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haiInsight

Tools and an interactive app to explore healthcare-associated infection (HAI) data derived from the BHAI package.
This package was developed as part of ETC5523: Communicating with Data at Monash University.

Installation

You can install the development version from GitHub:

# Install dependencies if needed
install.packages(c("devtools", "shiny", "ggplot2", "dplyr", "bslib", "kableExtra"))

# Install from GitHub
devtools::install_github("ETC5523-2025/assignment-4-packages-and-shiny-apps-jyovika")

Load the package:

Example usage 

# Load included data
data("hai_data_clean")

# Summarise total cases by infection type
summarise_hai(hai_data_clean, by = "Infection_Type")

# Visualise totals by age group
plot_hai_totals(hai_data_clean, by = "Age_Group")

These functions let you quickly compare infection distributions across groups - with built-in styling and optional proportional views.

Launch the Shiny app

This opens an interactive interface to:

  • Filter by infection type and sex

  • View totals by infection type, age group, or sex

  • Display both plots and summary tables with clear, styled output

Learn more

For detailed examples and explanation, check the vignettes:

Summarising HAI data Visualising totals with plot_hai_totals()

Acknowledgements

  • Data adapted from the BHAI dataset (Germany, 2011).
  • Zacher, Benedikt; Haller, Sebastian; Willrich, Niklas; Walter, Jan; Abu Sin, Muna; Cassini, Alessandro; Plachouras, Diamantis; Suetens, Carl; Behnke, Michael; Gastmeier, Petra; Wieler, Lothar H.; Eckmanns, Tim (2019). Application of a new methodology and R package reveals a high burden of healthcare-associated infections (HAI) in Germany compared to the average in the European Union/European Economic Area, 2011 to 2012.
    Euro Surveillance, 24(46):1900135.https://doi.org/10.2807/1560-7917.ES.2019.24.46.1900135
  • Developed by Jyovika Aswale for educational and demonstration purposes.

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