In most cases, a remote resource does not change over time. For example, the following “retail sales example” dataset is unlikely to change and can be retrieved as follows:

pin("https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv",
    name = "retail_sales")

To ensure this dataset does not change, we can retrieve a unique signature to identify this resource using pin_info() as follows:

pin_info("retail_sales", signature = TRUE)
# Source: local<example_retail_sales> [files]
# Signature: 5f7e575b23e3bb4a802358864be2dbc665ef1ab8
# Properties:
#   - path: example_retail_sales
#   - extension: csv

This signature can then be used to ensure the dataset does not change. For instance, you can share the following code with others to ensure they get the exact same dataset:

pin("https://raw.githubusercontent.com/facebook/prophet/master/examples/example_retail_sales.csv",
    signature = "5f7e575b23e3bb4a802358864be2dbc665ef1ab8")

You can also use signature with pin_get(). Notice that the operation will fail if the signatures don’t match.

In contrast, other datasets are expected to change and require us to think which particular version we are retrieving or sharing in a given board.

Versioning Boards

Currently, all boards support versioning; however, not all boards support versions by default. Boards like GitHub, Kaggle and RStudio Connect, support versioning by default and you can explore previous versions using pin_versions().

For instance, let’s take a look at previous versions of a Kaggle dataset:

pin_versions("new-york-city/nypd-motor-vehicle-collisions", board = "kaggle")
# A tibble: 10 x 4
   version created                  author      message                       
   <chr>   <chr>                    <chr>       <chr>                         
 1 12      2019-12-02T08:47:02.517Z Kaggle Team Automated data update 20191202
 2 11      2019-11-26T14:13:33.05Z  Kaggle Team Automated data update 20191126
 3 10      2019-11-22T19:39:42.85Z  Kaggle Team Automated data update 20191122
 4 9       2019-11-12T15:11:14.477Z Kaggle Team Automated data update 20191112
 5 8       2019-11-04T00:14:16.933Z Kaggle Team Automated data update 20191104
 6 7       2019-10-29T23:24:22.33Z  Kaggle Team Automated data update 20191029
 7 6       2019-10-24T08:44:45.307Z Kaggle Team Automated data update 20191024
 8 5       2019-10-16T20:07:22.91Z  Kaggle Team Automated data update 20191016
 9 4       2019-10-12T06:37:25.1Z   Kaggle Team Automated data update 20191012
10 3       2019-10-07T13:46:46.657Z Kaggle Team Automated data update 20191007

You can then retrieve specific versions with the version parameter in pin_get():

pin_get("new-york-city/nypd-motor-vehicle-collisions", version = 3, board = "kaggle")
[1] "nypd-motor-vehicle-collisions/_versions/3/Collision_DataDictionary.xlsx"    
[2] "nypd-motor-vehicle-collisions/_versions/3/nypd-motor-vehicle-collisions.csv"
[3] "nypd-motor-vehicle-collisions/_versions/3/socrata_metadata.json" 

Other boards, like the default local board, Azure, Digital Ocean, Google Cloud, and S3, do not support versions by default since additional storage cost is incurred. In such cases, one needs to opt-in to use versions when registering a board by setting the versions parameter to TRUE.

For instance, in your local computer you can register the local board with versioning as follows:

board_register("local", versions = TRUE)

From now on, when pin() is used, multiple versions will be stored locally:

# store iris as mtcars dataset
pin(iris, "mtcars", description = "the mtcars dataset")

# oops! we meant mtcars as mtcars
pin(mtcars, "mtcars", description = "the mtcars dataset")

Which you can then list with pin_versions(), notice that the most recent version is displayed first:

pin_versions("mtcars", board = "local")
# A tibble: 2 x 1
  version
  <chr>  
1 a5c5cb2
2 d93d422

You can then retrieve specific versions:

pin_get("mtcars", version = "d93d422", board = "local")
# A tibble: 150 x 5
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
 1          5.1         3.5          1.4         0.2 setosa 
 2          4.9         3            1.4         0.2 setosa 
 3          4.7         3.2          1.3         0.2 setosa 
 4          4.6         3.1          1.5         0.2 setosa 
 5          5           3.6          1.4         0.2 setosa 
 6          5.4         3.9          1.7         0.4 setosa 
 7          4.6         3.4          1.4         0.3 setosa 
 8          5           3.4          1.5         0.2 setosa 
 9          4.4         2.9          1.4         0.2 setosa 
10          4.9         3.1          1.5         0.1 setosa 
# … with 140 more rows

A similar approach can be followed with board_register_azure(), board_register_dospace(), board_register_gcloud(), board_register_s3(), and board_register_datatxt().