Symbol, name of the target. 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.

R code to run the target.

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.

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.

Character vector of library paths to try when loading packages.

Character of length 1. Must be "file", "url", or "aws_file". See the format argument of targets::tar_target() for details.

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().

Character of length 1, what to do if the target runs into an error. If "stop", the whole pipeline stops and throws an error. If "continue", the error is recorded, but the pipeline keeps going. error = "workspace" is just like error = "stop" except targets saves a special workspace file to support interactive debugging outside the pipeline. (Visit https://books.ropensci.org/targets/debugging.html to learn how to debug targets using saved workspaces.)

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 policy 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.

Logical, whether to run base::gc() just before the target runs.

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.

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()). Only applies to tar_make_future() and tar_make_clustermq() (not tar_make()). tar_make_future() with no extra settings is a drop-in replacement for tar_make() in this case.

A named list of computing resources. Uses:

• Template file wildcards for future::future() in tar_make_future().

• Template file wildcards clustermq::workers() in tar_make_clustermq().

• Custom target-level future::plan(), e.g. resources = list(plan = future.callr::callr).

• Custom curl handle if format = "url", e.g. resources = list(handle = curl::new_handle(nobody = TRUE)). In custom handles, most users should manually set nobody = TRUE so targets does not download the entire file when it only needs to check the time stamp and ETag.

• Custom preset for qs::qsave() if format = "qs", e.g. resources = list(handle = "archive").

• Arguments compression and compression_level to arrow::write_feather() and arrow:write_parquet() if format is "feather", "parquet", "aws_feather", or "aws_parquet".

• Custom compression level for fst::write_fst() if format is "fst", "fst_dt", or "fst_tbl", e.g. resources = list(compress = 100).

• AWS bucket and prefix for the "aws_" formats, e.g. resources = list(bucket = "your-bucket", prefix = "folder/name"). bucket is required for AWS formats. See the cloud computing chapter of the manual for details.

Character of length 1, only relevant to tar_make_clustermq() and tar_make_future(). If "main", the target's return value is sent back to the host machine and saved locally. If "worker", the worker saves the value.

Character of length 1, only relevant to tar_make_clustermq() and tar_make_future(). If "main", the target's dependencies are loaded on the host machine and sent to the worker before the target builds. If "worker", the worker loads the targets dependencies.

An optional object from tar_cue() to customize the rules that decide whether the target is up to date. Only applies to the downstream target. The upstream target always runs.