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, 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.
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.
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/Rtmpvoaxgw/pins-142d05cc7724a'
#> 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 '20231108T211157Z-8df40'
#> 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 2023.01.0 at <https://colorado.posit.co/rsc>
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_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 Amazon’s S3 (board_s3()), Azure’s blob storage
(board_azure()), and Google Cloud Storage (board_gcs()). Learn more
in vignette("pins").