Publishing from R

This section describes how to publish content to RStudio Connect from R. This can be done from the RStudio IDE's R Console as an alternative to push-button publishing, or for content types that do not support push-button publishing. You can also use this method to publish from R session in a terminal outside of the RStudio IDE.

This workflow uses the rsconnect R package. See the rsconnect documentation or the package help for details.

Shiny Applications

To publish a Shiny application, use the deployApp function, specifying your project's directory:

rsconnect::deployApp(appDir = '<project-dir>')

For more information:

install.packages('rsconnect')
library(rsconnect)
?rsconnect::deployApp

Plumber APIs

To get started with publishing Plumber API endpoints, create a directory with a plumber.R file defining your endpoints. From the R console, execute the following, replacing <project-dir> with your project's directory:

rsconnect::deployAPI(api = '<project-dir>')

Quarto Content

The rsconnect package can deploy all Quarto content supported by RStudio Connect (Quarto support is included in version 0.8.26).

To use rsconnect::deployApp() to deploy Quarto content, you need to pass the path to a Quarto binary to function's quarto argument:

rsconnect::deployApp(appDir, quarto = "path/to/quarto")

If Quarto is available on your PATH, you can use "quarto". If this doesn't work, you can use the function quarto_path() from the quarto package:

rsconnect::deployApp(quarto = quarto::quarto_path())

The function rsconnect::writeManifest() can also generate manifests for Quarto content when provided a quarto argument.

Tensorflow Model APIs

TensorFlow Model APIs are easy to deploy to RStudio Connect. Export your model:

# `library(tensorflow)` version
export_savedmodel(session, "mysavedmodel")

# `library(keras)` version
export_savedmodel(model, "mysavedmodel")

or from Python:

# Keras version
tf.keras.models.save_model(model, "mysavedmodel")

# Low-level TensorFlow version
tf.saved_model.save(model, "mysavedmodel")

Then deploy it to RStudio Connect using the rsconnect R package:

rsconnect::deployTFModel("mysavedmodel", account = "myaccount", server = "myserver")

Note

TensorFlow Saved Models up to TensorFlow version 1.13.1 are supported. To find out what version of TensorFlow is installed, you can run the following in the R console: tensorflow::tf_version().

If your installed TensorFlow version is greater than 1.13.1, you can install TensorFlow 1.13.1 by running the following in the R console: tensorflow::install_tensorflow(version = "1.13.1")".