knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
pathviewr defines trajectories as continuous movement from one side of a
region of interest to the other. Before trajectories can be defined, the region
of interest must be selected via
select_x_percent() in addition to all the
previous steps of the data import and cleaning pipeline as described in
the Data Import and Cleaning vignette.
We'll start with loading
pathviewr and a few packages from the
importing our data.
## If you do not already have pathviewr installed: # install.packages("devtools") # devtools::install_github("ropensci/pathviewr") library(pathviewr) library(ggplot2) library(magrittr) ## Import the example Motive data included in ## the package motive_data <- read_motive_csv( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr') )
We'll quickly run through the cleanup pipeline using one of
all-in-one cleaning functions as described in the Data Import and Cleaning vignette.
Since trajectories are defined in the
separate_trajectories() function, we'll
stop the all-in-one there by setting it and every step after it to
FALSE so we
can take a closer look at how trajectories are defined. Since all steps are set
TRUE by default, we don't actually need to list them in the function, just
those we want to switch to
FALSE or those that require additional arguments.
motive_cleaned <- motive_data %>% clean_viewr( desired_percent = 70, rename_viewr_characters = FALSE, separate_trajectories = FALSE, get_full_trajectories = FALSE ) ## Quick plot plot(motive_cleaned$position_length, motive_cleaned$position_width, asp = 1)
We've cleaned up our data and are ready to link individual data points into
continuous trajectories. Deciding exactly how to define a trajectory will depend
largely on the quality of data collected and the resolution required to answer a
given question about the data. If the data are fairly continuous and high
resolution is required, the default
max_frame_gap = 1 will not allow any frame
gaps--movement must be continuous.
motive_mfg1 <- motive_cleaned %>% separate_trajectories( max_frame_gap = 1 ) ## Quick plot plot(motive_mfg1$position_length, motive_mfg1$position_width, asp = 1, col = as.factor(motive_mfg1$file_sub_traj)) ## How many trajectories do we end up with? length(unique(motive_mfg1$file_sub_traj))
Plotting each trajectory reveals that perhaps some trajectories that should be
continuous have been split into two or more separate trajectories because of a
dropped frame or two. This data may require relaxing the stringent no-frame-gaps
requirement in order to link data points that should go together into a single
trajectory. Let's try setting
max_frame_gap = 5.
motive_mfg5 <- motive_cleaned %>% separate_trajectories( max_frame_gap = 5 ) ## Quick plot plot(motive_mfg5$position_length, motive_mfg5$position_width, asp = 1, col = as.factor(motive_mfg5$file_sub_traj)) ## How many trajectories do we end up with? length(unique(motive_mfg5$file_sub_traj))
By increasing the allowable frame gap, more chunks of data have been linked into
trajectories and so our trajectory count has dropped from 335 to 224. Because it
is hard to see all the trajectories piled up on top of each other, let's go
ahead and run this through
get_full_trajectories() to clean out bits of data
that do not span our region of interest. To inspect each trajectory
individually, we can then use the
plot_viewr_trajectories() function to take a
closer look at the quality of each trajectory. This can be computationally
expensive depending on your data set.
motive_mfg5_full <- motive_mfg5 %>% get_full_trajectories( span = .6 ) ## Quick plot plot(motive_mfg5_full$position_length, motive_mfg5_full$position_width, asp = 1, col = as.factor(motive_mfg5_full$file_sub_traj)) ## How many trajectories do we end up with? length(unique(motive_mfg5_full$file_sub_traj)) ## Plot each trajectory plot_viewr_trajectories(motive_mfg5_full, plot_axes = c("length", "width"), multi_plot = TRUE)
pathviewr has several tools to help users decide what frame gap allowances may
be appropriate depending on their data and resolution needs.
visualize_frame_gap_choice() runs the
separate_trajectories() function over
the same data set as many times as the user would like via the
each time with a different
max_frame_gap allowance. Each loop represents an
increase in the max frame gap value of 1. For example the default of
20 will run
separate_trajectories() over the data set 20 times, with an
increase in the
max_frame_gap argument of 1 each time.
motive_cleaned %>% visualize_frame_gap_choice( loops = 20 )
The output of
visualize_frame_gap_choice() is a tibble and plot of the number
of trajectories after running
max_frame_gap = 1,
max_frame_gap = 2,
max_frame_gap = 3, etc.
We can see that as the frame gap allowance increases, more bits of data are
being linked into continuous trajectories and thus the total number of
trajectories decreases. The vertical line on the plot indicates the "elbow" of
the plot or the point at which counts of trajectories appear to stabilize and
increases in the
max_frame_gap allowance no longer effect the trajectory count
max_frame_gap = "autodetect" rather than a numeric value uses the
"elbow" of the plots from
visualize_frame_gap_choice() to guesstimate the best
In addition to automatically selecting the
max_frame_gap depending on the
autodetect does so on a per subject basis rather than applying the same
allowable frame gap to all data in the data set since frame gaps can vary
The cap on how high the
max_frame_gap can go is defined as a proportion of the capture frame rate and
set by the
frame_rate_proportion argument, which defaults to
motive_auto <- motive_cleaned %>% separate_trajectories( max_frame_gap = "autodetect", frame_rate_proportion = 0.1, frame_gap_messaging = TRUE, frame_gap_plotting = TRUE ) ## How many trajectories do we end up with? length(unique(motive_auto$file_sub_traj))
Our sample data has 3 subjects so with
frame_gap_messaging = TRUE, the
max_frame_gap for each subject will be reported (the default is
frame_gap_plotting = TRUE will display the elbow plots for each subject, but
also defaults to
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