You can manage content settings for deployed pins just Once a pin has been deployed, it is easy to share the pin with colleagues. pin_write ( mtcars, name = "mtcars2", type = "csv", description = "Data extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).", metadata = ) Rather than using email or file systems to share dataįiles, use RStudio Connect to host these resources as pins.īoard. For example, models saved as Python objects aren't easy to You need to share resources that aren't structured for traditional tools
Using a scheduled Jupyter Notebook document. Use a pinned resource and update only the data. Rather than redeploying the content each time the data changes, Rather thanĬopying that data, each piece of content references a single source ofĬontent depends on data or model objects that need to be regularly Multiple pieces of content require the same input data. Sharing data can be useful in many situations, for example:
Networked drive or with dropbox), RStudio Connect, Amazon S3, and more. Stored on a variety of "boards", including local folders (to share on a Your resources may be text files (CSV, JSON, etc.), Arrow/Featherįiles, or any other format you use to share data. Library provides a way to easily share data sets, models, and other Python
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