View source: R/utility_functions.R
gather_tunnel_data | R Documentation |
Reformat viewr
data into a "tidy" format so that every row corresponds
to the position (and potentially rotation) of a single subject during an
observed frame and time.
gather_tunnel_data(obj_name, NA_drop = TRUE, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
NA_drop |
Should rows with NAs be dropped? Defaults to |
... |
Additional arguments that can be passed to other |
The tibble or data.frame that is fed in must have variables that
have subject names and axis names separated by underscores. Axis names must
be one of the following: position_length
, position_width
, or
position_height
. Each of these three dimensions must be present in the
data. Collectively, this means that names like bird01_position_length
or larry_position_height
are acceptable, but bird01_x
or
bird01_length
are not.
A tibble in "tidy" format which is formatted to have every row
correspond to the position (and potentially rotation) of a single subject
during an observed frame and time. Subjects' names are automatically parsed
from original variable names (e.g. subject1_rotation_width extracts
"subject1" as the subject name) and stored in a Subjects
column in the
returned tibble.
Vikram B. Baliga
Other data cleaning functions:
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()
,
visualize_frame_gap_choice()
library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## First use relabel_viewr_axes() to rename these variables using _length, ## _width, and _height instead motive_data_relabeled <- relabel_viewr_axes(motive_data) ## Now use gather_tunnel_data() to gather colums into tidy format motive_data_gathered <- gather_tunnel_data(motive_data_relabeled) ## Column names reflect the way in which data were reformatted: names(motive_data_gathered)
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