tarchetypes

The tarchetypes package provides functions to define row groups in a data frame, allowing downstream dynamic targets to automatically branch over these groups. This is useful for running the same analysis on different subsets of data.

Available grouping functions:

• tar_group_by(): Uses dplyr::group_by() semantics.
• tar_group_select(): Uses tidyselect semantics.
• tar_group_count(): Defines a specific number of row groups.
• tar_group_size(): Defines row groups of a specific size.

# _targets.R file:
library(targets)
library(tarchetypes)

produce_data <- function() {
  expand.grid(var1 = c("a", "b"), var2 = c("c", "d"), rep = c(1, 2, 3))
}

list(
  tar_group_by(data, produce_data(), var1, var2),
  tar_target(group, data, pattern = map(data))
)

You can install tarchetypes from CRAN, GitHub, or the rOpenSci development repository.

# From CRAN
install.packages("tarchetypes")

# From GitHub (requires remotes package)
remotes::install_github("ropensci/tarchetypes")

# From rOpenSci development repository
install.packages("tarchetypes", repos = "https://dev.ropensci.org")

The tar_render() archetype simplifies the process of rendering R Markdown reports within a targets pipeline. It automatically scans the report file (e.g., .Rmd) for mentions of targets via tar_load() or tar_read() and enforces the necessary dependency relationships. This ensures that the report is automatically re-rendered whenever its input data targets change.

# _targets.R
library(targets)
library(tarchetypes)

list(
  tar_target(dataset, data.frame(x = letters)),
  tar_render(report, "report.Rmd")
)

tar_plan() is a drop-in replacement for drake_plan() in the targets ecosystem. It allows you to write targets as simple name/command pairs, avoiding the need to wrap every target in an explicit tar_target() call. Note that when using target archetypes like tar_render() inside tar_plan(), they are still supported.

tar_plan(
  tar_file(raw_data_file, "data/raw_data.csv", format = "file"),
  
  # Simple name/command syntax:
  raw_data = read_csv(raw_data_file, col_types = cols()),
  data = raw_data %>% mutate(Ozone = replace_na(Ozone, mean(Ozone, na.rm = TRUE))),
  hist = create_plot(data),
  fit = biglm(Ozone ~ Wind + Temp, data),
  
  # Using an archetype within tar_plan:
  tar_render(report, "report.Rmd")
)