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Want to see who is using your Shiny apps and what they are doing while they are there?

Google Analytics is a popular way to track traffic to your website. With Google Analytics, you can see what sort of person comes to your website, where they arrive from, and what they do while they are there.

Since Shiny apps are web pages, you can also use Google Analytics to keep an eye on who visits your app and how they use it.

Add Google Analytics to a Shiny app is a new article at the Shiny dev center that will show you how to set up analytics for a Shiny app. Some knowledge of jQuery is required.

Shiny v0.10.1 has been released to CRAN. You can either install it from a CRAN mirror, or update it if you have installed a previous version.

install.packages('shiny', repos = '')
# or update your installed packages
# update.packages(ask = FALSE, repos = '')

The most prominent change in this patch release is that we added full Unicode support on Windows. Shiny apps running on Windows must use the UTF-8 encoding for ui.R and server.R (also the optional global.R,, and DESCRIPTION) if they contain non-ASCII characters. See this article for details and examples:

Chinese characters in a shiny app

Chinese characters in a shiny app

Please note although we require UTF-8 for the app components, UTF-8 is not a general requirement for any other files. If you read/write text files in an app, you are free to use any encoding you want, e.g. you can readLines('foo.txt', encoding = 'Windows-1252'). The article above has explained it in detail.

Other changes include:

  • runGitHub() also allows the 'username/repo' syntax now, which is equivalent to runGitHub('repo', 'username'). (#427)
  • navbarPage() now accepts a windowTitle parameter to set the web browser page title to something other than the title displayed in the navbar.
  • Added an inline argument to textOutput(), imageOutput(), plotOutput(), and htmlOutput(). When inline = TRUE, these outputs will be put in span() instead of the default div(). This occurs automatically when these outputs are created via the inline expressions (e.g. `r renderText(expr)`) in R Markdown documents. See an R Markdown example at (#512)
  • Added support for option groups in the select/selectize inputs. When the choices argument for selectInput()/selectizeInput() is a list of sub-lists and any sub-list is of length greater than 1, the HTML tag <optgroup> will be used. See an example at here (#542)

Please let us know if you have any comments or questions.

RStudio is very pleased to announce the general availability of Shiny Server Pro 1.2.

Download a free 45 day evaluation of Shiny Server Pro 1.2

Shiny Server Pro 1.2 adds support for R Markdown Interactive Documents in addition to Shiny applications. Learn more about Interactive Documents by registering for the Reproducible Reporting webinar August 13 and Interactive Reporting webinar September 3.

We are excited about the new ways in which you can now share your data analysis in Shiny Server Pro along with the security, management and performance tuning capabilities you and your IT teams need to scale.

Uncover all the features of Shiny Server Pro 1.2 in the updated Shiny Server admin guide…then give it a try!

We’ve added a new section of articles to the Shiny Development Center. These articles explain how to create interactive documents with Shiny and R Markdown.

You’ll learn how to

  • Use R Markdown to create reproducible, dynamic reports. R Markdown offers one of the most efficient workflows for writing up your R results.

  • Create interactive documents and slideshows by embedding Shiny elements into an R Markdown report. The Shiny + R Markdown combo does more than just enhance your reports; R Markdown provides one of the quickest ways to make light weight Shiny apps.

  • Take advantage of RStudio’s built in features that support R Markdown


Learn more at

The RStudio team recently rolled out new capabilities in RStudio, shiny, ggvis, dplyr, knitr, R Markdown, and packrat. The “Essential Tools for Data Science with R” free webinar series is the perfect place to learn more about the power of these R packages from the authors themselves.

Click to learn more and register for one or more webinar sessions. You must register for each separately. If you miss a live webinar or want to review them, recorded versions will be available to registrants within 30 days.

The Grammar and Graphics of Data Science
Live! Wednesday, July 30 at 11am Eastern Time US  Click to register

  • dplyr: a grammar of data manipulation – Hadley Wickham
  • ggvis: Interactive graphics in R – Winston Chang

Reproducible Reporting 
Live! Wednesday, August 13 at 11am Eastern Time US  Click to register

  • The Next Generation of R Markdown – Jeff Allen
  • Knitr Ninja – Yihui Xie
  • Packrat – A Dependency Management System for R – J.J. Allaire & Kevin Ushey

Interactive Reporting
Live! Wednesday, September 3 at 11am Eastern Time US  Click to register

  • Embedding Shiny Apps in R Markdown documents – Garrett Grolemund
  • Shiny: R made interactive – Joe Cheng


RStudio will teach the new essentials for doing data science in R at this year’s Strata NYC conference, Oct 15 2014.

R Day at Strata is a full day of tutorials that will cover some of the most useful topics in R. You’ll learn how to manipulate and visualize data with R, as well as how to write reproducible, interactive reports that foster collaboration. Topics include:

9:00am – 10:30am
A Grammar of Data Manipulation with dplyr
Speaker: Hadley Wickham

11:00am – 12:30pm
A Reactive Grammar of Graphics with ggvis
Speaker: Winston Chang

1:30pm – 3:00pm
Analytic Web Applications with Shiny
Speaker: Garrett Grolemund

3:30pm – 5:00pm
Reproducible R Reports with Packrat and Rmarkdown
Speaker: JJ Allaire & Yihui Xie

The tutorials are integrated into a cohesive day of instruction. Many of the tools that we’ll cover did not exist six months ago, so you are almost certain to learn something new. You will get the most out of the day if you already know how to load data into R and have some basic experience visualizing and manipulating data.

Visit to learn more and register! Early bird pricing ends July 31.

Not available on October 15? Check out Hadley’s Advanced R Workshop in New York City on September 8 and 9, 2014.


Shiny v0.10 comes with a quick, handy guide. Use the Shiny cheat sheet as a quick reference for building Shiny apps. The cheat sheet will guide you from structuring your app, to writing a reactive foundation with server.R, to laying out and deploying your app.




You can find the Shiny cheat sheet along with many more resources for using Shiny at the Shiny Dev Center,

(p.s. Visit the RStudio booth at useR! today for a free hard copy of the cheat sheet.)

Our first public release of ggvis, version 0.3, is now available on CRAN. What is ggvis? It’s a new package for data visualization. Like ggplot2, it is built on concepts from the grammar of graphics, but it also adds interactivity, a new data pipeline, and it renders in a web browser. Our goal is to make an interface that’s flexible, so that you can compose new kinds of visualizations, yet simple, so that it’s accessible to all R users.

Update: there was an issue affecting interactive plots in version 0.3. Version fixes the issue. The updated source package is now on CRAN, and Windows and Mac binary packages will be available shortly.


ggvis integrates with Shiny, so you can use dynamic, interactive ggvis graphics in Shiny applications. We hope that the combination of ggvis and Shiny will make it easy for you to create applications for interactive data exploration and presentation. ggvis plots are inherently reactive and they render in the browser, so they can take advantage of the capabilities provided by modern web browsers. You can use Shiny’s interactive components for interactivity as well as more direct forms of interaction with the plot, such as hovering, clicking, and brushing.

ggvis works seamlessly with R Markdown v2 and interactive documents, so you can easily add interactive graphics to your R Markdown documents:

shiny-doc-ggvis  ggvis_density

And don’t worry — ggvis isn’t only meant to be used with Shiny and interactive documents. Because the RStudio IDE is also a web browser, ggvis plots can display in the IDE, like any other R graphics:

ggvis in RStudio IDE

There’s much more to come with ggvis. To learn more, visit the ggvis website.

Please note that ggvis is still young, and lacks a number of important features from ggplot2. But we’re working hard on ggvis and expect many improvements in the months to come.

Shiny 0.10 is now available on CRAN.

Interactive documents

In this release, the biggest changes were under the hood to support the creation of interactive documents. If you haven’t had a chance to check out interactive documents, we really encourage you to do so—it may be the easiest way to learn Shiny.

New layout functions

Three new functions—flowLayout(), splitLayout(), and inputPanel()—were added for putting UI elements side by side.

  • flowPanel() lays out its children in a left-to-right, top-to-bottom arrangement.
  • splitLayout() evenly divides its horizontal space among its children (or unevenly divides if cellWidths argument is provided).
  • inputPanel() is like flowPanel(), but with a light grey background, and is intended for encapsulating small input controls wherever vertical space is at a premium.

A new logical argument inline was also added to checkboxGroupInput() and radioButtons() to arrange check boxes and radio buttons horizontally.

Custom validation error messages

Sometimes you don’t want your reactive expressions or output renderers in server.R to proceed unless certain input conditions are satisfied, e.g. a select input value has been chosen, or a sensible combination of inputs has been provided. In these cases, you might want to stop the render function quietly, or you might want to give the user a custom message. In shiny 0.10.0, we introduced the functions validate() and need() which you can use to enforce validation conditions. This won’t be the last word on input validation in Shiny, but it should be a lot safer and more convenient than how most of us have been doing it.

See the article Write error messages for your UI with validate for details and examples.

Sever-side processing for Selectize input

In the previous release of Shiny, we added support for Selectize, a powerful select box widget. At that time, our implementation passed all of the data to the web page and used JavaScript to do any paging, filtering, and sorting. It worked great for small numbers of items but didn’t scale well beyond a few thousand items.

For Shiny 0.10, we greatly improved the performance of our existing client-side Selectize binding, but also added a new mode that allows the paging, filtering, and sorting to all happen on the server. Only the results that are actually displayed are downloaded to the client. This approach works well for hundreds of thousands or millions of rows.

For more details and examples, see the article Using selectize input on


We also split off Shiny’s HTML generating library (tags and friends) into a separate htmltools package. If you’re writing a package that needs to generate HTML programmatically, it’s far easier and safer to use htmltools than to paste HTML strings together yourself. We’ll have more to share about htmltools in the months to come.

Other changes

  • New actionLink() input control: behaves like actionButton() but looks like a link
  • renderPlot() now calls print() on its result if it’s visible–no more explicit print() required for ggplot2
  • Sliders and select boxes now use a fixed horizontal size instead of filling up all available horizontal space; pass width="100%" if you need the old behavior
  • The session object that can be passed into a server function is now documented: see ?session
  • New reactive domains feature makes it easy to get callbacks when the current session ends, without having to pass session everywhere
  • Thanks to reactive domains, by default, observers now automatically stop executing when the Shiny session that created them ends
  • shinyUI and shinyServer

For the full list, you can take a look at the NEWS file. Please let us know if you have any comments or questions.

R Markdown’s new interactive documents provide a quick, light-weight way to use Shiny. An interactive document embeds Shiny elements in an R Markdown report. The report becomes “live”, a choose your own adventure that readers can control and explore. Interactive documents are easy to create and easy to share.

Create an interactive document

To create an interactive document use RStudio to create a new R Markdown file, choose the Shiny document template, then click “Run Document” to show a preview:


Embed R code chunks in your report where you like. Interactive documents use the same syntax as R Markdown and knitr. Set echo = FALSE. Your reader won’t see the code, just its results.



Include Shiny widgets and outputs in your code chunks. R Markdown will insert the widgets directly into your final document. When a reader toggles a widget, the parts of the document that depend on it will update instantly.


That’s it! No extra files are needed.

Note that in order to use interactive documents you should be running the latest version of RStudio (v0.98.932 or higher). Alternatively if you are not using RStudio be sure to follow the directions here to install all of the required components.

Share your document

Interactive documents can be run locally on the desktop or be deployed Shiny Server v1.2 or ShinyApps just like any other Shiny application. See the RMarkdown v2 website for more details on deploying interactive documents.

Use pre-packaged tools

Interactive documents make it easy to insert powerful tools into a report. For example, you can insert a kmeans clustering tool into your document with one line of code, as below. kmeans_cluster is a widget built from a Shiny app and intended for use in interactive documents.


You can build your own widgets with shinyApp, a new function that repackages Shiny apps as functions. shinyApp is easy to use. Its first argument takes the code that appears in an app’s ui.R file. The second argument takes the code that appears in the app’s server.R file. The source of kmeans_cluster reveals how simple this is.

Be a hero

Ready to be a hero? You can use the `shinyApp` function to make out of the box widgets that students, teachers, and data scientists will use everyday. Widgets can

  • fit models
  • compare distributions
  • visualize data
  • demonstrate teaching examples
  • act as quizzes or multiple choice questions
  • and more

These widgets are not made yet, they are low hanging fruit for any Shiny developer. If you know how to program with Shiny (or want to learn), and would like to make your mark on R, consider authoring a package that makes widgets available for interactive documents.

Get started!

To learn more about interactive documents visit



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