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Create a terra SpatVector target

Source: R/tar-terra-vect.R
tar_terra_vect.Rd

Provides a target format for terra::SpatVector objects.

Usage

tar_terra_vect(
  name,
  command,
  pattern = NULL,
  filetype = geotargets_option_get("gdal.vector.driver"),
  gdal = geotargets_option_get("gdal.vector.creation.options"),
  ...,
  packages = targets::tar_option_get("packages"),
  tidy_eval = targets::tar_option_get("tidy_eval"),
  library = targets::tar_option_get("library"),
  repository = targets::tar_option_get("repository"),
  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. A target name must be a valid name for a symbol in R, and it must not start with a dot. See targets::tar_target() for more information.

command

R code to run the target.

pattern

Code to define a dynamic branching pattern for a target. See targets::tar_target() for more information.

filetype

character. File format expressed as GDAL driver names passed to terra::writeVector(). See 'Note' for more details.

gdal

character. GDAL driver specific datasource creation options passed to terra::writeVector().

...

Additional arguments not yet used.

packages

Character vector of packages to load right before the target runs or the output data is reloaded for downstream targets. Use tar_option_set() to set packages globally for all subsequent targets you define.

tidy_eval

Logical, whether to enable tidy evaluation when interpreting command and pattern. If TRUE, you can use the "bang-bang" operator !! to programmatically insert the values of global objects.

library

Character vector of library paths to try when loading packages.

repository

Character of length 1, remote repository for target storage. Choices:

Note: if repository is not "local" and format is "file" then the target should create a single output file. That output file is uploaded to the cloud and tracked for changes where it exists in the cloud. The local file is deleted after the target runs.

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.

  • "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. If "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). If "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"), this memory strategy 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, whether to run base::gc() just before the target runs.

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 of length 1, only relevant to tar_make_clustermq() and tar_make_future(). 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": almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language. If you do use it, then the return value of the target is totally ignored when the target ends, but each downstream target still attempts to load the data file (except when retrieval = "none").

    If you select storage = "none", then the return value of the target's command is ignored, and the data is not saved automatically. As with dynamic files (format = "file") it is the responsibility of the user to write to the data store from inside the target.

    The distinguishing feature of storage = "none" (as opposed to format = "file") is that in the general case, downstream targets will automatically try to load the data from the data store as a dependency. As a corollary, storage = "none" is completely unnecessary if format is "file".

retrieval

Character of length 1, only relevant to tar_make_clustermq() and tar_make_future(). 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 targets dependencies.

  • "none": the dependencies are not loaded at all. This choice is almost never recommended. It is only for niche situations, e.g. the data needs to be loaded explicitly from another language.

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

target class "tar_stem" for use in a target pipeline

Details

terra::SpatVector objects do not contain vector data directly—they contain a C++ pointer to memory where the data is stored. As a result, these objects are not portable between R sessions without special handling, which causes problems when including them in targets pipelines with tar_target(). tar_terra_rast() handles this issue by writing and reading the target as a geospatial file (specified by filetype) rather than saving the SpatVector object itself.

Note

The iteration argument is unavailable because it is hard-coded to "list", the only option that works currently.

Although you may pass any supported GDAL vector driver to the filetype argument, not all formats are guaranteed to work with geotargets. At the moment, we have tested GeoJSON and ESRI Shapefile which both appear to work generally.

Examples

if (Sys.getenv("TAR_LONG_EXAMPLES") == "true") {
  targets::tar_dir({ # tar_dir() runs code from a temporary directory.
    targets::tar_script({
      lux_area <- function(projection = "EPSG:4326") {
        terra::project(
          terra::vect(system.file("ex", "lux.shp",
            package = "terra"
          )),
          projection
        )
      }
      list(
        geotargets::tar_terra_vect(
          terra_vect_example,
          lux_area()
        )
      )
    })
    targets::tar_make()
    x <- targets::tar_read(terra_vect_example)
  })
}