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Usage Guide

This guide walks you through how to process new data files and run the Shiny dashboard.

Step 1: Convert .mat Files to .rds and Append to TRAINING.csv

  1. Place new .mat files in the data directory configured via path_to_mat_files.
  2. Run the main script:
source("ExtractSaveData.R")

This will:

  • Identify new (unprocessed) .mat files
  • Convert them to .rds using ConvertToRDS.R
  • Extract metadata with ReadBcontrolData.R or ReadBpodData.R
  • Append results to shiny_app/TRAINING.csv

Already-processed files are skipped automatically.

Step 2: Launch the Shiny Dashboard

From within R or RStudio:

setwd("shiny_app")
shiny::runApp()

The app loads TRAINING.csv, applies cleaning and reshaping (via load_data.R), and generates interactive plots.

Notes

  • The pipeline uses parallel processing to speed up file conversion.
  • shiny_app/full_TRAINING.csv can be used to accumulate data across sessions or datasets.
  • Only sessions not already listed in the CSV will be added.
  • You can rerun ExtractSaveData.R safely without duplicating entries.

Troubleshooting

  • If the Shiny app fails to launch, check for formatting issues in TRAINING.csv (e.g., broken rows).
  • Ensure that all required packages are installed: tidyverse, R.matlab, shiny, ggpubr, ggrepel, parallel.