Uploaded March 2025 | Updated September 2026, 2 weeks ago
#ShinyConf #ShinyConf2024
Abstract: Combining Shiny apps with cloud services can support technical teams to rapidly generate insights, but challenges relating to data volume, storage and display must be overcome. In this case study, our client had developed a data analysis method that reliably led to business value. However, the method was time consuming, tedious and vulnerable to human error, as it required the team to switch between several pieces of software and manually copy and paste data into Excel. As demand for the team’s insights grew, they needed a way to make their process more efficient. In close collaboration with the client, we developed a Shiny app connected to an AWS-hosted data pipeline and database. The pipeline ingests images and tabular data and the Shiny app allows the users to query and visualise the data products. Throughout this project, we faced a number of interesting challenges. We will describe how we overcame issues relating to the large volume of data, the need to display images in a custom format, and why and how we used Python and R together in an R Shiny app. The completed pipeline, database and Shiny app have had a big impact on the team’s efficiency, allowing them to generate business insights in hours rather than days, and keep up with the growing demand for their work.
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Join us this year! Sign up for ShinyConf 2025 here: go.appsilon.com/register-shinyconf2025
#ShinyConf #ShinyConf2024
Abstract: Combining Shiny apps with cloud services can support technical teams to rapidly generate insights, but challenges relating to data volume, storage and display must be overcome. In this case study, our client had developed a data analysis method that reliably led to business value. However, the method was time consuming, tedious and vulnerable to human error, as it required the team to switch between several pieces of software and manually copy and paste data into Excel. As demand for the team’s insights grew, they needed a way to make their process more efficient. In close collaboration with the client, we developed a Shiny app connected to an AWS-hosted data pipeline and database. The pipeline ingests images and tabular data and the Shiny app allows the users to query and visualise the data products. Throughout this project, we faced a number of interesting challenges. We will describe how we overcame issues relating to the large volume of data, the need to display images in a custom format, and why and how we used Python and R together in an R Shiny app. The completed pipeline, database and Shiny app have had a big impact on the team’s efficiency, allowing them to generate business insights in hours rather than days, and keep up with the growing demand for their work.
____________________________
Join us this year! Sign up for ShinyConf 2025 here: go.appsilon.com/register-shinyconf2025
