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minex

reprex makes your bug reproducible. minex makes it minimal.

When you ask for help with R, you are told to post a minimal reproducible example. The reprex package runs and formats your code; it does not shrink it. minex shrinks it: paste in a failing script and it returns the smallest subset of statements that still throws the same error, finding it by delta debugging rather than by deleting lines and re-running by hand.

Installation

# install.packages("pak")
pak::pak("DIGlabUAB/minex")

Usage

library(minex)

script <- c(
  "a <- 10",
  "b <- 20",
  "log('not a number')"
)

minex(code = script)
#> <minex_result> 3 statement(s) reduced to 1 (3 oracle call(s))
#> target failure: non-numeric argument to mathematical function
#> ------------------------------------------------
#> log('not a number')

The setup lines are dropped and only the offending statement remains. A statement that the failure genuinely depends on is kept, because removing it changes the error and the reduction is rejected.

minex() also reads a file directly:

minex(file = "analysis.R")

And reduce_rows() does the same job for a data frame, returning the rows that still reproduce a failure:

df <- data.frame(id = 1:6, value = c(3, 8, 999, 2, 5, 7))
reduce_rows(df, function(d) any(d$value > 100))
#>   id value
#> 3  3   999

How it works

Each candidate reduction runs in a fresh R process (via callr) so that statement dependencies and side effects are respected. A candidate “reproduces” the failure when it errors with the same message as the original; matching on the condition class instead, or supplying a custom oracle, is also supported. The engine is ddmin(), an implementation of the delta debugging algorithm of Zeller and Hildebrandt (2002), exposed for reuse on any collection.

See vignette("minex") for the full walkthrough.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.