Hey R-People! This post will look at GitHub’s Copilot and how we can leverage it for our R Shiny development. We will learn how to integrate the AI-powered coding assistant into our IDE. We can not (yet) use our beloved RStudio IDE for this, but we will show how you can do it in Visual […]
Leveraging GitHub Copilot for Efficient R (Shiny) Programming Read More »
The R Shiny framework provides various layout systems to create responsive and flexible user interfaces. One of the most popular layout systems is the grid layout system. In this article, we will discuss the R Shiny grid layout system in detail and provide code examples to demonstrate how it works. What is the grid layout
Shiny’s grid layout or how to arrange elements side-by-side Read More »
shinydashboard is a popular R package that provides a framework for creating interactive dashboards in Shiny applications. One of the key elements of shinydashboard is the menuItem function, which allows you to create interactive menu items that can be used to navigate between different sections or pages of your dashboard. In this article, we will
Creating Interactive Menus with shinydashboard::menuItem in R Shiny Read More »
R Shiny is a powerful framework for creating interactive web applications. One of the key features of Shiny is the ability to create dynamic UI elements that update based on user input. In this article, we will explore how to use a slider in Shiny to dynamically generate selectInput elements, and how to add listeners
Dynamically generate UI elements in R Shiny Read More »
In this blog post, we will explore the different ways to load data into R Shiny applications. Uploading data from the user interface: One of the easiest ways to load data into a Shiny application is to allow users to upload their own data files. Shiny provides the fileInput function, which allows users to select
Load Data to R Shiny Read More »
Big data sets can affect the performance of R Shiny applications. This is why it is important to think about the architecture of the application, including the way data is stored and retrieved. An interesting approach is storing data in an SQLite database instead of R data files (e.g. *.rdata/*.rds) and only load the subset
Working with big data sets in R Shiny using SQLite Read More »
Plots in your application can give a great overview of events and allow you to observe characteristics like trends or seasonalities. However, often people reading these graphs want to dig deeper after the first glance. In your app, you can use Plotly’s event data to trigger filtered plots or tables with one click. Let’s see
Using Plotly Events for Drill Downs Read More »
A relevant icons can make UI element much more intuitive, as it helps us understand what we are looking at. That’s because our brain can process a simple picture much faster and more efficiently than a sentence or even a single word. Nevertheless, it is important to pick icons wisely in order not to confuse
Using Icons in R Shiny Read More »
It is good practice to validate the data before you visualize it in any form such as a table or plot. Shiny offers a simple and intuitive way to do that. As a result, the user does not see a confusing error in red, but an informative message that tells him the reason for the
Input/Output Validation in R Shiny Read More »
Shiny modules help you break down more complex applications into manageable bits. In this post, I will give you a short intro and an example of how you can use modules in your application.