[<img alt="alt_text" src="https://img.shields.io/badge/OSF-10.17605%2FOSF.IO%2FE2KCW-blue" />](https://osf.io/e2kcw/) 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/>) ## Repository 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 trials - `dat_fit`: dataset without the neutral facial expression and extra pre-processing steps used for model fitting - `cleaned/catch/`: contains data from catch trials used as attention check during the task - `dat_catch_ang`: dataset with catch trials and all pre-processing steps - `dat_catch_ang_acc`: dataset with catch trials and all pre-processing steps with computed accuracy - `cleaned/valid`: contains data from valid trials used for plotting and modelling - `dat_valid_ang`: contains all participants, valid trials and all pre-processing steps - `dat_valid_ang_final`: contains only good participants (excluded from catch trials) with all pre-processing steps - `figures/`: contains all figures included in the paper or supplementary materials - `tables/`: contains all tables included in the paper or supplementary materials - `objects/`: 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. - `docs/`: contains the `Rmd` script and all files to reproduce the supplementary materials document - `R/`: contains all custom functions used through the project. ## Model fitting 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. ## Packages - `rmarkdown` - `bookdown` - `knitr` - `devtools` - `flextable` - `ggplot2` - `here` - `officer` - `tidyverse` - `dplyr` - `kableExtra` - `magrittr` - `tidyr` - `cowplot` - `forcats` - `ggh4x` - `ggpubr` - `magick` - `stringr` - `brms` - `CircStats` - `circular` - `tidybayes` - `patchwork` - `tools` - `cli` - `renv` - `rlang` - `purrr` - `ftExtra` - `gghalves` - `latex2exp`