Radiant is an open-source platform-independent browser-based interface for business analytics in R. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vicent Nijs. Please use the issue tracker on GitHub to suggest enhancements or report problems: https://github.com/radiant-rstats/radiant/issues. For other questions and comments please use email@example.com.
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 Model > Estimate > Logistic regression (GLM)). This facilitates rapid 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 reproduce 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 R-code. Radiant provides a bridge to programming in R(studio) by exporting the functions used for analysis (i.e., you can conduct your analysis using the Radiant web-interface or by calling Radiant's functions directly from R-code). 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.
Documentation and tutorials are available at http://radiant-rstats.github.io/docs/ 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 https://github.com/radiant-rstats/radiant/issues
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 and 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 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
Refresh in the menu in the navigation bar to return to a clean/new 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.
numericInput("sm_comp_value", "Comparison value:", state_init('sm_comp_value', 0))
The source code is available on GitHub at https://github.com/radiant-rstats.
radiant.data, offers data loading, saving, viewing, visualizing, combining, and transforming tools.
radiant.design builds on
radiant.data and adds tools for experimental design, sampling, and sample size calculation.
radiant.basics covers the basics of statistical analysis (e.g., comparing means and proportions, cross-tabs, correlation, etc.) and includes a probability calculator.
radiant.model covers model estimation (e.g., logistic regression and neural networks), model evaluation (e.g., gains chart, profit curve, confusion matrix, etc.), and decision tools (e.g., decision analysis and simulation). Finally,
radiant.multivariate includes tools to generate brand maps and conduct cluster, factor, and conjoint analysis.
These tools are used in the Business Analytics, Quantitative Analysis, Research for Marketing Decisions, Consumer behavior, Experiments in firms, 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, rmardown, 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 as well as 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 firstname.lastname@example.org