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. This vignette will introduce you to the basics of pins.
library(pins)Every pin lives in a pin board, so you must start by creating a pin board. In this vignette I’ll use a temporary board which is automatically deleted when your R session is over:
board <- board_temp()In real-life, you’d pick a board depending on how you want to share the data. Here are a few options:
board <- board_local() # share data across R sessions on the same computer
board <- board_folder("~/Dropbox") # share data with others using dropbox
board <- board_folder("Z:\\my-team\pins") # share data using a shared network drive
board <- board_rsconnect() # share data with RStudio ConnectOnce you have a pin board, you can write data to it with pin_write():
mtcars <- tibble::as_tibble(mtcars)
board %>% pin_write(mtcars, "mtcars")
#> Guessing `type = 'rds'`
#> Creating new version '20120304T050607Z-2f9b5'
#> Writing to pin 'mtcars'The first argument is the object to save (usually a data frame, but it can be any R object), and the second argument gives the “name” of the pin. The name is basically equivalent to a file name: you’ll use it when you later want to read the data from the pin. The only rule for a pin name is that it can’t contain slashes.
As you can see from the output, pins has chosen to save this data to an .rds file. But you can choose another option depending on your goals:
type = "rds" uses writeRDS() to create a binary R data file. It can save any R object but it’s only readable from R, not other languages.type = "csv" uses write.csv() to create a .csv file. CSVs can read by any application, but only support simple columns (e.g. numbers, strings, dates), can take up a lot of disk space, and can be slow to read.type = "arrow" uses arrow::write_feather() to create an arrow/feather file. Arrow is a modern, language-independent, high-performance file format designed for data science. Not every tool can read arrow files, but support is growing rapidly.type = "json" uses jsonlite::write_json() to create a .json file. Pretty much every programming language can read json files, but they only work well for nested lists.type = "qs" uses qs::qsave() to create a binary R data file, like writeRDS(). This format achieves faster read/write speeds than RDS, and compresses data more efficiently, making it a good choice for larger objects. Read more on the qs package.After you’ve pinned an object, you can read it back with pin_read():
board %>% pin_read("mtcars")
#> # A tibble: 32 × 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 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4
#> # … with 22 more rowsYou don’t need to supply the file type when reading data from a pin because pins automatically stores the file type in the metadata, the topic of the next section.
Note that when the data lives elsewhere, pins takes care of downloading and caching so that it’s only re-downloaded when needed. That said, most boards transmit pins over HTTP, and this is going to be slow and possibly unreliable for very large pins. As a general rule of thumb, we don’t recommend using pins with files over 500 MB. If you find yourself routinely pinning data larger that this, you might need to reconsider your data engineering pipeline.
Every pin is accompanied by some metadata that you can access with pin_meta():
board %>% pin_meta("mtcars")
#> List of 11
#> $ file : chr "mtcars.rds"
#> $ file_size : 'fs_bytes' int 3.12K
#> $ pin_hash : chr "2f9b568eb4365daf"
#> $ type : chr "rds"
#> $ title : chr "mtcars: a pinned 32 x 11 data frame"
#> $ description: NULL
#> $ created : POSIXct[1:1], format: "2021-12-14 12:45:00"
#> $ api_version: num 1
#> $ user : list()
#> $ name : chr "mtcars"
#> $ local :List of 3
#> ..$ dir : 'fs_path' chr "/tmp/Rtmph4DofV/pins-64474bfd2bd6/mtcars/20120304T050607Z-2f9b5"
#> ..$ url : NULL
#> ..$ version: chr "20120304T050607Z-2f9b5"This shows you the metadata that’s generated by default. This includes:
title, a brief textual description of the dataset.
an optional description, where you can provide more details.
the date-time when the pin was created.
the file_size, in bytes, of the underlying files.
a unique pin_hash that you can supply to pin_read() to ensure that you’re reading exactly the data that you expect.
When creating the pin, you can override the default description or provide additional metadata that is stored with the data:
board %>% pin_write(mtcars,
description = "Data extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).",
metadata = list(
source = "Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411."
)
)
#> Using `name = 'mtcars'`
#> Guessing `type = 'rds'`
#> Replacing version '20120304T050607Z-2f9b5' with '20120304T050607Z-2f9b5'
#> Writing to pin 'mtcars'
board %>% pin_meta("mtcars")
#> List of 11
#> $ file : chr "mtcars.rds"
#> $ file_size : 'fs_bytes' int 3.12K
#> $ pin_hash : chr "2f9b568eb4365daf"
#> $ type : chr "rds"
#> $ title : chr "mtcars: a pinned 32 x 11 data frame"
#> $ description: chr "Data extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobi"| __truncated__
#> $ created : POSIXct[1:1], format: "2021-12-14 12:45:00"
#> $ api_version: num 1
#> $ user :List of 1
#> ..$ source: chr "Henderson and Velleman (1981), Building multiple regression models interactively. Biometrics, 37, 391–411."
#> $ name : chr "mtcars"
#> $ local :List of 3
#> ..$ dir : 'fs_path' chr "/tmp/Rtmph4DofV/pins-64474bfd2bd6/mtcars/20120304T050607Z-2f9b5"
#> ..$ url : NULL
#> ..$ version: chr "20120304T050607Z-2f9b5"While we’ll do our best to keep the automatically generated metadata consistent over time, I’d recommend manually capturing anything you really care about in metadata.
In many situations it’s useful to version pins, so that writing to an existing pin does not replace the existing data, but instead adds a new copy. There are two ways to turn versioning on:
When you create a board you can turn versioning on for every pin in that board:
board2 <- board_temp(versioned = TRUE)When you write a pin, you can specifically request that versioning be turned on for that pin:
board2 <- board_temp()
board2 %>% pin_write(mtcars, versioned = TRUE)Most boards have versioning on by default. The primary exception is board_folder() since that stores data on your computer, and there’s no automated way to clean up the data your saving.
Once you have turned versioning on, every pin_write() will create a new version:
board2 <- board_temp(versioned = TRUE)
board2 %>% pin_write(1:5, name = "x", type = "rds")
#> Creating new version '20120304T050607Z-ab444'
#> Writing to pin 'x'
board2 %>% pin_write(2:6, name = "x", type = "rds")
#> Creating new version '20120304T050607Z-a077a'
#> Writing to pin 'x'
board2 %>% pin_write(3:7, name = "x", type = "rds")
#> Creating new version '20120304T050607Z-0a284'
#> Writing to pin 'x'You can list all the available versions with pin_versions():
board2 %>% pin_versions("x")
#> # A tibble: 3 × 3
#> version created hash
#> <chr> <dttm> <chr>
#> 1 20120304T050607Z-0a284 2012-03-03 23:06:00 0a284
#> 2 20120304T050607Z-a077a 2012-03-03 23:06:00 a077a
#> 3 20120304T050607Z-ab444 2012-03-03 23:06:00 ab444(Pins does not currently provide any tool to remove old versions, but this is likely to be implemented in the future.)
By default, pin_read() will return the most recent version:
board2 %>% pin_read("x")
#> [1] 1 2 3 4 5But you can request an older version by supplying the version argument:
board2 %>% pin_read("x", version = "20210520T173110Z-49519")So far we’ve focussed on pin_write() and pin_read() which work with R objects. pins also provides the lower-level pin_upload() and pin_download() which work with files on disk. You can use them to share types of data that are otherwise unsupported by pins.
pin_upload() works like pin_write() but instead of an R object you give it a vector of paths. I’ll start by creating a few files in the temp directory:
paths <- file.path(tempdir(), c("mtcars.csv", "alphabet.txt"))
write.csv(mtcars, paths[[1]])
writeLines(letters, paths[[2]])Now I can upload those to the board:
board %>% pin_upload(paths, "example")
#> Creating new version '20120304T050607Z-e9d42'pin_download() returns a vector of paths:
board %>% pin_download("example")
#> [1] "/tmp/Rtmph4DofV/pins-64474bfd2bd6/example/20120304T050607Z-e9d42/mtcars.csv"
#> [2] "/tmp/Rtmph4DofV/pins-64474bfd2bd6/example/20120304T050607Z-e9d42/alphabet.txt"It’s now your job to handle them. You should treat these paths as internal implementation details — never modify them and never save them for use outside of pins.
Note that you can’t pin_read() something you pinned with pin_upload():
board %>% pin_read("example")
#> Error: Pin does not declare file type so can't be automatically read
#> ℹ Retrieve uploaded paths with `pin_download()`But you can pin_download() something that you’ve pinned with pin_write():
board %>% pin_download("mtcars")
#> [1] "/tmp/Rtmph4DofV/pins-64474bfd2bd6/mtcars/20120304T050607Z-2f9b5/mtcars.rds"The primary purpose of pins is to make it easy to share data. But pins is also designed to help you spend as little time as possible downloading data. pin_read() and pin_download() automatically cache remote pins: they maintain a local copy of the data (so it’s fast) but always check that it’s up-to-date (so your analysis doesn’t use stale data).
Wouldn’t it be nice if you could take advantage of this feature for any dataset on the internet? That’s the idea behind board_url() — you can assemble your own board from datasets, wherever they live on the internet. For example, this code creates a board containing a single pin, penguins, that refers to some fun data I found on GitHub:
my_data <- board_url(c(
"penguins" = "https://raw.githubusercontent.com/allisonhorst/palmerpenguins/master/inst/extdata/penguins_raw.csv"
))You can read this data by combining pin_download() with read.csv()1:
my_data %>%
pin_download("penguins") %>%
read.csv(check.names = FALSE) %>%
tibble::as_tibble()
#> # A tibble: 344 × 17
#> studyName `Sample Number` Species Region Island Stage `Individual ID`
#> <chr> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 PAL0708 1 Adelie Pengu… Anvers Torge… Adult,… N1A1
#> 2 PAL0708 2 Adelie Pengu… Anvers Torge… Adult,… N1A2
#> 3 PAL0708 3 Adelie Pengu… Anvers Torge… Adult,… N2A1
#> 4 PAL0708 4 Adelie Pengu… Anvers Torge… Adult,… N2A2
#> 5 PAL0708 5 Adelie Pengu… Anvers Torge… Adult,… N3A1
#> 6 PAL0708 6 Adelie Pengu… Anvers Torge… Adult,… N3A2
#> 7 PAL0708 7 Adelie Pengu… Anvers Torge… Adult,… N4A1
#> 8 PAL0708 8 Adelie Pengu… Anvers Torge… Adult,… N4A2
#> 9 PAL0708 9 Adelie Pengu… Anvers Torge… Adult,… N5A1
#> 10 PAL0708 10 Adelie Pengu… Anvers Torge… Adult,… N5A2
#> # … with 334 more rows, and 10 more variables: Clutch Completion <chr>,
#> # Date Egg <chr>, Culmen Length (mm) <dbl>, Culmen Depth (mm) <dbl>,
#> # Flipper Length (mm) <int>, Body Mass (g) <int>, Sex <chr>,
#> # Delta 15 N (o/oo) <dbl>, Delta 13 C (o/oo) <dbl>, Comments <chr>board_url() requires a bit of work compared to using download.file() or similar but it has a big payoff: the data will only be re-downloaded when it changes.
Here I’m using read.csv() to the reduce the dependencies of the pins package. For real code I’d recommend using data.table::fread() or readr::read_csv().↩︎