README.md
In mousetRajectory: Mouse Trajectory Analyses for Behavioural Scientists

mousetRajectory: Trajectory Analyses for Behavioural Scientists

Tool helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions computing spatial measures (maximum absolute deviation, area under the curve, sample entropy) or providing a shorthand for often-used procedures.

You can install mousetRajectory from CRAN with

install.packages("mousetRajectory")

Alternatively, you can keep up to date and install the latest development version of mousetRajectory from github.com/mc-schaaf/mousetRajectory with:

if(!require("devtools")){install.packages("devtools")}
devtools::install_github("mc-schaaf/mousetRajectory")

Currently, the following functions are featured:

Preprocessing:
is_monotonic() checks whether your timestamps make sense and warns you if they don’t.
is_monotonic_along_ideal() checks whether your trajectories make sense and warns you if they don’t.
time_circle_left() tells you the time at which the starting area was left.
time_circle_entered() tells you the time at which the end area was entered.
point_crosses() tells you how often a certain value on the x or y axis is crossed.
direction_changes() tells you how often the direction along the x or y axis changes.
interp1() directs you to the interpolation function from the awesome signal package. Thus, you do not have to call library("signal"). Such time-saving, much wow. Also, not having to attach the signal package avoids ambiguity between signal::filter() and dplyr::filter() in your search path.
interp2() is a convenience wrapper to interp1() that rescales the time for you.
Spatial measures:
starting_angle() computes (not only starting) angles.
auc() computes the (signed) Area Under the Curve (AUC).
max_ad() computes the (signed) Maximum Absolute Deviation (MAD).
curvature() computes the curvature.
index_max_velocity() computes the time to peak velocity, assuming equidistant times between data points.
index_max_acceleration() computes the time to peak acceleration, assuming equidistant times between data points.
Other measures
sampen() computes the sample entropy.