Understanding Boards

Whenever you run pin(resource), the resource is stored; by default, this means storing it locally on your computer for the purposes of caching it to avoid re-downloads, recomputing, etc. We can explore this local storage by creating a pin and then enumerating the board's storage folder:

tibble::tibble(paths = dir(board_local_storage(), full.names = TRUE))
# A tibble: 2 x 1
 1 /Users/username/Library/Caches/pins/local/data.txt   
 2 /Users/username/Library/Caches/pins/local/mtcars          

As you can see, each pin is stored inside the rappdirs::user_cache_dir() folder and a list of resources is tracked on a data.txt file. The function pin() and pin_get() store and retrieve data from this well-known location, while pin_find() makes use of the index file to search for resources and track other properties like cache expiration information. We can make explicit use of the default "local" board as follows:

pin(mtcars, name = "mtcars", board = "local")
pin_get("mtcars", board = "local")

A local board is quite useful for all the reasons mentioned in the Getting Started article; however, to take full advantage of pins, you can also consider storing pins in shared locations that others can access, for instance:

Azure : Azure is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers. Azure provides storage services for storing and accessing data on the cloud. You can learn more about using pins with Azure in the Azure Board article.

DigitalOcean : DigitalOcean is an American cloud infrastructure provider with data centers worldwide. DigitalOcean Spaces is an S3-compatible object storage service that lets you store and serve large amounts of data. You can learn more aboaut using pins with DigitalOcean in the DigitalOcean Board article.

GitHub : GitHub is an American company that provides hosting for software development version control using Git and a subsidiary of Microsoft. GitHub offers plans for free, professional, and enterprise accounts; but free accounts are commonly used to host open source projects. While GitHub is mostly known to be used as a code repository, it is a common practice to also use GitHub to store datasets. However, GitHub works best with datasets under 25MB before workarounds like Git LFS and release files to support up to 2GB resources. You can learn more about using pins with GitHub in the GitHub Board article.

Google Cloud : Google Cloud Platform, offered by Google, is a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search and YouTube. Google Cloud provides object storage with integrated edge caching to store unstructured data. You can learn more about using pins with Google Cloud in the Google Cloud Board article.

Kaggle : Kaggle is an online community of data scientists and machine learners, owned by Google LLC. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. Kaggle supports storing datasets up to 10GB in size and is free of charge for public datasets. You can learn more about using pins with GitHub in the Kaggle Board article.

RStudio Connect : RStudio Connect is a publishing platform for the work your teams create in R and Python. You can share Shiny applications, R Markdown reports, Plumber APIs, dashboards, plots, Jupyter Notebooks, and more in one convenient place. RStudio Connect can be used to store private datasets and is only limited by the physical storage capacity configured in your server, learn to configure RStudio Connect in the RStudio Connect Board article.

S3 : Amazon Web Services is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. Amazon Simple Storage Service (S3) is a service offered by Amazon Web Services that provides object storage through a web service interface. Amazon S3 uses the same scalable storage infrastructure that Amazon.com uses to run its global e-commerce network. You can learn more about using pins with Amazon S3 in the S3 Board article.

However, since there are many more storage locations to consider (see for instance Where should scientists store their data), pins also supports web-accessible locations through Website Boards and even custom boards to fully-support reading, writing, authentication and so on. You can learn how to create extensions at Extending Boards.

For instance, there are various ongoing projects to implement additional boards:

Databases : A database is an organized collection of data, generally stored and accessed electronically from a computer system. Where databases are more complex they are often developed using formal design and modeling techniques. In R, packages like DBI and odbc allow you to connect to many different databases. The connections package integrates DBI-compliant packages with the RStudio IDE’s Connection Pane and experimental support for pins.

Nextcloud : Nextcloud is a suite of client-server software for creating and using file hosting services. Nextcloud application functionally is similar to Dropbox. Unlike Dropbox, Nextcloud does not offer off-premises file storage hosting. Nextcloud is free and open-source, which means that anyone is allowed to install and operate it on their own private server devices. The nextcloudr package provides an interface to Nextcloud and experimental support for pins.

Please be mindful when using boards, you should only register boards you trust. As a general good practice, when using GitHub, it is not advisable to clone and build R packages from GitHub repos you don't trust. In a similar way, you should not register GitHub boards, nor Kaggle, nor RStudio Connect, nor Website boards that you don't trust.

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pins documentation built on Jan. 8, 2021, 2:28 a.m.