Dynamic Function-Oriented 'Make'-Like Declarative Pipelines
Deprecated: callr arguments.
Objects exported from other packages
RStudio addin to call tar_glimpse().
RStudio addin to call tar_load() on the symbol at the cursor.
RStudio addin to run tar_make() in the background.
RStudio addin to call tar_outdated().
RStudio addin to print tail(tar_progress()).
RStudio addin to call tar_read() on the symbol at the cursor.
RStudio addin to insert "tar_target()" at the cursor.
RStudio addin to call tar_visnetwork().
Show if the pipeline is running.
Assertions
Superseded: exponential backoff
Combine pipeline objects (deprecated).
Integer branch indexes
Branch names
Reconstruct the branch names and the names of their dependencies.
Deprecated: list built targets.
Identify the called targets function.
Default callr arguments.
Invoke a targets task from inside a callr function (without error ...
Cancel a target mid-execution under a custom condition.
List canceled targets.
Local CAS download.
Check existence in local CAS.
List keys in local CAS.
Local CAS upload.
List completed targets.
Contain an error condition and formatted traceback.
Conditions
Get configuration settings.
List projects.
Set configuration settings.
Unset configuration settings.
Read _targets.yaml.
Create a counter object.
Get crew worker info.
Declare the rules that cue a target.
Print instructions for debugging a target.
Deduplicate meta and progress databases (deprecated).
For developers only: get the definition of the current target.
Delete target output values.
Code dependencies
Select targets using their descriptions.
Destroy the data store.
Execute code in a temporary directory.
List dispatched targets.
Open the target script file for editing.
Target Markdown knitr engine
For developers only: get the environment of the current target.
Show targets environment variables.
List errored targets.
Check if target metadata exists.
Check if local output data exists for one or more targets.
Check if process metadata exists.
Check if progress metadata exists.
Check if the target script file exists.
Current storage format.
Define a custom target storage format.
Set up GitHub Actions to run a targets pipeline
Visualize an abridged fast dependency graph.
Group a data frame to iterate over subsets of rows.
Write a helper R script.
Run if Target Markdown interactive mode is on.
Delete one or more metadata records (e.g. to rerun a target).
Language
Load the values of all available targets.
Load globals for debugging, testing, and prototyping
Load the values of targets.
Superseded. Run a pipeline with persistent clustermq workers.
Superseded. Run a pipeline of targets in parallel with transient `futu...
Interactive mode pipeline
Run a pipeline of targets.
Produce a data frame of information about your targets.
mermaid.js dependency graph.
Delete metadata.
download local metadata to the cloud.
Synchronize cloud metadata.
Upload local metadata to the cloud.
Read a project's metadata.
Get the name of the target currently running.
Return the vertices and edges of a pipeline dependency graph.
List new targets
Run if Target Markdown interactive mode is not on.
List saved targets
List old targets
Export options.
Get a target option.
Reset all target options.
Set target options.
Check which targets are outdated.
Directory path to the support scripts of the current target script
Current target script path
Current data store path
Identify the file path where a target will be stored.
Deprecated: identify the file path where a target will be stored.
Emulate dynamic branching.
Get main process ID.
Declare a pipeline (deprecated).
Repeatedly poll progress in the R console.
Get main process info.
Tabulate the progress of dynamic branches.
Summarize target progress.
Read progress.
List targets that tar_prune() will remove.
Remove targets that are no longer part of the pipeline.
Random TCP port
Read a target's value from storage.
Set up package dependencies for compatibility with renv
Local CAS garbage collection
Local content-addressable storage (CAS) repository (an experimental fe...
Define a custom content-addressable storage (CAS) repository (an exper...
Reproducible example of targets with reprex
Target resources: Amazon Web Services (AWS) S3 storage
Target resources: clustermq high-performance computing
Target resources: crew high-performance computing
Target resources for custom storage formats
Target resources: feather storage formats
Target resources: fst storage formats
Target resources: future high-performance computing
Target resources: Google Cloud Platform (GCP) Google Cloud Storage (GC...
Target resources for network file systems.
Target resources: parquet storage formats
Target resources: qs storage formats
Target resources for custom storage formats
Target resources: URL storage formats
Target resources
Get the tar_runtime object.
Write a target script file.
Create a seed for a target.
Get the random number generator seed of the target currently running.
Set a seed to run a target.
Deprecated: get the seed of the current target.
Show the cue-by-cue status of each target.
List skipped targets.
Run R scripts.
Deprecated: list started targets.
Deprecated: current data store path
Declare a target.
Test code in a temporary directory.
Get the timestamp(s) of a target.
Choose code to run based on Target Markdown mode.
Get a target's traceback
Unblock the pipeline process
Remove target script helper files.
Delete cloud object version IDs from local metadata.
Validate a pipeline of targets.
visNetwork dependency graph.
Create the full tar_watch() app UI.
Shiny module server for tar_watch()
Shiny module UI for tar_watch()
Shiny app to watch the dependency graph.
Load a saved workspace and seed for debugging.
List saved target workspaces.
Internal function to run a target on a worker.
targets: Dynamic Function-Oriented Make-Like Declarative Pipelines for...
Use targets with Target Markdown.
Use targets
Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, <doi:10.21105/joss.00550>).
Useful links