x_percent().
motive_selected <-
motive_data %>%
relabel_viewr_axes() %>%
gather_tunnel_data() %>%
trim_tunnel_outliers() %>%
rotate_tunnel() %>%
select_x_percent(desired_percent = 50)
## Now separate trajectories using autodetect
motive_separated <-
motive_selected %>%
separate_trajectories(max_frame_gap = "autodetect",
frame_rate_proportion = 0.1)
## See new column file_sub_traj for trajectory labeling
names(motive_separated)
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