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pre[class*=language-]{background-color:rgba(var(--ui-12-rgb),.5);color:var(--text-01);text-shadow:none;padding:var(--spacing-00);border-radius:var(--spacing-00);font-size:smaller}","isGSAPEnabled":false,":type":"snowflake-site/components/markup-editor"},"responsivegrid":{"columnClassNames":{"quickstart_hero":"aem-GridColumn aem-GridColumn--default--12","flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","markup_editor":"aem-GridColumn aem-GridColumn--default--12"},"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnCount":12,":items":{"quickstart_hero":{"id":"quickstart-hero-7c238e1bb2","quickstartHeroFirstCertifiedTag":{"tagText":"Community Solution","tagColor":"#29B5E8","tagPath":"/content/cq:tags/snowflake-site/taxonomy/solution-center/certification/community-sourced","tagIcon":""},"isDeveloperGuidesPage":false,"quickstartHeroBreadcrumbs":[{"title":"Analyze Data with R using Posit Workbench and Snowflake","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake","currentPage":true},{"title":"Guides","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides","currentPage":false},{"title":"Snowflake for Developers","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers","currentPage":false}],"quickstartHeroTitle":{"lines":["Analyze Data with R using Posit Workbench and Snowflake"],"type":"heading2",":type":"snowflake-site/components/title-v2"},"quickstartHeroAuthor":"Daniel Chen, Sara Altman","quickstartHeroFirstSnowflakeFeatureTag":{"tagText":"Interactive 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Repo"},":type":"snowflake-site/components/quickstart/quickstart-hero","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/analyze-data-with-r-using-posit-workbench-and-snowflake"},"flexible_column_cont":{"id":"flexible-column-container-b989dcb679","propertiesId":"quickstart-template-main-flexible-container","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"small","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-46fe416e84",":items":{"contentfragment":{"id":"contentfragment-f94eda4375","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n","\u003Cp\u003EThis guide will walk you through using R to analyze data in Snowflake using the Posit\nWorkbench Native App. You'll learn how to launch the Posit Workbench Native App and use the available RStudio Pro IDE. You'll also learn how to use the \u003Ccode\u003E{dbplyr}\u003C/code\u003E package to translate R code into SQL, allowing you to run data operations directly in Snowflake's high-performance computing environment.\u003C/p\u003E\n","\u003Cp\u003EWe'll focus on a healthcare example by analyzing heart failure data. We'll then guide you through launching an R session, accessing the data, and performing data cleaning, transformation, and visualization. Finally, you'll see how to generate an HTML report, build an interactive Shiny app, and write data back to Snowflake—-completing an end-to-end R analysis \u003Cem\u003Eentirely within Snowflake\u003C/em\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/overview/architecture.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EFamiliarity with R\u003C/li\u003E\u003Cli\u003EThe ability to launch Posit Workbench from \u003Ca href=\"https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/\"\u003ESnowflake Native Applications\u003C/a\u003E. This can be provided by an administrator with the \u003Ccode\u003Eaccountadmin\u003C/code\u003E role.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create an R session within the RStudio Pro IDE that comes with the Posit Workbench Native App.\u003C/li\u003E\u003Cli\u003EHow to connect to your Snowflake data from R to create tables, visualizations, and more.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You’ll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn RStudio Pro IDE environment to use within Snowflake.\u003C/li\u003E\u003Cli\u003EA Quarto document that contains plots and tables built with R, using data stored in Snowflake.\u003C/li\u003E\u003Cli\u003EAn interactive Shiny application built with R, using data stored in Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EAlong the way, you will use R to analyze which variables are associated with survival among patients with heart failure.\nYou can follow along with this quickstart guide,\nor look at the materials provided in the accompanying repository:\n\u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r\"\u003Ehttps://github.com/posit-dev/snowflake-posit-quickstart-r\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch2\u003ESetup\u003C/h2\u003E\n","\u003Cp\u003EBefore we begin there are a few components we need to prepare. We need to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EAdd the heart failure data to Snowflake\u003C/li\u003E\u003Cli\u003ELaunch the Posit Workbench Native App\u003C/li\u003E\u003Cli\u003ECreate an RStudio Pro IDE session\u003C/li\u003E\u003Cli\u003EInstall R Packages\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EAdd the heart failure data to Snowflake\u003C/h3\u003E\n","\u003Cp\u003EFor this analysis, we'll use the \u003Ca href=\"https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records\"\u003EHeart Failure Clinical Records\u003C/a\u003E dataset. The data is available for download as a CSV from the \u003Ca href=\"https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records\"\u003EUCI Machine Learning Repository\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EWe'll walk through how to download the data from UCI and then upload it to Snowflake from a CSV.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Cem\u003EINTERACTIVITY NOTE:\u003C/em\u003E\u003C/strong\u003E If you have the necessary permissions in Snowflake, you can also import the data from this S3 bucket: s3://heart-failure-records/heart_failure.csv.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EStep 1: Download the data as a CSV\u003C/h4\u003E\n","\u003Cp\u003EDownload the data from UCI \u003Ca href=\"https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records\"\u003Ehere\u003C/a\u003E, and then unzip the downloaded file.\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Add data in Snowsight\u003C/h4\u003E\n","\u003Cp\u003ELog into \u003Ca href=\"https://docs.snowflake.com/en/user-guide/ui-snowsight\"\u003ESnowsight\u003C/a\u003E, then click \u003Ccode\u003ECreate\u003C/code\u003E > \u003Ccode\u003EAdd Data\u003C/code\u003E. You can find the \u003Ccode\u003ECreate\u003C/code\u003E button in the upper-left corner.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/02-add_data.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Load data\u003C/h4\u003E\n","\u003Cp\u003EChoose the \u003Ccode\u003ELoad Data into a Table\u003C/code\u003E option, then select your downloaded heart failure CSV. Specify an existing database or create a new one for the heart failure data (we called ours \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E). Then, select \u003Ccode\u003E+ Create a new table\u003C/code\u003E and name it \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003EOnce your find the database, you can load it into your Snowflake account by clicking the Get button on the right-hand side.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/03-load_data_into_table.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Confirm data\u003C/h4\u003E\n","\u003Cp\u003EYou should now be able to see the heart failure data in Snowsight. Navigate to \u003Ccode\u003EData\u003C/code\u003E > \u003Ccode\u003EDatabases\u003C/code\u003E, then select the database to which you added the data (e.g., \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E). Expand the database, schema, and tables until you see the \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/04-confirm_data.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ELaunch Posit Workbench\u003C/h3\u003E\n","\u003Cp\u003EWe can now start exploring the data using Posit Workbench.\nYou can find Posit Workbench as a Snowflake Native Application\nand use it to connect to your database.\u003C/p\u003E\n","\u003Ch4\u003EStep 1: Navigate to Apps\u003C/h4\u003E\n","\u003Cp\u003EIn your Snowflake account, Go to \u003Ccode\u003ECatalog\u003C/code\u003E > \u003Ccode\u003EApps\u003C/code\u003E to open the Native Apps collection. If Posit Workbench is not already installed, click \u003Ccode\u003EBrowse all apps\u003C/code\u003E and search for \u003Ccode\u003EPosit Workbench\u003C/code\u003E to find it. Please note that the Native App must be \u003Ca href=\"https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/install.html\"\u003Einstalled and configured\u003C/a\u003E by an administrator.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-05-find_posit_workbench.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Activate and launch Posit Workbench Native App\u003C/h4\u003E\n","\u003Cp\u003EOnce Posit Workbench is installed, click on the app under \u003Ccode\u003EInstalled Apps\u003C/code\u003E to launch the app. If you do not see the Posit Workbench app listed, ask your Snowflake account administrator for access to the app.\u003C/p\u003E\n","\u003Cp\u003EAfter clicking on the app, you will see a page with activation steps and a blue \u003Ccode\u003EActivate\u003C/code\u003E button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-06-Activate_App.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EClick on \u003Ccode\u003EActivate\u003C/code\u003E. This should now take you to the next step where you can see a \u003Ccode\u003ELaunch app\u003C/code\u003E button.\n\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-07-Launch_App.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo setup Oauth, click on \u003Ccode\u003EConnections\u003C/code\u003E tab, and then click on \u003Ccode\u003EConfigure\u003C/code\u003E in section titeld \u003Ccode\u003ESnowflake OAuth Integration\u003C/code\u003E. You will then be prompted to enter your username and password along with instrucions to setup OAuth.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-08-OAuth-setup.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-09-OAuth-username-password.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EFinally, to launch the app click on \u003Ccode\u003ELaunch app\u003C/code\u003E. This should take you to the webpage generated for the Workbench application. You may be prompted to first login to Snowflake using your regular credentials or authentication method.\u003C/p\u003E\n","\u003Ch3\u003ECreate an RStudio Pro Session\u003C/h3\u003E\n","\u003Cp\u003EPosit Workbench provides several IDEs, such as RStudio Pro, JupyterLab, and VS Code. For this analysis we will use an RStudio Pro IDE.\u003C/p\u003E\n","\u003Ch4\u003EStep 1: New Session\u003C/h4\u003E\n","\u003Cp\u003EWithin Posit Workbench, click \u003Ccode\u003ENew Session\u003C/code\u003E to launch a new session to spin up your coding environment.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/01-start_new_session.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 2: Select an IDE\u003C/h4\u003E\n","\u003Cp\u003EWhen prompted, select the RStudio Pro IDE.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/02-create_new_session_snowflake.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 3: Log into your Snowflake account\u003C/h4\u003E\n","\u003Cp\u003ENext, connect to your Snowflake account from within Posit Workbench.\nUnder \u003Ccode\u003ECredentials\u003C/code\u003E, click the button with the Snowflake icon to sign in to Snowflake.\nFollow the sign in prompts.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/03-snowflake_login.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf sign-in fails: verify OAuth and network policy.\u003C/p\u003E\n","\u003Cp\u003EIf the Snowflake sign-in in RStudio doesn’t complete (the Snowflake button doesn’t turn blue) or you see 403/blocked errors:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EOAuth setup for the app: Ensure the Posit Workbench Native App is configured to use OAuth so users can authenticate from inside RStudio. Follow the install steps: \u003Ca href=\"https://docs.posit.co/partnerships/snowflake/workbench/native-app/install.html#oauth\"\u003EInstall with OAuth\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EAccount network policy: If your account enforces a network policy, allow connections from the Posit Workbench Native App back to Snowflake. Guidance and remediation: \u003Ca href=\"https://docs.posit.co/partnerships/snowflake/workbench/native-app/troubleshooting.html#account-network-policies\"\u003EAccount network policies\u003C/a\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETry again: After updating OAuth and/or network policy, return to RStudio in Posit Workbench and click the Snowflake button to sign in.\u003C/p\u003E\n","\u003Cp\u003ETip: Most sign-in issues are due to incomplete OAuth configuration or a restrictive network policy. Ask an admin to review both if you don’t have permissions.\u003C/p\u003E\n","\u003Cp\u003EWhen you're successfully signed into Snowflake, the Snowflake button will turn blue\nand there will be a checkmark in the upper-left corner.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/04-snowflake_login_success.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 4: Launch the RStudio Pro IDE\u003C/h4\u003E\n","\u003Cp\u003EClick \u003Ccode\u003EStart Session\u003C/code\u003E to launch the RStudio Pro IDE.\u003C/p\u003E\n","\u003Cp\u003EOnce everything is ready,\nyou will be able to work with your Snowflake data\nin the familiar RStudio Pro IDE. Since the IDE is provided by the Posit Workbench Native App,\nyour entire analysis will occur securely within Snowflake.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/01-rstudio.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EStep 5: Access the Quickstart Materials\u003C/h4\u003E\n","\u003Cp\u003EThis Quickstart will step you through the analysis contained in \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd\"\u003Ehttps://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd\u003C/a\u003E.\nTo follow along, open the file in your RStudio Pro IDE. There are two ways to do this:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003ESimple copy-and-paste\u003C/strong\u003E Go to File > New File > Quarto Document and then copy the contents of \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd\"\u003Equarto.qmd\u003C/a\u003E into your new file.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003E\u003Cstrong\u003EStarting a new project linked to the GitHub repo.\u003C/strong\u003E To do this:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EGo to File > New Project in the RStudio IDE menu bar.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/03-new-project.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003ESelect Version Control in the New Project Wizard\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/04-project-wizard.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003ESelect Git\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/05-git.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EPaste the \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r/\"\u003EURL\u003C/a\u003E of the GitHub repo and click Create Project\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/06-create-project.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ERStudio will clone a local copy of the materials on GitHub. You can use the Files pane in the bottom right-hand corner of the IDE to navigate to \u003Ccode\u003Equarto.qmd\u003C/code\u003E. Click on the file to open it.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003ENote: SSH authentication is not available in Snowpark Container Services, so when creating projects from Git, you may need to authenticate Git operations over HTTPS, using a username and password or a personal access token.\u003C/p\u003E\n","\u003Ch3\u003EInstall R Packages\u003C/h3\u003E\n","\u003Cp\u003ENow that we're in a familiar R environment,\nwe need to prepare the packages we will use. For this analysis, we will use the \u003Ca href=\"https://www.tidyverse.org/\"\u003ETidyverse\u003C/a\u003E suite of packages, as well as a few others.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Einstall.packages(c("tidyverse", "DBI", "dbplyr", "gt", "gtExtras", "odbc"))\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAfter we install the packages, we load them.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Elibrary(tidyverse)\nlibrary(DBI)\nlibrary(dbplyr)\nlibrary(gt)\nlibrary(gtExtras)\nlibrary(odbc)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EAccess Snowflake data from R\u003C/h2\u003E\n","\u003Cp\u003EWe will run our code in the R environment created by the RStudio Pro IDE, but the code will use data stored in our database on Snowflake.\u003C/p\u003E\n","\u003Cp\u003ETo access this data, we will use the \u003Ccode\u003E{DBI}\u003C/code\u003E and \u003Ccode\u003E{odbc}\u003C/code\u003E R packages to connect to the database. We will then use \u003Ccode\u003E{dplyr}\u003C/code\u003E and \u003Ccode\u003E{dbplyr}\u003C/code\u003E to query the data with R,\nwithout having to write raw SQL. Let's take a look at how this works.\u003C/p\u003E\n","\u003Ch3\u003EConnect with \u003Ccode\u003E{DBI}\u003C/code\u003E\u003C/h3\u003E\n","\u003Cp\u003E\u003Ccode\u003E{DBI}\u003C/code\u003E is an R package that allows us to connect to databases with \u003Ccode\u003EDBI::dbConnect()\u003C/code\u003E.\u003C/p\u003E\n","\u003Cp\u003ETo connect to our Snowflake database, we will use a driver provided by the \u003Ccode\u003E{odbc}\u003C/code\u003E package. We will need to provide a \u003Ccode\u003Ewarehouse\u003C/code\u003E for compute, and a \u003Ccode\u003Edatabase\u003C/code\u003E to connect to.\nWe can also provide a \u003Ccode\u003Eschema\u003C/code\u003E here to make connecting to specific tables and views easier.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003E# Connect to the database\nconn <-\n  DBI::dbConnect(\n    odbc::snowflake(),\n    warehouse = "DEFAULT_WH",\n    database = "HEART_FAILURE",\n    schema = "PUBLIC"\n  )\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe will save our connection as an object, named \u003Ccode\u003Econn\u003C/code\u003E, which will make it easy to use.\u003C/p\u003E\n","\u003Cp\u003EOnce we build a connection, we can see the databases, schemas, and tables available to us in the RStudio IDE Connections pane. Click on the database icon to the right of a database to see its schemas. Click on the schema icon to the right of a schema to see its tables. Click on the table icon to the right of a table to see a preview of the table.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/02-connections.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch3\u003ECreate \u003Ccode\u003Etbl\u003C/code\u003Es that correspond to tables in the database\u003C/h3\u003E\n","\u003Cp\u003EOnce we build a connection, we can use \u003Ccode\u003Edplyr::tbl()\u003C/code\u003E to create \u003Ccode\u003Etbl\u003C/code\u003Es. A tbl is an R object that represents a table or view accessed through a connection.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure <- tbl(conn, "HEART_FAILURE")\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf we did not provide the \u003Ccode\u003Eschema\u003C/code\u003E argument into \u003Ccode\u003EDBI::dbConnect()\u003C/code\u003E earlier, we would need to specify the view with \u003Ccode\u003Etbl(conn, in_schema("PUBLIC", "HEART_FAILURE"))\u003C/code\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003ERely on \u003Ccode\u003E{dbplyr}\u003C/code\u003E to translate R to SQL\u003C/h3\u003E\n","\u003Cp\u003EWe can now use \u003Ccode\u003Eheart_failure\u003C/code\u003E as if it was a tibble in R. For example, we can filter rows and select columns from our data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure_young <-\n  heart_failure |> \n  filter(age < 50) |>\n  select(age, sex, smoking, death_event)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWhen we use \u003Ccode\u003Etbl\u003C/code\u003Es, \u003Ccode\u003E{dbplyr}\u003C/code\u003E translates our R code into SQL queries. Think of any object made downstream from a tbl like a view: it contains SQL that represents a table built from the tbl. If we want to see the SQL code that \u003Ccode\u003E{dbplyr}\u003C/code\u003E generates, we can run \u003Ccode\u003Edbplyr::show_query()\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure_young |>\n  show_query()\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003ESELECT\n  "AGE" AS "age",\n  "SEX" AS "sex",\n  "SMOKING" AS "smoking",\n  "DEATH_EVENT" AS "death_event"\nFROM "HEART_FAILURE"\nWHERE ("AGE" < 50.0)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo save compute, R waits to execute a query until we request the data that it represents.\u003C/p\u003E\n","\u003Cp\u003ETo save memory, R stores the data as a temporary file in the database (instead of in R's global environment). When we inspect the data, R only returns the first few rows of this file to display.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure_young\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E# Source:   SQL [?? x 4]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n     age   sex smoking death_event\n   <dbl> <dbl>   <dbl>       <dbl>\n 1    45     1       0           1\n 2    49     0       0           0\n 3    45     1       0           1\n 4    48     0       0           1\n 5    49     1       1           1\n 6    45     1       0           1\n 7    45     1       0           1\n 8    45     0       0           0\n 9    42     0       0           1\n10    41     1       1           0\n# ℹ more rows\n# ℹ Use `print(n = ...)` to see more rows\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003E\u003Ccode\u003Ecollect()\u003C/code\u003E\u003C/h3\u003E\n","\u003Cp\u003EWe can trigger R to execute a query and to return the \u003Cem\u003Eentire\u003C/em\u003E result as a tibble with \u003Ccode\u003Edbplyr::collect()\u003C/code\u003E. Keep in mind, that if your result is large, you may not want to collect it into R.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure_young_df <-\n  heart_failure_young |> \n  collect()\n  \nheart_failure_young_df\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E# A tibble: 47 × 4\n     age   sex smoking death_event\n   <dbl> <dbl>   <dbl>       <dbl>\n 1    45     1       0           1\n 2    49     0       0           0\n 3    45     1       0           1\n 4    48     0       0           1\n 5    49     1       1           1\n 6    45     1       0           1\n 7    45     1       0           1\n 8    45     0       0           0\n 9    42     0       0           1\n10    41     1       1           0\n# ℹ 37 more rows\n# ℹ Use `print(n = ...)` to see more rows\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch3\u003EIn summary\u003C/h3\u003E\n","\u003Cp\u003EThis system:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EKeeps our data in the database, saving memory in the R session\u003C/li\u003E\u003Cli\u003EPushes computations to the database, saving compute in the R session\u003C/li\u003E\u003Cli\u003EEvaluates queries lazily, saving compute in the database\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EWe don't need to manage the process, it happens automatically behind the scenes.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Ccode\u003E{dbplyr}\u003C/code\u003E can translate the most common tidyverse and base R functions to SQL. However, you may sometimes use a function that \u003Ccode\u003E{dbplyr}\u003C/code\u003E does not recognize or for which there is no SQL analogue. On these occasions, you will need to \u003Ccode\u003Ecollect()\u003C/code\u003E your data into R, where you can process it as a real tibble.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003ELearn more about \u003Ccode\u003E{dbplyr}\u003C/code\u003E at \u003Ca href=\"https://dbplyr.tidyverse.org/\"\u003Edbplyr.tidyverse.org\u003C/a\u003E\u003C/p\u003E\n","\u003Ch2\u003EWrite to a Snowflake database\u003C/h2\u003E\n","\u003Cp\u003EYou can also use \u003Ccode\u003E{DBI}\u003C/code\u003E to create a new table in a database or append to an existing table.\u003C/p\u003E\n","\u003Cp\u003ETo add a new table, use \u003Ccode\u003EDBI::dbWriteTable()\u003C/code\u003E. Note that \u003Ccode\u003Evalue\u003C/code\u003E needs to be a data.frame or tibble, which\nis why we passed \u003Ccode\u003Eheart_failure_young_df\u003C/code\u003E, the result of calling \u003Ccode\u003Ecollect()\u003C/code\u003E on \u003Ccode\u003Eheart_failure_young\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003EdbWriteTable(conn, name = "HEART_FAILURE_YOUNG", value = heart_failure_young_df)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ETo append to an existing table, specify \u003Ccode\u003Eappend = TRUE\u003C/code\u003E. If appending, the \u003Ccode\u003Ename\u003C/code\u003E\nargument should refer to an existing table.\u003C/p\u003E\n","\u003Cp\u003ENow that we understand how R will interact with the database, we can use R to perform our analysis.\u003C/p\u003E\n","\u003Ch2\u003EPrepare data with \u003Ccode\u003E{dplyr}\u003C/code\u003E\u003C/h2\u003E\n","\u003Cp\u003EWe want to understand which variables in \u003Ccode\u003EHEART_FAILURE\u003C/code\u003E are associated with survival\nof patients with heart failure.\u003C/p\u003E\n","\u003Cp\u003EFirst we convert the column names to lowercase, so we won't need to worry about capitalization.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003E# Standardize column names\nheart_failure <- \n  heart_failure |> \n  rename_with(str_to_lower)\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EWhen we are running these commands on a database connection, \u003Ccode\u003E{dbplyr}\u003C/code\u003E is translating the code into SQL for us under the hood.\nWe don't need to write raw SQL commands, and the compute is happening directly on the database.\nYou can pipe \u003Ccode\u003E|>\u003C/code\u003E the code into \u003Ccode\u003Eshow_query()\u003C/code\u003E if you want to see the generated SQL query.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EFilter ages\u003C/h3\u003E\n","\u003Cp\u003EFor now, we'll focus on just patients younger than 50. We also reduce the data to just the columns we're interested in.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure <-\n  heart_failure |> \n  filter(age < 50) |> \n  select(age, diabetes, serum_sodium, serum_creatinine, sex, death_event)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOur table now looks like this.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003E# Source:   SQL [?? x 6]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n     age diabetes serum_sodium serum_creatinine   sex death_event\n   <dbl>    <dbl>        <dbl>            <dbl> <dbl>       <dbl>\n 1    45        0          137             1.1      1           1\n 2    49        0          138             1        0           0\n 3    45        0          127             0.8      1           1\n 4    48        1          121             1.9      0           1\n 5    49        0          136             1.1      1           1\n 6    45        1          139             1        1           1\n 7    45        0          145             1        1           1\n 8    45        0          137             1.18     0           0\n 9    42        1          136             1.3      0           1\n10    41        0          140             0.8      1           0\n# ℹ more rows\n# ℹ Use `print(n = ...)` to see more rows\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EVisualize Data with \u003Ccode\u003E{ggplot2}\u003C/code\u003E\u003C/h2\u003E\n","\u003Cp\u003EThe heart failure data provides important insights that can help us:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EIdentify factors associated with increased risk of mortality after heart failure\u003C/li\u003E\u003Cli\u003EPredict future survival outcomes based on historical clinical data\u003C/li\u003E\u003Cli\u003EBenchmark patient outcomes based on clinical indicators like serum sodium levels\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EVisualizing clinical variables across different patient groups can help identify patterns.\u003C/p\u003E\n","\u003Ch3\u003EVisualize serum sodium levels\u003C/h3\u003E\n","\u003Cp\u003EWe can use \u003Ccode\u003E{ggplot2}\u003C/code\u003E to visually compare sodium levels across different patient groups. In this plot, we see the distribution of serum sodium based on whether the patients have diabetes and whether they survived (\u003Ccode\u003E0\u003C/code\u003E) or died (\u003Ccode\u003E1\u003C/code\u003E) during the follow-up period.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure |> \n  mutate(\n    death_event = as.character(death_event), \n    diabetes = as.character(diabetes)\n  ) |> \n  ggplot(aes(x = death_event, y = serum_sodium, fill = diabetes)) +\n  geom_boxplot() +\n  labs(\n    title = "Serum Sodium Levels by Diabetes Status and Survival Outcome",\n    x = "Survival Outcome (0 = Survived, 1 = Died)",\n    y = "Serum Sodium (mEq/L)",\n    fill = "Diabetes"\n  ) +\n  theme(legend.position = "bottom")\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/analysis/plot-sodium.png\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003E\u003Cem\u003EINTERACTIVITY NOTE:\u003C/em\u003E\u003C/strong\u003E  The code above allows for easy visualization of other variables. You can adjust the \u003Ccode\u003Eaes()\u003C/code\u003E function or filter the data to explore different clinical indicators and patient characteristics.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EMake publication-ready tables with \u003Ccode\u003E{gt}\u003C/code\u003E\u003C/h2\u003E\n","\u003Cp\u003EWe can continue exploring the heart failure dataset with visualizations or create a table that concisely displays multiple pieces of information at once. For example, we can use \u003Ccode\u003E{dplyr}\u003C/code\u003E verbs to calculate the median values for various clinical metrics across different patient groups.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Eheart_failure |> \n  summarize(\n    across(\n      c("age", "serum_creatinine", "serum_sodium"), \n      \\(x) median(x, na.rm = TRUE), \n      .names = "median_{.col}"\n    )\n  )\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E# Source:   SQL [1 x 3]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n  median_age median_serum_creatinine median_serum_sodium\n       <dbl>                   <dbl>               <dbl>\n1         45                       1                 137\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWith \u003Ccode\u003E{dplyr}\u003C/code\u003E's \u003Ccode\u003Egroup_by()\u003C/code\u003E command, we can compute separate metrics for each\ncombination of \u003Ccode\u003Edeath_event\u003C/code\u003E and \u003Ccode\u003Ediabetes\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Ecomparison <- \n  heart_failure |> \n  group_by(death_event, diabetes) |> \n  summarize(\n    across(\n      c("age", "serum_creatinine", "serum_sodium"), \n      \\(x) median(x, na.rm = TRUE), \n      .names = "median_{.col}"\n    ),\n    .groups = "drop"\n  )\n  \ncomparison\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cpre\u003E\u003Ccode\u003E# Source:   SQL [4 x 5]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n  death_event diabetes median_age median_serum_creatinine median_serum_sodium\n        <dbl>    <dbl>      <dbl>                   <dbl>               <dbl>\n1           0        0       42                      1                    139\n2           1        1       45.5                    1.45                 133\n3           1        0       45                      1.1                  136\n4           0        1       45                      0.9                  137\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThis is a useful way to examine the information for ourselves. However, if we wish to share the information with others, we might prefer to present the table in a more polished format. We can do this with commands from R's \u003Ca href=\"https://gt.rstudio.com/\"\u003E\u003Ccode\u003E{gt}\u003C/code\u003E package\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EThe following code creates a table displaying the information in \u003Ccode\u003Ecomparison\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Ecomparison |> \n  mutate(\n    death_event = case_when(\n      death_event == 1 ~ "Died",\n      death_event == 0 ~ "Survived"\n    ),\n    diabetes = case_when(\n      diabetes == 1 ~ "Yes",\n      diabetes == 0 ~ "No"\n    )\n  ) |> \n  arrange(desc(death_event), desc(diabetes)) |> \n  gt(rowname_col = "death_event") |> \n  cols_label(\n    diabetes = "Diabetes Status",\n    median_age = "Median Age",\n    median_serum_creatinine = "Median Serum Creatinine (mg/dL)",\n    median_serum_sodium = "Median Serum Sodium (mEq/L)"\n  ) |> \n  tab_header(\n    title = "Clinical Metrics by Survival Outcome and Diabetes Status"\n  ) |> \n  data_color(\n    columns = c(median_serum_creatinine, median_serum_sodium),\n    palette = "Blues"\n  ) \n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/gt/gt-table.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ENow that we've accumulated some insights, let's think about how we might present the results of our analysis to our colleagues.\u003C/p\u003E\n","\u003Ch2\u003EBuild Reports and Dashboards with Quarto\u003C/h2\u003E\n","\u003Cp\u003EWe've conveniently written our analysis in a Quarto (\u003Ccode\u003E.qmd\u003C/code\u003E) document, \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd\"\u003Equarto.qmd\u003C/a\u003E. \u003Ca href=\"https://quarto.org/\"\u003EQuarto\u003C/a\u003E\nis an open-source publishing system that makes it easy to create\n\u003Ca href=\"https://quarto.org/docs/guide/\"\u003Edata products\u003C/a\u003E such as\n\u003Ca href=\"https://quarto.org/docs/output-formats/html-basics.html\"\u003Edocuments\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/presentations/\"\u003Epresentations\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/dashboards/\"\u003Edashboards\u003C/a\u003E,\n\u003Ca href=\"https://quarto.org/docs/websites/\"\u003Ewebsites\u003C/a\u003E,\nand\n\u003Ca href=\"https://quarto.org/docs/books/\"\u003Ebooks\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EBy placing our work in a Quarto document, we have interwoven all of our code, results, output, and prose text into a single literate programming document.\nThis way everything can travel together in a reproducible data product.\u003C/p\u003E\n","\u003Cp\u003EA Quarto document can be thought of as a regular markdown document,\nbut with the ability to run code chunks.\u003C/p\u003E\n","\u003Cp\u003EYou can run any of the code chunks by clicking the play button above the chunk in the RStudio Pro IDE.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/quarto/run-chunk.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou can render the entire document into a polished report to share, with the \u003Ccode\u003ERender\u003C/code\u003E button.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/quarto/rstudio-render.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will run all the code in the document from top to bottom in a new R session,\nand generate an HTML file, by default, for you to view and share.\u003C/p\u003E\n","\u003Ch3\u003ELearn More about Quarto\u003C/h3\u003E\n","\u003Cp\u003EYou can learn more about Quarto here: \u003Ca href=\"https://quarto.org/\"\u003Ehttps://quarto.org/\u003C/a\u003E,\nand the documentation for all the various Quarto outputs here: \u003Ca href=\"https://quarto.org/docs/guide/\"\u003Ehttps://quarto.org/docs/guide/\u003C/a\u003E.\nQuarto works with R, Python, and Javascript Observable code out-of-the box,\nand is a great tool to communicate your data science analyses.\u003C/p\u003E\n","\u003Ch2\u003EShiny Application\u003C/h2\u003E\n","\u003Cp\u003EOne way to share our work and allow others to explore the heart failure dataset is to create an\ninteractive \u003Ca href=\"https://shiny.posit.co/\"\u003EShiny\u003C/a\u003E app.\u003C/p\u003E\n","\u003Cp\u003EWe've prepared an example Shiny app in the directory:\n\u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r\"\u003Ehttps://github.com/posit-dev/snowflake-posit-quickstart-r\u003C/a\u003E. Our app allows the user\nto explore different clinical metrics in one place.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/shiny/shiny.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo run the app, open \u003Ccode\u003Eapp.R\u003C/code\u003E and then click the Run App button at the top of the script in the RStudio Pro IDE.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/shiny/run.png\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EIf you face package missing errors like this: \u003Ccode\u003Ethere is no package called ‘xyz’\u003C/code\u003E, please install the required package by running \u003Ccode\u003Einstall.packages("xyz")\u003C/code\u003E in the R console.\u003C/p\u003E\n","\u003Cp\u003EChange the metric in the sidebar to control which metric is plotted.\u003C/p\u003E\n","\u003Ch3\u003ELearn More About Shiny\u003C/h3\u003E\n","\u003Cp\u003EYou can learn more about Shiny at: \u003Ca href=\"https://shiny.posit.co/\"\u003Ehttps://shiny.posit.co/\u003C/a\u003E.\nThis example uses Shiny for R, but\n\u003Ca href=\"https://shiny.posit.co/py/\"\u003EShiny for Python\u003C/a\u003E\nis also available!\u003C/p\u003E\n","\u003Cp\u003EIf you're new to Shiny, you can try it online with\n\u003Ca href=\"https://shinylive.io/r/\"\u003Eshinylive\u003C/a\u003E.\nIt too, comes in a \u003Ca href=\"https://shinylive.io/py\"\u003EPython\u003C/a\u003E version.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003ER is beloved by data scientists for its intuitive, concise syntax. You can now combine this syntax with the power and peace of mind of Snowflake. The Posit Workbench Native Application provides an IDE for R \u003Cem\u003Ewithin Snowflake\u003C/em\u003E. You can then use R's existing database packages---\u003Ccode\u003E{DBI}\u003C/code\u003E, \u003Ccode\u003E{odbc}\u003C/code\u003E, \u003Ccode\u003E{dbplyr}\u003C/code\u003E---to access your Snowflake databases.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to create an R session within the RStudio Pro IDE that comes with the Posit Workbench Native App.\u003C/li\u003E\u003Cli\u003EHow to connect to your Snowflake data from R to create tables, visualizations, and more.\u003C/li\u003E\u003Cli\u003EBuild an RStudio Pro IDE environment to use within Snowflake.\u003C/li\u003E\u003Cli\u003EBuild a Quarto document that contains plots and tables built with R, using data stored in Snowflake.\u003C/li\u003E\u003Cli\u003EBuild an interactive Shiny Application built with R, using data stored in Snowflake.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://github.com/posit-dev/snowflake-posit-quickstart-r\"\u003ESource Code on GitHub\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://posit.co/products/enterprise/workbench/\"\u003EMore about Posit Workbench\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://dbplyr.tidyverse.org/\"\u003E{tidyverse} package for data science in R\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://dbplyr.tidyverse.org/\"\u003E{dbplyr} package for database connections\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://gt.rstudio.com/\"\u003E{gt} package for tables\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://quarto.org/\"\u003EQuarto for reproducible documents, reports, and data products\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://shiny.posit.co/\"\u003EShiny for interactive dashboards and applications\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://shinylive.io/\"\u003EShinylive for serverless Shiny applications\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"/content/dam/snowflake-site/developers/2024/05/Data-Analysis-with-R-using-POSIT-workbench.pdf\"\u003EDownload Reference Architecture\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://medium.com/snowflake/analyze-data-in-r-with-posit-workbench-and-snowflake-7b837b27337b\"\u003ERead the Blog\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://youtu.be/ZscKe1Vl7Ec?list=TLGGZ4Kuet5QT6AyMjA5MjAyNQ\"\u003EWatch the Demo\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"Analyze Snowflake data using R in Posit Workbench for statistical computing, predictive modeling, and data visualization.","title":"Analyze Data with R using Posit Workbench and Snowflake",":items":{},":itemsOrder":[],"isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment","elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nThis guide will walk you through using R to analyze data in Snowflake using the Posit\nWorkbench Native App. You'll learn how to launch the Posit Workbench Native App and use the available RStudio Pro IDE. You'll also learn how to use the `{dbplyr}` package to translate R code into SQL, allowing you to run data operations directly in Snowflake's high-performance computing environment.\n\nWe'll focus on a healthcare example by analyzing heart failure data. We'll then guide you through launching an R session, accessing the data, and performing data cleaning, transformation, and visualization. Finally, you'll see how to generate an HTML report, build an interactive Shiny app, and write data back to Snowflake—-completing an end-to-end R analysis _entirely within Snowflake_.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/overview/architecture.png)\n\n### What You'll Need\n\n- Familiarity with R\n- The ability to launch Posit Workbench from [Snowflake Native Applications](https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/). This can be provided by an administrator with the `accountadmin` role.\n\n### What You’ll Learn\n\n- How to create an R session within the RStudio Pro IDE that comes with the Posit Workbench Native App.\n- How to connect to your Snowflake data from R to create tables, visualizations, and more.\n\n### What You’ll Build\n\n- An RStudio Pro IDE environment to use within Snowflake.\n- A Quarto document that contains plots and tables built with R, using data stored in Snowflake.\n- An interactive Shiny application built with R, using data stored in Snowflake.\n\nAlong the way, you will use R to analyze which variables are associated with survival among patients with heart failure.\nYou can follow along with this quickstart guide,\nor look at the materials provided in the accompanying repository:\n\u003Chttps://github.com/posit-dev/snowflake-posit-quickstart-r\u003E.\n\n## Setup\n\nBefore we begin there are a few components we need to prepare. We need to:\n\n- Add the heart failure data to Snowflake\n- Launch the Posit Workbench Native App\n- Create an RStudio Pro IDE session\n- Install R Packages\n\n### Add the heart failure data to Snowflake\n\nFor this analysis, we'll use the [Heart Failure Clinical Records](https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records) dataset. The data is available for download as a CSV from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records). \n\nWe'll walk through how to download the data from UCI and then upload it to Snowflake from a CSV.\n\n\u003E **_INTERACTIVITY NOTE:_** If you have the necessary permissions in Snowflake, you can also import the data from this S3 bucket: s3://heart-failure-records/heart_failure.csv.\n\n#### Step 1: Download the data as a CSV\n\nDownload the data from UCI [here](https://archive.ics.uci.edu/dataset/519/heart+failure+clinical+records), and then unzip the downloaded file. \n\n#### Step 2: Add data in Snowsight\n\nLog into [Snowsight](https://docs.snowflake.com/en/user-guide/ui-snowsight), then click `Create` \u003E `Add Data`. You can find the `Create` button in the upper-left corner. \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/02-add_data.png)\n\n#### Step 3: Load data\n\nChoose the `Load Data into a Table` option, then select your downloaded heart failure CSV. Specify an existing database or create a new one for the heart failure data (we called ours `HEART_FAILURE`). Then, select `+ Create a new table` and name it `HEART_FAILURE`.\n\nOnce your find the database, you can load it into your Snowflake account by clicking the Get button on the right-hand side.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/03-load_data_into_table.png)\n\n#### Step 4: Confirm data\n\nYou should now be able to see the heart failure data in Snowsight. Navigate to `Data` \u003E `Databases`, then select the database to which you added the data (e.g., `HEART_FAILURE`). Expand the database, schema, and tables until you see the `HEART_FAILURE` table. \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/04-confirm_data.png)\n\n### Launch Posit Workbench\n\nWe can now start exploring the data using Posit Workbench.\nYou can find Posit Workbench as a Snowflake Native Application\nand use it to connect to your database.\n\n#### Step 1: Navigate to Apps\n\nIn your Snowflake account, Go to `Catalog` \u003E `Apps` to open the Native Apps collection. If Posit Workbench is not already installed, click `Browse all apps` and search for `Posit Workbench` to find it. Please note that the Native App must be [installed and configured](https://docs.posit.co/ide/server-pro/integration/snowflake/native-app/install.html) by an administrator. \n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-05-find_posit_workbench.png)\n\n#### Step 2: Activate and launch Posit Workbench Native App\n\nOnce Posit Workbench is installed, click on the app under `Installed Apps` to launch the app. If you do not see the Posit Workbench app listed, ask your Snowflake account administrator for access to the app.\n\nAfter clicking on the app, you will see a page with activation steps and a blue `Activate` button.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-06-Activate_App.png)\n\nClick on `Activate`. This should now take you to the next step where you can see a `Launch app` button.\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-07-Launch_App.png)\n\nTo setup Oauth, click on `Connections` tab, and then click on `Configure` in section titeld `Snowflake OAuth Integration`. You will then be prompted to enter your username and password along with instrucions to setup OAuth.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-08-OAuth-setup.png)\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/snowflake/v2-09-OAuth-username-password.png)\n\nFinally, to launch the app click on `Launch app`. This should take you to the webpage generated for the Workbench application. You may be prompted to first login to Snowflake using your regular credentials or authentication method.\n\n### Create an RStudio Pro Session \n\nPosit Workbench provides several IDEs, such as RStudio Pro, JupyterLab, and VS Code. For this analysis we will use an RStudio Pro IDE.\n\n#### Step 1: New Session\n\nWithin Posit Workbench, click `New Session` to launch a new session to spin up your coding environment.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/01-start_new_session.png)\n\n#### Step 2: Select an IDE\n\nWhen prompted, select the RStudio Pro IDE.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/02-create_new_session_snowflake.png)\n\n#### Step 3: Log into your Snowflake account\n\nNext, connect to your Snowflake account from within Posit Workbench.\nUnder `Credentials`, click the button with the Snowflake icon to sign in to Snowflake.\nFollow the sign in prompts.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/03-snowflake_login.png)\n\nIf sign-in fails: verify OAuth and network policy.\n\nIf the Snowflake sign-in in RStudio doesn’t complete (the Snowflake button doesn’t turn blue) or you see 403/blocked errors:\n* OAuth setup for the app: Ensure the Posit Workbench Native App is configured to use OAuth so users can authenticate from inside RStudio. Follow the install steps: [Install with OAuth](https://docs.posit.co/partnerships/snowflake/workbench/native-app/install.html#oauth).\n* Account network policy: If your account enforces a network policy, allow connections from the Posit Workbench Native App back to Snowflake. Guidance and remediation: [Account network policies](https://docs.posit.co/partnerships/snowflake/workbench/native-app/troubleshooting.html#account-network-policies).\n\nTry again: After updating OAuth and/or network policy, return to RStudio in Posit Workbench and click the Snowflake button to sign in.\n\nTip: Most sign-in issues are due to incomplete OAuth configuration or a restrictive network policy. Ask an admin to review both if you don’t have permissions.\n\n\nWhen you're successfully signed into Snowflake, the Snowflake button will turn blue\nand there will be a checkmark in the upper-left corner.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/posit_workbench/04-snowflake_login_success.png)\n\n#### Step 4: Launch the RStudio Pro IDE\n\nClick `Start Session` to launch the RStudio Pro IDE.\n\nOnce everything is ready,\nyou will be able to work with your Snowflake data\nin the familiar RStudio Pro IDE. Since the IDE is provided by the Posit Workbench Native App, \nyour entire analysis will occur securely within Snowflake.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/01-rstudio.png)\n\n#### Step 5: Access the Quickstart Materials\n\nThis Quickstart will step you through the analysis contained in \u003Chttps://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd\u003E.\nTo follow along, open the file in your RStudio Pro IDE. There are two ways to do this:\n\n1. **Simple copy-and-paste** Go to File \u003E New File \u003E Quarto Document and then copy the contents of [quarto.qmd](https://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd) into your new file.\n2. **Starting a new project linked to the GitHub repo.** To do this:\n\n    1.  Go to File \u003E New Project in the RStudio IDE menu bar.\n\n    ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/03-new-project.png)\n\n    2.  Select Version Control in the New Project Wizard\n\n    ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/04-project-wizard.png)\n\n    3.  Select Git\n\n    ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/05-git.png)\n\n    4.  Paste the [URL](https://github.com/posit-dev/snowflake-posit-quickstart-r/) of the GitHub repo and click Create Project\n\n    ![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/06-create-project.png)\n\n    RStudio will clone a local copy of the materials on GitHub. You can use the Files pane in the bottom right-hand corner of the IDE to navigate to `quarto.qmd`. Click on the file to open it.\n\n\nNote: SSH authentication is not available in Snowpark Container Services, so when creating projects from Git, you may need to authenticate Git operations over HTTPS, using a username and password or a personal access token.\n\n\n### Install R Packages\n\nNow that we're in a familiar R environment,\nwe need to prepare the packages we will use. For this analysis, we will use the [Tidyverse](https://www.tidyverse.org/) suite of packages, as well as a few others.\n\n```r\ninstall.packages(c(\"tidyverse\", \"DBI\", \"dbplyr\", \"gt\", \"gtExtras\", \"odbc\"))\n```\n\nAfter we install the packages, we load them.\n\n```r\nlibrary(tidyverse)\nlibrary(DBI)\nlibrary(dbplyr)\nlibrary(gt)\nlibrary(gtExtras)\nlibrary(odbc)\n```\n\n## Access Snowflake data from R\n\nWe will run our code in the R environment created by the RStudio Pro IDE, but the code will use data stored in our database on Snowflake.\n\nTo access this data, we will use the `{DBI}` and `{odbc}` R packages to connect to the database. We will then use `{dplyr}` and `{dbplyr}` to query the data with R,\nwithout having to write raw SQL. Let's take a look at how this works.\n\n### Connect with `{DBI}`\n\n`{DBI}` is an R package that allows us to connect to databases with `DBI::dbConnect()`.\n\nTo connect to our Snowflake database, we will use a driver provided by the `{odbc}` package. We will need to provide a `warehouse` for compute, and a `database` to connect to.\nWe can also provide a `schema` here to make connecting to specific tables and views easier.\n\n```r\n# Connect to the database\nconn \u003C-\n  DBI::dbConnect(\n    odbc::snowflake(),\n    warehouse = \"DEFAULT_WH\",\n    database = \"HEART_FAILURE\",\n    schema = \"PUBLIC\"\n  )\n```\n\nWe will save our connection as an object, named `conn`, which will make it easy to use.\n\nOnce we build a connection, we can see the databases, schemas, and tables available to us in the RStudio IDE Connections pane. Click on the database icon to the right of a database to see its schemas. Click on the schema icon to the right of a schema to see its tables. Click on the table icon to the right of a table to see a preview of the table.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/rstudio/02-connections.png)\n\n### Create `tbl`s that correspond to tables in the database\n\nOnce we build a connection, we can use `dplyr::tbl()` to create `tbl`s. A tbl is an R object that represents a table or view accessed through a connection.\n\n\n```r\nheart_failure \u003C- tbl(conn, \"HEART_FAILURE\")\n```\n\n\u003E If we did not provide the `schema` argument into `DBI::dbConnect()` earlier, we would need to specify the view with `tbl(conn, in_schema(\"PUBLIC\", \"HEART_FAILURE\"))`.\n\n### Rely on `{dbplyr}` to translate R to SQL\n\nWe can now use `heart_failure` as if it was a tibble in R. For example, we can filter rows and select columns from our data.\n\n```r\nheart_failure_young \u003C-\n  heart_failure |\u003E \n  filter(age \u003C 50) |\u003E\n  select(age, sex, smoking, death_event)\n```\n\nWhen we use `tbl`s, `{dbplyr}` translates our R code into SQL queries. Think of any object made downstream from a tbl like a view: it contains SQL that represents a table built from the tbl. If we want to see the SQL code that `{dbplyr}` generates, we can run `dbplyr::show_query()`.\n\n```r\nheart_failure_young |\u003E\n  show_query()\n```\n```\nSELECT\n  \"AGE\" AS \"age\",\n  \"SEX\" AS \"sex\",\n  \"SMOKING\" AS \"smoking\",\n  \"DEATH_EVENT\" AS \"death_event\"\nFROM \"HEART_FAILURE\"\nWHERE (\"AGE\" \u003C 50.0)\n```\n\nTo save compute, R waits to execute a query until we request the data that it represents.\n\nTo save memory, R stores the data as a temporary file in the database (instead of in R's global environment). When we inspect the data, R only returns the first few rows of this file to display.\n\n```r\nheart_failure_young\n```\n```\n# Source:   SQL [?? x 4]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n     age   sex smoking death_event\n   \u003Cdbl\u003E \u003Cdbl\u003E   \u003Cdbl\u003E       \u003Cdbl\u003E\n 1    45     1       0           1\n 2    49     0       0           0\n 3    45     1       0           1\n 4    48     0       0           1\n 5    49     1       1           1\n 6    45     1       0           1\n 7    45     1       0           1\n 8    45     0       0           0\n 9    42     0       0           1\n10    41     1       1           0\n# ℹ more rows\n# ℹ Use `print(n = ...)` to see more rows\n```\n\n### `collect()`\n\nWe can trigger R to execute a query and to return the _entire_ result as a tibble with `dbplyr::collect()`. Keep in mind, that if your result is large, you may not want to collect it into R.\n\n```r\nheart_failure_young_df \u003C-\n  heart_failure_young |\u003E \n  collect()\n  \nheart_failure_young_df\n```\n```\n# A tibble: 47 × 4\n     age   sex smoking death_event\n   \u003Cdbl\u003E \u003Cdbl\u003E   \u003Cdbl\u003E       \u003Cdbl\u003E\n 1    45     1       0           1\n 2    49     0       0           0\n 3    45     1       0           1\n 4    48     0       0           1\n 5    49     1       1           1\n 6    45     1       0           1\n 7    45     1       0           1\n 8    45     0       0           0\n 9    42     0       0           1\n10    41     1       1           0\n# ℹ 37 more rows\n# ℹ Use `print(n = ...)` to see more rows\n```\n\n### In summary\n\nThis system:\n\n1. Keeps our data in the database, saving memory in the R session\n2. Pushes computations to the database, saving compute in the R session\n3. Evaluates queries lazily, saving compute in the database\n\nWe don't need to manage the process, it happens automatically behind the scenes.\n\n\u003E `{dbplyr}` can translate the most common tidyverse and base R functions to SQL. However, you may sometimes use a function that `{dbplyr}` does not recognize or for which there is no SQL analogue. On these occasions, you will need to `collect()` your data into R, where you can process it as a real tibble.\n\nLearn more about `{dbplyr}` at [dbplyr.tidyverse.org](https://dbplyr.tidyverse.org/)\n\n## Write to a Snowflake database\n\nYou can also use `{DBI}` to create a new table in a database or append to an existing table.\n\nTo add a new table, use `DBI::dbWriteTable()`. Note that `value` needs to be a data.frame or tibble, which\nis why we passed `heart_failure_young_df`, the result of calling `collect()` on `heart_failure_young`.\n\n```r\ndbWriteTable(conn, name = \"HEART_FAILURE_YOUNG\", value = heart_failure_young_df)\n```\n\nTo append to an existing table, specify `append = TRUE`. If appending, the `name` \nargument should refer to an existing table.\n\nNow that we understand how R will interact with the database, we can use R to perform our analysis.\n\n## Prepare data with `{dplyr}`\n\nWe want to understand which variables in `HEART_FAILURE` are associated with survival\nof patients with heart failure.\n\nFirst we convert the column names to lowercase, so we won't need to worry about capitalization.\n\n```r\n# Standardize column names\nheart_failure \u003C- \n  heart_failure |\u003E \n  rename_with(str_to_lower)\n```\n\n\u003E When we are running these commands on a database connection, `{dbplyr}` is translating the code into SQL for us under the hood.\n\u003E We don't need to write raw SQL commands, and the compute is happening directly on the database.\n\u003E You can pipe `|\u003E` the code into `show_query()` if you want to see the generated SQL query.\n\n\n### Filter ages\n\nFor now, we'll focus on just patients younger than 50. We also reduce the data to just the columns we're interested in.\n\n```r\nheart_failure \u003C-\n  heart_failure |\u003E \n  filter(age \u003C 50) |\u003E \n  select(age, diabetes, serum_sodium, serum_creatinine, sex, death_event)\n```\n\nOur table now looks like this.\n\n```\n# Source:   SQL [?? x 6]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n     age diabetes serum_sodium serum_creatinine   sex death_event\n   \u003Cdbl\u003E    \u003Cdbl\u003E        \u003Cdbl\u003E            \u003Cdbl\u003E \u003Cdbl\u003E       \u003Cdbl\u003E\n 1    45        0          137             1.1      1           1\n 2    49        0          138             1        0           0\n 3    45        0          127             0.8      1           1\n 4    48        1          121             1.9      0           1\n 5    49        0          136             1.1      1           1\n 6    45        1          139             1        1           1\n 7    45        0          145             1        1           1\n 8    45        0          137             1.18     0           0\n 9    42        1          136             1.3      0           1\n10    41        0          140             0.8      1           0\n# ℹ more rows\n# ℹ Use `print(n = ...)` to see more rows\n```\n\n## Visualize Data with `{ggplot2}`\n\nThe heart failure data provides important insights that can help us:\n\n- Identify factors associated with increased risk of mortality after heart failure\n- Predict future survival outcomes based on historical clinical data\n- Benchmark patient outcomes based on clinical indicators like serum sodium levels\n\nVisualizing clinical variables across different patient groups can help identify patterns.\n\n### Visualize serum sodium levels\n\nWe can use `{ggplot2}` to visually compare sodium levels across different patient groups. In this plot, we see the distribution of serum sodium based on whether the patients have diabetes and whether they survived (`0`) or died (`1`) during the follow-up period.\n\n```r\nheart_failure |\u003E \n  mutate(\n    death_event = as.character(death_event), \n    diabetes = as.character(diabetes)\n  ) |\u003E \n  ggplot(aes(x = death_event, y = serum_sodium, fill = diabetes)) +\n  geom_boxplot() +\n  labs(\n    title = \"Serum Sodium Levels by Diabetes Status and Survival Outcome\",\n    x = \"Survival Outcome (0 = Survived, 1 = Died)\",\n    y = \"Serum Sodium (mEq/L)\",\n    fill = \"Diabetes\"\n  ) +\n  theme(legend.position = \"bottom\")\n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/analysis/plot-sodium.png)\n\n\u003E **_INTERACTIVITY NOTE:_**  The code above allows for easy visualization of other variables. You can adjust the `aes()` function or filter the data to explore different clinical indicators and patient characteristics.\n\n## Make publication-ready tables with `{gt}`\n\nWe can continue exploring the heart failure dataset with visualizations or create a table that concisely displays multiple pieces of information at once. For example, we can use `{dplyr}` verbs to calculate the median values for various clinical metrics across different patient groups.\n\n```r\nheart_failure |\u003E \n  summarize(\n    across(\n      c(\"age\", \"serum_creatinine\", \"serum_sodium\"), \n      \\(x) median(x, na.rm = TRUE), \n      .names = \"median_{.col}\"\n    )\n  )\n```\n\n```\n# Source:   SQL [1 x 3]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n  median_age median_serum_creatinine median_serum_sodium\n       \u003Cdbl\u003E                   \u003Cdbl\u003E               \u003Cdbl\u003E\n1         45                       1                 137\n```\n\nWith `{dplyr}`'s `group_by()` command, we can compute separate metrics for each\ncombination of `death_event` and `diabetes`.\n\n```r\ncomparison \u003C- \n  heart_failure |\u003E \n  group_by(death_event, diabetes) |\u003E \n  summarize(\n    across(\n      c(\"age\", \"serum_creatinine\", \"serum_sodium\"), \n      \\(x) median(x, na.rm = TRUE), \n      .names = \"median_{.col}\"\n    ),\n    .groups = \"drop\"\n  )\n  \ncomparison\n```\n\n```\n# Source:   SQL [4 x 5]\n# Database: Snowflake 8.39.2[@Snowflake/HEART_FAILURE]\n  death_event diabetes median_age median_serum_creatinine median_serum_sodium\n        \u003Cdbl\u003E    \u003Cdbl\u003E      \u003Cdbl\u003E                   \u003Cdbl\u003E               \u003Cdbl\u003E\n1           0        0       42                      1                    139\n2           1        1       45.5                    1.45                 133\n3           1        0       45                      1.1                  136\n4           0        1       45                      0.9                  137\n```\n\nThis is a useful way to examine the information for ourselves. However, if we wish to share the information with others, we might prefer to present the table in a more polished format. We can do this with commands from R's [`{gt}` package](https://gt.rstudio.com/).\n\nThe following code creates a table displaying the information in `comparison`.\n\n```r\ncomparison |\u003E \n  mutate(\n    death_event = case_when(\n      death_event == 1 ~ \"Died\",\n      death_event == 0 ~ \"Survived\"\n    ),\n    diabetes = case_when(\n      diabetes == 1 ~ \"Yes\",\n      diabetes == 0 ~ \"No\"\n    )\n  ) |\u003E \n  arrange(desc(death_event), desc(diabetes)) |\u003E \n  gt(rowname_col = \"death_event\") |\u003E \n  cols_label(\n    diabetes = \"Diabetes Status\",\n    median_age = \"Median Age\",\n    median_serum_creatinine = \"Median Serum Creatinine (mg/dL)\",\n    median_serum_sodium = \"Median Serum Sodium (mEq/L)\"\n  ) |\u003E \n  tab_header(\n    title = \"Clinical Metrics by Survival Outcome and Diabetes Status\"\n  ) |\u003E \n  data_color(\n    columns = c(median_serum_creatinine, median_serum_sodium),\n    palette = \"Blues\"\n  ) \n```\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/gt/gt-table.png)\n\nNow that we've accumulated some insights, let's think about how we might present the results of our analysis to our colleagues.\n\n## Build Reports and Dashboards with Quarto\n\nWe've conveniently written our analysis in a Quarto (`.qmd`) document, [quarto.qmd](https://github.com/posit-dev/snowflake-posit-quickstart-r/blob/main/quarto.qmd). [Quarto](https://quarto.org/)\nis an open-source publishing system that makes it easy to create\n[data products](https://quarto.org/docs/guide/) such as\n[documents](https://quarto.org/docs/output-formats/html-basics.html),\n[presentations](https://quarto.org/docs/presentations/),\n[dashboards](https://quarto.org/docs/dashboards/),\n[websites](https://quarto.org/docs/websites/),\nand\n[books](https://quarto.org/docs/books/).\n\nBy placing our work in a Quarto document, we have interwoven all of our code, results, output, and prose text into a single literate programming document.\nThis way everything can travel together in a reproducible data product.\n\nA Quarto document can be thought of as a regular markdown document,\nbut with the ability to run code chunks.\n\nYou can run any of the code chunks by clicking the play button above the chunk in the RStudio Pro IDE.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/quarto/run-chunk.png)\n\nYou can render the entire document into a polished report to share, with the `Render` button.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/quarto/rstudio-render.png)\n\nThis will run all the code in the document from top to bottom in a new R session,\nand generate an HTML file, by default, for you to view and share.\n\n### Learn More about Quarto\n\nYou can learn more about Quarto here: \u003Chttps://quarto.org/\u003E,\nand the documentation for all the various Quarto outputs here: \u003Chttps://quarto.org/docs/guide/\u003E.\nQuarto works with R, Python, and Javascript Observable code out-of-the box,\nand is a great tool to communicate your data science analyses.\n\n\n## Shiny Application\n\nOne way to share our work and allow others to explore the heart failure dataset is to create an\ninteractive [Shiny](https://shiny.posit.co/) app. \n\nWe've prepared an example Shiny app in the directory:\n\u003Chttps://github.com/posit-dev/snowflake-posit-quickstart-r\u003E. Our app allows the user\nto explore different clinical metrics in one place.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/shiny/shiny.png)\n\nTo run the app, open `app.R` and then click the Run App button at the top of the script in the RStudio Pro IDE.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/analyze-data-with-r-using-posit-workbench-and-snowflake/shiny/run.png)\n\nIf you face package missing errors like this: `there is no package called ‘xyz’`, please install the required package by running `install.packages(\"xyz\")` in the R console.\n\nChange the metric in the sidebar to control which metric is plotted.\n\n### Learn More About Shiny\n\nYou can learn more about Shiny at: \u003Chttps://shiny.posit.co/\u003E.\nThis example uses Shiny for R, but\n[Shiny for Python](https://shiny.posit.co/py/)\nis also available!\n\nIf you're new to Shiny, you can try it online with\n[shinylive](https://shinylive.io/r/).\nIt too, comes in a [Python](https://shinylive.io/py) version.\n\n## Conclusion and Resources\n\nR is beloved by data scientists for its intuitive, concise syntax. You can now combine this syntax with the power and peace of mind of Snowflake. The Posit Workbench Native Application provides an IDE for R _within Snowflake_. You can then use R's existing database packages---`{DBI}`, `{odbc}`, `{dbplyr}`---to access your Snowflake databases.\n\n### What You Learned\n\n- How to create an R session within the RStudio Pro IDE that comes with the Posit Workbench Native App.\n- How to connect to your Snowflake data from R to create tables, visualizations, and more.\n- Build an RStudio Pro IDE environment to use within Snowflake.\n- Build a Quarto document that contains plots and tables built with R, using data stored in Snowflake.\n- Build an interactive Shiny Application built with R, using data stored in Snowflake.\n\n\n### Resources\n\n- [Source Code on GitHub](https://github.com/posit-dev/snowflake-posit-quickstart-r)\n- [More about Posit Workbench](https://posit.co/products/enterprise/workbench/)\n- [{tidyverse} package for data science in R](https://dbplyr.tidyverse.org/)\n- [{dbplyr} package for database connections](https://dbplyr.tidyverse.org/)\n- [{gt} package for tables](https://gt.rstudio.com/)\n- [Quarto for reproducible documents, reports, and data products](https://quarto.org/)\n- [Shiny for interactive dashboards and applications](https://shiny.posit.co/)\n- [Shinylive for serverless Shiny applications](https://shinylive.io/)\n- [Download Reference Architecture](/content/dam/snowflake-site/developers/2024/05/Data-Analysis-with-R-using-POSIT-workbench.pdf)\n- [Read the Blog](https://medium.com/snowflake/analyze-data-in-r-with-posit-workbench-and-snowflake-7b837b27337b)\n- [Watch the Demo](https://youtu.be/ZscKe1Vl7Ec?list=TLGGZ4Kuet5QT6AyMjA5MjAyNQ)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-c2d28b9865","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-409cf2814d",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-59fe495e54","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2025-12-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-3e7599e717","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances\u003C/span\u003E\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"}},":itemsOrder":["quickstart_last_modi","text"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-bf9c31d01d",":items":{},":itemsOrder":[],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"}},":itemsOrder":["contentfragment","flexible_column_cont"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"flexible_column_content_container_2":{"layout":"SIMPLE","id":"container-1fb619d7ad",":items":{"quickstart_table_of_":{"layout":"SIMPLE","id":"container-83540fa6b6","isDeveloperGuidesPage":false,":items":{"quickstart_table_of_":{"id":"quickstart-table-of-content-a9eda4c41a",":type":"snowflake-site/components/quickstart/quickstart-table-of-content","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/analyze-data-with-r-using-posit-workbench-and-snowflake","headings":["\u003Ch2\u003EOverview\u003C/h2\u003E","\u003Ch2\u003ESetup\u003C/h2\u003E","\u003Ch2\u003EAccess Snowflake data from R\u003C/h2\u003E","\u003Ch2\u003EWrite to a Snowflake database\u003C/h2\u003E","\u003Ch2\u003EPrepare data with \u003Ccode\u003E{dplyr}\u003C/code\u003E\u003C/h2\u003E","\u003Ch2\u003EVisualize Data with \u003Ccode\u003E{ggplot2}\u003C/code\u003E\u003C/h2\u003E","\u003Ch2\u003EMake publication-ready tables with \u003Ccode\u003E{gt}\u003C/code\u003E\u003C/h2\u003E","\u003Ch2\u003EBuild Reports and Dashboards with Quarto\u003C/h2\u003E","\u003Ch2\u003EShiny Application\u003C/h2\u003E","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E"]},"quickstart_button":{"id":"quickstart-button-fe25197f6f",":type":"snowflake-site/components/quickstart/quickstart-button","fragmentPath":"/content/dam/snowflake-site/en/content-fragments/quickstarts/analyze-data-with-r-using-posit-workbench-and-snowflake","appliedCssClassNames":"snowflake-responsive-component-top-padding-none"}},":itemsOrder":["quickstart_table_of_","quickstart_button"],":type":"snowflake-site/components/quickstart/quickstart-table-of-content/quickstart-table-of-content-container"}},":itemsOrder":["quickstart_table_of_"],":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container"},"isBlogPage":false,"isActiveTOC":false,":type":"snowflake-site/components/flexible-column-container"},"markup_editor":{"id":"markup-editor-eaf2ab8456","title":"Page CSS","cssContent":"#quickstart-template-main-flexible-container{padding:24px}#quickstart-template-main-flexible-container \u003E 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