pins

The pins package helps you publish data sets, 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 “boards”, including local folders (to share on a networked drive or with dropbox), RStudio connect, Amazon S3, and more.

Installation

# Install the released version from CRAN:
install.packages("pins")

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 specific. Here I’ll use board_temp() which because it creates a temporary board that will evaporate when your R session ends:

library(pins)

b <- board_temp()
b
#> Pin board <pins_board_local>
#> Path: '/tmp/RtmpXz6NnB/pins-108ca22945b75'
#> With no pins.

Next you can to store some data in that board with pin_write(). The first argument is the object to pin (normally a data frame) , and the second argument is the name you’ll use to later retrieve it:

b %>% pin_write(head(mtcars), "mtcars")
#> Guessing `type = 'rds'`
#> Creating new version 'f879756e28a960ae'

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

Later, in a different R session, you can retrieve the data with pin_read():

b %>% 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

This can be convenient when working locally, but the real power of pins comes when you use a shared board, because because the writer and reader can be different people (or automated processes). For example, you can RStudio Connect you can pin data to board that’s accessible to your team:

b <- board_rsconnect()
b %>% pin_write(tidy_sales_data, "sales-summary")
#> Saving to hadley/sales-summary

Then someone else (or an automated Rmd report) can read it:

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

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

As well as RStudio connect, you can share your pins:

• On GitHub: board_github().
• In Microsoft Azure’s storage: board_azure().
• On Amazon’s S3: board_s3().

Learn more in vignette("pins").