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.

R code for the upstream change-inducing target.

Whether to invoke tidy evaluation (e.g. the !! operator from rlang) as soon as the target is defined (before tar_make()). Applies to arguments command and change.

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.

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. Possible 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 the resources argument, e.g. tar_target(..., resources = list(preset = "archive")). Requires the qs package (not installed by default).

• "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 the resources argument, e.g. tar_target(..., resources = list(compression = ...)). 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 the resources argument, e.g. tar_target(..., resources = list(compression = ...)). 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 the resources argument, e.g. tar_target(..., resources = list(compress = 100)). 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.)

• "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 the resources argument, e.g. tar_target(..., resources = list(handle = curl::new_handle(nobody = TRUE))). # nolint 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": AWS-powered versions of the respective formats "rds", "qs", etc. The only difference is that the data file is uploaded to the AWS S3 bucket you supply to resources. 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.

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.