---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: eb6343fd16095dd4aad04d726ef6142fc6e2df1ef1b47c383ba0520c109dc63b
  pageDirectory: concepts
  sources:
    - databricks-connect-for-r-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - renv-for-databricks-connect-projects
    - RFDCP
  citations:
    - file: databricks-connect-for-r-databricks-on-aws.md
title: renv for Databricks Connect projects
description: Using renv (R package for reproducible environments) to manage project-specific R package dependencies when developing with Databricks Connect in RStudio.
tags:
  - R
  - reproducibility
  - environments
timestamp: "2026-06-19T09:48:36.464Z"
---

# renv for Databricks Connect Projects

**renv** is an R package that creates and manages project-local R library directories, enabling reproducible R environments with isolated package dependencies. In the context of [Databricks Connect for R](/concepts/databricks-connect-for-r.md), `renv` is used to capture and restore the exact versions of packages required by a Databricks Connect project, such as `sparklyr`, `pysparklyr`, `reticulate`, and others. ^[databricks-connect-for-r-databricks-on-aws.md]

## Usage in the Databricks Connect Tutorial

When creating a new Databricks Connect project in RStudio Desktop, the tutorial instructs users to check the **Use renv with this project** option in the **New Project** wizard. If an updated version of the `renv` package is available, the wizard prompts the user to install it. This initializes an `renv` environment that records all subsequently installed R packages in a project-local library, ensuring that the same package versions are used each time the project is opened on the same or another machine. ^[databricks-connect-for-r-databricks-on-aws.md]

After the project is created, users install the required packages — `sparklyr`, `pysparklyr`, `reticulate`, `usethis`, `dplyr`, and `dbplyr` — using RStudio’s **Install Packages** dialog. Because `renv` is active, these packages are installed into the project’s private library rather than the user’s global R library. The `renv.lock` file generated at the end of the session can be committed to version control to reproduce the environment later. ^[databricks-connect-for-r-databricks-on-aws.md]

## Benefits

- **Reproducibility**: The exact state of all R dependencies is captured in a lock file, making the project portable across systems.
- **Isolation**: Package versions used for the Databricks Connect project do not interfere with other R projects or the system library.
- **Version Control**: The `renv.lock` file can be tracked in Git, allowing team members to restore the same environment with `renv::restore()`.

## Related Concepts

- [Databricks Connect for R](/concepts/databricks-connect-for-r.md) — The integration framework that `renv` supports.
- [sparklyr](/concepts/sparklyr.md) — The primary R interface to Apache Spark used with Databricks Connect.
- [pysparklyr](/concepts/pysparklyr.md) — A companion package that bridges PySpark and `sparklyr`.
- reticulate — Used to manage the Python environment required by Databricks Connect.
- [R package environment management](/concepts/renv-environment-management-for-databricks-connect.md) — General best practices for R project isolation.

## Sources

- databricks-connect-for-r-databricks-on-aws.md

# Citations

1. [databricks-connect-for-r-databricks-on-aws.md](/references/databricks-connect-for-r-databricks-on-aws-29a91e1a.md)
