Note this is an active development (with permission) of the archived CRAN package eyetrackingR. The archived version is still available at https://github.com/jwdink/eyetrackingr
- Warnings given by latest versions of dplyr and ggplot2 have been fixed.
- Support for plotting predictions of binomial models using glmer, glmmTMB and glmmPQL
- Samuel Forbes (samuel.h.forbes@gmail.com)
- Jacob Dink (jacobwdink@gmail.com)
- Brock Ferguson (brock.ferguson@gmail.com)
This package is designed to make dealing with eye-tracking data easier. It addresses tasks along the pipeline from raw data to analysis and visualization. It offers several popular types of analyses, including growth-curve analysis, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches.
To install from CRAN:
install.packages('eyetrackingR')
To load:
library(eyetrackingR)
For the development version (make sure you have run install.packages("devtools") to get devtools first):
devtools::install_github("samhforbes/eyetrackingR")
EyetrackingR only requires that your data is in an R dataframe and has a few necessary columns. For that reason, eyetrackingR is compatible with any eyetracker, so long as you can export your data to a table and import it into R. See the preparing your data vignette.
Once your data is in R, you can prepare it for eyetrackingR by running the make_eyetrackingr_data function, e.g.:
data <- make_eyetrackingr_data(your_original_data,
participant_column = "ParticipantName",
trial_column = "Trial",
time_column = "Timestamp",
trackloss_column = "TrackLoss",
treat_non_aoi_looks_as_missing = TRUE
)
From here, all of eyetrackingR's functionality becomes available for this data. Check out the eyetrackingR workflow to get an accesible overview of this functionality, or check out the vignettes for guides on how to clean your data, visualize it, and perform analyses.
Copyright (c) 2021, Samuel Forbes, Jacob Dink and Brock Ferguson
Released under the MIT License (see LICENSE for details)