The commands in workflow plan data frames can have wildcard symbols that can stand for datasets, parameters, function arguments, etc. These wildcards can be evaluated over a set of possible values using evaluate_plan.

evaluate_plan(plan, rules = NULL, wildcard = NULL, values = NULL,
  expand = TRUE)

Arguments

plan

workflow plan data frame, similar to one produced by drake_plan()

rules

Named list with wildcards as names and vectors of replacements as values. This is a way to evaluate multiple wildcards at once. When not NULL, rules overrules wildcard and values if not NULL.

wildcard

character scalar denoting a wildcard placeholder

values

vector of values to replace the wildcard in the drake instructions. Will be treated as a character vector. Must be the same length as plan$command if expand is TRUE.

expand

If TRUE, create a new rows in the workflow plan data frame if multiple values are assigned to a single wildcard. If FALSE, each occurrence of the wildcard is replaced with the next entry in the values vector, and the values are recycled.

Value

A workflow plan data frame with the wildcards evaluated.

Details

Specify a single wildcard with the wildcard and values arguments. In each command, the text in wildcard will be replaced by each value in values in turn. Specify multiple wildcards with the rules argument, which overrules wildcard and values if not NULL. Here, rules should be a list with wildcards as names and vectors of possible values as list elements.

Examples

# Create the part of the workflow plan for the datasets. datasets <- drake_plan( small = simulate(5), large = simulate(50)) # Create a template workflow plan for the analyses. methods <- drake_plan( regression1 = reg1(dataset__), regression2 = reg2(dataset__)) # Evaluate the wildcards in the template # to produce the actual part of the workflow plan # that encodes the analyses of the datasets. # Create one analysis for each combination of dataset and method. evaluate_plan(methods, wildcard = "dataset__", values = datasets$target)
#> # A tibble: 4 x 2 #> target command #> <chr> <chr> #> 1 regression1_small reg1(small) #> 2 regression1_large reg1(large) #> 3 regression2_small reg2(small) #> 4 regression2_large reg2(large)
# Only choose some combinations of dataset and analysis method. ans <- evaluate_plan(methods, wildcard = "dataset__", values = datasets$target, expand = FALSE) ans
#> # A tibble: 2 x 2 #> target command #> <chr> <chr> #> 1 regression1 reg1(small) #> 2 regression2 reg2(large)
# For the complete workflow plan, row bind the pieces together. my_plan <- rbind(datasets, ans) my_plan
#> # A tibble: 4 x 2 #> target command #> <chr> <chr> #> 1 small simulate(5) #> 2 large simulate(50) #> 3 regression1 reg1(small) #> 4 regression2 reg2(large)
# Workflow plans can have multiple wildcards. # Each combination of wildcard values will be used # Except when expand is FALSE. x <- drake_plan(draws = rnorm(mean = Mean, sd = Sd)) evaluate_plan(x, rules = list(Mean = 1:3, Sd = c(1, 10)))
#> # A tibble: 6 x 2 #> target command #> <chr> <chr> #> 1 draws_1_1 rnorm(mean = 1, sd = 1) #> 2 draws_1_10 rnorm(mean = 1, sd = 10) #> 3 draws_2_1 rnorm(mean = 2, sd = 1) #> 4 draws_2_10 rnorm(mean = 2, sd = 10) #> 5 draws_3_1 rnorm(mean = 3, sd = 1) #> 6 draws_3_10 rnorm(mean = 3, sd = 10)