Production Deployment

Safely Deploy R to Production

Table of Contents

Data products built in R, such as dashboards, web applications, reports, and APIs, are increasingly deployed to production. While specific definitions of production can vary, everyone agrees that production content should be stable. Most production systems use a variation of the Snapshot and Restore strategy. Here, we focuse on examples of production systems that apply the snapshot and restore strategy. This page also describes staging environments, acceptable differences between development and production environments, and strategies for upgrading production environments.

This page focuses on reproducible environments for production content. There are other important concerns for placing R code in production:

Development to Production

Production systems come in different shapes and sizes. Some organizations store code in Git and use continuous integration tools like Jenkins to deploy content. Other organizations might use containers and an orchestration tool like Kubernetes. Or, you may use infrastructure-as-code tooling like Chef or Puppet to deploy products onto physical, virtual, or cloud servers.

Regardless of the specific implementation, there are three basic steps required to deploy R environments to production:

  1. In the development environment, the data product’s dependencies should be snapshotted.
  2. In production, an isolated environment should be created for the data product.
  3. In the isolated environment, the dependencies should be restored exactly, with matching versions.

These steps are the heart of the “snapshot and restore” strategy for reproducing environments. The following two examples showcase implementations of this strategy in production systems. These examples are not exhaustive, and you can certainly design other processes. All implementations should meet the key requirements of snapshot, isolate, and restore.

Example: RStudio Connect

RStudio Connect Implementation
Snapshot Manifest file is created during publication
Isolate RStudio Connect creates an isolated package library for each piece of content
Restore Connect installs the packages listed in the manifest into the isolated library

RStudio Connect is a publishing platform for data products, that automatically implements the snapshot and restore strategy when users publish content. If you’re using RStudio Connect, you don’t need to manually manage this process. Here is what happens when a data product is deployed:

  1. The version of R in use, the current repository, and the list of R packages are recorded in a manifest file. Users typically don’t see this file, but if you’d like to explore it, run rsconnect::writeManifest() from within your project’s working directory in the development environment. Here is a sample from a manifest file:

  "version": 1,
  "locale": "en_US",
  "platform": "3.4.4",
  "metadata": {
    "appmode": "api",
    "primary_rmd": null,
    "primary_html": null,
    "content_category": null,
    "has_parameters": false
  "packages": {
    "BH": {
      "Source": "CRAN",
      "Repository": "",
      "description": {
        "Package": "BH",
        "Type": "Package",
        "Title": "Boost C++ Header Files",
        "Version": "1.66.0-1",
        "Date": "2018-02-12",
        "Author": "Dirk Eddelbuettel, John W. Emerson and Michael J. Kane",
        "Maintainer": "Dirk Eddelbuettel <>",
  1. The manifest file, application code, and supporting files are sent to the production RStudio Connect server.

  2. The RStudio Connect server creates an isolated library for each piece of content.

  3. The required packages are restored into the isolated library using the manifest file. As an example, if content A depends on ISLR 1.0 and content B depends on ISLR 2.0, the appropriate version will be installed into the separate content libraries. RStudio Connect maintains a cache so that packages are appropriately reused when possible.

Example: renv and Docker

In this example, a Docker container is used to isolate the data product, and renv is used to snapshot and restore the appropriate package environment. More details are available for using R with Docker here.

Docker + renv Implementation
Snapshot renv::snapshot() creates a lock file from the development environment
Isolate The Docker container creates an isolated environment for the data product
Restore renv::restore() is run in the Docker container to recreate the package environment
  1. In the development environment, create a renv.lock file for the project by running renv::snapshot(). The lock file records the version of the R packages in use. Commit this lock file alongside the code.

  2. Create a Dockerfile, starting with the appropriate version of R:

# start with the appropriate version of R
FROM rstudio/r-base:3.4-bionic

# install git
RUN apt-get install -y git

# clone the code base
RUN git clone

# install renv
RUN R -e 'install.packages("renv", repos = "")'

# restore the package environment
RUN R -e 'setwd("./project"); renv::restore()'

# run the data product
CMD ...

In this example, the version of R is controlled by the base image, using an image provided by RStudio that includes R. Other alternatives also work, such as including the commands to install R. You can determine the R version from the renv lock file:

cat renv.lock | grep -A1 "\[R\]" | sed -En "s/Version=(.*)$/\1/p"
  1. Build and deploy the docker image. This image contains the environment for your production code. The image can then be used as the basis for containers to execute your code.

Testing and Staging Environments

The focus so far has been deploying R environments to production systems. With proper record keeping and environment isolation, there is a high chance that deployed content will work as expected. However, for systems that require minimal downtime, it is still imperative to test content in a pre-production system before officially deploying to production. The concept is simple:

  1. Create and maintain a clone of the production system.
  2. Follow the steps described above to deploy the data product to the production clone.
  3. If everything works as expected, re-run the steps to deploy the data product into production.

While conceptually simple, in practice there are two challenges:

  1. Ensuring the staging environment is a true clone of production.
  2. Repeating the same steps on both the clone and production system.

To solve these problems, most organizations use either containers or infrastructure-as-code. Luckily the idea is straight forward: instead of manually running this process, automate as much as possible by writing explicit code that accomplishes steps 1 and 2. The specific details for implementing these steps would require an entire website all their own, but most R users do not need to worry about re-inventing this process. Typically organizations will have a “DevOps” team or strategy in place for staging content. The main task for the R user is explaining how those tools should be adapted for data products using R. The adaptation is simply including our snapshot, isolate, and restore steps.

Example: RStudio Connect with Git, Jenkins, and Chef

In this example, a DevOps team maintains staging and production servers using Chef. They also maintain an enterprise Git application and use Jenkins for continuous integration. The current DevOps strategy relies on Git branches. Branches of a repository are automatically deployed to staging, whereas the master branch is deployed to production. To integrate R based data products:

  1. The DevOps team should create Chef recipes responsible for installing multiple versions of R onto the staging and production servers.

  2. The DevOps team should create a Chef recipe to install and configure RStudio Connect.

  3. The DevOps team should configure Jenkins to deploy a repository’s branches to the staging environment. Jenkins should also be configured to deploy the master branch to production. In both cases, the Jenkins pipeline will consist of bash shell scripts that clone the Git repository, create a tar file, and then call RStudio Connect API endpoints to deploy. Example shell scripts are available.

  4. When the R user is ready to deploy content, they should start by running rsconnect::writeManifest() inside of the development environment. The resulting manifest file should be included alongside the application code in a Git commit to a staging branch.

  5. Following the commit, Jenkins will deploy the code to the staging RStudio Connect environment, using the automatic process described above. The R user should confirm the content looks correct.

  6. The user or admin can merge the staging branch into the master branch. This merge triggers a deployment to the production server.

More details are available here.

Acceptable Differences

We’ve now described three environments: development, staging/testing, and production. The key to success is keeping these environments as similar as possible. However, what happens if your development environment is a Windows desktop, and production is a Linux server? This section outlines differences that are acceptable: the R patch version and the R source.

R Patch Version

R’s version scheme has there components, the major version, the minor version, and a patch. For example, R version 3.5.2 has:
- Major version: 3
- Minor version: 5
- Patch version: 2

Major versions are released rarely. Minor versions are released once a year in the spring. Patch versions are released on a regular, as-needed basis. R packages are compatible across patch versions, but not major or minor versions!. As an example, a package built on version 3.5.1 will work on 3.5.2, but is not guaranteed to work on 3.6.0.

For the reason above, we recommend that development and production systems have the same available major.minor version, but the patch version could vary. For example, content created in the development environment using R version 3.5.1 could be deployed to a production environment using 3.5.2.

R packages do not follow these same rules, and package versions should match exactly!

R Package Source

It is possible for development and production environments to have different operating systems. For example, development could be performed on a Windows desktop, while production lives on a Linux server.

While possible, this setup is not recommended. Instead, many organizations prefer to standardize on a single operating system, usually Linux. RStudio Server (Pro) makes it easy for R users to develop in an environment that more closely resembles production.

In the case where operating systems vary, the source of an R package may vary as well. Using the scenario above, R packages on Windows are typically installed from pre-compiled CRAN binaries. When the same packages are restored on a Linux system, they are normally installed from source. This difference will not impact behavior, but it explains why information about the package (name, version, repository) is transferred as opposed to transferring the installed package library.

Environment Upgrades

In production, one does not simply upgrade packages or system dependencies! These tips can enable successful maintenance of your production system overtime: