Projecticum Manuel, Philine en Yusra
This repository contains the Rshiny app for the “Hack the VITEK” project.
General information
- Title: Hack the VITEK project
- Authors: Manuel Leeuwerik, Philine van Bekkum and Yusra Kunduzi
- Institution: University of Applied Sciences Utrecht (HU)
- Date of Data Collection: 2024-2025
License information
This repository and the data provided are licensed under the MIT. Please refer to the LICENSE file for more details.
Files
- data_raw/: Contains the raw, unprocessed data files obtained from the VITEK system
- data_bewerkt/: Contains the cleaned and tidy datasets prepared for analysis and visualization
- Rshiny/: Contains all the scripts that were used to build and run the RShiny app
- app.R: The main script for running the entire RShiny app, combining UI and server functionality
- server.R: Defines the server-side logic for the RShiny app
- ui.R: Defines the user interface (UI) of the RShiny app
- functions/: Contains individual R scripts with reusable functions that support the app's functionality
- ATCC.R: Functions to build ATCC database
- extinctie_mapping.R: Function to create mapping for user friendly labels
- identificatie_tabel.R: Processes NIDresults.log files to extract and tidy test result data
- interpolated_data.R: Function to interpolate the input data for the mitis_oralis.R
- categories_mapping.R: Function to define the different categories of the biochemical tests
- meta_data.R: Extracts and tidies metadata from NIDresults.log files in specified folders and saves the processed data as CSV files
- mitis_oralis.R: Compares test results against predefined ranges for Streptococcus mitis and Streptococcus oralis to classify identifiers
- mutate_tidyfile.R: Combines data into a single data frame, while appending an identifier from each file
- outliers_boxplot.R: Function for detecting outliers and generating boxplots for visual analysis
- pca_heatmap.R: Function for performing PCA (Principal Component Analysis) and creating heatmaps
- statistische_test.R: Function for detecting outliers and generating boxplots for visual analysis
- tidy_function.R: Function for tidying the data
- mitis_oralis_ranges/: Contains CSV files with combined datasets for mitis and oralis species ranges
- mitis_ranges.csv: ranges for S. mitis based on ATCC bacteria
- oralis_ranges.csv: ranges for S. oralis based on ATCC bacteria
Methodological information
To be able to reproduce the Rshiny app, make sure you have downloaded the following packages and loaded the following libraries.
# Install the packages
install.packages(c("shiny", "shinyFiles", "dplyr", "readr", "here", "ggplot2", "plotly" "webshot2", "DT", "tidyverse", "shinydashboard", "pheatmap", "scales", "tidyr"))
# Install the libraries
library(shiny) # For building interactive web applications in R
library(shinyFiles) # Provides file and folder selection in Shiny applications
library(dplyr) # For data manipulation and transformation using a grammar of data
library(readr) # For fast and friendly reading of rectangular data files (CSV, TSV, etc.)
library(here) # Simplifies path handling by managing working directories in a reproducible way
library(webshot2) # Allows users to take screenshots of web pages and save them as image files
library(plotly) # For creating interactive, web-based visualizations
library(ggplot2) # For creating elegant and complex data visualizations
library(DT) # For integrating interactive data tables in R, particularly in Shiny apps
library(tidyverse) # A collection of R packages for data science workflows
library(shinydashboard) # For creating dashboards with Shiny using a simple layout system
library(pheatmap) # For creating beautiful heatmaps with additional features
library(scales) # For generating color gradients and discrete color scales for visualization
library(tidyr) # For data tidying, reshaping and organizing data into a clean and structured format