Learn R Programming
⚠️There's a newer version (0.6.2) of this package.Take me there.

PupilPre (version 0.6.1)

Preprocessing Pupil Size Data

Description

Pupillometric data collected using SR Research Eyelink eye trackers requires significant preprocessing. This package contains functions for preparing pupil dilation data for visualization and statistical analysis. Specifically, it provides a pipeline of functions which aid in data validation, the removal of blinks/artifacts, downsampling, and baselining, among others. Additionally, plotting functions for creating grand average and conditional average plots are provided. See the vignette for samples of the functionality. The package is designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer.

Copy Link

Version

Install

install.packages('PupilPre')

Monthly Downloads

171

Version

0.6.1

License

GPL-3

Maintainer

Aki-Juhani Kyr<c3><b6>l<c3><a4>inen

Last Published

December 18th, 2019

Functions in PupilPre (0.6.1)

PupilPre

PupilPre: Preprocessing Pupil Size Data.
NA_summary

Check missing data
downsample

Downsample the data
compare_summary

A utility function to compare pupil size data before and after applying the cleanup
clean_blink

Automatically clean Eyelink marked blinks.
interpolate_NAs

Interpolation for missing data.
Pupilex4

This is an example dataset to illustrate certain functionality
ppl_rm_extra_DVcols

Checks for and removes unnecessary DV output columns.
Pupilex5

This is an example dataset to illustrate certain functionality
ppl_select_recorded_eye

Select the eye used during recording
apply_cleanup_change

Applies user-selected changes to auto cleanup
plot_events

Plot each event within a group to a directory
plot_compare_app

Plots comparison of Pupil and Pupil_Previous by event.
apply_butter

Applies a Butterworth filter to each event.
verify_cleanup_app

Interactive app for verifying auto cleanup.
blink_summary

Check blinks
check_baseline

Check baseline window for missing data
clean_artifact

Automatically clean artifacts.
ppl_plot_avg

Plots average Pupil.
recode_off_screen

Check for samples off-screen and marks as NA.
ppl_plot_avg_cdiff

Plots average difference between two conditions.
rm_sparse_events

Removes events with excessive missing data
apply_user_cleanup

Applies manual cleanup to the data
baseline

Baseline correct the data
ppl_plot_avg_contour

Plots average contour surface of pupil data.
butter_filter_app

Plots the effect of Butterworth filtering by event.
trim_filtered

Trim the beginning and end of filtered events.
ppl_prep_data

Check the classes of specific columns and re-assigns as necessary.
user_cleanup_app

Interactive app for manually cleaning pupil data.
plot_summary_app

Plots summary of subject or item.
ppl_check_eye_recording

Check which eyes were recorded during the experiment
Pupilex6

This is an example dataset to illustrate certain functionality
Pupildat

This is a sample pupil size dataset included in the package
Pupilex7

This is an example dataset to illustrate certain functionality
Pupilex2

This is an example dataset to illustrate certain functionality
Pupilex3

This is an example dataset to illustrate certain functionality
Pupilex1

This is an example dataset to illustrate certain functionality