8.46.3
==============================================

:doc:`ReleaseNotes` | :doc:`Contributors`

.. image:: https://travis-ci.org/rstudio/rstudio.svg?branch=master
    :target: https://travis-ci.org/rstudio/rstudio

.. image:: https://img.shields.io/github/license/rstudio/rstudio.svg
   :target: https://www.gnu.org/licenses/gpl-3.0.html

.. image:: https://img.shields.io/badge/version/8.46.3?style=flat
   :target: https://www.rstudio.com/download/release_notes/8.46.3/

.. image:: https://img.shields.io/badge/platforms/windows%20%20linux%20%20osx?style=flat
   :target: https://www.rstudio.com/download/

.. toctree::
    :maxdepth: 2
    :caption: Installation

    installing-system-requirements
    installing-R
    installing-rstudio
    installing-addins
    installing-packages
    installing-python-2-7
    installing-python-3-6
    installing-java
    installing-shiny
    installing-shinydashboard
    installing-rt-package-manager

.. toctree::
    :maxdepth: 1
    :caption: User Interface

    rstudio_interface

.. toctree::
    :maxdepth: 2
    :caption: Shiny Applications

    creating-a-shiny-app
    working-with-shiny-apps
    sharing-shiny-apps

.. toctree::
    :maxdepth: 2
    :caption: Documentation

    documenting-your-work
    documenting-functions
    documenting-packages

.. toctree::
    :maxdepth: 1
    :caption: Package Development

    creating-a-package
    package-development-overview
    documentation-in-packages
    testing-packages
    publishing-packages

.. toctree::
    :maxdepth: 2
    :caption: Package Management

    installing-addins
    managing-dependencies
    using-R-package-manager
    using-conda

.. toctree::
    :maxdepth: 1
    :caption: Advanced Topics

    customizing-rstudio
    troubleshooting
    security-and-privacy
    performance
    contributing
    glossary
    faq


RStudio Server 8.46.3 Release Notes
===================================

Release Date: July 9th, 2019

.. note:: RStudio Server is a commercial product developed and maintained by RStudio, PBC. You can find more information about RStudio Server at http://www.rstudio.com/products/server/.

New Features
------------

### Addins for R Markdown

Addins are small packages that can be loaded in a Shiny app to add custom functionality to the app. This release adds two new addins: `rmarkdown_addin` and `knitr_addin`. These addins allow you to render R Markdown files and knitr documents directly from within your Shiny app, respectively. You can find more information about addins in our `adding-packages` page and our `creating-a-shiny-app` tutorial.

### Shiny Web App Server

Shiny Web App Server is a free and open source product that allows you to run and host R Shiny apps on your own server. This release includes several improvements to Shiny Web App Server, including improved support for large data sets, better security features, and better performance when running multiple apps simultaneously. You can find more information about Shiny Web App Server at http://www.rstudio.com/products/shiny/web/.

### Improved R Markdown Support

This release includes several improvements to our support for rendering R Markdown files in Shiny apps, including better support for custom templates and improved performance when rendering large documents. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Performance When Running Multiple Apps Simultaneously

We have made several improvements to the performance of Shiny Web App Server that should make it more efficient at running multiple apps simultaneously. These changes should result in faster startup times and improved overall performance when running multiple apps on a single server.

Bug Fixes
----------

### Improved Support for Large Data Sets

We have made several improvements to our support for rendering large data sets in Shiny apps, including better support for custom templates and improved performance when rendering large documents. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Support for Large Data Sets in Custom Templates

We have made several improvements to our support for custom templates in Shiny apps, including improved support for rendering large data sets and better performance when rendering large documents. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Security Features

We have made several improvements to the security of Shiny Web App Server, including better support for SSL/TLS encryption and improved protection against common web application vulnerabilities such as SQL injection attacks. You can find more information about security in our `security-and-privacy` page.

### Improved Performance When Running Multiple Apps Simultaneously

We have made several improvements to the performance of Shiny Web App Server that should make it more efficient at running multiple apps simultaneously. These changes should result in faster startup times and improved overall performance when running multiple apps on a single server.

Known Issues
------------

### Improved Support for Large Data Sets

While we have made significant improvements to our support for rendering large data sets in Shiny apps, there are still some limitations to this feature. In particular, it may be difficult to render very large data sets (e.g., those with millions of rows) in Shiny apps due to memory constraints. We recommend using tools like `sparkR` or `dplyr::bind_rows` to process and manipulate large data sets before rendering them in Shiny apps. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Support for Large Data Sets in Custom Templates

While we have made significant improvements to our support for custom templates in Shiny apps, there are still some limitations to this feature. In particular, it may be difficult to render very large data sets (e.g., those with millions of rows) in custom templates due to memory constraints. We recommend using tools like `sparkR` or `dplyr::bind_rows` to process and manipulate large data sets before rendering them in custom templates. You can find more information about R Markdown in our `documenting-functions` page and our `creating-a-shiny-app` tutorial.

### Improved Security Features

While we have made significant improvements to the security of Shiny Web App Server, there are still some potential vulnerabilities that you should be aware of when using this product. In particular, it is important to keep your server software up to date and to use strong passwords for all user accounts. You can find more information about security in our `security-and-privacy` page.

### Improved Performance When Running Multiple Apps Simultaneously

While we have made significant improvements to the performance of Shiny Web App Server, there are still some limitations to this feature. In particular, it may be difficult to run very large numbers of Shiny apps simultaneously on a single server due to resource constraints. We recommend using a load balancer or a cluster of servers to distribute the workload across multiple machines. You can find more information about Shiny Web App Server in our `shiny-web-app-server` page.