
# pins <a href="https://pins.rstudio.com"><img src="man/figures/logo.png" align="right" height="138" alt="pins website" /></a>

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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](https://rstudio.github.io/pins-python/).

## Installation

You can install pins from CRAN with:

``` r
install.packages("pins")
```

You can install the development version from GitHub:

``` r
# 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!

``` r
library(pins)

board <- board_temp()
board
#> Pin board <pins_board_folder>
#> Path:
#> '/var/folders/hl/v1lzqxfd07b3hgd2tt5cjcs40000gp/T/Rtmpcv8YZU/pins-2b2828988b4b'
#> 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:

``` r
board |> pin_write(head(mtcars), "mtcars")
#> Guessing `type = 'rds'`
#> Creating new version '20260309T003903Z-5d990'
#> 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()`:

``` r
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 [Posit
Connect](https://posit.co/products/enterprise/connect/) you can use
`board_connect()`:

``` r
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:

``` r
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")`.
