The tarchetypes R package is a collection of target and pipeline
archetypes for the targets
package. These archetypes express complicated pipelines with concise
syntax, which enhances readability and thus reproducibility. Archetypes
are possible because of the flexible metaprogramming capabilities of
targets. In
targets, one can define a
target as an object outside the central pipeline, and the
tar_target_raw()
function completely avoids non-standard evaluation. That means anyone
can write their own niche interfaces for specialized projects.
tarchetypes aims to include the most common and versatile archetypes
and usage patterns.
tarchetypes has functions for easy dynamic branching over subsets of
data frames:
tar_group_by(): define row groups usingdplyr::group_by()semantics.tar_group_select(): define row groups usingtidyselectsemantics.tar_group_count(): define a given number row groups.tar_group_size(): define row groups of a given size.
If you define a target with one of these functions, all downstream dynamic targets will automatically branch over the row groups.
# _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))
)# R console:
library(targets)
tar_make()
#> ▶ dispatched target data
#> ● completed target data [0.007 seconds]
#> ▶ dispatched branch group_b3d7d010
#> ● completed branch group_b3d7d010 [0 seconds]
#> ▶ dispatched branch group_6a76c5c0
#> ● completed branch group_6a76c5c0 [0 seconds]
#> ▶ dispatched branch group_164b16bf
#> ● completed branch group_164b16bf [0 seconds]
#> ▶ dispatched branch group_f5aae602
#> ● completed branch group_f5aae602 [0 seconds]
#> ● completed pattern group
#> ▶ completed pipeline [0.104 seconds]
# First row group:
tar_read(group, branches = 1)
#> # A tibble: 3 × 4
#> var1 var2 rep tar_group
#> <fct> <fct> <dbl> <int>
#> 1 a c 1 1
#> 2 a c 2 1
#> 3 a c 3 1
# Second row group:
tar_read(group, branches = 2)
#> # A tibble: 3 × 4
#> var1 var2 rep tar_group
#> <fct> <fct> <dbl> <int>
#> 1 a d 1 2
#> 2 a d 2 2
#> 3 a d 3 2Consider the following R Markdown report.
---
title: report
output: html_document
---
```{r}
library(targets)
tar_read(dataset)
```
We want to define a target to render the report. And because the report
calls tar_read(dataset), this target needs to depend on dataset.
Without tarchetypes, it is cumbersome to set up the pipeline
correctly.
# _targets.R
library(targets)
list(
tar_target(dataset, data.frame(x = letters)),
tar_target(
report, {
# Explicitly mention the symbol `dataset`.
list(dataset)
# Return relative paths to keep the project portable.
fs::path_rel(
# Need to return/track all input/output files.
c(
rmarkdown::render(
input = "report.Rmd",
# Always run from the project root
# so the report can find _targets/.
knit_root_dir = getwd(),
quiet = TRUE
),
"report.Rmd"
)
)
},
# Track the input and output files.
format = "file",
# Avoid building small reports on HPC.
deployment = "main"
)
)With tarchetypes, we can simplify the pipeline with the tar_render()
archetype.
# _targets.R
library(targets)
library(tarchetypes)
list(
tar_target(dataset, data.frame(x = letters)),
tar_render(report, "report.Rmd")
)Above, tar_render() scans code chunks for mentions of targets in
tar_load() and tar_read(), and it enforces the dependency
relationships it finds. In our case, it reads report.Rmd and then
forces report to depend on dataset. That way, tar_make() always
processes dataset before report, and it automatically reruns
report.Rmd whenever dataset changes.
tar_plan()
is a drop-in replacement for
drake_plan()
in the targets ecosystem. It
lets users write targets as name/command pairs without having to call
tar_target().
tar_plan(
tar_file(raw_data_file, "data/raw_data.csv", format = "file"),
# Simple drake-like 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),
# Needs tar_render() because it is a target archetype:
tar_render(report, "report.Rmd")
)| Type | Source | Command |
|---|---|---|
| Release | CRAN | install.packages("tarchetypes") |
| Development | GitHub | remotes::install_github("ropensci/tarchetypes") |
| Development | rOpenSci | install.packages("tarchetypes", repos = "https://dev.ropensci.org") |
For specific documentation on tarchetypes, including the help files of
all user-side functions, please visit the reference
website. For documentation on
targets in general, please
visit the targets reference
website. Many of the linked
resources use tarchetypes functions such as
tar_render().
Please read the help
guide to learn how best
to ask for help using targets and tarchetypes.
Please note that this package is released with a Contributor Code of Conduct.
citation("tarchetypes")
#> To cite tarchetypes in publications use:
#>
#> William Michael Landau (2021). tarchetypes: Archetypes for Targets.
#> https://docs.ropensci.org/tarchetypes/,
#> https://github.com/ropensci/tarchetypes.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {tarchetypes: Archetypes for Targets},
#> author = {William Michael Landau},
#> year = {2021},
#> note = {{https://docs.ropensci.org/tarchetypes/, https://github.com/ropensci/tarchetypes}},
#> }
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