Addresses tasks along the pipeline from raw
data to analysis and visualization for eye-tracking data. Offers several
popular types of analyses, including linear and growth curve time analyses,
onset-contingent reaction time analyses, as well as several non-parametric
bootstrapping approaches. For references to the approach see Mirman,
Dixon & Magnuson (2008)
EyetrackingR is now compatible with dplyr > 0.5.0.
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("jwdink/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) 2015, Jacob Dink and Brock Ferguson
Released under the MIT License (see LICENSE for details)
Changes in 0.1.8:
Changes in 0.1.7:
Changes in 0.1.6:
lm or lmer time-bin or cluster analysis, via the "treatment_level" argument.Changes in 0.1.5:
lme4 package.Changes in 0.1.4:
Changes in 0.1.3:
analyze_time_bins and therefore cluster-analyses have been re-written internally. Full support for (g)lm, (g)lmer, wilcox. Support for interaction terms/predictors. Experimental support for using boot-splines within cluster analysis.analyze_time_binsanalyze_time_bins and cluster analysesmake_boot_splines_data and analyze_boot_splines are now deprecated. To perform this type of analysis, use test="boot_splines" in analyze_time_bins.analyze_time_bins.analyze_time_clusters function now checks that the extra arguments passed to it are the same as the arguments passedsimulate_eyetrackingr_data function to generate fake data for simulations.Changes in 0.1.1:
clean_by_trackloss. Previously did not work for certain column names.make_eyetrackingr_data. Previously did not work correctly with treat_non_aoi_as_missing = TRUE.analyze_time_clusters: previously did not compute permutation-distribution correctly.make_time_window_data or make_time_sequence_data to summarize. This DV can then be plotted and used in downstream functions (like analyze_time_bins or make_time_cluster_data)analyze_time_bins and functions that call this (e.g make_time_cluster_data).analyze_time_clusters, allowing the user to take advantage of multiple cores to speed up this relatively slow function.get_time_clusters for getting information about clusters in a data.frame (rather than a printed summary-- better for programming).