Archetypes for Targets
Counter constructor.
Add data to an existing counter object.
Objects exported from other packages
Create a target that runs when the last run gets old
Append statically mapped values to target output.
An assignment-based pipeline DSL
Target that responds to an arbitrary change.
Static aggregation
Cue to run a target when the last output reaches a certain age
Cue to force a target to run if a condition is true
Cue to skip a target if a condition is true
Download multiple URLs and return the local paths.
Target that downloads URLs.
Evaluate multiple expressions created with symbol substitution.
Track a file and read the contents.
Dynamic branching over input files or URLs
Dynamic branching over output or input files.
Convert a condition into a change.
Target with a custom condition to force execution.
Nanoparquet format
Superseded target factories for storage formats
Target factories for storage formats
Generate a grouped data frame within tar_group_by()
Group a data frame target by one or more variables.
Generate the tar_group column for tar_group_count().
Generate a grouped data frame within tar_group_count().
Group the rows of a data frame into a given number groups
Generate a grouped data frame within tar_group_select()
Group a data frame target with tidyselect semantics.
Generate the tar_group column for tar_group_size().
Generate a grouped data frame within tar_group_size()
Group the rows of a data frame into groups of a given size.
Hook to prepend code
Hook to wrap dependencies
Hook to wrap commands
Run a knitr report inside a tar_knit() target.
Target with a knitr document.
Expression with literate programming dependencies.
List literate programming dependencies.
Dynamic batched replication within static branches for data frames.
Static branching.
Dynamic-within-static branching for data frames (count batching).
Append the tar_group variable to a tar_map2() target.
Run a rep in a tar_map2()-powered function.
Run a dynamic batch of a tar_map2() target.
Dynamic-within-static branching for data frames (size batching).
Batched dynamic-within-static branching for data frames.
Nanoparquet convert method
Nanoparquet read method
Nanoparquet write method
A drake-plan-like pipeline DSL
Get Source Files From Quarto Inspect
Quarto file detection
Run a rep in a tar_quarto_rep().
Prepare Quarto parameters for tar_quarto_rep().
Render a batch of parameterized Quarto reports inside a `tar_quarto_re...
Parameterized Quarto with dynamic branching.
Render a Quarto project inside a tar_quarto() target.
Target with a Quarto project.
Run a rep in a tar_render_rep().
Prepare R Markdown parameters for tar_render_rep().
Render a batch of parameterized R Markdown reports inside a `tar_rende...
Parameterized R Markdown with dynamic branching.
Render an R Markdown report inside a tar_render() target.
Target with an R Markdown document.
Get overall rep index.
Dynamic batched computation downstream of tar_rep()(raw; deprecated)...
Dynamic batched computation downstream of tar_rep() (deprecated).
Run a rep in tar_rep().
Run a tar_rep() batch.
Batched replication with dynamic branching.
Run a rep in a tar_rep2()-powered function.
Run tar_rep2() batches.
Dynamic batched computation downstream of tar_rep()
Select target names from a target list
Select target definition objects from a target list
Target with a custom cancellation condition.
Create multiple expressions with symbol substitution.
Convert Quarto or R Markdown to a pipeline
targets: Archetypes for Targets
Static code analysis for tarchetypes.
Code analysis for knitr reports.
Function-oriented Make-like declarative pipelines for Statistics and data science are supported in the 'targets' R package. As an extension to 'targets', the 'tarchetypes' package provides convenient user-side functions to make 'targets' easier to use. By establishing reusable archetypes for common kinds of targets and pipelines, these functions help express complicated reproducible pipelines concisely and compactly. The methods in this package were influenced by the 'targets' R package. by Will Landau (2018) <doi:10.21105/joss.00550>.
Useful links