Merge pull request #202673 from jesscioffi/main

v-stsavell · web-flow · commit e2f03bd7d0b5 · 2022-06-23T16:39:16.000-05:00
Updating DSVM Rstudio Documentation
diff --git a/articles/machine-learning/data-science-virtual-machine/dsvm-tools-development.md b/articles/machine-learning/data-science-virtual-machine/dsvm-tools-development.md
@@ -6,10 +6,10 @@ keywords: data science tools, data science virtual machine, tools for data scien
 services: machine-learning
 ms.service: data-science-vm
 
-author: timoklimmer
-ms.author: tklimmer
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: conceptual
-ms.date: 05/12/2021
+ms.date: 06/23/2022
 ---
 
 # Development tools on the Azure Data Science Virtual Machine
@@ -38,15 +38,6 @@ The Data Science Virtual Machine (DSVM) bundles several popular tools in a highl
 | Typical uses | Code editor and Git integration |
 | How to use and run it | Desktop shortcut (`C:\Program Files (x86)\Microsoft VS Code\Code.exe`) in Windows, desktop shortcut or terminal (`code`) in Linux |
 
-## RStudio Desktop
-
-| Category | Value |
-|--|--|
-| What is it? | Client IDE for R language |
-| Supported DSVM versions | Windows, Linux |
-| Typical uses | R development |
-| How to use and run it | Desktop shortcut (`C:\Program Files\RStudio\bin\rstudio.exe`) on Windows, desktop shortcut (`/usr/bin/rstudio`) on Linux |
-
 ## PyCharm
 
 | Category | Value |
diff --git a/articles/machine-learning/data-science-virtual-machine/dsvm-tools-languages.md b/articles/machine-learning/data-science-virtual-machine/dsvm-tools-languages.md
@@ -7,10 +7,10 @@ services: machine-learning
 ms.service: data-science-vm
 ms.custom: devx-track-python
 
-author: timoklimmer
-ms.author: tklimmer
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: conceptual
-ms.date: 05/12/2021
+ms.date: 06/23/2022
 
 
 ---
@@ -73,18 +73,14 @@ artificial intelligence (AI) applications. Here are some of the notable ones.
 
   Open a command prompt and type `R`.
 
-* Use in an IDE:
-
-  To edit R scripts in an IDE, you can use RStudio, which is installed on the DSVM images by default.
-
 * Use in Jupyter Lab
 
   Open a Launcher tab in Jupyter Lab and select the type and kernel of your new document. If you want your document to be
   placed in a certain folder, navigate to that folder in the File Browser on the left side first.
 
 * Install R packages:
 
-  You can install new R packages either by using the `install.packages()` function or by using RStudio.
+  You can install new R packages by using the `install.packages()` function.
 
 ## Julia
 
diff --git a/articles/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough.md b/articles/machine-learning/data-science-virtual-machine/linux-dsvm-walkthrough.md
@@ -5,10 +5,10 @@ description: Learn how to complete several common data science tasks by using th
 services: machine-learning
 ms.service: data-science-vm
 
-author: timoklimmer
-ms.author: tklimmer
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: conceptual
-ms.date: 05/10/2021
+ms.date: 06/23/2022
 
 ---
 
@@ -74,8 +74,7 @@ To get copies of the code samples that are used in this walkthrough, use git to
 git clone https://github.com/Azure/Azure-MachineLearning-DataScience.git
 ```
 
-Open a terminal window and start a new R session in the R interactive console. You also can use RStudio, which is preinstalled on the DSVM.
-
+Open a terminal window and start a new R session in the R interactive console.
 To import the data and set up the environment:
 
 ```R
@@ -532,4 +531,4 @@ select top 10 spam, char_freq_dollar from spam;
 GO
 ```
 
-You can also query by using SQuirreL SQL. Follow steps similar to PostgreSQL by using the SQL Server JDBC driver. The JDBC driver is in the /usr/share/java/jdbcdrivers/sqljdbc42.jar folder.
+You can also query by using SQuirreL SQL. Follow steps similar to PostgreSQL by using the SQL Server JDBC driver. The JDBC driver is in the /usr/share/java/jdbcdrivers/sqljdbc42.jar folder.
diff --git a/articles/machine-learning/data-science-virtual-machine/overview.md b/articles/machine-learning/data-science-virtual-machine/overview.md
@@ -6,10 +6,10 @@ keywords: data science tools, data science virtual machine, tools for data scien
 services: machine-learning
 ms.service: data-science-vm
 
-author: jeff-shepherd
-ms.author: jeffshep
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: overview
-ms.date: 04/02/2020
+ms.date: 06/23/2022
 
 ---
 
@@ -58,7 +58,7 @@ The key differences between these two product offerings are detailed below:
 |Built-in<br>Hosted Notebooks     |   No<br>(requires additional configuration)      |      Yes   |
 |Built-in SSO     | No <br>(requires additional configuration)         |    Yes     |
 |Built-in Collaboration     | No         | Yes        |
-|Pre-installed Tools     |  Jupyter(lab), RStudio Server, VSCode,<br> Visual Studio, PyCharm, Juno,<br>Power BI Desktop, SSMS, <br>Microsoft Office 365, Apache Drill       |     Jupyter(lab)<br> RStudio Server   |
+|Pre-installed Tools     |  Jupyter(lab), VSCode,<br> Visual Studio, PyCharm, Juno,<br>Power BI Desktop, SSMS, <br>Microsoft Office 365, Apache Drill       |     Jupyter(lab) |
 
 ## Sample use cases
 
diff --git a/articles/machine-learning/data-science-virtual-machine/reference-ubuntu-vm.md b/articles/machine-learning/data-science-virtual-machine/reference-ubuntu-vm.md
@@ -2,13 +2,13 @@
 title: 'Reference: Ubuntu Data Science Virtual Machine'
 titleSuffix: Azure Data Science Virtual Machine 
 description: Details on tools included in the Ubuntu Data Science Virtual Machine
-author: timoklimmer
+author: jesscioffi
 services: machine-learning
 ms.service: data-science-vm
 ms.custom: devx-track-python
 
-ms.author: tklimmer
-ms.date: 05/12/2021
+ms.author: jcioffi
+ms.date: 06/23/2022
 ms.topic: reference
 
 
@@ -83,9 +83,9 @@ easier to troubleshoot issues, compared to developing on a Spark cluster.
 
 ## IDEs and editors
 
-You have a choice of several code editors, including VS.Code, PyCharm, RStudio, IntelliJ, vi/Vim, Emacs. 
+You have a choice of several code editors, including VS.Code, PyCharm, IntelliJ, vi/Vim, Emacs. 
 
-VS.Code, PyCharm, RStudio, and IntelliJ are graphical editors. To use them, you need to be signed in to a graphical
+VS.Code, PyCharm, and IntelliJ are graphical editors. To use them, you need to be signed in to a graphical
 desktop. You open them by using desktop and application menu shortcuts.
 
 Vim and Emacs are text-based editors. On Emacs, the ESS add-on package makes working with R easier within the Emacs
@@ -246,4 +246,4 @@ You can exit Rattle and R. Now you can modify the generated R script. Or, use th
 
 ## Next steps
 
-Have additional questions? Consider creating a [support ticket](https://azure.microsoft.com/support/create-ticket/).
+Have additional questions? Consider creating a [support ticket](https://azure.microsoft.com/support/create-ticket/).
diff --git a/articles/machine-learning/data-science-virtual-machine/tools-included.md b/articles/machine-learning/data-science-virtual-machine/tools-included.md
@@ -6,10 +6,10 @@ keywords: data science tools, data science virtual machine, tools for data scien
 services: machine-learning
 ms.service: data-science-vm
 
-author: v-jeevkumar
-ms.author: v-jeevkumar
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: reference
-ms.date: 05/12/2021
+ms.date: 06/23/2022
 ms.custom: contperf-fy20q4
 
 ---
@@ -100,8 +100,6 @@ The Data Science Virtual Machine comes with the most useful data-science tools p
 | [Nano](https://www.nano-editor.org/) | <span class='green-check'>&#9989;</span></br> | <span class='red-x'>&#10060;</span></br> | <span class='red-x'>&#10060;</span></br> | |
 | [Visual Studio 2019 Community Edition](https://www.visualstudio.com/community/) | <span class='green-check'>&#9989;</span> | <span class='red-x'>&#10060;</span> | <span class='red-x'>&#10060;</span> | [Visual Studio on the DSVM](dsvm-tools-development.md#visual-studio-community-edition) |
 | [Visual Studio Code](https://code.visualstudio.com/) | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | [Visual Studio Code on the DSVM](./dsvm-tools-development.md#visual-studio-code) |
-| [RStudio Desktop](https://www.rstudio.com/products/rstudio/#Desktop) | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | [RStudio Desktop on the DSVM](./dsvm-tools-development.md#rstudio-desktop) |
-| [RStudio Server](https://www.rstudio.com/products/rstudio/#Server) <br/> (disabled by default) | <span class='red-x'>&#10060;</span> | <span class='green-check'>&#9989;</span> | <span class='green-check'>&#9989;</span> |  |
 | [PyCharm Community Edition](https://www.jetbrains.com/pycharm/) | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | [PyCharm on the DSVM](./dsvm-tools-development.md#pycharm) |
 | [IntelliJ IDEA](https://www.jetbrains.com/idea/) | <span class='red-x'>&#10060;</span> | <span class='green-check'>&#9989;</span> | <span class='green-check'>&#9989;</span> | |
 | [Vim](https://www.vim.org) | <span class='red-x'>&#10060;</span> | <span class='green-check'>&#9989;</span></br> | <span class='green-check'>&#9989;</span></br> | |
diff --git a/articles/machine-learning/data-science-virtual-machine/vm-do-ten-things.md b/articles/machine-learning/data-science-virtual-machine/vm-do-ten-things.md
@@ -6,18 +6,18 @@ services: machine-learning
 ms.service: data-science-vm
 ms.custom: devx-track-python, devx-track-azurepowershell
 
-author: Blackmist
-ms.author: larryfr
+author: jesscioffi
+ms.author: jcioffi
 ms.topic: conceptual
-ms.date: 05/08/2020
+ms.date: 06/23/2022
 
 ---
 
 # Data science with a Windows Data Science Virtual Machine
 
 The Windows Data Science Virtual Machine (DSVM) is a powerful data science development environment where you can perform data exploration and modeling tasks. The environment comes already built and bundled with several popular data analytics tools that make it easy to get started with your analysis for on-premises, cloud, or hybrid deployments. 
 
-The DSVM works closely with Azure services. It can read and process data that's already stored on Azure, in Azure Synapse (formerly SQL DW),Azure Data Lake, Azure Storage, or Azure Cosmos DB. It can also take advantage of other analytics tools, such as Azure Machine Learning.
+The DSVM works closely with Azure services. It can read and process data that's already stored on Azure, in Azure Synapse (formerly SQL DW), Azure Data Lake, Azure Storage, or Azure Cosmos DB. It can also take advantage of other analytics tools, such as Azure Machine Learning.
 
 In this article, you'll learn how to use your DSVM to perform data science tasks and interact with other Azure services. Here are some of the things you can do on the DSVM:
 
@@ -73,7 +73,7 @@ When you're in the notebook, you can explore your data, build the model, and tes
 
 You can use languages like R and Python to do your data analytics right on the DSVM.
 
-For R, you can use an IDE like RStudio that can be found on the start menu or on the desktop. Or you can use R Tools for Visual Studio. Microsoft has provided additional libraries on top of the open-source CRAN R to enable scalable analytics and the ability to analyze data larger than the memory size allowed in parallel chunked analysis. 
+For R, you can use R Tools for Visual Studio. Microsoft has provided additional libraries on top of the open-source CRAN R to enable scalable analytics and the ability to analyze data larger than the memory size allowed in parallel chunked analysis. 
 
 For Python, you can use an IDE like Visual Studio Community Edition, which has the Python Tools for Visual Studio (PTVS) extension pre-installed. By default, only Python 3.6, the root Conda environment, is configured on PTVS. To enable Anaconda Python 2.7, take the following steps:
 
