Target that downloads URLs.

Create a target that downloads file from one or more URLs and automatically reruns when the remote data changes (according to the ETags or last-modified time stamps).

Usage

tar_download(
  name,
  urls,
  paths,
  method = NULL,
  quiet = TRUE,
  mode = "w",
  cacheOK = TRUE,
  extra = NULL,
  headers = NULL,
  iteration = targets::tar_option_get("iteration"),
  error = targets::tar_option_get("error"),
  memory = targets::tar_option_get("memory"),
  garbage_collection = targets::tar_option_get("garbage_collection"),
  deployment = targets::tar_option_get("deployment"),
  priority = targets::tar_option_get("priority"),
  resources = targets::tar_option_get("resources"),
  storage = targets::tar_option_get("storage"),
  retrieval = targets::tar_option_get("retrieval"),
  cue = targets::tar_option_get("cue"),
  description = targets::tar_option_get("description")
)

Arguments

name

Symbol, name of the target. In tar_target(), name is an unevaluated symbol, e.g. tar_target(name = data). In tar_target_raw(), name is a character string, e.g. tar_target_raw(name = "data").

A target name must be a valid name for a symbol in R, and it must not start with a dot. Subsequent targets can refer to this name symbolically to induce a dependency relationship: e.g. tar_target(downstream_target, f(upstream_target)) is a target named downstream_target which depends on a target upstream_target and a function f(). In addition, a target's name determines its random number generator seed. In this way, each target runs with a reproducible seed so someone else running the same pipeline should get the same results, and no two targets in the same pipeline share the same seed. (Even dynamic branches have different names and thus different seeds.) You can recover the seed of a completed target with tar_meta(your_target, seed) and run tar_seed_set() on the result to locally recreate the target's initial RNG state.

urls

Character vector of URLs to track and download. Must be known and declared before the pipeline runs.

paths

Character vector of local file paths to download each of the URLs. Must be known and declared before the pipeline runs.

method

Method to be used for downloading files. Current download methods are "internal", "libcurl", "wget", "curl" and "wininet" (Windows only), and there is a value "auto": see ‘Details’ and ‘Note’.

The method can also be set through the option "download.file.method": see options().

quiet

If TRUE, suppress status messages (if any), and the progress bar.

mode

character. The mode with which to write the file. Useful values are "w", "wb" (binary), "a" (append) and "ab". Not used for methods "wget" and "curl". See also ‘Details’, notably about using "wb" for Windows.

cacheOK

logical. Is a server-side cached value acceptable?

extra

character vector of additional command-line arguments for the "wget" and "curl" methods.

headers

named character vector of additional HTTP headers to use in HTTP[S] requests. It is ignored for non-HTTP[S] URLs. The User-Agent header taken from the HTTPUserAgent option (see options) is automatically used as the first header.

iteration

Character of length 1, name of the iteration mode of the target. Choices:

• "vector": branching happens with vctrs::vec_slice() and aggregation happens with vctrs::vec_c().

• "list", branching happens with [[]] and aggregation happens with list().

• "group": dplyr::group_by()-like functionality to branch over subsets of a non-dynamic data frame. For iteration = "group", the target must not by dynamic (the pattern argument of tar_target() must be left NULL). The target's return value must be a data frame with a special tar_group column of consecutive integers from 1 through the number of groups. Each integer designates a group, and a branch is created for each collection of rows in a group. See the tar_group() function to see how you can create the special tar_group column with dplyr::group_by().

error

Character of length 1, what to do if the target stops and throws an error. Options:

• "stop": the whole pipeline stops and throws an error.

• "continue": the whole pipeline keeps going.

• "null": The errored target continues and returns NULL. The data hash is deliberately wrong so the target is not up to date for the next run of the pipeline. In addition, as of version 1.8.0.9011, a value of NULL is given to upstream dependencies with error = "null" if loading fails.

• "abridge": any currently running targets keep running, but no new targets launch after that.

• "trim": all currently running targets stay running. A queued target is allowed to start if:

  1. It is not downstream of the error, and

  2. It is not a sibling branch from the same tar_target() call (if the error happened in a dynamic branch).

  The idea is to avoid starting any new work that the immediate error impacts. error = "trim" is just like error = "abridge", but it allows potentially healthy regions of the dependency graph to begin running. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

memory

Character of length 1, memory strategy. Possible values:

• "auto": new in targets version 1.8.0.9011, memory = "auto" is equivalent to memory = "transient" for dynamic branching (a non-null pattern argument) and memory = "persistent" for targets that do not use dynamic branching.

• "persistent": the target stays in memory until the end of the pipeline (unless storage is "worker", in which case targets unloads the value from memory right after storing it in order to avoid sending copious data over a network).

• "transient": the target gets unloaded after every new target completes. Either way, the target gets automatically loaded into memory whenever another target needs the value.

For cloud-based dynamic files (e.g. format = "file" with repository = "aws"), the memory option applies to the temporary local copy of the file: "persistent" means it remains until the end of the pipeline and is then deleted, and "transient" means it gets deleted as soon as possible. The former conserves bandwidth, and the latter conserves local storage.

garbage_collection

Logical: TRUE to run base::gc() just before the target runs, FALSE to omit garbage collection. In the case of high-performance computing, gc() runs both locally and on the parallel worker. All this garbage collection is skipped if the actual target is skipped in the pipeline. Non-logical values of garbage_collection are converted to TRUE or FALSE using isTRUE(). In other words, non-logical values are converted FALSE. For example, garbage_collection = 2 is equivalent to garbage_collection = FALSE.

deployment

Character of length 1. If deployment is "main", then the target will run on the central controlling R process. Otherwise, if deployment is "worker" and you set up the pipeline with distributed/parallel computing, then the target runs on a parallel worker. For more on distributed/parallel computing in targets, please visit https://books.ropensci.org/targets/crew.html.

priority

Numeric of length 1 between 0 and 1. Controls which targets get deployed first when multiple competing targets are ready simultaneously. Targets with priorities closer to 1 get dispatched earlier (and polled earlier in tar_make_future()).

resources

Object returned by tar_resources() with optional settings for high-performance computing functionality, alternative data storage formats, and other optional capabilities of targets. See tar_resources() for details.

storage

Character string to control when the output of the target is saved to storage. Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values:

• "main": the target's return value is sent back to the host machine and saved/uploaded locally.

• "worker": the worker saves/uploads the value.

• "none": targets makes no attempt to save the result of the target to storage in the location where targets expects it to be. Saving to storage is the responsibility of the user. Use with caution.

retrieval

Character string to control when the current target loads its dependencies into memory before running. (Here, a "dependency" is another target upstream that the current one depends on.) Only relevant when using targets with parallel workers (https://books.ropensci.org/targets/crew.html). Must be one of the following values:

• "main": the target's dependencies are loaded on the host machine and sent to the worker before the target runs.

• "worker": the worker loads the target's dependencies.

• "none": targets makes no attempt to load its dependencies. With retrieval = "none", loading dependencies is the responsibility of the user. Use with caution.

cue

An optional object from tar_cue() to customize the rules that decide whether the target is up to date.

description

Character of length 1, a custom free-form human-readable text description of the target. Descriptions appear as target labels in functions like tar_manifest() and tar_visnetwork(), and they let you select subsets of targets for the names argument of functions like tar_make(). For example, tar_manifest(names = tar_described_as(starts_with("survival model"))) lists all the targets whose descriptions start with the character string "survival model".

Value

A list of two target objects, one upstream and one downstream. The upstream one watches a URL for changes, and the downstream one downloads it. See the "Target objects" section for background.

Details

tar_download() creates a pair of targets, one upstream and one downstream. The upstream target uses format = "url" (see targets::tar_target()) to track files at one or more URLs, and automatically invalidate the target if the ETags or last-modified time stamps change. The downstream target depends on the upstream one, downloads the files, and tracks them using format = "file".

Target objects

Most tarchetypes functions are target factories, which means they return target objects or lists of target objects. Target objects represent skippable steps of the analysis pipeline as described at https://books.ropensci.org/targets/. Please read the walkthrough at https://books.ropensci.org/targets/walkthrough.html to understand the role of target objects in analysis pipelines.

For developers, https://wlandau.github.io/targetopia/contributing.html#target-factories explains target factories (functions like this one which generate targets) and the design specification at https://books.ropensci.org/targets-design/ details the structure and composition of target objects.

See also

Other targets with custom invalidation rules: tar_change(), tar_force(), tar_skip()

Examples

if (identical(Sys.getenv("TAR_LONG_EXAMPLES"), "true")) {
targets::tar_dir({ # tar_dir() runs code from a temporary directory.
targets::tar_script({
  list(
    tarchetypes::tar_download(
      x,
      urls = c("https://httpbin.org/etag/test", "https://r-project.org"),
      paths = c("downloaded_file_1", "downloaded_file_2")
    )
  )
})
targets::tar_make()
targets::tar_read(x)
})
}