RInno – An Open Source Solution to Help Install R Shiny Applications

By Jon Hill, FI Consulting


If you are trying to improve your company’s data science infrastructure, you will need people who can code in R, Python, or Java. Doing data science without staff with these skills would be like trying to build a database without staff who know SQL. R, Python and Java perennially top the list of tools Data Scientists use, with R and Python battling for the #1 spot. Java is often described as the production tool, the code you will find in the data engineering infrastructures of Facebook, LinkedIn, and Google. However, R and Python are great for rapidly prototyping and deploying sleek analytics applications, like Shiny apps.
In order to help your company’s data science team, FI Consulting developed a free, open source R software package called RInno (“R Inno”), which installs Shiny apps on desktop computers, providing an experience similar to installing any other program (next, next, … finish). Many organizations start using R by installing it on a few desktops for their analysts, maybe trying to reduce the cost of SAS. RInno is designed for organizations at this stage of development.
Because Shiny apps are designed to run on a server, you either have to install Shiny Server or RStudio Connect on premise or use a cloud service. That could be very difficult to achieve depending on how well resourced your IT department is. RInno, however, does not require IT support because it uses, and optionally provides, a local copy of R to run your applications. It can connect to software repositories like GitHub or Bitbucket, which your data scientists should be using, to automatically update your apps on start up. This ensures that users always have the same version installed and it is easy for analysts/data scientists to make updates. In short, it shares Shiny apps with a broad audience of business users who your data scientists/analysts would have difficulty reaching on their own.
If you would like a more detailed description of RInno’s features and how to use it, check out our FI Labs blog post. RInno is an open source project, so we will continue to develop new features on GitHub and publish stable versions to CRAN. Please visit our GitHub Issues page and submit your ideas, bugs, and user experience recommendations. We would love to see more people get involved.