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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 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AI","url":"https://www.snowflake.com/content/snowflake-site/global/en/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex","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}]},"flexible_column_cont":{"id":"flexible-column-container-7ef5b36b04","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-99d7bcc293",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"contentfragment":{"id":"contentfragment-9de25e7b29","paragraphs":["\u003Ch2\u003EOverview\u003C/h2\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003ELearn more about the Posit Package Manager that just entered Public Preview at \u003Ca href=\"https://posit.co/preview-link/node/14816/976bf2ac-6506-4ae1-99d7-62a19524b94b\"\u003Ethis blog\u003C/a\u003E and more about the Posit &amp; Snowflake partnership \u003Ca href=\"https://posit.co/preview-link/node/14811/6e8fefc5-a12f-40bc-8257-e141d80ae49f\"\u003Ehere\u003C/a\u003E.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003EIn this guide, we'll use the Posit Team Snowflake Native App to build an interactive dashboard that lets users explore Home Mortgage Disclosure Act (HMDA) data. In Posit Workbench, we'll use Positron Assistant and Databot with Snowflake Cortex AI to do some quick, yet powerful exploratory data analysis. Then, we'll create an interactive Shiny app and deploy it to Posit Connect in Snowflake with one-click publishing for easy sharing across your organization.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/guide-overview.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EWe'll also make sure the shared dashboard respects built-in Snowflake security and authorization settings, ensuring anyone who views the dashboard on\nConnect only sees data they have access to.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/overview-architecture.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EBy the end of this guide, we'll have a fully functional dashboard where users can explore the data through interactive visualizations and filters, viewing insights about loan amounts, key metrics (total loans, average loan amounts, and total loan volume), and year-over-year lending trends.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Will Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to securely connect to your Snowflake databases from Workbench and the Positron Pro IDE\u003C/li\u003E\u003Cli\u003EHow to leverage Cortex AI using Databot and Positron Assistant for exploratory data analysis\u003C/li\u003E\u003Cli\u003EHow to create an interactive Shiny dashboard for data exploration and use Positron Assistant to refine it\u003C/li\u003E\u003Cli\u003EHow to deploy and share the dashboard to Connect in Snowflake with one-click publishing\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You Will Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EAn interactive Shiny dashboard with dynamic visualizations and filters for exploring mortgage data, accessible to your team on Connect in the Posit Team Snowflake Native App\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA \u003Ca href=\"https://signup.snowflake.com/?utm_source=snowflake-devrel&amp;utm_medium=developer-guides&amp;utm_cta=developer-guides\"\u003ESnowflake account\u003C/a\u003E with Cortex AI enabled\u003C/li\u003E\u003Cli\u003EThe \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZMCB9S/posit-pbc-posit-team\"\u003EPosit Team Snowflake Native App\u003C/a\u003E must already be installed and configured by an administrator with the \u003Ccode\u003Eaccountadmin\u003C/code\u003E role. You must have been granted access to this app\u003C/li\u003E\u003Cli\u003EAccess to the \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free\"\u003E\u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E database\u003C/a\u003E in Snowsight\u003C/li\u003E\u003Cli\u003EFamiliarity with SQL and R\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EPhase 1: Set Up Your Environment\u003C/h2\u003E\n","\u003Ch3\u003EStep 1: Access the HMDA Dataset\u003C/h3\u003E\n","\u003Cp\u003EFor this analysis, we'll use the Home Mortgage Disclosure Act (HMDA) dataset from Snowflake's free public database. This dataset contains mortgage application and\norigination data collected under the HMDA, including information about loan types, applicant demographics, property characteristics, and loan outcomes across different geographic areas.\u003C/p\u003E\n","\u003Cp\u003EThe HMDA dataset we'll use is located at:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EDatabase:\u003C/strong\u003E \u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESchema:\u003C/strong\u003E \u003Ccode\u003EPUBLIC_DATA_FREE\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ETable:\u003C/strong\u003E \u003Ccode\u003EHOME_MORTGAGE_DISCLOSURE_ATTRIBUTES\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ETo verify you have access to this data, navigate to Snowsight and click \u003Cstrong\u003E+\u003C/strong\u003E &gt; \u003Cstrong\u003ESQL File\u003C/strong\u003E . Then run the following query:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT *\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES\nLIMIT 10;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou should see the first 10 rows of the HMDA dataset, which includes columns about mortgage applications, loan details, applicant information, and property characteristics.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you find that you do not have access to this dataset, please contact your account administrator.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EStep 2: Launch Posit Workbench from the Posit Team Native App\u003C/h3\u003E\n","\u003Cp\u003EWe can now start exploring the data using Posit Workbench. You can find Workbench within the Posit Team Native App, and use it to connect to your database.\u003C/p\u003E\n","\u003Ch4\u003EOpen the Posit Team Native App\u003C/h4\u003E\n","\u003Cp\u003EIn Snowsight, navigate to \u003Cstrong\u003EHorizon Catalog\u003C/strong\u003E &gt; \u003Cstrong\u003ECatalog\u003C/strong\u003E &gt; \u003Cstrong\u003EApps\u003C/strong\u003E &gt; Posit Team.\u003C/p\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ELaunch app\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/snowflake-launch-app.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you do not see the Posit Team Native App listed, contact your Snowflake account administrator to:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EInstall the \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZMCB9S/posit-pbc-posit-team\"\u003EPosit Team Snowflake Native App\u003C/a\u003E from the Marketplace\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.posit.co/partnerships/snowflake/posit-team/\"\u003EConfigure\u003C/a\u003E the Posit Team Native App and its products\u003C/li\u003E\u003Cli\u003EGrant you access to the app\u003C/li\u003E\u003C/ul\u003E\n\u003C/blockquote\u003E\n","\u003Ch4\u003EOpen Workbench from the Posit Team Native App\u003C/h4\u003E\n","\u003Cp\u003EFrom the Posit Team Native App, click \u003Cstrong\u003EPosit Workbench\u003C/strong\u003E.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/snowflake-launch-workbench.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EYou might be prompted to log in to Snowflake using your regular credentials or authentication method.\u003C/p\u003E\n","\u003Ch3\u003EStep 3: Create a Positron Pro Session\u003C/h3\u003E\n","\u003Cp\u003EWorkbench provides several IDEs, including Positron Pro, VS Code, RStudio Pro, and JupyterLab. For this analysis, we will use Positron, the next-generation\ndata science IDE built for Python and R. It combines the power of a full-featured IDE with interactive data science tools for Python and R.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWithin Workbench, click \u003Cstrong\u003E+ New Session\u003C/strong\u003E to launch a new session.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/workbench-start-new-session.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EWhen prompted, select the Positron Pro IDE. You can optionally give your session a unique name.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/workbench-create-new-session.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EUnder \u003Cstrong\u003ESession Credentials\u003C/strong\u003E, click the button with the Snowflake icon to sign in to Snowflake. Follow any sign in prompts.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/workbench-snowflake-login-success.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThis lets your Workbench session securely inherit your Snowflake role, which grants you access to data warehouses and compute resources using your existing Snowflake identity.\u003C/li\u003E\u003Cli\u003EFor more information on how Workbench uses your Snowflake credentials, see the \u003Ca href=\"https://docs.posit.co/ide/server-pro/user/posit-workbench/managed-credentials/snowflake.html\"\u003EWorkbench-managed Snowflake credentials\u003C/a\u003E section of the Workbench user guide.\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003EClick \u003Cstrong\u003ELaunch\u003C/strong\u003E to launch Positron Pro. If desired, you can check the \u003Cstrong\u003EAuto-join session\u003C/strong\u003E option to automatically open the IDE when the session is ready.\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EEnsure You Have the Necessary Extensions\u003C/h4\u003E\n","\u003Cp\u003EThe analysis contained in this guide requires you to have some extensions installed and enabled. You can verify that you have them from the \u003Ca href=\"https://docs.posit.co/ide/server-pro/user/positron/guide/extensions.html\"\u003EExtensions view\u003C/a\u003E.\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EThe \u003Ca href=\"https://open-vsx.org/extension/posit/shiny\"\u003EShiny extension\u003C/a\u003E supports the development of Shiny apps in Positron.\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.posit.co/connect/user/publishing-positron-vscode/\"\u003EPosit Publisher\u003C/a\u003E lets you start the deployment of projects to Connect from Positron with a single click.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EBoth of these extensions are included automatically in Positron as \u003Ca href=\"https://positron.posit.co/extensions.html#bootstrapped-extensions\"\u003Ebootstrapped extensions\u003C/a\u003E. Before we dive into our data analysis, let's make sure we have them installed and enabled:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen the Positron Extensions view: on the left-hand side of Positron Pro, click the Extensions icon in the activity bar to open the Extensions Marketplace.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESearch for &quot;Shiny&quot; or &quot;Posit Publisher&quot; to find the extensions. For example:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/positron-shiny-extension.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003EVerify that you have each extension:\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003EIf an extension is already installed and enabled, you will see a wheel icon.\u003C/li\u003E\u003Cli\u003EIf it is not already installed, click \u003Cstrong\u003EInstall\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003EIf you cannot install the extensions yourself or you find that they are disabled, ask your administrator for access.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EPhase 2: Prepare Your Development Environment\u003C/h2\u003E\n","\u003Ch3\u003EStep 4: Access this Guide's Materials\u003C/h3\u003E\n","\u003Cp\u003EThis guide will walk you through the steps contained in \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard\"\u003Ehttps://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard\u003C/a\u003E. To follow along, clone the repository by following the steps below.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen your home folder in Positron:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPress \u003Ccode\u003ECtrl/Cmd+Shift+P\u003C/code\u003E to open the Command Palette.\u003C/li\u003E\u003Cli\u003EType &quot;File: Open Folder&quot;, and press \u003Ccode\u003Eenter\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003ENavigate to your home directory and click \u003Cstrong\u003EOK\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClone the \u003Ca href=\"https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard\"\u003EGitHub repo\u003C/a\u003E by running the following command in a terminal:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-bash\"\u003Egit clone https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard/\n\u003C/code\u003E\u003C/pre\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EIf you don't already see a terminal open, open the Command Palette (\u003Ccode\u003ECtrl/Cmd+Shift+P\u003C/code\u003E), then select \u003Cstrong\u003ETerminal: Create New Terminal\u003C/strong\u003E to open one.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E This guide uses HTTPS for git authentication. Standard git authentication procedures apply.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen the cloned repository folder:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003EPress \u003Ccode\u003ECtrl/Cmd+Shift+P\u003C/code\u003E to open the Command Palette.\u003C/li\u003E\u003Cli\u003ESelect \u003Cstrong\u003EFile: Open Folder\u003C/strong\u003E.\u003C/li\u003E\u003Cli\u003ENavigate to \u003Ccode\u003Esnowflake-posit-build-deploy-interactive-dashboard\u003C/code\u003E and click \u003Cstrong\u003EOK\u003C/strong\u003E.\u003C/li\u003E\u003C/ul\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch4\u003EExplore Quarto\u003C/h4\u003E\n","\u003Cp\u003EBefore we dive into our data analysis, let's first discuss Quarto. We've documented the code for this guide in a Quarto document,\n\u003Ca href=\"https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard/blob/main/quarto.qmd\"\u003Equarto.qmd\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EA Quarto document can be thought of as a regular markdown document, but with the ability to run code chunks. You can run any of the code chunks by clicking the \u003Ccode\u003ERun Cell\u003C/code\u003E button above the chunk in Positron Pro.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/quarto-run-chunk.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ETo render and preview the entire document, click the \u003Ccode\u003EPreview\u003C/code\u003E button or run \u003Ccode\u003Equarto preview quarto.qmd\u003C/code\u003E from the terminal.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/quarto-preview.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003EThis will run all the code in the document from top to bottom and generate an HTML file, by default, for you to view and share.\u003C/p\u003E\n","\u003Cp\u003ELearn more about Quarto here: \u003Ca href=\"https://quarto.org/\"\u003Ehttps://quarto.org/\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EStep 5: Install R Packages from \u003Ccode\u003Erenv.lock\u003C/code\u003E\u003C/h3\u003E\n","\u003Cp\u003EOur analysis uses the following R packages: \u003Ca href=\"https://github.com/posit-dev/connectcreds\"\u003E{connectcreds}\u003C/a\u003E, \u003Ca href=\"https://dbi.r-dbi.org/\"\u003E{DBI}\u003C/a\u003E,\n\u003Ca href=\"https://dplyr.tidyverse.org/\"\u003E{dplyr}\u003C/a\u003E, \u003Ca href=\"https://dbplyr.tidyverse.org/articles/dbplyr.html\"\u003E{dbplyr}\u003C/a\u003E,\n\u003Ca href=\"https://ggplot2.tidyverse.org/\"\u003E{ggplot2}\u003C/a\u003E, \u003Ca href=\"https://scales.r-lib.org/\"\u003E{scales}\u003C/a\u003E, and \u003Ca href=\"https://shiny.posit.co/r/getstarted/shiny-basics/lesson1/\"\u003E{shiny}\u003C/a\u003E,\nplus more for advanced data analysis and deployment to Connect.\u003C/p\u003E\n","\u003Cp\u003EThese dependencies are all found in the file \u003Ccode\u003Erenv.lock\u003C/code\u003E. We can automatically install all dependencies by running the \u003Ccode\u003Erenv::restore()\u003C/code\u003E\nfunction. These dependencies will carry over to our Connect deployment as well, ensuring your content runs smoothly.\u003C/p\u003E\n","\u003Cp\u003EOpen the \u003Ccode\u003Equarto.qmd\u003C/code\u003E file in your current directory in Positron. Then run the following code chunk to install the \u003Ccode\u003Erenv\u003C/code\u003E package:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Einstall.packages(&quot;renv&quot;)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ENext, activate the \u003Ccode\u003Erenv\u003C/code\u003E environment and run:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Erenv::activate()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EFinally, you need to restore all required packages from the lockfile by running:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Erenv::restore()\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EYou might need to restart your R session once all dependencies are set. Restart your session by clicking the refresh icon above the R console.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/positron-restart-r.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch2\u003EPhase 3: Connect and Explore\u003C/h2\u003E\n","\u003Ch3\u003EStep 6: Connect to Snowflake Data\u003C/h3\u003E\n","\u003Cp\u003ENow that we have our Positron Pro session started with the necessary extensions and dependencies, we can connect to our data in Snowflake.\nThere are two ways we can do this: automatically by prompting Databot, or by running some code ourselves.\u003C/p\u003E\n","\u003Ch4\u003EUse Databot to Connect\u003C/h4\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003EImportant:\u003C/strong\u003E Databot is currently in research preview.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Cp\u003E\u003Ca href=\"https://positron.posit.co/databot.html\"\u003EDatabot\u003C/a\u003E is an AI assistant designed to dramatically accelerate exploratory data analysis for data scientists fluent in R or Python, allowing them to do in minutes what might usually take hours.\u003C/p\u003E\n","\u003Cp\u003EInstead of manually writing connection code, you can use Databot's built-in Snowflake skill to guide you through the connection process. This is especially helpful when you're working in Workbench, as Databot can automatically detect and use your Snowflake credentials.\u003C/p\u003E\n","\u003Cp\u003EDatabot runs with your available Cortex AI LLMs, keeping your data secure and private. Choose which model you would like to use by clicking the model in the lower right-hand corner of the Databot window.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/databot-choose-model.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E You must be running Databot v0.0.41 or higher to use it to connect to Snowflake data. To check your Databot version, click the Extensions icon on the left-hand side, and then search for &quot;Databot.&quot; Select the extension in the list to open its details page, where the installed version number is listed in the top-right corner.\u003C/p\u003E\n\u003C/blockquote\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EOpen the Command Palette (\u003Ccode\u003ECmd/Ctrl+Shift+P\u003C/code\u003E).\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EType &quot;Databot&quot; and select \u003Cstrong\u003EOpen Databot in Editor Panel\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EThe Databot panel will open, ready to analyze your mortgage data. Ensure you are still in your R session within the Databot dialog.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EIn the Databot panel, enter:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHelp me connect to Snowflake.\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDatabot will:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EUse your Cortex AI LLM.\u003C/li\u003E\u003Cli\u003EDetect if you're in Workbench with integrated authentication.\u003C/li\u003E\u003Cli\u003EProvide the appropriate connection code for your environment.\u003C/li\u003E\u003Cli\u003EGuide you through discovering available databases, schemas, and tables.\u003C/li\u003E\u003Cli\u003EHelp you explore Semantic Views if available.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EOnce connected, you can move on to the next section, which is to \u003Ca href=\"#step-7-explore-the-data-with-databot\"\u003Eexplore the data with Databot\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch4\u003EConnect with Code\u003C/h4\u003E\n","\u003Cp\u003EYou can also connect to your data using the \u003Ccode\u003Equarto.qmd\u003C/code\u003E file from \u003Ccode\u003Esnowflake-posit-build-deploy-interactive-dashboard\u003C/code\u003E. Click the \u003Cstrong\u003ERun Cell\u003C/strong\u003E button to run the next R code chunk in the \u003Ccode\u003Equarto.qmd\u003C/code\u003E file.\u003C/p\u003E\n","\u003Cp\u003EThe code uses {dplyr}, which provides an intuitive way to work with database tables in R. To ensure our connection works across different environments (development,\nWorkbench, and Connect), the code uses {dbplyr}, {DBI}, {odbc}, and {connectcreds} to create a flexible connection function.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003Elibrary(DBI)\nlibrary(odbc)\nlibrary(dplyr)\nlibrary(dbplyr)\nlibrary(connectcreds)\n\nget_connection &lt;- function() {\n  warehouse &lt;- &quot;DEFAULT_WH&quot;\n  database &lt;- &quot;SNOWFLAKE_PUBLIC_DATA_FREE&quot;\n  schema &lt;- &quot;PUBLIC_DATA_FREE&quot;\n  account &lt;- Sys.getenv(&quot;SNOWFLAKE_ACCOUNT&quot;)\n\n  con &lt;- DBI::dbConnect(\n    odbc::snowflake(),\n    account = account,\n    warehouse = warehouse,\n    database = database,\n    schema = schema\n  )\n\n  return(con)\n}\n\ncon &lt;- get_connection()\nmortgage_data &lt;- tbl(con, &quot;HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES&quot;)\nmessage(&quot;Successfully established secure connection to Snowflake!&quot;)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EWe have now used Workbench, Positron, and R to connect to the HMDA mortgage data in Snowflake's public dataset, all securely within Snowflake.\u003C/p\u003E\n","\u003Ch3\u003EStep 7: Explore the Data with Databot\u003C/h3\u003E\n","\u003Cp\u003EBefore building our dashboard, let's use Databot to explore the mortgage data. Unlike general coding assistants, Databot is purpose-built for EDA with rapid iteration of short code snippets that execute quickly.\u003C/p\u003E\n","\u003Cp\u003EWith your connection to the \u003Ccode\u003EHOME_MORTGAGE_DISCLOSURE_ATTRIBUTES\u003C/code\u003E table established (from the previous section), you can now ask Databot\nto explore the data. Try these prompts:\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EUnderstand the dataset structure:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EExplore `HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES` from the `SNOWFLAKE_PUBLIC_DATA_FREE` database and summarize its structure\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDatabot will generate and execute code to show you the columns, data types, and basic statistics.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EInvestigate specific patterns:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EExplore the relationship between loan amounts and property types\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDatabot will create visualizations and statistical summaries to help you understand lending patterns.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECompare trends across geography:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EHow do loan approval rates vary across different states?\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDatabot will analyze geographic patterns and create appropriate visualizations.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EIdentify data quality issues:\u003C/strong\u003E\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECheck for missing values and data quality issues in the mortgage data\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EDatabot will examine the dataset for completeness and potential problems.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECreate a Quarto report:\u003C/strong\u003E\u003C/p\u003E\n","\u003Cp\u003EOnce you are done exploring the data, you can create a Quarto report so you can reproduce the analysis another time or share the information with your team. Just ask Databot to create a report by calling\n\u003Ccode\u003E/report\u003C/code\u003E, or by entering:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003ECreate a Quarto report with your findings\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Ch2\u003EPhase 4: Build Your Dashboard\u003C/h2\u003E\n","\u003Ch3\u003EStep 8: Build the Dashboard\u003C/h3\u003E\n","\u003Cp\u003ENow that we've done some exploratory data analysis, let's create our interactive dashboard. We'll build a Shiny app that lets users explore the HMDA data through interactive filters and visualizations.\u003C/p\u003E\n","\u003Ch4\u003EBuild a Shiny App\u003C/h4\u003E\n","\u003Cp\u003ELet's run the following code, which will build a Shiny app to explore the data interactively.\nRunning this code will create a new \u003Ccode\u003Eapp.R\u003C/code\u003E file in the current directory that contains all of your already-established connection settings.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-r\"\u003E# Create app.R file with Shiny application code\napp_code &lt;- '\nlibrary(shiny)\nlibrary(DBI)\nlibrary(odbc)\nlibrary(dplyr)\nlibrary(dbplyr)\nlibrary(connectcreds)\nlibrary(ggplot2)\nlibrary(scales)\n\nget_connection &lt;- function() {\n  warehouse &lt;- &quot;DEFAULT_WH&quot;\n  database &lt;- &quot;SNOWFLAKE_PUBLIC_DATA_FREE&quot;\n  schema &lt;- &quot;PUBLIC_DATA_FREE&quot;\n  account &lt;- Sys.getenv(&quot;SNOWFLAKE_ACCOUNT&quot;)\n\n  con &lt;- DBI::dbConnect(\n    odbc::snowflake(),\n    account = account,\n    warehouse = warehouse,\n    database = database,\n    schema = schema\n  )\n\n  return(con)\n}\n\n# Define UI\nui &lt;- fluidPage(\n  titlePanel(&quot;HMDA Mortgage Data Explorer&quot;),\n  sidebarLayout(\n    sidebarPanel(\n      selectInput(&quot;loan_type&quot;, &quot;Select Loan Type:&quot;,\n                  choices = c(&quot;All&quot;, &quot;Conventional&quot;, &quot;FHA&quot;, &quot;VA&quot;, &quot;FSA/RHS&quot;)),\n      selectInput(&quot;state&quot;, &quot;Select State:&quot;,\n                  choices = c(&quot;All&quot;, &quot;California&quot;, &quot;Texas&quot;, &quot;Florida&quot;, &quot;New York&quot;, &quot;Pennsylvania&quot;)),\n      sliderInput(&quot;loan_amount&quot;, &quot;Loan Amount Range:&quot;,\n                  min = 0, max = 1000000, value = c(0, 500000),\n                  step = 50000)\n    ),\n    mainPanel(\n      h4(&quot;Loan Distribution:&quot;),\n      plotOutput(&quot;loan_plot&quot;),\n      h4(&quot;Key Metrics:&quot;),\n      verbatimTextOutput(&quot;key_metrics&quot;),\n      h4(&quot;Year Trends:&quot;),\n      plotOutput(&quot;year_plot&quot;)\n    )\n  )\n)\n\n# Define server\nserver &lt;- function(input, output, session) {\n  # Initialize connection\n  con &lt;- get_connection()\n  mortgage_data &lt;- tbl(con, &quot;HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES&quot;)\n\n  # Close connection when session ends\n  onStop(function() {\n    dbDisconnect(con)\n  })\n\n  # Reactive filtered data\n  filtered_data &lt;- reactive({\n    # Extract slider values into local variables before using in filter\n    min_amount &lt;- input$loan_amount[1]\n    max_amount &lt;- input$loan_amount[2]\n\n    data &lt;- mortgage_data %&gt;%\n      filter(LOAN_AMOUNT &gt;= min_amount,\n             LOAN_AMOUNT &lt;= max_amount)\n\n    if (input$loan_type != &quot;All&quot;) {\n      data &lt;- data %&gt;% filter(LOAN_TYPE == input$loan_type)\n    }\n\n    if (input$state != &quot;All&quot;) {\n      data &lt;- data %&gt;% filter(STATE_NAME == input$state)\n    }\n\n    # Limit to 10,000 rows for performance\n    result &lt;- data %&gt;%\n      head(10000) %&gt;%\n      collect()\n\n    result\n  })\n\n  output$loan_plot &lt;- renderPlot({\n    data &lt;- filtered_data()\n\n    if (nrow(data) == 0) {\n      plot.new()\n      text(0.5, 0.5, &quot;No data available for the selected filters&quot;, cex = 1.5)\n    } else {\n      ggplot(data, aes(x = LOAN_AMOUNT)) +\n        geom_histogram(bins = 30, fill = &quot;steelblue&quot;, color = &quot;white&quot;) +\n        scale_x_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +\n        theme_minimal() +\n        labs(title = paste(&quot;Distribution of Loan Amounts (&quot;, nrow(data), &quot;loans)&quot;),\n             x = &quot;Loan Amount&quot;,\n             y = &quot;Count&quot;)\n    }\n  })\n\n  output$key_metrics &lt;- renderPrint({\n    data &lt;- filtered_data()\n    if (nrow(data) == 0) {\n      cat(&quot;No data available for the selected filters&quot;)\n    } else {\n      total_loans &lt;- nrow(data)\n      avg_loan &lt;- mean(data$LOAN_AMOUNT, na.rm = TRUE)\n      total_volume &lt;- sum(data$LOAN_AMOUNT, na.rm = TRUE)\n\n      cat(sprintf(&quot;Total Loans: %s\\\\n&quot;, format(total_loans, big.mark = &quot;,&quot;)))\n      cat(sprintf(&quot;Average Loan Amount: %s\\\\n&quot;, dollar(avg_loan)))\n      cat(sprintf(&quot;Total Loan Volume: %s\\\\n&quot;, dollar(total_volume)))\n    }\n  })\n\n  output$year_plot &lt;- renderPlot({\n    data &lt;- filtered_data()\n\n    if (nrow(data) == 0) {\n      plot.new()\n      text(0.5, 0.5, &quot;No data available for the selected filters&quot;, cex = 1.5)\n    } else {\n      year_summary &lt;- data %&gt;%\n        group_by(YEAR) %&gt;%\n        summarise(count = n(), .groups = &quot;drop&quot;)\n\n      ggplot(year_summary, aes(x = factor(YEAR), y = count)) +\n        geom_col(fill = &quot;steelblue&quot;) +\n        theme_minimal() +\n        labs(title = &quot;Loan Count by Year&quot;,\n             x = &quot;Year&quot;,\n             y = &quot;Number of Loans&quot;) +\n        scale_y_continuous(labels = label_comma())\n    }\n  })\n}\n\n# Run the application\nshinyApp(ui = ui, server = server)\n'\n\n# Write the app code to app.R file\nwriteLines(app_code, &quot;app.R&quot;)\n\nmessage(&quot;Shiny app created successfully!&quot;)\nmessage(&quot;\\nRun the app with: shiny::runApp(\\&quot;app.R\\&quot;)&quot;)\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EOpen the new \u003Ccode\u003Eapp.R\u003C/code\u003E file and click the \u003Cstrong\u003ERun App\u003C/strong\u003E icon to view and use the Shiny app in the \u003Cstrong\u003EViewer\u003C/strong\u003E pane.\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/shiny-run-app.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Ch4\u003EEnhance Your Dashboard with Positron Assistant\u003C/h4\u003E\n","\u003Cp\u003EThe code above provided a simple Shiny app with basic functionality and appearance. You can use Positron Assistant with Cortex AI to enhance the dashboard with additional features, better styling, or more complex visualizations.\u003C/p\u003E\n","\u003Cp\u003ETo start a chat with Positron Assistant, click on the Positron Assistant icon in the toolbar:\u003C/p\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/positron-assistant.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n","\u003Cp\u003ESelect a model from the available options. For best results with Cortex AI, we recommend using Claude Sonnet 4.5 or later models.\u003C/p\u003E\n","\u003Cp\u003EYou can ask Positron Assistant to help improve your Shiny app. Try prompts like:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EAdd a second tab that shows approval rates by state\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003Eor\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode\u003EImprove the visual styling of the dashboard with better colors and layout\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EContinue to make changes to the app with Positron Assistant until you are happy with how it looks and behaves.\u003C/p\u003E\n","\u003Ch2\u003EPhase 5: Deploy and Share\u003C/h2\u003E\n","\u003Ch3\u003EStep 9: Obtain your Connect API Key\u003C/h3\u003E\n","\u003Cp\u003EBefore we start the process to deploy the dashboard to Connect, you need to create and save an API key.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EAccess Posit Connect from the Posit Team Snowflake Native App.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/snowflake-launch-connect.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003EClick on your account in the upper right-hand corner of Connect, and then click \u003Cstrong\u003EManage Your API Keys\u003C/strong\u003E.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E&lt;img src=&quot;assets/connect-manage-api-keys.png&quot; height=&quot;400px&quot;&gt;\u003C/p\u003E\n\u003Col start=\"3\"\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003E+ New API Key\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECreate a name for your API key, and select the \u003Cstrong\u003EPublisher\u003C/strong\u003E role permission.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EClick \u003Cstrong\u003ECreate Key\u003C/strong\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ECopy the key to somewhere secure. You will need it when deploying your content in the next step.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Ch3\u003EStep 10: Deploy to Posit Connect\u003C/h3\u003E\n","\u003Cp\u003ENow that your dashboard works locally and looks how you'd like it to, let's deploy it to Connect so your team can access it. Deployment is an incredibly simple process. Because Workbench and Connect run within the same Native App, the complex network and authentication challenges are eliminated.\u003C/p\u003E\n","\u003Cp\u003EOnce you click deploy in Positron, Connect handles dependency management and ensures your code runs successfully as a deployed artifact.\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EIn the Positron tool menu, click the Posit Publisher icon.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/posit-publisher.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"2\"\u003E\u003Cli\u003E\n","\u003Cp\u003EUnder \u003Cstrong\u003EDeployment\u003C/strong\u003E, click the \u003Cstrong\u003ESelect...\u003C/strong\u003E dropdown. Since this is the first time we've deployed this content,\nyou'll be prompted to create a new deployment. Select the \u003Ccode\u003Eapp.R\u003C/code\u003E file to deploy.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the Connect deployment or create a new one with the URL: \u003Ccode\u003Ehttps://connect/\u003C/code\u003E.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E\u003Cimg src=\"https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/deploy-connect.png?v=ff263759\" alt=\"\"\u003E\u003C/p\u003E\n\u003Col start=\"4\"\u003E\u003Cli\u003E\n","\u003Cp\u003EEnter the API key you created in the steps above.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ESelect the files to include:\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cul\u003E\u003Cli\u003E[x] \u003Ccode\u003Eapp.R\u003C/code\u003E\u003C/li\u003E\u003Cli\u003E[x] \u003Ccode\u003Erenv.lock\u003C/code\u003E\u003C/li\u003E\u003C/ul\u003E\n\u003Col start=\"6\"\u003E\u003Cli\u003EClick \u003Cstrong\u003EIntegration requests\u003C/strong\u003E &gt; \u003Cstrong\u003E+\u003C/strong\u003E &gt; the available Snowflake integration\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003E&lt;img src=&quot;assets/publisher-snowflake-integration.png&quot; height=&quot;400px&quot;&gt;\u003C/p\u003E\n\u003Col start=\"7\"\u003E\u003Cli\u003EClick the \u003Cstrong\u003EDeploy your project\u003C/strong\u003E button.\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EFor more information on the deployment process, see \u003Ca href=\"https://docs.posit.co/connect/user/publishing-positron-vscode/\"\u003EPublishing from VS Code or Positron\u003C/a\u003E in the Connect user guide.\u003C/p\u003E\n","\u003Ch3\u003EStep 11: Access Your Dashboard on Connect\u003C/h3\u003E\n\u003Col\u003E\u003Cli\u003E\n","\u003Cp\u003EGo back to Connect in the Posit Team Native App. Click the \u003Cstrong\u003EContent\u003C/strong\u003E tab, and then your deployed dashboard.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETest your deployed dashboard to ensure it's working as expected.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003ETo share your dashboard with others, click the \u003Cstrong\u003ESettings\u003C/strong\u003E pane in the upper-right-hand corner of the content page.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EIn the \u003Cstrong\u003EContent URL\u003C/strong\u003E section, copy the URL.\u003C/p\u003E\n\u003C/li\u003E\u003Cli\u003E\n","\u003Cp\u003EShare this URL with your team.\u003C/p\u003E\n\u003C/li\u003E\u003C/ol\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003E\u003Cstrong\u003ENote:\u003C/strong\u003E Since you added the Snowflake integration to your content before deploying it, your dashboard automatically uses viewer-level authentication. Each user who accesses the dashboard will connect with their own Snowflake credentials, ensuring they only see data they have permission to access.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Ch3\u003EOverview\u003C/h3\u003E\n","\u003Cp\u003EIn this guide, we connected securely to Snowflake data using Posit Workbench in the Posit Team Native App, explored the data with Databot powered by Cortex AI, developed a Shiny application using Positron Assistant,\nand deployed the dashboard to Posit Connect where your team can access it securely.\u003C/p\u003E\n","\u003Cp\u003EThe steps we took along the way easily transfer to other datasets and use cases. This pattern of combining Snowflake's data platform and Cortex AI with Posit's authoring and publishing tools enables you to build and share powerful data applications quickly.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EHow to access Snowflake public datasets using Workbench, Positron, and R\u003C/li\u003E\u003Cli\u003EHow to use Databot with Snowflake Cortex AI for exploratory data analysis\u003C/li\u003E\u003Cli\u003EHow to build with multiple environments in mind using connection code that works seamlessly in Workbench and Connect\u003C/li\u003E\u003Cli\u003EHow to implement viewer-level authentication to ensure each user connects to Snowflake with their own credentials\u003C/li\u003E\u003Cli\u003EHow to create interactive Shiny dashboards with dynamic filters and visualizations for data exploration\u003C/li\u003E\u003Cli\u003EHow to publish Shiny applications to Connect with one-click deployment from Workbench\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EResources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Cortex AI Documentation\u003C/strong\u003E: \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions\"\u003ECortex AI Functions\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ESnowflake Public Data\u003C/strong\u003E: \u003Ca href=\"https://docs.snowflake.com/en/user-guide/data-marketplace\"\u003EUsing Snowflake Data Marketplace\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPositron\u003C/strong\u003E: \u003Ca href=\"https://positron.posit.co/\"\u003EPositron Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EDatabot\u003C/strong\u003E: \u003Ca href=\"https://positron.posit.co/databot.html\"\u003EDatabot Documentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EShiny for R\u003C/strong\u003E: \u003Ca href=\"https://shiny.posit.co/\"\u003EDocumentation\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPosit Workbench\u003C/strong\u003E: \u003Ca href=\"https://docs.posit.co/ide/server-pro/user/\"\u003EUser Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EPosit Connect\u003C/strong\u003E: \u003Ca href=\"https://docs.posit.co/connect/user/\"\u003EUser Guide\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003ERelated Guides\u003C/strong\u003E: \u003Ca href=\"https://quickstarts.snowflake.com/guide/analyze-data-with-python-using-posit-team/\"\u003EAnalyze Data with Python Using Posit Team\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Build and Deploy an Interactive Shiny Dashboard with the Posit Team Native App and Snowflake Cortex AI","isDeveloperGuidesPage":false,":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\n\u003E Learn more about the Posit Package Manager that just entered Public Preview at [this blog](https://posit.co/preview-link/node/14816/976bf2ac-6506-4ae1-99d7-62a19524b94b) and more about the Posit & Snowflake partnership [here](https://posit.co/preview-link/node/14811/6e8fefc5-a12f-40bc-8257-e141d80ae49f).\n\nIn this guide, we'll use the Posit Team Snowflake Native App to build an interactive dashboard that lets users explore Home Mortgage Disclosure Act (HMDA) data. In Posit Workbench, we'll use Positron Assistant and Databot with Snowflake Cortex AI to do some quick, yet powerful exploratory data analysis. Then, we'll create an interactive Shiny app and deploy it to Posit Connect in Snowflake with one-click publishing for easy sharing across your organization.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/guide-overview.png?v=ff263759)\n\nWe'll also make sure the shared dashboard respects built-in Snowflake security and authorization settings, ensuring anyone who views the dashboard on\nConnect only sees data they have access to.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/overview-architecture.png?v=ff263759)\n\nBy the end of this guide, we'll have a fully functional dashboard where users can explore the data through interactive visualizations and filters, viewing insights about loan amounts, key metrics (total loans, average loan amounts, and total loan volume), and year-over-year lending trends.\n\n### What You Will Learn\n\n- How to securely connect to your Snowflake databases from Workbench and the Positron Pro IDE\n- How to leverage Cortex AI using Databot and Positron Assistant for exploratory data analysis\n- How to create an interactive Shiny dashboard for data exploration and use Positron Assistant to refine it\n- How to deploy and share the dashboard to Connect in Snowflake with one-click publishing\n\n### What You Will Build\n\n- An interactive Shiny dashboard with dynamic visualizations and filters for exploring mortgage data, accessible to your team on Connect in the Posit Team Snowflake Native App\n\n### Prerequisites\n\n- A [Snowflake account](https://signup.snowflake.com/?utm_source=snowflake-devrel&utm_medium=developer-guides&utm_cta=developer-guides) with Cortex AI enabled\n- The [Posit Team Snowflake Native App](https://app.snowflake.com/marketplace/listing/GZTSZMCB9S/posit-pbc-posit-team) must already be installed and configured by an administrator with the `accountadmin` role. You must have been granted access to this app\n- Access to the [`SNOWFLAKE_PUBLIC_DATA_FREE` database](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255/snowflake-public-data-products-snowflake-public-data-free) in Snowsight\n- Familiarity with SQL and R\n\n## Phase 1: Set Up Your Environment\n\n### Step 1: Access the HMDA Dataset\n\nFor this analysis, we'll use the Home Mortgage Disclosure Act (HMDA) dataset from Snowflake's free public database. This dataset contains mortgage application and\norigination data collected under the HMDA, including information about loan types, applicant demographics, property characteristics, and loan outcomes across different geographic areas.\n\nThe HMDA dataset we'll use is located at:\n\n- **Database:** `SNOWFLAKE_PUBLIC_DATA_FREE`\n- **Schema:** `PUBLIC_DATA_FREE`\n- **Table:** `HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES`\n\nTo verify you have access to this data, navigate to Snowsight and click **+** \u003E **SQL File** . Then run the following query:\n\n```sql\nSELECT *\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES\nLIMIT 10;\n```\n\nYou should see the first 10 rows of the HMDA dataset, which includes columns about mortgage applications, loan details, applicant information, and property characteristics.\n\n\u003E If you find that you do not have access to this dataset, please contact your account administrator.\n\n### Step 2: Launch Posit Workbench from the Posit Team Native App\n\nWe can now start exploring the data using Posit Workbench. You can find Workbench within the Posit Team Native App, and use it to connect to your database.\n\n#### Open the Posit Team Native App\n\nIn Snowsight, navigate to **Horizon Catalog** \u003E **Catalog** \u003E **Apps** \u003E Posit Team.\n\nClick **Launch app**.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/snowflake-launch-app.png?v=ff263759)\n\n\u003E If you do not see the Posit Team Native App listed, contact your Snowflake account administrator to:\n\u003E - Install the [Posit Team Snowflake Native App](https://app.snowflake.com/marketplace/listing/GZTSZMCB9S/posit-pbc-posit-team) from the Marketplace\n\u003E - [Configure](https://docs.posit.co/partnerships/snowflake/posit-team/) the Posit Team Native App and its products\n\u003E - Grant you access to the app\n\n#### Open Workbench from the Posit Team Native App\n\nFrom the Posit Team Native App, click **Posit Workbench**.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/snowflake-launch-workbench.png?v=ff263759)\n\nYou might be prompted to log in to Snowflake using your regular credentials or authentication method.\n\n### Step 3: Create a Positron Pro Session\n\nWorkbench provides several IDEs, including Positron Pro, VS Code, RStudio Pro, and JupyterLab. For this analysis, we will use Positron, the next-generation\ndata science IDE built for Python and R. It combines the power of a full-featured IDE with interactive data science tools for Python and R.\n\n1. Within Workbench, click **+ New Session** to launch a new session.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/workbench-start-new-session.png?v=ff263759)\n\n2. When prompted, select the Positron Pro IDE. You can optionally give your session a unique name.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/workbench-create-new-session.png?v=ff263759)\n\n3. Under **Session Credentials**, click the button with the Snowflake icon to sign in to Snowflake. Follow any sign in prompts.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/workbench-snowflake-login-success.png?v=ff263759)\n\n  - This lets your Workbench session securely inherit your Snowflake role, which grants you access to data warehouses and compute resources using your existing Snowflake identity.\n  - For more information on how Workbench uses your Snowflake credentials, see the [Workbench-managed Snowflake credentials](https://docs.posit.co/ide/server-pro/user/posit-workbench/managed-credentials/snowflake.html) section of the Workbench user guide.\n\n4. Click **Launch** to launch Positron Pro. If desired, you can check the **Auto-join session** option to automatically open the IDE when the session is ready.\n\n#### Ensure You Have the Necessary Extensions\n\nThe analysis contained in this guide requires you to have some extensions installed and enabled. You can verify that you have them from the [Extensions view](https://docs.posit.co/ide/server-pro/user/positron/guide/extensions.html).\n\n- The [Shiny extension](https://open-vsx.org/extension/posit/shiny) supports the development of Shiny apps in Positron.\n- [Posit Publisher](https://docs.posit.co/connect/user/publishing-positron-vscode/) lets you start the deployment of projects to Connect from Positron with a single click.\n\nBoth of these extensions are included automatically in Positron as [bootstrapped extensions](https://positron.posit.co/extensions.html#bootstrapped-extensions). Before we dive into our data analysis, let's make sure we have them installed and enabled:\n\n1. Open the Positron Extensions view: on the left-hand side of Positron Pro, click the Extensions icon in the activity bar to open the Extensions Marketplace.\n\n2. Search for \"Shiny\" or \"Posit Publisher\" to find the extensions. For example:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/positron-shiny-extension.png?v=ff263759)\n\n3. Verify that you have each extension:\n  - If an extension is already installed and enabled, you will see a wheel icon.\n  - If it is not already installed, click **Install**.\n  - If you cannot install the extensions yourself or you find that they are disabled, ask your administrator for access.\n\n## Phase 2: Prepare Your Development Environment\n\n### Step 4: Access this Guide's Materials\n\nThis guide will walk you through the steps contained in \u003Chttps://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard\u003E. To follow along, clone the repository by following the steps below.\n\n1. Open your home folder in Positron:\n\n   - Press `Ctrl/Cmd+Shift+P` to open the Command Palette.\n   - Type \"File: Open Folder\", and press `enter`.\n   - Navigate to your home directory and click **OK**.\n\n2. Clone the [GitHub repo](https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard) by running the following command in a terminal:\n\n   ```bash\n   git clone https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard/\n   ```\n\n   \u003E If you don't already see a terminal open, open the Command Palette (`Ctrl/Cmd+Shift+P`), then select **Terminal: Create New Terminal** to open one.\n\n   \u003E **Note:** This guide uses HTTPS for git authentication. Standard git authentication procedures apply.\n\n3. Open the cloned repository folder:\n\n   - Press `Ctrl/Cmd+Shift+P` to open the Command Palette.\n   - Select **File: Open Folder**.\n   - Navigate to `snowflake-posit-build-deploy-interactive-dashboard` and click **OK**.\n\n#### Explore Quarto\n\nBefore we dive into our data analysis, let's first discuss Quarto. We've documented the code for this guide in a Quarto document,\n[quarto.qmd](https://github.com/posit-dev/snowflake-posit-build-deploy-interactive-dashboard/blob/main/quarto.qmd).\n\nA Quarto document can be thought of as a regular markdown document, but with the ability to run code chunks. You can run any of the code chunks by clicking the `Run Cell` button above the chunk in Positron Pro.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/quarto-run-chunk.png?v=ff263759)\n\nTo render and preview the entire document, click the `Preview` button or run `quarto preview quarto.qmd` from the terminal.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/quarto-preview.png?v=ff263759)\n\nThis will run all the code in the document from top to bottom and generate an HTML file, by default, for you to view and share.\n\nLearn more about Quarto here: \u003Chttps://quarto.org/\u003E.\n\n### Step 5: Install R Packages from `renv.lock`\n\nOur analysis uses the following R packages: [{connectcreds}](https://github.com/posit-dev/connectcreds), [{DBI}](https://dbi.r-dbi.org/),\n[{dplyr}](https://dplyr.tidyverse.org/), [{dbplyr}](https://dbplyr.tidyverse.org/articles/dbplyr.html),\n[{ggplot2}](https://ggplot2.tidyverse.org/), [{scales}](https://scales.r-lib.org/), and [{shiny}](https://shiny.posit.co/r/getstarted/shiny-basics/lesson1/),\nplus more for advanced data analysis and deployment to Connect.\n\nThese dependencies are all found in the file `renv.lock`. We can automatically install all dependencies by running the `renv::restore()`\nfunction. These dependencies will carry over to our Connect deployment as well, ensuring your content runs smoothly.\n\nOpen the `quarto.qmd` file in your current directory in Positron. Then run the following code chunk to install the `renv` package:\n\n```r\ninstall.packages(\"renv\")\n```\n\nNext, activate the `renv` environment and run:\n\n```r\nrenv::activate()\n```\n\nFinally, you need to restore all required packages from the lockfile by running:\n\n```r\nrenv::restore()\n```\n\nYou might need to restart your R session once all dependencies are set. Restart your session by clicking the refresh icon above the R console.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/positron-restart-r.png?v=ff263759)\n\n## Phase 3: Connect and Explore\n\n### Step 6: Connect to Snowflake Data\n\nNow that we have our Positron Pro session started with the necessary extensions and dependencies, we can connect to our data in Snowflake.\nThere are two ways we can do this: automatically by prompting Databot, or by running some code ourselves.\n\n#### Use Databot to Connect\n\n   \u003E **Important:** Databot is currently in research preview.\n\n[Databot](https://positron.posit.co/databot.html) is an AI assistant designed to dramatically accelerate exploratory data analysis for data scientists fluent in R or Python, allowing them to do in minutes what might usually take hours.\n\nInstead of manually writing connection code, you can use Databot's built-in Snowflake skill to guide you through the connection process. This is especially helpful when you're working in Workbench, as Databot can automatically detect and use your Snowflake credentials.\n\nDatabot runs with your available Cortex AI LLMs, keeping your data secure and private. Choose which model you would like to use by clicking the model in the lower right-hand corner of the Databot window.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/databot-choose-model.png?v=ff263759)\n\n  \u003E **Note:** You must be running Databot v0.0.41 or higher to use it to connect to Snowflake data. To check your Databot version, click the Extensions icon on the left-hand side, and then search for \"Databot.\" Select the extension in the list to open its details page, where the installed version number is listed in the top-right corner.\n\n1. Open the Command Palette (`Cmd/Ctrl+Shift+P`).\n\n2. Type \"Databot\" and select **Open Databot in Editor Panel**.\n\n3. The Databot panel will open, ready to analyze your mortgage data. Ensure you are still in your R session within the Databot dialog.\n\nIn the Databot panel, enter:\n\n```\nHelp me connect to Snowflake.\n```\n\nDatabot will:\n\n1. Use your Cortex AI LLM.\n2. Detect if you're in Workbench with integrated authentication.\n3. Provide the appropriate connection code for your environment.\n4. Guide you through discovering available databases, schemas, and tables.\n5. Help you explore Semantic Views if available.\n\nOnce connected, you can move on to the next section, which is to [explore the data with Databot](#step-7-explore-the-data-with-databot).\n\n#### Connect with Code\n\nYou can also connect to your data using the `quarto.qmd` file from `snowflake-posit-build-deploy-interactive-dashboard`. Click the **Run Cell** button to run the next R code chunk in the `quarto.qmd` file.\n\nThe code uses {dplyr}, which provides an intuitive way to work with database tables in R. To ensure our connection works across different environments (development,\nWorkbench, and Connect), the code uses {dbplyr}, {DBI}, {odbc}, and {connectcreds} to create a flexible connection function.\n\n```r\nlibrary(DBI)\nlibrary(odbc)\nlibrary(dplyr)\nlibrary(dbplyr)\nlibrary(connectcreds)\n\nget_connection \u003C- function() {\n  warehouse \u003C- \"DEFAULT_WH\"\n  database \u003C- \"SNOWFLAKE_PUBLIC_DATA_FREE\"\n  schema \u003C- \"PUBLIC_DATA_FREE\"\n  account \u003C- Sys.getenv(\"SNOWFLAKE_ACCOUNT\")\n\n  con \u003C- DBI::dbConnect(\n    odbc::snowflake(),\n    account = account,\n    warehouse = warehouse,\n    database = database,\n    schema = schema\n  )\n\n  return(con)\n}\n\ncon \u003C- get_connection()\nmortgage_data \u003C- tbl(con, \"HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES\")\nmessage(\"Successfully established secure connection to Snowflake!\")\n```\n\nWe have now used Workbench, Positron, and R to connect to the HMDA mortgage data in Snowflake's public dataset, all securely within Snowflake.\n\n### Step 7: Explore the Data with Databot\n\nBefore building our dashboard, let's use Databot to explore the mortgage data. Unlike general coding assistants, Databot is purpose-built for EDA with rapid iteration of short code snippets that execute quickly.\n\nWith your connection to the `HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES` table established (from the previous section), you can now ask Databot\nto explore the data. Try these prompts:\n\n**Understand the dataset structure:**\n\n```\nExplore `HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES` from the `SNOWFLAKE_PUBLIC_DATA_FREE` database and summarize its structure\n```\n\nDatabot will generate and execute code to show you the columns, data types, and basic statistics.\n\n**Investigate specific patterns:**\n\n```\nExplore the relationship between loan amounts and property types\n```\n\nDatabot will create visualizations and statistical summaries to help you understand lending patterns.\n\n**Compare trends across geography:**\n\n```\nHow do loan approval rates vary across different states?\n```\n\nDatabot will analyze geographic patterns and create appropriate visualizations.\n\n**Identify data quality issues:**\n\n```\nCheck for missing values and data quality issues in the mortgage data\n```\n\nDatabot will examine the dataset for completeness and potential problems.\n\n**Create a Quarto report:**\n\nOnce you are done exploring the data, you can create a Quarto report so you can reproduce the analysis another time or share the information with your team. Just ask Databot to create a report by calling\n`/report`, or by entering:\n\n```\nCreate a Quarto report with your findings\n```\n\n## Phase 4: Build Your Dashboard\n\n### Step 8: Build the Dashboard\n\nNow that we've done some exploratory data analysis, let's create our interactive dashboard. We'll build a Shiny app that lets users explore the HMDA data through interactive filters and visualizations.\n\n#### Build a Shiny App\n\nLet's run the following code, which will build a Shiny app to explore the data interactively.\nRunning this code will create a new `app.R` file in the current directory that contains all of your already-established connection settings.\n\n```r\n# Create app.R file with Shiny application code\napp_code \u003C- '\nlibrary(shiny)\nlibrary(DBI)\nlibrary(odbc)\nlibrary(dplyr)\nlibrary(dbplyr)\nlibrary(connectcreds)\nlibrary(ggplot2)\nlibrary(scales)\n\nget_connection \u003C- function() {\n  warehouse \u003C- \"DEFAULT_WH\"\n  database \u003C- \"SNOWFLAKE_PUBLIC_DATA_FREE\"\n  schema \u003C- \"PUBLIC_DATA_FREE\"\n  account \u003C- Sys.getenv(\"SNOWFLAKE_ACCOUNT\")\n\n  con \u003C- DBI::dbConnect(\n    odbc::snowflake(),\n    account = account,\n    warehouse = warehouse,\n    database = database,\n    schema = schema\n  )\n\n  return(con)\n}\n\n# Define UI\nui \u003C- fluidPage(\n  titlePanel(\"HMDA Mortgage Data Explorer\"),\n  sidebarLayout(\n    sidebarPanel(\n      selectInput(\"loan_type\", \"Select Loan Type:\",\n                  choices = c(\"All\", \"Conventional\", \"FHA\", \"VA\", \"FSA/RHS\")),\n      selectInput(\"state\", \"Select State:\",\n                  choices = c(\"All\", \"California\", \"Texas\", \"Florida\", \"New York\", \"Pennsylvania\")),\n      sliderInput(\"loan_amount\", \"Loan Amount Range:\",\n                  min = 0, max = 1000000, value = c(0, 500000),\n                  step = 50000)\n    ),\n    mainPanel(\n      h4(\"Loan Distribution:\"),\n      plotOutput(\"loan_plot\"),\n      h4(\"Key Metrics:\"),\n      verbatimTextOutput(\"key_metrics\"),\n      h4(\"Year Trends:\"),\n      plotOutput(\"year_plot\")\n    )\n  )\n)\n\n# Define server\nserver \u003C- function(input, output, session) {\n  # Initialize connection\n  con \u003C- get_connection()\n  mortgage_data \u003C- tbl(con, \"HOME_MORTGAGE_DISCLOSURE_ATTRIBUTES\")\n\n  # Close connection when session ends\n  onStop(function() {\n    dbDisconnect(con)\n  })\n\n  # Reactive filtered data\n  filtered_data \u003C- reactive({\n    # Extract slider values into local variables before using in filter\n    min_amount \u003C- input$loan_amount[1]\n    max_amount \u003C- input$loan_amount[2]\n\n    data \u003C- mortgage_data %\u003E%\n      filter(LOAN_AMOUNT \u003E= min_amount,\n             LOAN_AMOUNT \u003C= max_amount)\n\n    if (input$loan_type != \"All\") {\n      data \u003C- data %\u003E% filter(LOAN_TYPE == input$loan_type)\n    }\n\n    if (input$state != \"All\") {\n      data \u003C- data %\u003E% filter(STATE_NAME == input$state)\n    }\n\n    # Limit to 10,000 rows for performance\n    result \u003C- data %\u003E%\n      head(10000) %\u003E%\n      collect()\n\n    result\n  })\n\n  output$loan_plot \u003C- renderPlot({\n    data \u003C- filtered_data()\n\n    if (nrow(data) == 0) {\n      plot.new()\n      text(0.5, 0.5, \"No data available for the selected filters\", cex = 1.5)\n    } else {\n      ggplot(data, aes(x = LOAN_AMOUNT)) +\n        geom_histogram(bins = 30, fill = \"steelblue\", color = \"white\") +\n        scale_x_continuous(labels = label_dollar(scale_cut = cut_short_scale())) +\n        theme_minimal() +\n        labs(title = paste(\"Distribution of Loan Amounts (\", nrow(data), \"loans)\"),\n             x = \"Loan Amount\",\n             y = \"Count\")\n    }\n  })\n\n  output$key_metrics \u003C- renderPrint({\n    data \u003C- filtered_data()\n    if (nrow(data) == 0) {\n      cat(\"No data available for the selected filters\")\n    } else {\n      total_loans \u003C- nrow(data)\n      avg_loan \u003C- mean(data$LOAN_AMOUNT, na.rm = TRUE)\n      total_volume \u003C- sum(data$LOAN_AMOUNT, na.rm = TRUE)\n\n      cat(sprintf(\"Total Loans: %s\\\\n\", format(total_loans, big.mark = \",\")))\n      cat(sprintf(\"Average Loan Amount: %s\\\\n\", dollar(avg_loan)))\n      cat(sprintf(\"Total Loan Volume: %s\\\\n\", dollar(total_volume)))\n    }\n  })\n\n  output$year_plot \u003C- renderPlot({\n    data \u003C- filtered_data()\n\n    if (nrow(data) == 0) {\n      plot.new()\n      text(0.5, 0.5, \"No data available for the selected filters\", cex = 1.5)\n    } else {\n      year_summary \u003C- data %\u003E%\n        group_by(YEAR) %\u003E%\n        summarise(count = n(), .groups = \"drop\")\n\n      ggplot(year_summary, aes(x = factor(YEAR), y = count)) +\n        geom_col(fill = \"steelblue\") +\n        theme_minimal() +\n        labs(title = \"Loan Count by Year\",\n             x = \"Year\",\n             y = \"Number of Loans\") +\n        scale_y_continuous(labels = label_comma())\n    }\n  })\n}\n\n# Run the application\nshinyApp(ui = ui, server = server)\n'\n\n# Write the app code to app.R file\nwriteLines(app_code, \"app.R\")\n\nmessage(\"Shiny app created successfully!\")\nmessage(\"\\nRun the app with: shiny::runApp(\\\"app.R\\\")\")\n```\n\nOpen the new `app.R` file and click the **Run App** icon to view and use the Shiny app in the **Viewer** pane.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/shiny-run-app.png?v=ff263759)\n\n#### Enhance Your Dashboard with Positron Assistant\n\nThe code above provided a simple Shiny app with basic functionality and appearance. You can use Positron Assistant with Cortex AI to enhance the dashboard with additional features, better styling, or more complex visualizations.\n\nTo start a chat with Positron Assistant, click on the Positron Assistant icon in the toolbar:\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/positron-assistant.png?v=ff263759)\n\nSelect a model from the available options. For best results with Cortex AI, we recommend using Claude Sonnet 4.5 or later models.\n\nYou can ask Positron Assistant to help improve your Shiny app. Try prompts like:\n\n```\nAdd a second tab that shows approval rates by state\n```\n\nor\n\n```\nImprove the visual styling of the dashboard with better colors and layout\n```\n\nContinue to make changes to the app with Positron Assistant until you are happy with how it looks and behaves.\n\n## Phase 5: Deploy and Share\n\n### Step 9: Obtain your Connect API Key\n\nBefore we start the process to deploy the dashboard to Connect, you need to create and save an API key.\n\n1. Access Posit Connect from the Posit Team Snowflake Native App.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/snowflake-launch-connect.png?v=ff263759)\n\n2. Click on your account in the upper right-hand corner of Connect, and then click **Manage Your API Keys**.\n\n\u003Cimg src=\"assets/connect-manage-api-keys.png\" height=\"400px\"\u003E\n\n3. Click **+ New API Key**.\n\n4. Create a name for your API key, and select the **Publisher** role permission.\n\n5. Click **Create Key**.\n\n6. Copy the key to somewhere secure. You will need it when deploying your content in the next step.\n\n### Step 10: Deploy to Posit Connect\n\nNow that your dashboard works locally and looks how you'd like it to, let's deploy it to Connect so your team can access it. Deployment is an incredibly simple process. Because Workbench and Connect run within the same Native App, the complex network and authentication challenges are eliminated.\n\nOnce you click deploy in Positron, Connect handles dependency management and ensures your code runs successfully as a deployed artifact.\n\n1. In the Positron tool menu, click the Posit Publisher icon.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/posit-publisher.png?v=ff263759)\n\n2. Under **Deployment**, click the **Select...** dropdown. Since this is the first time we've deployed this content,\nyou'll be prompted to create a new deployment. Select the `app.R` file to deploy.\n\n3. Select the Connect deployment or create a new one with the URL: `https://connect/`.\n\n![](https://www.snowflake.com/content/dam/snowflake-site/developers/guides/build-and-deploy-interactive-dashboard-with-posit-team-and-cortex/deploy-connect.png?v=ff263759)\n\n4. Enter the API key you created in the steps above.\n\n5. Select the files to include:\n- [x] `app.R`\n- [x] `renv.lock`\n\n6. Click **Integration requests** \u003E **+** \u003E the available Snowflake integration\n\n\u003Cimg src=\"assets/publisher-snowflake-integration.png\" height=\"400px\"\u003E\n\n7. Click the **Deploy your project** button.\n\nFor more information on the deployment process, see [Publishing from VS Code or Positron](https://docs.posit.co/connect/user/publishing-positron-vscode/) in the Connect user guide.\n\n### Step 11: Access Your Dashboard on Connect\n\n1. Go back to Connect in the Posit Team Native App. Click the **Content** tab, and then your deployed dashboard.\n\n2. Test your deployed dashboard to ensure it's working as expected.\n\n3. To share your dashboard with others, click the **Settings** pane in the upper-right-hand corner of the content page.\n\n4. In the **Content URL** section, copy the URL.\n\n5. Share this URL with your team.\n\n\u003E **Note:** Since you added the Snowflake integration to your content before deploying it, your dashboard automatically uses viewer-level authentication. Each user who accesses the dashboard will connect with their own Snowflake credentials, ensuring they only see data they have permission to access.\n\n## Conclusion and Resources\n\n### Overview\n\nIn this guide, we connected securely to Snowflake data using Posit Workbench in the Posit Team Native App, explored the data with Databot powered by Cortex AI, developed a Shiny application using Positron Assistant,\nand deployed the dashboard to Posit Connect where your team can access it securely.\n\nThe steps we took along the way easily transfer to other datasets and use cases. This pattern of combining Snowflake's data platform and Cortex AI with Posit's authoring and publishing tools enables you to build and share powerful data applications quickly.\n\n### What You Learned\n\n- How to access Snowflake public datasets using Workbench, Positron, and R\n- How to use Databot with Snowflake Cortex AI for exploratory data analysis\n- How to build with multiple environments in mind using connection code that works seamlessly in Workbench and Connect\n- How to implement viewer-level authentication to ensure each user connects to Snowflake with their own credentials\n- How to create interactive Shiny dashboards with dynamic filters and visualizations for data exploration\n- How to publish Shiny applications to Connect with one-click deployment from Workbench\n\n### Resources\n\n- **Snowflake Cortex AI Documentation**: [Cortex AI Functions](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions)\n- **Snowflake Public Data**: [Using Snowflake Data Marketplace](https://docs.snowflake.com/en/user-guide/data-marketplace)\n- **Positron**: [Positron Documentation](https://positron.posit.co/)\n- **Databot**: [Databot Documentation](https://positron.posit.co/databot.html)\n- **Shiny for R**: [Documentation](https://shiny.posit.co/)\n- **Posit Workbench**: [User Guide](https://docs.posit.co/ide/server-pro/user/)\n- **Posit Connect**: [User Guide](https://docs.posit.co/connect/user/)\n- **Related Guides**: [Analyze Data with Python Using Posit Team](https://quickstarts.snowflake.com/guide/analyze-data-with-python-using-posit-team/)\n",":type":"text/x-markdown","multiValue":false},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image",":type":"text/plain","multiValue":false}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-2673765604","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-6efec1ea20",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-7ab3fc6395","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-05-20",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-25f70efb4f","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. 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