The Radiant Data menu 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.
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 Vincent Nijs. Please use the issue tracker on GitHub to suggest enhancements or report problems: https://github.com/radiant-rstats/radiant.data/issues. For other questions and comments please use [email protected].
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.
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.
In Rstudio you can start and update Radiant through the
Addins menu at the top of the screen. 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 = "")
Once all packages are installed, select
Start radiant from the
Addins menu in Rstudio or use the command below to launch the app:
To launch Radiant in Rstudio's viewer pane use the command below:
To launch Radiant in an Rstudio Window use the command below:
To easily update Radiant and the required packages, install the
radiant.update package using:
install.packages("radiant.update", repos = "")
Update radiant from the
Addins menu in Rstudio or use the command below:
See the installing radiant page additional for details.
Optional: You can also create a launcher on your Desktop to start Radiant by typing
radiant::launcher() in the R(studio) console and pressing return. 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://radiant-rstats.github.io/docs/ and in the Radiant web interface (the icons on each page and the icon in the navigation bar).
Individual Radiant packages also each have their own pkgdown sites:
Want some help getting started? Watch the tutorials on the documentation site.
Please use the GitHub issue tracker at github.com/radiant-rstats/radiant/issues if you have any problems using Radiant.
Not ready to install Radiant on your computer? Try it online at the link below:
Do not upload sensitive data to this public server. The size of data upload has been restricted to 10MB for security reasons.
To run your own instance of Radiant on shinyapps.io first install Radiant and its dependencies. Then clone the radiant repo and ensure you have the latest version of the Radiant packages installed by running
radiant/inst/app/for.shinyapps.io.R. Finally, open
radiant/inst/app/ui.R and deploy the application.
You can also host Radiant using shiny-server. First, install radiant on the server using the command below:
install.packages("radiant", repos = "")
Then clone the radiant repo and point shiny-server to the
inst/app/ directory. As a courtesy, please let me know if you intend to use Radiant on a server.
When running Radiant on a server, by default, file uploads are limited to 10MB and R-code in Report > Rmd and Report > R will not be evaluated for security reasons. If you have
sudo access to the server and have appropriate security in place you can change these settings by adding the following lines to
.Rprofile for the
shiny user on the server.
options ## no file size limitoptions
To run radiant in the cloud you can use the customized Docker container. See https://github.com/radiant-rstats/docker for details
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 radiant state file (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-example.state.rda by clicking on the icon in the navbar and then on
Load radiant state file. Go to Data > View and Data > Visualize to see some of the settings from the previous "state" of the app. There is also a report in Report > Rmd that was created using the Radiant interface. The html file
radiant-example.nb.html contains the output.
A related feature in Radiant is that state is maintained if you accidentally navigate to another web 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.
Loading and saving state also works with Rstudio. If you start Radiant from Rstudio and use >
Stop to stop the app, lists called
r_state will be put into Rstudio's global workspace. If you start radiant again using
radiant::radiant() it will use these lists to restore state. 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.
numericInput("sm_comp_value", "Comparison value:", state_init("sm_comp_value", 0))
The source code for the radiant application is available on GitHub at https://github.com/radiant-rstats.
radiant.data, offers tools to load, save, view, visualize, summarize, combine, and transform data.
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, Pricing, 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, rmarkdown, and DT. For an overview of other packages that Radiant relies on please see the about page.
Radiant is licensed under the AGPLv3. 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.
The documentation, images, and videos for the
radiant.data package are licensed under the creative commons attribution and share-alike license CC-BY-SA. All other documentation and videos on this site, as well as the help files for
radiant.multivariate, are licensed under the creative commons attribution, non-commercial, share-alike license CC-NC-SA.
If you are interested in using any of the radiant packages please email me at [email protected]
summarytoolsdue to breaking changes
iterm) and non-linear term (
qterm) creation if character strings rather than integers are passed to the function
radiant_prefix to all attributes, except
description, to avoid conflicts with other packages (e.g.,
stringi::stri_trans_generalto replace special symbols in Rmarkdown that may cause problems
sas7bdatfiles through the
read filesbutton in Report > Rmd and Report > R.
qscatterplot similar to the function of the same name in Stata
ylimare set in
shinyAceinput for the R-code log in Data > Transform
options(radiant.autosave = c(10, 180)); radiant::radiant()to auto-save the application state to the
~/.radiant.sessionfolder every 10 minutes for the next 180 minutes. This can be useful if radiant is being used during an exam, for example.
~/.radiant.session/r_some_id.state.rda. The files should be automatically loaded when needed but can also be loaded as a regular radiant state file
.rdafrom from a URL in Data > Manage to load
ggplot::labsno longer accepts a list as input
radiant.model. This action now generates a pop-up in the browser interface
shiny::runAppwhen starting radiant such as the port to use. For example, radiant.data::radiant.data("https://github.com/radiant-rstats/docs/raw/gh-pages/examples/demo-dvd-rnd.state.rda", port = 8080)
pred_data = ""
format_dfwhen the data.frame contains missing values. This fix is relevant for several
summaryfunctions run in Report > Rmd or Report > R
Knit reportin Report > Rmd and Report > R without an Rstudio project. Will now correctly default to the working directory used in R(studio)
smoothsetting for histograms with a density plot
pfunet al. calculate summary statistics elementwise across multiple vectors
Desktopas a default directory to show in the
deregisterfunction to remove data in radiant from memory and the
Data > Pivot
summarytoolsto generate summary information for datasets in Data > Manage
radiant.data::fix_namesto files loaded into radiant to ensure valid R-object names
Store filtered data asinput to name the csv download in Data > View
Read filesin Report > Rmd and Report > R
loesscurves based on a selected
colorvariable for scatter plots in Data > Visualize
radiant.project_dirwhen no Rstudio project is used which could cause incorrect relative paths to be used
is_doubleto ensure dates are not treated as numeric variables in Data > View
Read filesbutton in Report > Rmd or Report > R
shinyFilesto provide convenient access to data located on a server
data(...)into the current environment rather than defaulting only to the global environment
file.renamefailed using docker on windows when saving a report. Using
sf_volumesused to set the root directories to load and save files
Sys.getlocale(category = "LC_ALL")what set to something other than "C"
radiant.sf_volumesused for the
pngfor plots in
_Report > Rmd_ and _Report > R_.svg` scatter plots with many point get to big for practical use on servers that have to transfer images to a local browser
desc(n)in reports and replace by
radiant.data::explorewhen variable names contain an underscore
find_gdrivewhen drive is not being synched
deparsein R 3.5
dev = "svg"for plots in Report > Rmd and Report > R
dtab.data.frameto format specified columns as a percentage
Show R-codecheckbox displays the R-code used to load or save the current dataset
shiny::makeReactiveBinding. The advantage is that the code generated for Report > Rmd and Report > R will no longer have to use a list (
r_data) to store and access data. This means that code generated and used in the Radiant browser interface will be directly usable without the browser interface as well
make_funsfunctions as they are no longer needed
sum_rmfunctions as they are no longer needed
save_clipto load and save data to the clipboard on Windows and macOS
r_dataenvironment. This means that the return value from
ls()will be much cleaner
loadrwhich add that data to the Datasets dropdown
visualizewill default to a scatter plot if
yvarare specified but no plot
typeis provided in the function call
read_filesfunction to interactively generate R-code (or Rmarkdown code-chunks) to read files in various format (e.g., SQLite, rds, csv, xlsx, css, jpg, etc.). Supports relative paths and uses
read_filesfunction to interactively generate R-code or Rmarkdown code-chunks to read files in various format (e.g., SQLite, rds, csv, xlsx, css, jpg, etc.). Supports relative paths and uses
rounddfto ignore dates
fixMSto replace curly quotes, em dash, etc. when using Data > Transform > Create
xtileand when binning data with too many groups
DiagrammeRbased plots in Rmarkdown reports
read_filesfor SQLite data names
selectizeInputin Rstudio Viewer shiny #1916
Stoplink in the navbar. As a work-around, use Rstudio's stop buttons instead.
find_projectfunction based on
find_gdrivefunctions to enhances reproducibility.
codeblocks in HTML reports generated in Report > Rmd
Stop & Reportoption in navbar
plotlyfor interactive plots in Report > Rmd
plotlyfor grids of interactive plots in Report > Rmd
res = 96for
dpi = 96for
visualizeto set plot colors when no
colorvariable has been selected
find_gdriveto determine the path to a user's local Google Drive folder if available
fixMsfor encoding in reports on Windows
sdprop, etc. for working with proportions
visualizefor multiple plots
refactorfunction to keep only a subset of levels in a factor and recode the remaining (and first) level to, for example, other
registerfunction to add a (transformed) dataset to the dataset dropdown
renamebutton, without changing the name, the dataset was set to NULL (thanks @kmezhoud, https://github.com/radiant-rstats/radiant/issues/5)
error = TRUEfor rmarkdown for consistency with knitr as used in Report > Rmd