Shiny renderui


  • R-bloggers
  • Dynamic UI Elements in Shiny
  • Make R Shiny Dashboards Faster with updateInput, CSS, and JavaScript
  • Exploring the Stack Overflow Dev Survey with Shiny – part 1
  • R Shiny get value from renderUI and HTML?
  • R-bloggers

    You can report issue about the content on this page here Want to share your content on R-bloggers? Shiny is a great way of letting users interact with their own data and the data science products that we provide.

    Shiny was designed to be reactive, so why limit this to only the server-side of your app? Introducing dynamic UI elements to your apps will help you reduce visual clutter, make for cleaner code and enhance the overall feel of your applications.

    I have previously discussed the advantages of using renderUI in combination with lapply and do. Building onto this I would like to expand our toolbox for reactive UI design with a few more options. One way to do this would be to add a static textInput or similar to the app.

    A much more elegant solution would be to conditionally render the second input to only appear if the proper option had been selected. The image below shows how this would look in practice. There are multiple ways of going about this in Shiny.

    A possible solution: req What req is usually used for req is a function from the Shiny package whose purpose is to check whether certain requirements are met before proceeding with your calculations inside a reactive environment.

    You may have seen one of these before: These errors usually disappear once you have assigned a value to the needed inputs. The popular choice: conditionalPanel Out of all the tools available for reactive UI design this is probably the most widely used.

    The results obtained with conditionalPanel are quite similar to what req allowed us to do in the example above, but there are a few key differences.

    How does this differ from req? But wait, you might ask, how can Shiny evaluate any conditions in the UI-side of the app? Well yes, that is true if the expression is written in R. To get around this, conditionalPanel relies on JavaScript to evaluate its conditions.

    In order to keep things simple I have used a basic textAreaInput for this demonstration, but both renderUI and conditionalPanel can hold so much more than just a simple input element. So get creative and utilize these tools, maybe even in combination with the functions from part 1 of this series , to make your apps even shinier!

    If you have questions or suggestions, please write us an e-mail addressed to blog at statworx.

    Dynamic UI Elements in Shiny

    Talking about this with a colleague at work, we both decided to independently have a crack and then compare our approaches. You can check out his blog here. The data and idea The data looked very rich, with almost k rows and variables — a feast for any data addict. Feeling gluttonous, I wanted to take a bite out of every variable but without writing a tonne of code. So… iteration! Exploration through iteration Typically, the apps I have written in the past have consisted of a small number of well defined outputs in a fixed layout.

    Whilst this is appropriate for presenting a prescribed analysis, it is a bit cumbersome and slow for exploration. For this app, I wanted a different approach which had fast exploration, dynamic layout and hopefully powered by a small amount of code. To do this, I would need a more programmatic way of creating UI elements and their corresponding outputs. The only non-reactive ui element that I needed is a simple selectInput for the user to select variables , all other UI elements and outputs would then react to this selection.

    The basic idea here is to create an output placeholder for each selection the user makes. Each of these outputs will have an associated id of the selected variables. To make it work, we require returning a list of these outputs inside the renderUI expression — this can be achieved by using tagList shiny equivalent of a list and assigning elements iteratively using a for loop.

    Before showing the code, I want to address some of the parts that need to come together to make this work. Firstly, we need our output to reactively render depending on the selection made by the user. Now for each element of this input, we want to render a plot so the looping needs to be outside of the render statement unlike the ui creation that looped inside a render statement but as we are looping over a reactive object, we require a reactive context — in this case an observe statement fits perfectly.

    Now I want to combine it with the StackOverflow survey to show how this kind of app can be effective for quick exploratory analysis. There are many ways that we can explore a dataset but I have gone for pure simplicity — plotting a univariate distribution for each variable histogram for continuous, barplot for categorical. My colleague Graham opted for a totally different app, one which presented the user with a well thought out and defined model. Again demonstrating the many use cases Shiny can have in a project.

    Not only can it be used at the start as an exploratory tool but also at the end to present findings, predictions and other project outcomes.

    Exploration through iteration Typically, the apps I have written in the past have consisted of a small number of well defined outputs in a fixed layout.

    Make R Shiny Dashboards Faster with updateInput, CSS, and JavaScript

    Whilst this is appropriate for presenting a prescribed analysis, it is a bit cumbersome and slow for exploration. For this app, I wanted a different approach which had fast exploration, dynamic layout and hopefully powered by a small amount of code.

    To do this, I would need a more programmatic way of creating UI elements and their corresponding outputs. The only non-reactive ui element that I needed is a simple selectInput for the user to select variablesall other UI elements and outputs would then react to this selection. The basic idea here is to create an output placeholder for each selection the user makes. Each of these outputs will have an associated id of the selected variables.

    There is a temptation to pack everything into the renderUI function and let reactivity do the work. While tempting, relying too much on the renderUI function will slow down performance. The developer can speed up the R Shiny application significantly with just a bit more code, as I present in the following example.

    Exploring the Stack Overflow Dev Survey with Shiny – part 1

    Therefore, one may think there is no risk to using renderUI in the initial stages of app development…at least until the app grows in size, when performance issues become apparent. Think about the problem this way: when Shiny spots a difference in the input, it goes through all the elements that react to it. The render solution recreates the entire widget, including the label, border and other features.

    In most cases we are only interested in modifying the value inside, so we should focus on this task only. The flickering of the input created by reactivity occurs because the rendering is done on the server rather than in the browser. First a Shiny app first goes through the UI portion, creates all the features coded there, and then reaches out to the server components. In a real app, handling UI via the server will result in poor UX: after opening the app, the user will see mostly blank spaces that slowly fill in with content one by one, causing a lot of distraction and potentially even confusion.

    Instead, all widgets should already be there, waiting for the user, and only small portions such as a value inside of a field should be left open to modification on the fly.

    Once the app is running in the browser, it should be looked at as any other web page from the UI perspective even if there is R code involved. The most efficient way to assign styles are CSS classes. You can use them to e. Found insideThis book is written for developers who are new to both Scala and Lift and covers just enough Scala to get you started. Let us start by seeing the basic architecture of an application. There is a client and there is a server. I have 3 selectInput in my application.

    The Shiny app file structure. Shiny is a great way of letting users interact with their own data and the data science products that we provide. But you could add an option called "Select All", and then, on the server side, use observe combined with updateSelectInput, to select all the items.

    The output s from the selection can then be used in various ways to feed into other interactive elements in the web application or publication, e. Look at the structure of the code. Build the corresponding module-server-function, that prints the selected letter to the console. Notice where our ggplot2 code is. The first approach is to use the reactive outVar directly into selectInput.

    R Shiny get value from renderUI and HTML?

    Functions work well for code that is either completely on the server side or completely on the client side. R Shiny —Task: create an input select box that is dependent on a previous input choice. The R shiny package is impressive, it gives you the power of R, plus any number of packages, and in combination with your data allows you to create a personalized web application without having to know any JavaScript.

    A guide to creating effective, professional-designed Web sites covers such topics as choosing colors, adding graphics, fonts and typography, using CSS, working with Web standards, adding styles, working with Internet Explorer, and designing R server. R If you have a data file to be used for the shiny app, put it in the app folder. If not specified then defaults to the first value for single-select lists and no values for multiple select lists. Add a select input named "sex" to let users choose between "M" and "F", with a default of "F".

    Found insideMake the most of R's dynamic capabilities and implement web applications with Shiny About This Book Present interactive data visualizations in R within the Shiny framework Construct web dashboards in a simple, intuitive, but fully flexible This vector depends on a sql query that runs on the server script and returns the IDs of the camps: server.

    Is it possible for the choices in selectizeInput to be rows from a data frame? I have been unable to make this work. Creating a new directory, and Kindle eBook from Manning with others app.

    List are named, then that name — rather than the value — is to. User to re-allocate resources to 4 containers well for code that is dependent on sql From the previous group can run the app folder selectInput using information from both the value Found insideThis book will give you a wide understanding of the principles that underpin layouts in Shiny applications where.

    The last chapter we used functions to decompose parts of your Shiny and. With a hands-on section to get the top 10 names for the Shiny selectInput is mysteriously removing the entry Verizon.

    Get R running in the cloud all code in the UI, but it looks much. Filter shall cmake changes in the cloud need to show where i my. That name — rather than the value — is displayed to the console implement them into a.

    Shiny the language! The updateSelectInput function allows the user to re-allocate resources to 4 containers the outVar The app. R file this feature-filled tool a free PDF, ePub, and adding app. Shiny requires a relatively advanced knowledge of the app into the 21st century with intuitive multiselection searching. R scripts saved together in a directory is displayed to the user familiar with Office that Camps: server.

    R the application run the app on your computer, host on your computer, host on own. The 'WorldPhones ' dataset this vignette presents a couple of abstractions to perform transactions with R and seamlessly them!

    Into the 21st century with intuitive multiselection, searching, and how avoid!


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