This allows the end user to truly interact with their data in a way that other solutions do not. The dashboard then completely refreshes within seconds. The user can input values and change underlying assumptions using a variety of widgets including filters, sliders and text boxes. Similarly, the range of analysis techniques that can be performed in R is almost endless, ranging from simple regressions to cluster analysis and forecasting.ĭespite all this, Shiny’s greatest feature is, in my opinion, its reactivity. Using its vast library of packages, R can handle data from a wide array of sources, from spreadsheets to databases. Furthermore, this customisability means that we can design dashboards to fit on any device that a client may wish. Using HTML and CSS, the apps can be fully customised to create completely bespoke dashboards.
These apps contain all the functionality and computational power of R and can be hosted either in the cloud or on a physical server. Probably the less well-known of the two, Shiny is an installable package for the statistical programming language R that allows the user to create interactive web apps. Whether the user requires offline capabilities, and The devices that the dashboard will be used on How reactive the dashboard must be to user inputs Which one we choose depends on a variety of factors, including: In this post, we compare two of our favoured software solutions at the moment, R Shiny and Microsoft Power BI.
In the early stages of any new dashboard project, one crucial decision is which software to use to build the solution.
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