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The target() function is a way to configure individual targets in a drake plan. Its most common use is to invoke static branching and dynamic branching, and it can also set the values of custom columns such as format, elapsed, retries, and max_expand. Details are at Note: drake_plan(my_target = my_command()) is equivalent to drake_plan(my_target = target(my_command()).


target(command = NULL, transform = NULL, dynamic = NULL, ...)



The command to build the target.


A call to map(), split(), cross(), or combine() to apply a static transformation. Details:


A call to map(), cross(), or group() to apply a dynamic transformation. Details:


Optional columns of the plan for a given target. See the Columns section of this help file for a selection of special columns that drake understands.


A one-row workflow plan data frame with the named arguments as columns.


target() must be called inside drake_plan(). It is invalid otherwise.


drake_plan() creates a special data frame. At minimum, that data frame must have columns target and command with the target names and the R code chunks to build them, respectively.

You can add custom columns yourself, either with target() (e.g. drake_plan(y = target(f(x), transform = map(c(1, 2)), format = "fst"))) or by appending columns post-hoc (e.g. plan$col <- vals).

Some of these custom columns are special. They are optional, but drake looks for them at various points in the workflow.

  • transform: a call to map(), split(), cross(), or combine() to create and manipulate large collections of targets. Details: ( # nolint

  • format: set a storage format to save big targets more efficiently. See the "Formats" section of this help file for more details.

  • trigger: rule to decide whether a target needs to run. It is recommended that you define this one with target(). Details:

  • hpc: logical values (TRUE/FALSE/NA) whether to send each target to parallel workers. Visit to learn more.

  • resources: target-specific lists of resources for a computing cluster. See for details.

  • caching: overrides the caching argument of make() for each target individually. Possible values:

    • "main": tell the main process to store the target in the cache.

    • "worker": tell the HPC worker to store the target in the cache.

    • NA: default to the caching argument of make().

  • elapsed and cpu: number of seconds to wait for the target to build before timing out (elapsed for elapsed time and cpu for CPU time).

  • retries: number of times to retry building a target in the event of an error.

  • seed: an optional pseudo-random number generator (RNG) seed for each target. drake usually comes up with its own unique reproducible target-specific seeds using the global seed (the seed argument to make() and drake_config()) and the target names, but you can overwrite these automatic seeds. NA entries default back to drake's automatic seeds.

  • max_expand: for dynamic branching only. Same as the max_expand argument of make(), but on a target-by-target basis. Limits the number of sub-targets created for a given target.


drake_plan() understands special keyword functions for your commands. With the exception of target(), each one is a proper function with its own help file.

  • target(): give the target more than just a command. Using target(), you can apply a transformation (examples:, # nolint supply a trigger (, # nolint or set any number of custom columns.

  • file_in(): declare an input file dependency.

  • file_out(): declare an output file to be produced when the target is built.

  • knitr_in(): declare a knitr file dependency such as an R Markdown (*.Rmd) or R LaTeX (*.Rnw) file.

  • ignore(): force drake to entirely ignore a piece of code: do not track it for changes and do not analyze it for dependencies.

  • no_deps(): tell drake to not track the dependencies of a piece of code. drake still tracks the code itself for changes.

  • id_chr(): Get the name of the current target.

  • drake_envir(): get the environment where drake builds targets. Intended for advanced custom memory management.


Specialized target formats increase efficiency and flexibility. Some allow you to save specialized objects like keras models, while others increase the speed while conserving storage and memory. You can declare target-specific formats in the plan (e.g. drake_plan(x = target(big_data_frame, format = "fst"))) or supply a global default format for all targets in make(). Either way, most formats have specialized installation requirements (e.g. R packages) that are not installed with drake by default. You will need to install them separately yourself. Available formats:

  • "file": Dynamic files. To use this format, simply create local files and directories yourself and then return a character vector of paths as the target's value. Then, drake will watch for changes to those files in subsequent calls to make(). This is a more flexible alternative to file_in() and file_out(), and it is compatible with dynamic branching. See for an example.

  • "fst": save big data frames fast. Requires the fst package. Note: this format strips non-data-frame attributes such as the

  • "fst_tbl": Like "fst", but for tibble objects. Requires the fst and tibble packages. Strips away non-data-frame non-tibble attributes.

  • "fst_dt": Like "fst" format, but for data.table objects. Requires the fst and data.table packages. Strips away non-data-frame non-data-table attributes.

  • "diskframe": Stores disk.frame objects, which could potentially be larger than memory. Requires the fst and disk.frame packages. Coerces objects to disk.frames. Note: disk.frame objects get moved to the drake cache (a subfolder of .drake/ for most workflows). To ensure this data transfer is fast, it is best to save your disk.frame objects to the same physical storage drive as the drake cache, as.disk.frame(your_dataset, outdir = drake_tempfile()).

  • "keras": save Keras models as HDF5 files. Requires the keras package.

  • "qs": save any R object that can be properly serialized with the qs package. Requires the qs package. Uses qsave() and qread(). Uses the default settings in qs version 0.20.2.

  • "rds": save any R object that can be properly serialized. Requires R version >= 3.5.0 due to ALTREP. Note: the "rds" format uses gzip compression, which is slow. "qs" is a superior format.

See also


# Use target() to create your own custom columns in a drake plan.
# See ?triggers for more on triggers.
  website_data = target(
    trigger = "always",
    custom_column = 5
  analysis = analyze(website_data)
#> # A tibble: 2 × 4
#>   target       command                           trigger       custom_column
#>   <chr>        <expr_lst>                        <expr_lst>            <dbl>
#> 1 website_data download_data("") "always"                  5
#> 2 analysis     analyze(website_data)             NA_character_            NA
models <- c("glm", "hierarchical")
plan <- drake_plan(
  data = target(
    transform = map(x = c("simulated", "survey"))
  analysis = target(
    analyze_data(data, model),
    transform = cross(data, model = !!models, .id = c(x, model))
  summary = target(
    transform = map(analysis, .id = c(x, model))
  results = target(
    transform = combine(summary, .by = data)
#> # A tibble: 12 × 2
#>    target                          command                                      
#>    <chr>                           <expr_lst>                                   
#>  1 analysis_simulated_glm          analyze_data(data_simulated, "glm")         …
#>  2 analysis_simulated_hierarchical analyze_data(data_simulated, "hierarchical")…
#>  3 analysis_survey_glm             analyze_data(data_survey, "glm")            …
#>  4 analysis_survey_hierarchical    analyze_data(data_survey, "hierarchical")   …
#>  5 data_simulated                  get_data("simulated")                       …
#>  6 data_survey                     get_data("survey")                          …
#>  7 results_data_simulated          bind_rows(summary_simulated_glm, summary_sim…
#>  8 results_data_survey             bind_rows(summary_survey_glm, summary_survey…
#>  9 summary_simulated_glm           summarize_analysis(analysis_simulated_glm)  …
#> 10 summary_simulated_hierarchical  summarize_analysis(analysis_simulated_hierar…
#> 11 summary_survey_glm              summarize_analysis(analysis_survey_glm)     …
#> 12 summary_survey_hierarchical     summarize_analysis(analysis_survey_hierarchi…
if (requireNamespace("styler", quietly = TRUE)) {
#> drake_plan(
#>   analysis_simulated_glm = analyze_data(data_simulated, "glm"),
#>   analysis_simulated_hierarchical = analyze_data(data_simulated, "hierarchical"),
#>   analysis_survey_glm = analyze_data(data_survey, "glm"),
#>   analysis_survey_hierarchical = analyze_data(data_survey, "hierarchical"),
#>   data_simulated = get_data("simulated"),
#>   data_survey = get_data("survey"),
#>   results_data_simulated = bind_rows(summary_simulated_glm, summary_simulated_hierarchical),
#>   results_data_survey = bind_rows(summary_survey_glm, summary_survey_hierarchical),
#>   summary_simulated_glm = summarize_analysis(analysis_simulated_glm),
#>   summary_simulated_hierarchical = summarize_analysis(analysis_simulated_hierarchical),
#>   summary_survey_glm = summarize_analysis(analysis_survey_glm),
#>   summary_survey_hierarchical = summarize_analysis(analysis_survey_hierarchical)
#> )