pins

The pins package publishes data, models, and other R objects, making it easy to share them across projects and with your colleagues. You can pin objects to a variety of pin boards, including folders (to share on a networked drive or with services like DropBox), RStudio Connect, Amazon S3, Azure storage and Microsoft 365 (OneDrive and SharePoint). Pins can be automatically versioned, making it straightforward to track changes, re-run analyses on historical data, and undo mistakes.

pins 1.0.0 includes a new more explicit API and greater support for versioning. The legacy API (pin(), pin_get(), and board_register()) will continue to work, but new features will only be implemented with the new API, so we encourage you to switch to the modern API as quickly as possible. Learn more in vignette("pins-update").

You can use pins from Python as well as R. For example, you can use one language to read a pin created with the other. Learn more about pins for Python.

Installation

You can install pins from CRAN with:

install.packages("pins")

You can install the development version from GitHub:

remotes::install_github("rstudio/pins-r")

Usage

To use the pins package, you must first create a pin board. A good place to start is board_folder(), which stores pins in a directory you specify. Here I’ll use a special version of board_folder() called board_temp() which creates a temporary board that’s automatically deleted when your R session ends. This is great for examples, but obviously you shouldn’t use it for real work!

library(pins)

board <- board_temp()
board
#> Pin board <pins_board_folder>
#> Path: '/tmp/RtmpAYa45m/pins-6cce258ca9cf'
#> Cache size: 0

You can “pin” (save) data to a board with pin_write(). It takes three arguments: the board to pin to, an object, and a name:

board %>% pin_write(head(mtcars), "mtcars")
#> Guessing `type = 'rds'`
#> Creating new version '20211116T161305Z-e8160'
#> Writing to pin 'mtcars'

As you can see, the data saved as an .rds by default, but depending on what you’re saving and who else you want to read it, you might use the type argument to instead save it as a csv, json, or arrow file.

You can later retrieve the pinned data with pin_read():

board %>% pin_read("mtcars")
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

A board on your computer is good place to start, but the real power of pins comes when you use a board that’s shared with multiple people. To get started, you can use board_folder() with a directory on a shared drive or in dropbox, or if you use RStudio Connect you can use board_rsconnect():

board <- board_rsconnect()
#> Connecting to RSC 1.9.0.1 at <https://connect.rstudioservices.com>
board %>% pin_write(tidy_sales_data, "sales-summary", type = "rds")
#> Writing to pin 'hadley/sales-summary'

Then, someone else (or an automated Rmd report) can read and use your pin:

board <- board_rsconnect()
board %>% pin_read("hadley/sales-summary")

You can easily control who gets to access the data using the RStudio Connect permissions pane.

The pins package also includes boards that allow you to share data on services like Amazon’s S3 (board_s3()), Azure’s blob storage (board_azure()), and Microsoft SharePoint (board_ms365()). Learn more in vignette("pins").