R Shiny – customising data exploration apps for researchers

Dr Rebecca Lange1
1Curtin University, Bentley, Australia

Title R Shiny – customising data exploration apps for researchers
Synopsis There are many computational bottlenecks slowing down or preventing scientific discovery for many researchers across all fields. In this new era of big data, data analysis practices need to scale to the volume of data processing and analysis needed for researchers to compete in a world-class arena.

In this showcase I will present two examples of implementing the R Shiny web app framework to help researchers with their data exploration, analysis and dissemination. This approach is especially useful to researchers who are new to big data and have limited or no programming skills to analyse this kind of data influx. The first Shiny app I will demonstrate was developed to support the Multimodal Analysis Group at Curtin University in their study of Online Extremist Communications. The second Shiny app I will demonstrate was built to share and explore data generated from a study of temple architectures across south-east Asia.

Format of demonstration Live Demonstration + Slide Show
Presenter(s) Dr. Rebecca Lange, Data Scientist, Curtin Institute for Computation
Target research community Humanities and all other areas interested in making their own dashboards
Statement of Research Impact Researchers have been able to utilise the custom build R Shiny apps to investigate key research questions that would not have otherwise been possible. For example, researchers have been able to study how extremist communication works and changes over time and how their propaganda images are spread.


Rebecca Lange received her PhD in astronomy from the International Centre for Radio Astronomy Research at the University of Western Australia.

Before Rebecca moved to Australia she studied Astronomy and Physics at Nottingham Trent University where she also worked as a research assistant in scientific imaging for art conservation and archaeology. Her work there included the development and testing of instruments and software for imaging and spectroscopy as well as the organisation and supervision of field trips, which often required liaising with art curators and conservators.

Throughout her studies and research Rebecca has gained extensive programming as well as data analytics and visualisation experience in various programming languages.

Currently she is working as a data scientist for the Curtin Institute for Computation where she helps researchers by providing data analytics and computational support and training.

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