Radiant development has been moved to https://github.com/radiant-rstats. Updated documentation is available at https://radiant-rstats.github.io/docs/
Radiant is a platform-independent browser-based interface for business analytics in R, based on the Shiny package. Developed by Vincent Nijs. Please use the issue tracker on GitHub to suggest enhancements or report problems: https://github.com/vnijs/radiant/issues. For other questions and comments please use [email protected].
- 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 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
Radiant is interactive. Results update immediately when inputs are changed (i.e., no separate dialog boxes) and/or when a button is pressed (e.g., Estimate in Regression > Logistic regression (GLM)). This greatly facilitates exploration and understanding of the data.
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.
Note: For Windows users with data that contain multibyte characters please make sure your data are in ANSI format so R(adiant) can load characters correctly.
To conduct high-quality analysis, simply saving output is not enough. You need the ability to recreate results for the same data and/or when new data become available. Moreover, others may want to review your analysis 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. See also the section on Saving and loading state below
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.
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.
Radiant focuses on business data and decisions. It offers tools, examples, and documentation relevant for that context, effectively reducing the business analytics learning curve.
- Required: R version 3.2.4 or later
- Required: A modern browser (e.g., Chrome or Safari). Internet Explorer (version 11 or higher) should work as well
- Recommended: Rstudio
If you use Rstudio (version 0.99.893 or later) you can start and update Radiant through the Addins menu at the top of the screen. For Quantitative Analysis or Business Analytics click Radiant - Quant, for Marketing Research click Radiant - Marketing, and for Customer Analytics click Radiant - Analytics:
To install the latest version of Radiant for Windows or Mac with complete documentation for off-line access open R(studio) and copy-and-paste the command below:
install.packages("radiant", repos = "https://vnijs.github.io/radiant_miniCRAN/", type = 'binary')Once all packages are installed use the command below to launch the app (use either "base", "quant", "marketing", or "analytics"), or use the Addins menu in Rstudio:
radiant::radiant("analytics")See the installing radiant page for more details.
You can also create a launcher on your Desktop to start Radiant by typing radiant::launcher("analytics") in the R(studio) console and pressing return. Again, enter either "base", "quant", "marketing" or "analytics" depending on the app you want to use. A file called radiant.bat (windows) or radiant.command (mac) will be created that you can double-click to start Radiant in your default browser. The launcher command will also create a file called update_radiant.bat (windows) or update_radiant.command (mac) that you can double-click to update Radiant to the latest release.
When Radiant starts you will see data on diamond prices. To close the application click the icon in the navigation bar and then click Stop. The Radiant process will stop and the browser window will close (Chrome) or gray-out.
Documentation and tutorials are available at https://vnijs.github.io/radiant/ and in the Radiant web interface (the icons on each page and the icon in the navigation bar).
Want some help getting started? Watch the tutorials on the documentation site.
Please use the GitHub issue tracker at github.com/vnijs/radiant/issues if you have any problems using Radiant.
Not ready to install Radiant on your computer? Try it online at the links below:
You can run Radiant on shinyapps.io or a (linux) server supported by Shiny server.
To run your own server instance copy/fork the repo from github and deploy to shinyapps.io as usual. Shinyapps.io may complain about paths but you shouldn’t have any trouble if you know how to deploy to shinyapps.io. If you do run into difficulties, also clone the repo at https://github.com/vnijs/shinyapps and run the deploy2shinyapp.R file in the Radiant/build folder.
You can also host Radiant using shiny-server. Just point shiny-server to the directory in inst/ you want to use. As a courtesy, please let me know if you intend to use Radiant on a server.
To save your analyses save the state of the app to a file by clicking on the icon in the navbar and then on Save state (see also the Data > Manage tab). You can open this state-file at a later time or on another computer to continue where you left off. You can also share the file with others that may want to replicate your analyses. As an example, load the state_file radiant-state.rda through the Data > Manage tab. Go to Data > View, Data > Visualize to see some of the settings. There is also a report in R > Report that was created using the Radiant interface. The html file radiant-state.html contains the output.
A related feature in Radiant is that state is maintained if you accidentally navigate to another page, close (and reopen) the browser, and/or hit refresh. Use Reset in the menu in the navigation bar to return to a clean/new state.
Loading and saving state also works with Rstudio. If you start Radiant from Rstudio and use > Stop to stop the app, lists called r_data and r_state will be put into Rstudio's global workspace. If you start radiant again using radiant() it will use these lists to restore state. This can be convenient if you want to make changes to a data file in Rstudio and load it back into Radiant. Also, if you load a state-file directly into Rstudio it will be used when you start Radiant to recreate a previous state.
Technical note: Loading state works as follows in Radiant: When an input is initialized in a Shiny app you set a default value in the call to, for example, numericInput. In Radiant, when a state-file has been loaded and an input is initialized it looks to see if there is a value for an input of that name in a list called r_state. If there is, this value is used. The r_state list is created when saving state using reactiveValuesToList(input). An example of a call to numericInput is given below where the state_init function from radiant.R is used to check if a value from r_state can be used. sm_args$comp_value is the default value specified in the single_mean function call.
numericInput("sm_comp_value", "Comparison value:", state_init('sm_comp_value',sm_args$comp_value))The source code is available on GitHub at https://github.com/vnijs/radiant. Three (related) apps are included in the inst/ directory. Base, offers data loading, saving, viewing, visualizing, merging, and transforming tools. The quant app sources the code from base and extends it. This app is used in the Quantitative Analysis class at the Rady School of Management (UCSD). Finally, the marketing app sources the code from base and quant and extends it with additional tools. The quant app focuses on (basic) quantitative analysis (e.g., comparing means, regression, etc.). The marketing app focuses on marketing analytics by adding clustering, principle component analysis, conjoint analysis, etc. This app is used in the Business Analytics, Quantitative Analysis, Research for Marketing Decisions, and Customer Analytics classes at the Rady School of Management (UCSD).
Radiant would not be possible without R and Shiny. I would like to thank Joe Cheng, Winston Chang, and Yihui Xie for answering questions, providing suggestions, and creating amazing tools for the R community. Other key components used in Radiant are ggplot2, dplyr, tidyr, magrittr, broom, shinyAce, knitr, and DT. For an overview of other packages that Radiant relies on please see the about page.
Radiant is licensed under the AGPLv3. The documentation and videos on this site and the radiant help files are licensed under the creative commons attribution, non-commercial, share-alike license CC-NC-SA.
As a summary, the AGPLv3 license requires, attribution, including copyright and license information in copies of the software, stating changes if the code is modified, and disclosure of all source code. Details are in the COPYING file.
If you are interested in using Radiant please email me at [email protected]
© Vincent Nijs (2016)
