R Shiny can be used for far more applications than just creating advanced dashboards. In this first blog, I would like to tell you more about this. By Aniek, data scientist at Datalab
The number one use of R Shiny is of course creating beautiful dashboards. This is fairly straightforward, and that is why many people start using R Shiny specifically for this purpose. What is all too often overlooked is that you can develop much more with R Shiny. Tools with which you can, for example, create, manipulate or store data.
What is R Shiny?
R Shiny is a package that can be used within 'R' to create interactive web apps. Where you normally need HTML (content and structure), CSS (styling) and JavaScript (web page interactivity) to build a web app, with R Shiny you can build a complete web app using only 'R'. Under the bonnet, HTML, CSS and JavaScript are still used, but R Shiny automatically converts the 'R code' into them. This means you can programme quite precisely, so you can determine exactly how your web app should look. It is therefore no longer necessary to hire a web developer. That saves costs.
Want something slightly different from the standard web app? Or do you want the styling just a bit different? You can do all of that within R Shiny as well. This also immediately demonstrates the enormous added value of R Shiny compared to other data visualisation tools such as Power BI, Tableau or IBM Cognos.
Untapped potential of R Shiny
This brings us to my next point: many of R Shiny's applications are not yet fully exploited. Most of us use R Shiny only for creating dashboards. But precisely because of R Shiny's flexibility, you can easily build other types of tools. Think beyond just a different type of visualisation or different styling. This is about applications such as a data entry tool to edit and store data in one place, a query tool with which you can 'query' the data from your data warehouse in a user-friendly way, or a tool with which users can work through a questionnaire or step-by-step plan. Extremely useful extras.
Safe and up-to-date
Take the aforementioned example: the data entry tool. Most companies currently use Excel to enter and edit data. Convenient and familiar, but when using Excel, multiple versions quickly circulate, files are stored in multiple places simultaneously, and perhaps most importantly: there is no direct connection to a data warehouse or other central storage location. With a data entry tool, you could, for example, import data, edit it, combine it with other data from a data warehouse and store it. This way, data is safely stored in one place and is therefore always up-to-date.
There are many more possibilities along these lines. If you are thinking about acquiring a new type of tool and have a data analyst or data scientist at your disposal, first see whether it can be solved with R Shiny!