Package: VWPre 1.2.5

VWPre: Tools for Preprocessing Visual World Data

Gaze data from the Visual World Paradigm requires significant preprocessing prior to plotting and analyzing the data. This package provides functions for preparing visual world eye-tracking data for statistical analysis and plotting. It can prepare data for linear analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM). Additionally, it contains various plotting functions for creating grand average and conditional average plots. See the vignette for samples of the functionality. Currently, the functions in this package are designed for handling data collected with SR Research Eyelink eye trackers using Sample Reports created in SR Research Data Viewer. While we would like to add functionality for data collected with other systems in the future, the current package is considered to be feature-complete; further updates will mainly entail maintenance and the addition of minor functionality.

Authors:Vincent Porretta [aut, cre], Aki-Juhani Kyröläinen [aut], Jacolien van Rij [ctb], Juhani Järvikivi [ctb]

VWPre_1.2.5.tar.gz
VWPre_1.2.5.zip(r-4.7-any)VWPre_1.2.5.zip(r-4.6-any)VWPre_1.2.5.zip(r-4.5-any)
VWPre_1.2.5.tgz(r-4.6-any)VWPre_1.2.5.tgz(r-4.5-any)
VWPre_1.2.5.tar.gz(r-4.7-any)VWPre_1.2.5.tar.gz(r-4.6-any)
VWPre_1.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
VWPre/json (API)

# Install 'VWPre' in R:
install.packages('VWPre', repos = c('https://vincentporretta.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • VWdat - This is a sample eye-tracking dataset included in the package

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

4.38 score 1 stars 1 packages 99 scripts 361 downloads 2 mentions 31 exports 56 dependencies

Last updated from:db3e07402f. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK142
source / vignettesOK198
linux-releaseOK149
macos-releaseOK153
macos-oldrelOK204
windows-develOK91
windows-releaseOK106
windows-oldrelOK93
wasm-releaseOK102

Exports:align_msgbin_propcheck_all_msgscheck_eye_recordingcheck_iacheck_msg_timecheck_samples_per_bincheck_samplingratecheck_time_seriescreate_binomialcreate_time_seriescustom_iads_optionsfasttrackmake_pelogit_fncmark_tracklossplot_avgplot_avg_cdiffplot_avg_contourplot_avg_diffplot_indiv_appplot_transformation_appplot_var_appprep_datarecode_iarelabel_narename_columnsrm_extra_DVcolsrm_trackloss_eventsselect_recorded_eyetransform_to_elogit

Dependencies:base64encbslibcachemclicommonmarkcpp11digestdplyrfarverfastmapfontawesomefsgenericsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMatrixmemoisemgcvmimenlmeotelpillarpkgconfigpromisespurrrR6rappdirsRColorBrewerRcpprlangS7sassscalesshinysourcetoolsstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithrxtable

Aligning Data to a Specific Sample Message
Before aligning | Aligning to a specific message | Creating the Time series column | Proceed with preprocessing
Rendered fromVWPre_Message_Alignment.Rmdusingknitr::rmarkdown

Last update: 2025-12-06
Started: 2019-07-09

Basic VWP Preprocessing
Before using VWPre | Loading the package and the data | Preparing the data | Verifying and creating necessary columns | Remove unnecessary columns | Relabel NA samples as outside any interest area | Check encoding of interest areas | Creating the Time series column | Selecting which eye to use | Trackloss | Binning the data | Empirical logits | Binomial data | Fasttrack function | Renaming interest area columns | Saving the data | Subsetting and ordering | Saving to a file | Plotting
Rendered fromVWPre_Basic_Preprocessing.Rmdusingknitr::rmarkdown

Last update: 2025-12-06
Started: 2019-07-09

Plotting VWP Data Processed with VWPre
Plotting the data | Averaged data | Conditional averages | Error bars and Error bands | Confidence intervals (pointwise/simultaneous) | Single interest area by condition | Calculation of averages - Mean vs. Grand mean | Difference plots (between looks to two interest areas) | Difference plots (between two conditions for looks to one interest area) | Conditional contour surface | Extracting plotting data | Backtransformation of predicted values | Interactive/Utility plotting apps | Shiny app for understanding the empirical logit transformation | Shiny app plots for data inspection
Rendered fromVWPre_Plotting.Rmdusingknitr::rmarkdown

Last update: 2025-12-06
Started: 2019-07-09

Relabeling or Defining Interest Areas
Basic interest area relabelling | Check interest area coding | Providing new labels and IDs for existing interest areas | Remapping gaze position to newly defined interest areas | Creating the look-up table | Performing the gaze remapping
Rendered fromVWPre_Interest_Areas.Rmdusingknitr::rmarkdown

Last update: 2019-07-09
Started: 2019-07-09

Readme and manuals

Help Manual

Help pageTopics
Internal helper function, not intended to be called externally..check_for_PupilPre
Aligns samples to a specific message.align_msg
Bins the sample data and calculates proportion looks by interest areabin_prop
Output all messages with timestampscheck_all_msgs
Check which eyes were recorded during the experimentcheck_eye_recording
Check the interest area IDs and labelscheck_ia
Check the time value(s) at a specific messagecheck_msg_time
Check the number of samples in each bincheck_samples_per_bin
Determine the sampling rate present in the datacheck_samplingrate
Check the new time seriescheck_time_series
Creates a success/failure column for each IA based on counts.create_binomial
Create a time series columncreate_time_series
Map gaze data to newly defined interest areascustom_ia
Determine downsampling options based on current sampling rateds_options
Fast-track basic preprocessingfasttrack
Create function for back-transforming empirical logits to proportionsmake_pelogit_fnc
Mark trackloss by blink and/or screen sizemark_trackloss
Plots average looks to interest areas.plot_avg
Plots average difference between two conditions.plot_avg_cdiff
Plots average contour surface of looks to a given interest area.plot_avg_contour
Plots average difference between looks to two interest areas.plot_avg_diff
Plots diagnostic average plots of subjects/items.plot_indiv_app
Plots diagnostic plots of the empirical logit transformation.plot_transformation_app
Plots diagnostic plots of subject/item variance.plot_var_app
Check the classes of specific columns and re-assigns as necessary.prep_data
Recode interest area IDs and/or interest area labelsrecode_ia
Relabel samples containing 'NA' as outside any interest arearelabel_na
Rename default column names for interest areas.rename_columns
Checks for and removes unnecessary DV output columns.rm_extra_DVcols
Removes events with excessive tracklossrm_trackloss_events
Select the eye used during recordingselect_recorded_eye
Transforms proportion looks to empirical logits.transform_to_elogit
This is a sample eye-tracking dataset included in the packageVWdat
VWPre: Tools for Preprocessing Visual World Data.VWPre _PACKAGE