facetsviz
Parse and Visualize Facets Output Files
Utilities to parse output files from 'Facets' (a software widely used for multi-facet Rasch measurement) and build accurate visualizations from the measurement reports. For more details on the underlying measurement framework, see Linacre (1994, ISBN:0-941938-02-6) and Linacre (2023) < https://www.winsteps.com/a/Facets-Manual.pdf>.
facetsviz
Overview
facetsviz is an R package for parsing FACETS .out files and producing
reusable diagnostic visualizations from multi-facet Rasch measurement
output. It provides a suite of functions to easily create beautiful,
publication-ready plots using ggplot2.
plot_wright()plot_probability()plot_distribution()plot_usage()plot_fairness()
library(facetsviz)
facets <- read_facets("peerAssessment.out")
plot_wright(facets)
Installation
# Install from local source
install.packages("facetsviz_0.1.1.tar.gz", repos = NULL, type = "source")
# Or using devtools from GitHub
devtools::install_github("myprabowo/facetsviz")
Usage
facetsviz provides R functions for reading data, plotting, batch rendering, and an interactive Shiny app.
library(facetsviz)
# 1. Read FACETS output
facets <- read_facets("peerAssessment.out")
# 2. Inspect available measurement reports
names(facets$measurement_reports)
# 3. Create individual plots
plot_wright(facets)
plot_probability(facets)
# 4. Render all diagnostic plots to a folder
render_plots(facets, output_dir = "figures", formats = "png")
# 5. Launch the interactive Shiny app
facetsviz::run_app()
Examples
Use the plotting functions to inspect your Rasch measurement data:
plot_wright(facets): Visualizes person measures and item difficulties on a common logit scale.plot_probability(facets): Shows category probability curves.plot_distribution(facets): Displays the distribution of measures for a specific facet.plot_usage(facets): Shows how rating scale categories are used.