knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
devtools::load_all()
pkgs <- get_all_packages()
This repository contains all data and scripts to reproduce statistical analysis, figures and tables from the paper "Mapping the Perception-space of Facial Expressions in the Era of Face Masks" by Verroca, de Rienzo, Gambarota and Sessa (2022). The project is also on Open Science Framework (https://osf.io/e2kcw/). Supplementary materials are available here
The repository is organized with the following structure:
data/
: contains the cleaned data. The only pre-processing step is to combine different files from the Gorilla platform within a single dataset for each participant.dat_clean.rds
: dataset with all participants, catch and valid trialsdat_fit
: dataset without the neutral facial expression and extra pre-processing steps used for model fittingcleaned/catch/
: contains data from catch trials used as attention check during the taskdat_catch_ang
: dataset with catch trials and all pre-processing stepsdat_catch_ang_acc
: dataset with catch trials and all pre-processing steps with computed accuracycleaned/valid
: contains data from valid trials used for plotting and modellingdat_valid_ang
: contains all participants, valid trials and all pre-processing stepsdat_valid_ang_final
: contains only good participants (excluded from catch trials) with all pre-processing stepsfigures/
: contains all figures included in the paper or supplementary materialstables/
: contains all tables included in the paper or supplementary materialsobjects/
: contains all R objects used through the project. In particular contains all post-processed fitted models.scripts/
: contains all scripts to produce datasets, models, figures and tables. The numbering suggest the order to correctly reproduce the analysis.files/
: contains extra files used in the projectdocs/
: contains the Rmd
script and all files to reproduce the supplementary materials documentR/
: contains all custom functions used through the project.All models are computed within a Bayesian framework using the brms
R package. Models were fitted using cluster computing in order to speed-up the process. The final size of each model is on average more than ~200mb and for this reason we included only post-processing information (within the objects/
folder, created with 05a/b_post_processing_*.R
scripts). Running again the 04a/b_*_models.R
scripts will reproduce the same results.
pkgs <- sprintf("- `%s`", pkgs) cat(pkgs, sep = "\n")
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