Databricks provides robust support for R programming, allowing users to leverage their existing R skills for big data analytics and machine learning tasks within the Databricks environment. Here’s how you can use R in Databricks:

Key Features and Benefits:

• SparkR Integration: Databricks seamlessly integrates with SparkR, the R API for Apache Spark. This allows you to analyze large datasets distributedly using familiar R syntax.
• R Notebooks: You can create and run R code directly in Databricks notebooks, making it easy to experiment, visualize data, and develop R-based applications.
• R Libraries: Databricks provide access to many popular R libraries that have been pre-installed, and you can easily install additional packages from CRAN or other sources.
• Machine Learning:  Utilize Spark MLlib or other R-based machine learning libraries to build and deploy models on Databricks.
• RStudio Integration: You can connect RStudio Desktop or RStudio Server to Databricks clusters, enabling a familiar development environment for R users.

Getting Started:

1. Databricks Account: If you don’t have one, create a free Databricks Community Edition account or explore options for enterprise solutions.
2. Workspace: Within your Databricks workspace, create a new notebook and select “R” as the language.
3. SparkR:  Start using the Sparklyr package to connect to a Spark cluster and work with your data.
4. Libraries: Use the install.packages() function within the notebook to install any additional R packages you need.
5. RStudio:  If you prefer, follow the instructions to connect your RStudio instance to your Databricks cluster.