Tasks and Templates

Introduction

This vignette covers advanced features of rmake that help manage complex build processes: tasks for organizing and selectively executing groups of rules, and templates for efficiently generating multiple similar rules with parameterization.

For basic usage and an introduction to rmake, see the Getting Started with rmake vignette. For information on specific rule types, see the Build Rules vignette. For project management basics, see the rmake Project Management vignette.

Tasks

Tasks allow grouping rules that can be executed together. Each rule is a member of the "all" task by default. Rules can belong to multiple tasks.

Executing Tasks

Execute a task:

make('all')
make('preview')

Assigning Rules to Tasks

Assign rules to tasks:

library(rmake)
job <- c(
  "data.csv" %>>% rRule("preprocess.R") %>>% "data.rds",
  "data.rds" %>>% markdownRule("preview.Rmd", task = "preview") %>>% 
    "preview.pdf",
  "data.rds" %>>% markdownRule("final.Rmd", task = "final") %>>% 
    "final.pdf"
)
makefile(job, "Makefile")

Running make("preview") creates data.rds and preview.pdf but not final.pdf.

Use Cases for Tasks

Tasks are useful for:

Incremental development: Create a “preview” task for quick iterations and a “final” task for complete analysis
Different outputs: Separate tasks for different report formats or audiences
Testing: Create a “test” task that runs on sample data before processing the full dataset

Parameterized Execution

Pass parameters to scripts via the params argument:

library(rmake)
job <- c(
  "data.csv" %>>% rRule("fit.R", params = list(alpha = 0.1)) %>>% "out-0.1.rds",
  "data.csv" %>>% rRule("fit.R", params = list(alpha = 0.2)) %>>% "out-0.2.rds",
  "data.csv" %>>% rRule("fit.R", params = list(alpha = 0.3)) %>>% "out-0.3.rds"
)
makefile(job, "Makefile")

Accessing Parameters in Scripts

Parameters are available in scripts as the params global variable:

# fit.R
str(params)
# List of 5
#  $ .target : chr "out-0.1.rds"
#  $ .script : chr "fit.R"
#  $ .depends: chr "data.csv"
#  $ .task   : chr "all"
#  $ alpha   : num 0.1

Using getParam()

Use getParam() to access parameters safely:

# fit.R
library(rmake)

dataName <- getParam(".depends")
resultName <- getParam(".target")
alpha <- getParam("alpha")

# Use parameters...
cat("Processing with alpha =", alpha, "\n")

getParam() with default values:

dataName <- getParam(".depends", "data.csv")
resultName <- getParam(".target", "result.rds")
alpha <- getParam("alpha", 0.2)

Built-in Parameters

Every rule automatically has the following parameters available:

.target - The target file(s) being created
.script - The script being executed
.depends - The dependency file(s)
.task - The task name(s)

Rule Templates

Rule templates avoid repetitive rule definitions using expandTemplate(). This is particularly useful when you need to run the same analysis with different parameters or on different datasets.

Simple Template

tmpl <- "data-$[NUM].csv" %>>% 
  rRule("process.R") %>>% 
  "result-$[NUM].csv"
variants <- data.frame(NUM = 1:99)
job <- expandTemplate(tmpl, variants)

This creates 99 rules, one for each value of NUM.

Template with Multiple Variables

variants <- expand.grid(DATA = c("dataSimple", "dataComplex"),
                        TYPE = c("lm", "rf", "nnet"))
print(variants)
#>          DATA TYPE
#> 1  dataSimple   lm
#> 2 dataComplex   lm
#> 3  dataSimple   rf
#> 4 dataComplex   rf
#> 5  dataSimple nnet
#> 6 dataComplex nnet

tmpl <- "$[DATA].csv" %>>% 
  rRule("fit-$[TYPE].R") %>>%
  "result-$[DATA]_$[TYPE].csv"
job <- expandTemplate(tmpl, variants)
print(job)
#> [[1]]
#> (fit-lm.R, dataSimple.csv) -> R -> (result-dataSimple_lm.csv)
#> [[2]]
#> (fit-lm.R, dataComplex.csv) -> R -> (result-dataComplex_lm.csv)
#> [[3]]
#> (fit-rf.R, dataSimple.csv) -> R -> (result-dataSimple_rf.csv)
#> [[4]]
#> (fit-rf.R, dataComplex.csv) -> R -> (result-dataComplex_rf.csv)
#> [[5]]
#> (fit-nnet.R, dataSimple.csv) -> R -> (result-dataSimple_nnet.csv)
#> [[6]]
#> (fit-nnet.R, dataComplex.csv) -> R -> (result-dataComplex_nnet.csv)

Combining Templates with Parameters

Duplicate rules are automatically removed:

tmpl <- "data.csv" %>>%
  rRule("pre.R") %>>% "pre.rds" %>>%
  rRule("comp.R", params = list(alpha = "$[NUM]")) %>>% 
  "result-$[NUM].csv"
variants <- data.frame(NUM = 1:5)
job <- expandTemplate(tmpl, variants)
#> Warning in expandTemplate(tmpl, variants): Converting all values in `vars` to
#> character vectors.
print(job)
#> [[1]]
#> (pre.R, data.csv) -> R -> (pre.rds)
#> [[2]]
#> (comp.R, pre.rds) -> R -> (result-1.csv)
#> [[3]]
#> (comp.R, pre.rds) -> R -> (result-2.csv)
#> [[4]]
#> (comp.R, pre.rds) -> R -> (result-3.csv)
#> [[5]]
#> (comp.R, pre.rds) -> R -> (result-4.csv)
#> [[6]]
#> (comp.R, pre.rds) -> R -> (result-5.csv)

Template Variables in Parameters

Template variables (like $[NUM]) can be used in: - File names (targets and dependencies) - Script names - Parameters passed to rules

Common Pitfall: Multiple Rules for Same Target

Warning: Different rules producing the same target will cause an error:

tmpl <- "data-$[TYPE].csv" %>>% 
  markdownRule("report.Rmd") %>>% "report.pdf"
variants <- data.frame(TYPE = c("a", "b", "c"))
job <- expandTemplate(tmpl, variants)
print(job)
#> [[1]]
#> (report.Rmd, data-a.csv) -> markdown -> (report.pdf)
#> [[2]]
#> (report.Rmd, data-b.csv) -> markdown -> (report.pdf)
#> [[3]]
#> (report.Rmd, data-c.csv) -> markdown -> (report.pdf)

# This would error:
# makefile(job)
# Error: Multiple rules detected for the same target

Combining Tasks and Templates

You can combine tasks and templates for powerful workflow management:

# Create template for different models
tmpl <- "data.csv" %>>% 
  rRule("fit-$[MODEL].R", params = list(model = "$[MODEL]")) %>>%
  "model-$[MODEL].rds"

# Expand for different models
variants <- data.frame(MODEL = c("linear", "rf", "xgboost"))
training_job <- expandTemplate(tmpl, variants)

# Add evaluation tasks
eval_job <- c(
  c("model-linear.rds", "model-rf.rds", "model-xgboost.rds") %>>%
    rRule("evaluate.R") %>>% "evaluation.rds",
  "evaluation.rds" %>>% 
    markdownRule("report.Rmd", task = "report") %>>% "report.pdf"
)

# Combine all rules
job <- c(training_job, eval_job)
makefile(job, "Makefile")

Best Practices

For Tasks

Use meaningful names: Choose task names that clearly describe their purpose
Keep “all” minimal: Only include essential outputs in the default “all” task
Create incremental tasks: Build up complexity with tasks like “quick”, “full”, “validate”

For Templates

Use data frames for variants: This makes it easy to generate all combinations with expand.grid()
Test with small variants first: Before generating hundreds of rules, test with 2-3 variants
Use consistent naming: Develop a naming convention for template variables
Document your templates: Add comments explaining what each template generates

Summary

This vignette covered:

Tasks: Organizing rules into groups that can be executed selectively
Parameterized Execution: Passing parameters to scripts for flexible processing
Rule Templates: Efficiently generating multiple similar rules
Combining Features: Using tasks and templates together for complex workflows

For more information on basic usage and rule types, see the other vignettes: - Getting Started with rmake - rmake Project Management - Build Rules

Michal Burda

2026-01-09