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The pins R bundle offers a method for R users to quickly share information sets, designs, and other R items. Another Point of View may be text files (CSV, JSON, etc), R things (. Rds,. Rda, and so on) or any other format you use to share data. Pinned items can be kept on a variety of "boards", including regional folders (to share on a networked drive or with dropbox), RStudio Link, Amazon S3, and more.

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Rather than copying that data, each piece of material recommendations a single source of reality hosted on RStudio Connect. Material depends on data or model objects that need to be routinely upgraded. Rather than redeploying the content each time the data modifications, use a pinned resource and upgrade only the information.

Your content will read the latest data on each run. You need to share resources that aren't structured for standard tools like databases. For example, designs saved as R items aren't easy to store in a database. Rather than using email or file systems to share information files, use RStudio Link to host these resources as pins.

As a general general rule, we do not suggest utilizing pins with files over 500 MB. If you discover yourself routinely pinning data larger than this, then you might require to reconsider your data engineering pipeline. Please see Reading and composing data to find out more. Develop a Pin Board RStudio Link is simple to utilize as a board for pinning R objects.

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This function takes an auth argument which informs how you will verify to RStudio Connect. Usage auth="envvar" if you have currently defined CONNECT_SERVER and CONNECT_API_KEY as environment variables in your R session. library( pins) board.