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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), Posit Connect, Databricks, Amazon S3, Google Cloud Storage, 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.
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.
You can install pins from CRAN with:
install.packages("pins")You can install the development version from GitHub:
# install.packages("pak")
pak::pak("rstudio/pins-r")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:
#> '/var/folders/hv/hzsmmyk9393_m7q3nscx1slc0000gn/T/Rtmp4ETVv9/pins-28f423d40e5'
#> Cache size: 0You 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 '20241003T231743Z-8dce8'
#> 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
Parquet, Arrow, CSV, or JSON 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 1A 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 Posit Connect
you can use board_connect():
board <- board_connect()
#> Connecting to Posit Connect 2024.08.0 at <https://pub.current.posit.team>
board %>% pin_write(tidy_sales_data, "sales-summary", type = "rds")
#> Writing to pin 'hadley/sales-summary'Then, someone else (or an automated Quarto report) can read and use your pin:
board <- board_connect()
board %>% pin_read("hadley/sales-summary")You can easily control who gets to access the data using the Posit Connect permissions pane.
The pins package also includes boards that allow you to share data on
services like Databricks Volumes (board_databricks()),
Amazon’s S3 (board_s3()), Azure’s blob storage
(board_azure()), and Google Cloud Storage
(board_gcs()). Learn more in
vignette("pins").
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.