domino logo
Get started with Python
Step 0: Orient yourself to DominoStep 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Get started with R
Step 0: Orient yourself to Domino (R Tutorial)Step 1: Create a projectStep 2: Configure your projectStep 3: Start a workspaceStep 4: Get your files and dataStep 5: Develop your modelStep 6: Clean up WorkspacesStep 7: Deploy your model
Domino Reference
Projects
Projects OverviewProjects PortfolioUpload Files to Domino using your BrowserFork and Merge ProjectsSearchSharing and CollaborationCompare File RevisionsArchive a Project
Advanced Project Settings
Project DependenciesProject TagsRename a ProjectSet up your Project to Ignore FilesUpload files larger than 550MBExporting Files as a Python or R PackageTransfer Project Ownership
Domino Runs
JobsDiagnostic Statistics with dominostats.jsonNotificationsResultsRun Comparison
Advanced Options for Domino Runs
Run StatesDomino Environment VariablesEnvironment Variables for Secure Credential StorageAccessing the shell for a Domino Run with SSHUse Apache Airflow with Domino
Scheduled Jobs
Domino Workspaces
WorkspacesUse Visual Studio Code in Domino WorkspacesPersist RStudio PreferencesAccess Multiple Hosted Applications in one Workspace Session
Customize the Domino Software Environment
Environment ManagementDomino Standard EnvironmentsInstall Packages and DependenciesAdd Workspace IDEs
Advanced Options for Domino Software Environment
Install Custom Packages in Domino with Git IntegrationAdd Custom DNS Servers to Your Domino EnvironmentConfigure a Compute Environment to User Private Cran/Conda/PyPi MirrorsScala notebooksUse TensorBoard in Jupyter WorkspacesUse MATLAB as a WorkspaceCreate a SAS Data Science Workspace Environment
Publish your Work
Publish a Model API
Model Publishing OverviewModel Invocation SettingsModel Access and CollaborationModel Deployment ConfigurationPromote Projects to Production
Publish a Web Application
App Publishing OverviewGet Started with DashGet Started with ShinyGet Started with Flask
Advanced Web Application Settings in Domino
App Scaling and PerformanceHost HTML Pages from DominoHow to Get the Domino Username of an App Viewer
Launchers
Launchers OverviewAdvanced Launcher Editor
Assets Portfolio Overview
Connect to your Data
Domino Datasets
Datasets OverviewDatasets Best PracticesAbout domino.yamlDatasets Advanced Mode TutorialDatasets Scratch SpacesConvert Legacy Data Sets to Domino Datasets
Data Sources OverviewConnect to Data Sources
Git and Domino
Git Repositories in DominoWork From a Commit ID in Git
Work with Data Best Practices
Work with Big Data in DominoWork with Lots of FilesMove Data Over a Network
Hadoop and Spark
Connect to a Cloudera CDH5 cluster from DominoConnect to a Hortonworks cluster from DominoConnect to a MapR cluster from DominoConnect to an Amazon EMR cluster from DominoHadoop and Spark overviewKerberos authenticationRun local Spark on a Domino executorUse PySpark in Jupyter Workspaces
Advanced User Configuration Settings
Two-factor authenticationUser API KeysOrganizations Overview
Use the Domino Command Line Interface (CLI)
Install the Domino Command Line (CLI)Domino CLI ReferenceDownload Files with the CLIForce-Restore a Local ProjectMove a Project Between Domino DeploymentsUse the Domino CLI Behind a Proxy
Browser Support
Get Help with Domino
Additional ResourcesGet Domino VersionContact Domino Technical Support
domino logo
About Domino
Domino Data LabKnowledge BaseData Science BlogTraining
User Guide
>
Get started with R
>
Step 3: Start a workspace

Step 3: Start a workspace

Workspace sessions are interactive sessions hosted by a Domino executor where you can interact with code notebooks like Jupyter and RStudio. The software tools and associated configurations available to you are called Workspaces.

For this tutorial, we will start an Rstudio Workspace.

Workspaces page r

  1. Click Workspaces.

  2. Click Rstudio.

  3. Click Launch Rstudio Workspace.

When you launch a workspace, a new containerized session is created on a machine (also known as an executor) in the required hardware tier. The workspace tool you requested is launched in that container, and your browser is automatically redirected to the workspace’s interface when it’s ready.

Workspace Starting r

rstudio start

After your workspace is up and running, you will see a fresh Rstudio interface. If you’re brand new to Rstudio, you might find the Rstudio intro and cheatsheet helpful.

If you are interested in adding additional Workspaces for tools that are available by default, see the pluggable notebooks section of your Domino environment documentation.

Domino Data LabKnowledge BaseData Science BlogTraining
Copyright © 2022 Domino Data Lab. All rights reserved.