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.
Installation
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")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:
#> '/var/folders/hl/v1lzqxfd07b3hgd2tt5cjcs40000gp/T/Rtmpp4kE0d/pins-2a644bd98c4c'
#> 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 = 'parquet'`
#> Creating new version '20260313T164232Z-c8df2'
#> Writing to pin 'mtcars'As you can see, the data frame was saved as a .parquet file by default. 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 an RDS, Arrow, CSV, or JSON file.
You can later retrieve the pinned data with pin_read():
board |> pin_read("mtcars")
#> # A data frame: 6 × 11
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 18.1 6 225 105 2.76 3.46 20.2 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").