RStudio Connect API-driven Shiny application deployment example.
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Deploying with APIs

This directory contains a Shiny application with supporting tools to deploy that application into an RStudio Connect instance. The deployment tools use the experimental content management APIs to create content, upload code, and deploy that code so it can be served. A Docker image is provided to help you use these deployment tools if their prerequisites are not available.

Use this directory as a starting point when building your own deployment tools. Let us ( know about your experience!

What's here?

  • app.R -- A Shiny application that builds word clouds from "A Midsummer Night's Dream", "The Merchant of Venice", and "Romeo and Juliet".

    Uses the R packages shiny, tm, wordcloud, and memoise.

    Install these packages so you can try this example in your environment:

    From an R console:

  • manifest.json -- JSON file describing the requirements of this Shiny application. Created with rsconnect::writeManifest.

    From the command-line:

    # This directory should be your current working directory.
    Rscript -e 'rsconnect::writeManifest()'

    From an R console:

    # This directory should be your current working directory.

    We recommend committing the manifest.json into your source control system and regenerating it whenever you push new versions of your code -- especially when updating packages or otherwise changing its dependencies!

  • data -- Directory containing one compressed text file for each work.

  • deploy -- Directory containing deployment scripts for this content.

  • docker -- Directory defining a Docker image that can optionally be used to help with content deploys.


The RStudio Connect API Reference contains documentation explaining each HTTP endpoint used by these scripts. The RStudio Connect User Guide has code recipes used while building these deployment tools.

Deployment scripts

The deployment scripts in this directory let you create content in your RStudio Connect instance and repeatedly deploy code for that content.



The deployment scripts configure the target server and user authentication with environment variables.

The CONNECT_SERVER environment variable indicates the target RStudio Connect server. This environment variable must be the base URL of your instance and end with a trailing slash.


Some configurations have RStudio Connect behind a proxy and expose Connect under a specific sub-path; use that proxied path in the environment variable.


The CONNECT_API_KEY environment variable indicates an API key owned by the target "publisher" account in the CONNECT_SERVER RStudio Connect server.

export CONNECT_API_KEY='jIsDWwtuWWsRAwu0XoYpbyok2rlXfRWa'

If you do not already have an API key, you can create one in the RStudio Connect dashboard. Visit your RStudio Connect account profile to create and manage your API keys.

API keys are specific to a particular account and server. If you need to target multiple RStudio Connect instances or user accounts, you will need to manage multiple API keys. Password managers such as LastPass or 1Password can help you track secrets like API keys. Be sure to annotate the password record with the associated account and server!

Creating Content

The script creates a new content item in your target RStudio Connect server. The command-line arguments are taken as the title of your content. Duplicate titles are permitted.

./deploy/ "Shakespeare Word Clouds"
# => Created application: 491b772f-a58f-47e0-b358-3da7e288939c

The GUID that is returned (491b772f-a58f-47e0-b358-3da7e288939c) identifies the created content. You will use that GUID when you go to deploy your code.

Deploying Content

The script bundles your code into a .tar.gz archive, uploads that file to RStudio Connect, and requests that archive be "deployed". Progress of the deployment operation is tracked and final status reported.

./deploy/ 491b772f-a58f-47e0-b358-3da7e288939c
# => Creating bundle archive: bundle.tar.gz
# => Created bundle: 468
# => Deployment task: meQSUN9aqjKexyqN
# => Building Shiny application...
# => Bundle requested R version 3.5.1; using /usr/lib/R/bin/R which has version 3.4.4
# => ...
# => Completed packrat build against R version: '3.4.4'
# => Launching Shiny application...
# => Task: meQSUN9aqjKexyqN Complete.

You can repeatedly deploy code to a content item. Deploy new versions of a Shiny application as you incorporate feedback from your colleagues. Publish new versions of an R Markdown document as you make edits and expand your research.

You cannot convert a content item from one type to another -- R Markdown reports cannot become Shiny applications -- create a new content item to contain the different type of content.

Using Docker for deployments

The docker directory defines an Ubuntu 18.04 LTS (Bionic) image. This image can be helpful whenever deploying from locations that may not have the deployment script dependencies available, such as in continuous integration environments.

The Docker image installs following deployment script dependencies:

You do not need this Docker image if your deployment environment already has these tools.

You can build this image using docker build. Here, we give it the tag rstudio-connect-deployer:latest.

docker build -t rstudio-connect-deployer:latest docker

You can use the Docker image combined with the deployment scripts to publish your content. First, let's create a new content entry:

docker run --rm \
    -e CONNECT_SERVER="" \
    -e CONNECT_API_KEY="jIsDWwtuWWsRAwu0XoYpbyok2rlXfRWa" \
    -v $(pwd):/content \
    -w /content \
    rstudio-connect-deployer:latest \
    /content/deploy/ "Content created with Docker"
# => Created application: 3dac1a27-260c-4e56-b8c0-c6f0913d9ac5

The GUID that is returned (3dac1a27-260c-4e56-b8c0-c6f0913d9ac5) identifies the created content. Use that GUID to deploy your code.

Now, let's deploy the Shiny application to that entry.

docker run --rm \
    -e CONNECT_SERVER="" \
    -e CONNECT_API_KEY="jIsDWwtuWWsRAwu0XoYpbyok2rlXfRWa" \
    -v $(pwd):/content \
    -w /content \
    rstudio-connect-deployer:latest \
    /content/deploy/ 3dac1a27-260c-4e56-b8c0-c6f0913d9ac5
# => Creating bundle archive: bundle.tar.gz
# => Created bundle: 470
# => Deployment task: fXqbZXSlVeZIzABx
# => Building Shiny application...
# => Bundle requested R version 3.5.1; using /usr/lib/R/bin/R which has version 3.4.4
# => ...
# => Completed packrat build against R version: '3.4.4'
# => Launching Shiny application...
# => Task: fXqbZXSlVeZIzABx Complete.

Workflow considerations

The deployment scripts are meant to help you to start using the RStudio Connect content management APIs. They will probably need some adjustment to suit your environment. Here are some considerations to keep in mind:

  • Your API key is a secret and should be treated as such. Most continuous integration systems can handle sensitive data like passwords and keys.

    Do not commit your API key into the source repository with your application code.

  • This directory layout is presented as one way of collecting R code and the supporting deployment tools into a single hierarchy. Your organization may prefer managing deployment code separate from data analysis artifacts.

    For the R user: produce your R code (Shiny application, R Markdown document, Plumber API, etc.) together with the manifest.json.

    For the deploying engineer: use the tools in the deploy directory as example that need adapting to your environment. The docker directory provides a very basic Docker image that can help make some scripting tools available.

  • The scripts do not prescribe when you should create new content versus deploying new code to existing content. That depends on the requirements of your organization. Some situations may call for always creating new content.

    Here is a way of creating content with the current time as a component of the title.

    NOW=$(date "+%Y-%m-%d %H:%M:%S")
    ./deploy/ "Word Cloud Snapshot ${NOW}"
  • Create new tools specific to your workflows. Maybe you have two servers: one for staging and one for production. Control updates to these environment with separate scripts.

    #!/usr/bin/env bash
    # Deploy to staging
    export CONNECT_SERVER=""
    export STAGING_CONTENT="491b772f-a58f-47e0-b358-3da7e288939c"
    ./deploy/scripts/upload-and-deploy "${STAGING_CONTENT}"
    #!/usr/bin/env bash
    # Deploy to production
    export CONNECT_SERVER=""
    export PRODUCTION_CONTENT="3dac1a27-260c-4e56-b8c0-c6f0913d9ac5"
    ./deploy/scripts/upload-and-deploy "${PRODUCTION_CONTENT}"

    In this example, staging and production are different RStudio Connect instances and use distinct API keys. Your deploying environment needs to configure the CONNECT_API_KEY environment variable with the correct API key depending on the target environment.


We are using the Word Cloud example Shiny application from The R code has been updated to let the text files containing book data reside in a data directory. The original globals.R, ui.R, and server.R have been merged into a single app.R. The DisplayMode: Showcase entry has been removed from the DESCRIPTION file.

A simple word cloud generator, based on this blog post by PirateGrunt.