Re-introducing Radiant: A shiny interface for R

September 24, 2016
By

(This article was first published on R(adiant) news, and kindly contributed to R-bloggers)

Radiant is a platform-independent browser-based interface for business analytics in R. I first introduced Radiant through R-bloggers on 5/2/2015 and, according to Dean Attali, the post was reasonably popular. So I decided to write a post about the changes to the tool since then.

Radiant is back on CRAN and the code and documentation have been moved to a GitHub organization radiant-rstats. Note that the app is only available for R 3.3.0 or later.

There have been numerous changes to the functionality and structure of Radiant. The app is now made up of 5 different menus, each in a separate package. The Data menu (radiant.data) includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application. The Design menu (radiant.design) includes interfaces for design of experiments, sampling, and sample size calculation. The Basics menu (radiant.basics) includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The Model menu (radiant.model) includes interfaces for linear and logistic regression, Neural Networks, model evaluation, decision analysis, and simulation. The Multivariate menu (radiant.multivariate) includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. Finally, the radiant package combines the functionality from each of these 5 packages.

More functionality is in the works. For example, naive Bayes, boosted decision trees, random forests, and choice models will be added to the Model menu (radiant.model). I’m also planning to add a Text menu (radiant.text) to provide functionality to view, process, and analyze text.

If you are interested in contributing to, or extending, Radiant, take a look at the code for the radiant.design package on GitHub. This the simplest menu and should give you a good idea of how you can build on the functionality in the radiant.data package that is the basis for all other packages and menus.

Want to know more about Radiant? Although you could take look at the original Introducing Radiant blog post, quite a few of the links and references have changed. So to make things a bit easier, I’m including an updated version of the original post below.

If you have questions or comments please email me at [email protected]

Key features

  • Explore: Quickly and easily summarize, visualize, and analyze your data
  • Cross-platform: It runs in a browser on Windows, Mac, and Linux
  • Reproducible: Recreate results at any time and share work with others as a state file or an Rmarkdown report
  • Programming: Integrate Radiant’s analysis functions into your own R-code
  • Context: Data and examples focus on business applications

Explore

Radiant is interactive. Results update immediately when inputs are changed (i.e., no separate dialog boxes). This greatly facilitates exploration and understanding of the data.

Cross-platform

Radiant works on Windows, Mac, or Linux. It can run without an Internet connection and no data will leave your computer. You can also run the app as a web application on a server.

Reproducible

Simply saving output is not enough. You need the ability to recreate results for the same data and/or when new data becomes available. Moreover, others may want to review your analyses and results. Save and load the state of the application to continue your work at a later time or on another computer. Share state files with others and create reproducible reports using Rmarkdown.

If you are using Radiant on a server you can even share the url (include the SSUID) with others so they can see what you are working on. Thanks for this feature go to Joe Cheng.

Programming

Although Radiant’s web-interface can handle quite a few data and analysis tasks, you may prefer to write your own code. Radiant provides a bridge to programming in R(studio) by exporting the functions used for analysis. For more information about programming with Radiant see the programming page on the documentation site.

Context

Radiant focuses on business data and decisions. It offers context-relevant tools, examples, and documentation to reduce the business analytics learning curve.

How to install Radiant

  • Required: R version 3.3.0 or later
  • Required: A modern browser (e.g., Chrome or Safari). Internet Explorer (version 11 or higher) or Edge should work as well
  • Recommended: Rstudio

Radiant is available on CRAN. However, to install the latest version of the different packages with complete documentation for offline access open R(studio) and copy-and-paste the command below into the console:

install.packages("radiant", repos = "http://radiant-rstats.github.io/minicran/")

Once all packages and dependencies are installed use the following command to launch the app in your default browser:

radiant::radiant()

If you have a recent version of Rstudio installed you can also start the app from the Addins dropdown. That dropdown will also provide an option to upgrade Radiant to the latest version available on the github minicran repo.

If you currently only have R on your computer and want to make sure you have all supporting software installed as well (e.g., Rstudio, MikTex, etc.) open R, copy-and-paste the command below, and follow along as different dialogs are opened:

source("https://raw.githubusercontent.com/radiant-rstats/minicran/gh-pages/install.R")

More detailed instructions are available on the install radiant page.

Documentation

Documentation and tutorials are available at http://radiant-rstats.github.io/docs/ and in the Radiant web interface (the ? icons and the Help menu).

Want some help getting started? Watch the tutorials on the documentation site

Radiant on a server

If you have access to a server you can use shiny-server to run radiant. First, start R on the server with sudo R and install radiant using install.packages("radiant"). Then clone the radiant repo and point shiny-server to the inst/app/ directory.

If you have Rstudio server running and the Radiant package is installed, you can start Radiant from the addins menu as well. To deploy Radiant using Docker take a look the example and documentation at:

https://github.com/radiant-rstats/docker-radiant

Not ready to install Radiant, either locally or on a server? Try it out on shinyapps.io at the link below:

vnijs.shinyapps.io/radiant

Send questions and comments to: [email protected]


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