DataScience.com has partnered with RStudio to bring our platform customers seamless integrations with RStudio’s suite of tools for R, including its enterprise integrated development environment RStudio Server Pro. To help you take advantage of these new features, we’re going to walk you through how RStudio functions in the DataScience.com Platform.

If you'd like a more in-depth tutorial, register now for our June 15th instructional webinar, which will be co-hosted by RStudio. We'll be explaining how to solve an inventory management problem in the DataScience.com Platform using RStudio and data from Uber. 

Now let's get started!  

Configuring a Session

It only takes a few clicks to launch an interactive RStudio session in the DataScience.com Platform. 

 


Simply choose which branch of your repository you would like to work on and the hardware size your analysis will require. In seconds, you can start coding in this familiar environment.

 

 

 
Locating Files
 

Our native Git integrations will automatically add your branch’s files to a folder in your working directory. You can use RStudio’s file browser to upload, download, delete, copy, move, or rename files. If you make any changes that you would like to sync back to your repo, it just takes one click in the platform.

 
 
 
 
 
Adding Dependencies
 

We start you off with some commonly used packages pre-installed in our standard toolkit, but you can add additional dependencies. Install them just as you would in your local version of RStudio, either through the interface or directly in your script.

 
 
 
 
 
Viewing Environment Variables
 

From any script, you can reference environment variable keys that you set in the platform. 

 


This allows you, for example, to connect to data sources with your personal credentials without committing your secrets to your repository.

 
 
Publishing Reports
 

To publish a R Markdown document, start from an RStudio session on the DataScience.com Platform and run the R Markdown file from top to bottom, loading all the charts, tables, or other outputs into the view. Then in the RStudio console, run the following command:

rmarkdown::render('my-analysis.Rmd') 


Ensuring that the header is set as:

title: "My Analysis"
output: html_document


Once this new HTML file is synced back to the project’s repository, you can publish it as a beautiful report to share with any teammate or stakeholder.

 

 

 

Learn More

Ready to start using RStudio in the DataScience.com Platform? Request a demo to try it yourself or register for our upcoming webinar to learn how to solve an inventory management problem using RStudio Server Pro and data from Uber.