targets: tar_target – R documentation

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tar_target

Declare a target.

Description

A target is a single step of computation in a pipeline. It runs an R command and returns a value. This value gets treated as an R object that can be used by the commands of targets downstream. Targets that are already up to date are skipped. See the user manual for more details.

Usage

tar_target(
  name,
  command,
  pattern = NULL,
  tidy_eval = targets::tar_option_get("tidy_eval"),
  packages = targets::tar_option_get("packages"),
  library = targets::tar_option_get("library"),
  format = targets::tar_option_get("format"),
  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")
)

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. 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 `set.seed()` on the result to locally recreate the target's initial RNG state.
`command`	R code to run the target.
`pattern`	Language to define branching for a target. For example, in a pipeline with numeric vector targets `x` and `y`, `tar_target(z, x + y, pattern = map(x, y))` implicitly defines branches of `z` that each compute `x[1] + y[1]`, `x[2] + y[2]`, and so on. See the user manual for details.
`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.
`packages`	Character vector of packages to load right before the target builds. Use `tar_option_set()` to set packages globally for all subsequent targets you define.
`library`	Character vector of library paths to try when loading `packages`.
`format`	Optional storage format for the target's return value. With the exception of `format = "file"`, each target gets a file in `_targets/objects`, and each format is a different way to save and load this file. See the "Storage formats" section for a detailed list of possible data storage formats.
`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 data frame. 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. `"abridge"`: any currently running targets keep running, but no new targets launch after that. (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 such as `format = "aws_file"`, this memory strategy applies to temporary local copies of the file in `_targets/scratch/"`: `"persistent"` means they remain until the end of the pipeline, and `"transient"` means they get deleted from the file system 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, only relevant to `tar_make_clustermq()` and `tar_make_future()`. If `"worker"`, the target builds on a parallel worker. If `"main"`, the target builds on the host machine / process managing the pipeline.
`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 built 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"` or `"aws_file"`) it is the responsibility of the user to write to `tar_path()` from inside the target. An example target could look something like tar_target(x, saveRDS("value", tar_path(create_dir = TRUE)); "ignored", storage = "none")'. The distinguishing feature of `storage = "none"` (as opposed to `format = "file"` or `"aws_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"` or `"aws_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 builds. `"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.

Value

A target object. Users should not modify these directly, just feed them to list() in your target script file (default: _targets.R).

Target objects

Functions like tar_target() produce target objects, special objects with specialized sets of S3 classes. 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.

Storage formats

"rds": Default, uses saveRDS() and readRDS(). Should work for most objects, but slow.
"qs": Uses qs::qsave() and qs::qread(). Should work for most objects, much faster than "rds". Optionally set the preset for qsave() through tar_resources() and tar_resources_qs().
"feather": Uses arrow::write_feather() and arrow::read_feather() (version 2.0). Much faster than "rds", but the value must be a data frame. Optionally set compression and compression_level in arrow::write_feather() through tar_resources() and tar_resources_feather(). Requires the arrow package (not installed by default).
"parquet": Uses arrow::write_parquet() and arrow::read_parquet() (version 2.0). Much faster than "rds", but the value must be a data frame. Optionally set compression and compression_level in arrow::write_parquet() through tar_resources() and tar_resources_parquet(). Requires the arrow package (not installed by default).
"fst": Uses fst::write_fst() and fst::read_fst(). Much faster than "rds", but the value must be a data frame. Optionally set the compression level for fst::write_fst() through tar_resources() and tar_resources_fst(). Requires the fst package (not installed by default).
"fst_dt": Same as "fst", but the value is a data.table. Optionally set the compression level the same way as for "fst".
"fst_tbl": Same as "fst", but the value is a tibble. Optionally set the compression level the same way as for "fst".
"keras": Uses keras::save_model_hdf5() and keras::load_model_hdf5(). The value must be a Keras model. Requires the keras package (not installed by default).
"torch": Uses torch::torch_save() and torch::torch_load(). The value must be an object from the torch package such as a tensor or neural network module. Requires the torch package (not installed by default).
"file": A dynamic file. To use this format, the target needs to manually identify or save some data and return a character vector of paths to the data. (These paths must be existing files and nonempty directories.) Then, targets automatically checks those files and cues the appropriate build decisions if those files are out of date. Those paths must point to files or directories, and they must not contain characters | or *. All the files and directories you return must actually exist, or else targets will throw an error. (And if storage is "worker", targets will first stall out trying to wait for the file to arrive over a network file system.) If the target does not create any files, the return value should be character(0).
"url": A dynamic input URL. It works like format = "file" except the return value of the target is a URL that already exists and serves as input data for downstream targets. Optionally supply a custom curl handle through tar_resources() and tar_resources_url(). in new_handle(), nobody = TRUE is important because it ensures targets just downloads the metadata instead of the entire data file when it checks time stamps and hashes. The data file at the URL needs to have an ETag or a Last-Modified time stamp, or else the target will throw an error because it cannot track the data. Also, use extreme caution when trying to use format = "url" to track uploads. You must be absolutely certain the ETag and Last-Modified time stamp are fully updated and available by the time the target's command finishes running. targets makes no attempt to wait for the web server.
"aws_rds", "aws_qs", "aws_parquet", "aws_fst", "aws_fst_dt", "aws_fst_tbl", "aws_keras": versions of the respective formats "rds", "qs", etc. powered by Amazon Web Services (AWS) Simple Storage Service (S3). The only difference is that the data file is uploaded to the AWS S3 bucket you supply to tar_resources_aws(). See the cloud computing chapter of the manual for details.
"aws_file": arbitrary dynamic files on AWS S3. The target should return a path to a temporary local file, then targets will automatically upload this file to an S3 bucket and track it for you. Unlike format = "file", format = "aws_file" can only handle one single file, and that file must not be a directory. tar_read() and downstream targets download the file to _targets/scratch/ locally and return the path. _targets/scratch/ gets deleted at the end of tar_make(). Requires the same resources and other configuration details as the other AWS-powered formats. See the cloud computing chapter of the manual for details.
An entirely custom specification produced by tar_format().

Examples

# Defining targets does not run them.
data <- tar_target(target_name, get_data(), packages = "tidyverse")
analysis <- tar_target(analysis, analyze(x), pattern = map(x))
# Pipelines accept targets.
pipeline <- list(data, analysis)
# Tidy evaluation
tar_option_set(envir = environment())
n_rows <- 30L
data <- tar_target(target_name, get_data(!!n_rows))
print(data)
# Disable tidy evaluation:
data <- tar_target(target_name, get_data(!!n_rows), tidy_eval = FALSE)
print(data)
tar_option_reset()
# In a pipeline:
if (identical(Sys.getenv("TAR_EXAMPLES"), "true")) {
tar_dir({ # tar_dir() runs code from a temporary directory.
tar_script(tar_target(x, 1 + 1), ask = FALSE)
tar_make()
tar_read(x)
})
}

targets

Dynamic Function-Oriented 'Make'-Like Declarative Workflows

v0.10.0

MIT + file LICENSE

Authors

William Michael Landau [aut, cre] (<https://orcid.org/0000-0003-1878-3253>), Matthew T. Warkentin [ctb], Samantha Oliver [rev] (<https://orcid.org/0000-0001-5668-1165>), Tristan Mahr [rev] (<https://orcid.org/0000-0002-8890-5116>), Eli Lilly and Company [cph]

Initial release