Piggyback Data atop your GitHub Repository!

library(piggyback)
library(magrittr)

Why piggyback?

piggyback grew out of the needs of students both in my classroom and in my research group, who frequently need to work with data files somewhat larger than one can conveniently manage by committing directly to GitHub. As we frequently want to share and run code that depends on >50MB data files on each of our own machines, on continuous integration, and on larger computational servers, data sharing quickly becomes a bottleneck.

GitHub allows repositories to attach files of up to 2 GB each to releases as a way to distribute large files associated with the project source code. There is no limit on the number of files or bandwidth to deliver them.

Authentication

No authentication is required to download data from public GitHub repositories using piggyback. Nevertheless, we recommends setting a token when possible to avoid rate limits. To upload data to any repository, or to download data from private repositories, you will need to authenticate first.

piggyback uses the same GitHub Personal Access Token (PAT) that devtools, usethis, and friends use (gh::gh_token()). The current best practice for managing your GitHub credentials is detailed in this usethis vignette.

You can also add the token as an environment variable, which may be useful in situations where you use piggyback non-interactively (i.e. automated scripts). Here are the relevant steps:

  • Create a GitHub Token
  • Add the environment variable:
    • via project-specific Renviron:
      • usethis::use_git_ignore(".Renviron") to update your gitignore - this prevents accidentally committing your token to GitHub
      • usethis::edit_r_environ("project") to open the Renviron file, and then add your token, e.g. GITHUB_PAT=ghp_a1b2c3d4e5f6g7
    • via Sys.setenv(GITHUB_PAT = "ghp_a1b2c3d4e5f6g7") in your console for adhoc usage. Avoid adding this line to your R scripts – remember, the goal here is to avoid writing your private token in any file that might be shared, even privately.

Download Files

Download a file from a release:

pb_download(
  file = "iris2.tsv.gz", 
  dest = tempdir(),
  repo = "cboettig/piggyback-tests",
  tag = "v0.0.1"
  )
#> ℹ Downloading "iris2.tsv.gz"...
#>   |======================================================| 100%
fs::dir_tree(tempdir())
#> /tmp/RtmpWxJSZj
#> └── iris2.tsv.gz

Some default behaviors to know about:

  1. The repo argument in most piggyback functions will default to detecting the relevant GitHub repo based on your current working directory’s git configs, so in many cases you can omit the repo argument.
  2. The tag argument in most functions defaults to “latest”, which typically refers to the most recently created release of the repository, unless there is a release specifically named “latest” or if you have marked a different release as “latest” via the GitHub UI.
  3. The dest argument defaults to your current working directory ("."). We use tempdir() to meet CRAN policies for the purposes of examples.
  4. The file argument in pb_download defaults to NULL, which will download all files connected to a given release:
pb_download(
  repo = "cboettig/piggyback-tests",
  tag = "v0.0.1",
  dest = tempdir()
)
#> ℹ Downloading "diamonds.tsv.gz"...
#>   |======================================================| 100%
#> ℹ Downloading "iris.tsv.gz"...
#>   |======================================================| 100%
#> ℹ Downloading "iris.tsv.xz"...
#>   |======================================================| 100%
fs::dir_tree(tempdir())
#> /tmp/RtmpWxJSZj
#> ├── diamonds.tsv.gz
#> ├── iris.tsv.gz
#> ├── iris.tsv.xz
#> └── iris2.tsv.gz
  1. The use_timestamps argument defaults to TRUE - notice that above, iris2.tsv.gz was not downloaded. If use_timestamps is TRUE, pb_download() will compare the local file timestamp against the GitHub file timestamp, and only download the file if it has changed.

pb_download() also includes arguments to control the progress bar or if any particular files should not be downloaded.

Download URLs

Sometimes it is preferable to have a URL from which the data can be read in directly. These URL can then be passed into another R function, which can be more elegant and performant than having to first download the files locally. Enter pb_download_url():

pb_download_url(repo = "cboettig/piggyback-tests", tag = "v0.0.1")
#> [1] "https://github.com/cboettig/piggyback-tests/releases/download/v0.0.1/diamonds.tsv.gz"
#> [2] "https://github.com/cboettig/piggyback-tests/releases/download/v0.0.1/iris.tsv.gz"    
#> [3] "https://github.com/cboettig/piggyback-tests/releases/download/v0.0.1/iris.tsv.xz"    
#> [4] "https://github.com/cboettig/piggyback-tests/releases/download/v0.0.1/iris2.tsv.gz"

By default, this function returns the same download URL that you would get by visiting the release page, right-clicking on the file, and copying the link (aka the “browser_download_url”). This URL is served by GitHub’s web servers and not its API servers, and therefore not as restrictive with rate-limiting.

However, this URL is not accessible for private repositories, since the auth tokens are handled by the GitHub API. You can retrieve the API download url for private repositories by passing in "api" to the url_type argument:

pb_download_url(repo = "cboettig/piggyback-tests", tag = "v0.0.1", url_type = "api")
#> [1] https://api.github.com/repos/cboettig/piggyback-tests/releases/assets/44261315
#> [2] https://api.github.com/repos/cboettig/piggyback-tests/releases/assets/41841778
#> [3] https://api.github.com/repos/cboettig/piggyback-tests/releases/assets/18538636
#> [4] https://api.github.com/repos/cboettig/piggyback-tests/releases/assets/8990141

pb_download_url otherwise shares similar default behaviors with pb_download for the file, repo, and tag arguments.

Reading data for R usage

piggyback supports several general patterns for reading data into R, with increasing degrees of performance/efficiency (and complexity):

  • pb_download() files to disk and then reading files with a function that reads from disk into memory
  • pb_download_url() a set of URLs and then passing those URLs to a function that retrieves those URLs directly into memory
  • Disk-based workflows which require downloading all files first but then can perform queries before reading into memory
  • Cloud-native workflows which can perform queries directly on the URLs before reading into memory

We recommend the latter two approaches in cases where performance and efficiency matter, and have some vignettes with examples: - cloud native workflows - disk native workflows

Reading files

pb_read() is a wrapper on the first pattern - it downloads the file to a temp file, then reads that file into memory, then deletes the temporary file. It works for both public and private repositories, handling authentication under the hood:

pb_read("mtcars.rds", repo = "tanho63/piggyback-private")
#> # A data.frame: 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
#> # ℹ 27 more rows
#> # ℹ 1 more variable: carb <dbl>
pb_read("mtcars.parquet", repo = "tanho63/piggyback-private")
#> # A data.frame: 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
#> # ℹ 27 more rows
#> # ℹ 1 more variable: carb <dbl>

By default, pb_read is programmed to use the following read_function for the corresponding file extensions:

  • “.csv”, “.csv.gz”, “.csv.xz” are read with utils::read.csv()
  • “.tsv”, “.tsv.gz”, “.tsv.xz” are read with utils::read.delim()
  • “.rds” is read with readRDS()
  • “.json” is read with jsonlite::fromJSON()
  • “.parquet” is read with arrow::read_parquet()
  • “.txt” is read with readLines()

If a file extension is not on this list, pb_read will raise an error and ask you to provide a read_function - you can also use this parameter to override the default read_function yourself:

pb_read(
  file = "play_by_play_2023.qs", 
  repo = "nflverse/nflverse-data",
  tag = "pbp",
  read_function = qs::qread
)
#> # A tibble: 42,251 × 372
#>   play_id game_id        old_game_id home_team away_team season_type  week posteam
#>     <dbl> <chr>          <chr>       <chr>     <chr>     <chr>       <int> <chr>  
#> 1       1 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 NA     
#> 2      39 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> 3      55 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> 4      77 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> 5     102 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> # ℹ 42,246 more rows
#> # ℹ 364 more variables: posteam_type <chr>, defteam <chr>, side_of_field <chr>,
#> #   yardline_100 <dbl>, game_date <chr>, quarter_seconds_remaining <dbl>,
#> #   half_seconds_remaining <dbl>, game_seconds_remaining <dbl>, game_half <chr>,
#> #   quarter_end <dbl>, drive <dbl>, sp <dbl>, qtr <dbl>, down <dbl>,
#> #   goal_to_go <dbl>, time <chr>, yrdln <chr>, ydstogo <dbl>, ydsnet <dbl>,
#> #   desc <chr>, play_type <chr>, yards_gained <dbl>, shotgun <dbl>, …

Any read_function can be provided so long as it accepts the filename as the first argument, and you can pass any additional parameters via ...:

pb_read(
  file = "play_by_play_2023.csv", 
  n_max = 10,
  repo = "nflverse/nflverse-data",
  tag = "pbp",
  read_function = readr::read_csv
)
#> # A tibble: 10 × 372
#>   play_id game_id        old_game_id home_team away_team season_type  week posteam
#>     <dbl> <chr>          <chr>       <chr>     <chr>     <chr>       <int> <chr>  
#> 1       1 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 NA     
#> 2      39 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> 3      55 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> 4      77 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> 5     102 2023_01_ARI_W… 2023091007  WAS       ARI       REG             1 WAS    
#> # ℹ 5 more rows
#> # ℹ 364 more variables: posteam_type <chr>, defteam <chr>, side_of_field <chr>,
#> #   yardline_100 <dbl>, game_date <chr>, quarter_seconds_remaining <dbl>,
#> #   half_seconds_remaining <dbl>, game_seconds_remaining <dbl>, game_half <chr>,
#> #   quarter_end <dbl>, drive <dbl>, sp <dbl>, qtr <dbl>, down <dbl>,
#> #   goal_to_go <dbl>, time <chr>, yrdln <chr>, ydstogo <dbl>, ydsnet <dbl>,
#> #   desc <chr>, play_type <chr>, yards_gained <dbl>, shotgun <dbl>, …

Reading from URLs

More efficiently, many read functions accept URLs, including read.csv(), arrow::read_parquet(), readr::read_csv(), data.table::fread(), and jsonlite::fromJSON(), so reading in one file can be done by passing along the output of pb_download_url():

pb_download_url("mtcars.csv", repo = "tanho63/piggyback-tests", tag = "v0.0.2") %>%
  read.csv()
#> # A data.frame: 32 × 12
#>   X        mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear
#>   <chr>  <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int>
#> 1 Mazda…  21       6   160   110  3.9   2.62  16.5     0     1     4
#> 2 Mazda…  21       6   160   110  3.9   2.88  17.0     0     1     4
#> 3 Datsu…  22.8     4   108    93  3.85  2.32  18.6     1     1     4
#> 4 Horne…  21.4     6   258   110  3.08  3.22  19.4     1     0     3
#> 5 Horne…  18.7     8   360   175  3.15  3.44  17.0     0     0     3
#> # ℹ 27 more rows
#> # ℹ 1 more variable: carb <int>
#> # ℹ Use `print(n = ...)` to see more rows

Some functions also accept URLs when converted into a connection by wrapping it in url(), e.g. for readRDS():

pb_url <- pb_download_url("mtcars.rds", repo = "tanho63/piggyback-tests", tag = "v0.0.2") %>%
  url()
readRDS(pb_url)
#> # A data.frame: 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
#> # ℹ 27 more rows
#> # ℹ Use `print(n = ...)` to see more rows
close(pb_url)

Note that using url() requires that we close the connection after reading it, or else we will receive warnings about leaving open connections.

This url() approach allows us to pass along authentication for private repos, e.g.

pb_url <- pb_download_url("mtcars.rds", repo = "tanho63/piggyback-private", url_type = "api") %>%
  url(
    headers = c(
      "Accept" = "application/octet-stream",
      "Authorization" = paste("Bearer", gh::gh_token())
    )
  )
readRDS(pb_url)
#> # 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
#> # ℹ 27 more rows
#> # ℹ Use `print(n = ...)` to see more rows
close(pb_url)

Note that arrow does not accept a url() connection at this time, so you should default to pb_read() if using private repositories.

Uploading data

piggyback uploads data to GitHub releases. If your repository doesn’t have a release yet, piggyback will prompt you to create one - you can create a release with:

pb_release_create(repo = "cboettig/piggyback-tests", tag = "v0.0.2")
#> ✔ Created new release "v0.0.2".

Create new releases to manage multiple versions of a given data file, or to organize sets of files under a common topic. While you can create releases as often as you like, making a new release is not necessary each time you upload a file. If maintaining old versions of the data is not useful, you can stick with a single release and upload all of your data there.

Once we have at least one release available, we are ready to upload files. By default, pb_upload will attach data to the latest release.

## We'll need some example data first.
## Pro tip: compress your tabular data to save space & speed upload/downloads
readr::write_tsv(mtcars, "mtcars.tsv.gz")

pb_upload("mtcars.tsv.gz", repo = "cboettig/piggyback-tests")
#> ℹ Uploading to latest release: "v0.0.2".
#> ℹ Uploading mtcars.tsv.gz ...
#>   |===================================================| 100%

Like pb_download(), pb_upload() will overwrite any file of the same name already attached to the release file by default, unless the timestamp of the previously uploaded version is more recent. You can toggle these settings with the overwrite parameter.

pb_upload also accepts a vector of multiple files to upload:

library(magrittr)
## upload a folder of data
list.files("data") %>% 
  pb_upload(repo = "cboettig/piggyback-tests", tag = "v0.0.1")

## upload certain file extensions
list.files(pattern = c("*.tsv.gz", "*.tif", "*.zip")) %>% 
  pb_upload(repo = "cboettig/piggyback-tests", tag = "v0.0.1")

Write R object directly to release

pb_write wraps the above process, essentially allowing you to upload directly to a release by providing an object, filename, and repo/tag:

pb_write(mtcars, "mtcars.rds", repo = "cboettig/piggyback-tests")
#> ℹ Uploading to latest release: "v0.0.2".
#> ℹ Uploading mtcars.rds ...
#>   |===================================================| 100%

Similar to pb_read, pb_write has some pre-programmed write_functions for the following file extensions: - “.csv”, “.csv.gz”, “.csv.xz” are written with utils::write.csv() - “.tsv”, “.tsv.gz”, “.tsv.xz” are written with utils::write.csv(x, filename, sep = '\t') - “.rds” is written with saveRDS() - “.json” is written with jsonlite::write_json() - “.parquet” is written with arrow::write_parquet() - “.txt” is written with writeLines()

and you can pass custom functions with the write_function parameter:

pb_write(
  x = mtcars, 
  file = "mtcars.csv.gz", 
  repo = "cboettig/piggyback-tests", 
  write_function = data.table::fwrite
)
#> ℹ Uploading to latest release: "v0.0.2".
#> ℹ Uploading mtcars.csv.gz ...
#>   |===================================================| 100%

Deleting Files

Delete a file from a release:

pb_delete(file = "mtcars.tsv.gz", 
          repo = "cboettig/piggyback-tests", 
          tag = "v0.0.1")
#> ℹ Deleted "mtcars.tsv.gz" from "v0.0.1" release on "cboettig/piggyback-tests"

Note that this is irreversible unless you have a copy of the data elsewhere.

Listing Files

List all files currently piggybacking on a given release. Omit tag to see files on all releases.

pb_list(repo = "cboettig/piggyback-tests", tag = "v0.0.1")
#>         file_name   size           timestamp    tag    owner            repo
#> 1 diamonds.tsv.gz 571664 2021-09-07 23:38:31 v0.0.1 cboettig piggyback-tests
#> 2     iris.tsv.gz    846 2021-08-05 20:00:09 v0.0.1 cboettig piggyback-tests
#> 3     iris.tsv.xz    848 2020-03-07 06:18:32 v0.0.1 cboettig piggyback-tests
#> 4    iris2.tsv.gz    846 2018-10-05 17:04:33 v0.0.1 cboettig piggyback-tests

Caching

To reduce GitHub API calls, piggyback caches pb_releases and pb_list with a timeout of 10 minutes by default. This avoids repeating identical requests to update its internal record of the repository data (releases, assets, timestamps, etc) during programmatic use. You can increase or decrease this delay by setting the environment variable in seconds, e.g. Sys.setenv("piggyback_cache_duration" = 3600) for a longer cache or Sys.setenv("piggyback_cache_duration" = 0) to disable caching, and then restarting R.

Valid file names

GitHub assets attached to a release do not support file paths, and will sometimes convert most special characters (#, %, etc) to . or throw an error (e.g.  for file names containing $, @, /). piggyback will default to using the basename() of the file only (i.e. will only use "mtcars.csv" if provided a file path like "data/mtcars.csv")

A Note on GitHub Releases vs Data Archiving

piggyback is not intended as a data archiving solution. Importantly, bear in mind that there is nothing special about multiple “versions” in releases, as far as data assets uploaded by piggyback are concerned. The data files piggyback attaches to a Release can be deleted or modified at any time – creating a new release to store data assets is the functional equivalent of just creating new directories v0.1, v0.2 to store your data. (GitHub Releases are always pinned to a particular git tag, so the code/git-managed contents associated with repo are more immutable, but remember our data assets just piggyback on top of the repo).

Permanent, published data should always be archived in a proper data repository with a DOI, such as zenodo.org. Zenodo can freely archive public research data files up to 50 GB in size, and data is strictly versioned (once released, a DOI always refers to the same version of the data, new releases are given new DOIs). piggyback is meant only to lower the friction of working with data during the research process, (e.g. provide data accessible to collaborators or continuous integration systems during research process, including for private repositories.)

What will GitHub think of this?

GitHub documentation at the time of writing endorses the use of attachments to releases as a solution for distributing large files as part of your project:

screenshot of GitHub docs linked above