![]() The publishing computer may be using a different package repository from the Connect server. Verify that the Packages.HTTPProxy and Packages.HTTPSProxy configuration settings to point to your outbound proxy server. If the package repository is accessed be accessed through a proxy server, the proxy server must be configured in the Connect configuration file. Verify that the package repository is reachable from the Connect server. There may be network problem preventing the Posit Connect server from connecting to the package repository, or the package repository might be down. Posit Connect cannot download a package because the R package repository (for example, CRAN or Posit Package Manager) cannot be accessed. These are assigned error codes, which are listed below along with possible causes and solutions. Posit Connect analyzes the log output and recognizes a set of common errors. Your Connect administrator should review the Runtime Caches documentation in the Posit Connect Admin Guide to see if this apples. In rare situations, a change to the system running Posit Connect may be incompatible with the previously installed R and Python packages. If you have encountered an error that Posit Connect is not detecting or for which Posit Connect is not providing adequate troubleshooting steps, contact your Connect administrator or your Posit Support representative for additional assistance. Posit Connect scans your logs for common errors and suggests troubleshooting steps if an error is found. If no version of R on the Posit Connect server matches the version of R that you used to deploy your content, Posit Connect will find the closest available R version and log that you requested a specific R version but that it is using some other specific R version because the requested one is not available. Posit Connect will log the URL of all requests made to fetch packages, which may show problems accessing some of the R package repositories in the Connect configuration or used as options(repos) settings during content development. If there are issues related to packages not being present in repositories, this may help to diagnose them. This may occur when your organization has a security policy that permits you to use a particular public repository on your workstation but requires a secure private repository on the Posit Connect server. Posit Connect will log any overrides to the repos option set by your server administrator. If there are issues related to shared object files used by R, this may help to diagnose them. Posit Connect will log the distribution of Linux used by the server. Posit Connect includes extra information in the packrat restore, content build, and content execution logs that may help diagnose common problems with publishing content. You can check your logs in the Posit Connect dashboard, under the Logs section for your deployed content. We recommend first checking your application logs for any errors in your application. Share and manage access to R- and Python-based interactive applications, dashboards, and APIs, all in a single place.There are a variety of reasons why an application that appears to work locally may have problems when running on Posit Connect. Leverage a single infrastructure to launch and manage Jupyter Notebooks, JupyterLab, VSCode and the RStudio IDE, while giving your team easy access to Kubernetes and other resources. ![]() In this webinar, you will learn how RStudio helps organizations tackle these challenges, with a focus on some of the recent additions to our products that have helped deepen the happy relationship between R and Python:Įasily combine R and Python in a single Data Science project using a single IDE. ![]() Even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. Data Science leaders and their business partners find it difficult to make key data science content easily discoverable and available for decision-making, while IT Admins and DevOps engineers grapple with how to efficiently support these teams without duplicating infrastructure. Many Data Science teams today leverage both R and Python in their work, but struggle to use them together.
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