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Re-run only the cases impacted by an input diff

Source: R/rerun_affected.R
rerun_affected.Rd

Filters the original mask of r0 down to the cases that referenced inputs reported as changed, removed, or added by diff_inputs(), and re-runs genproc() on that subset.

Usage

rerun_affected(
  r0,
  diff,
  f,
  parallel = NULL,
  nonblocking = NULL,
  track_inputs = TRUE,
  input_cols = NULL,
  skip_input_cols = NULL
)

Arguments

r0

A genproc_result produced by genproc(). Its $reproducibility$mask_snapshot provides the original mask; it must contain track_inputs = TRUE (the default).

diff

A genproc_input_diff produced by diff_inputs().

f

A function. Typically the same function passed to the original genproc() call. The result object does not store f, so it must be supplied here.

parallel, nonblocking, track_inputs, input_cols, skip_input_cols

Forwarded to genproc() for the re-run. By default, these inherit a sensible behaviour: track_inputs = TRUE (so the re-run is itself comparable), the other arguments default to NULL (sequential, blocking, automatic input tracking).

Value

A new genproc_result covering only the affected cases. Its case_ids are local to the subset (re-numbered starting at case_0001); the link back to the original r0 is via the matching rows of r0$reproducibility$mask_snapshot. If diff reports no affected cases, the function returns NULL with a message — there is nothing to re-run.

Details

This is the actionable end of the reproducibility layer: when an upstream file silently drifts, you do not need to re-run the whole mask. rerun_affected() produces a smaller run that refreshes only the impacted outputs.

See also

diff_inputs(), genproc()

Examples

# \donttest{
  # Set up a tiny workspace with one tracked input file.
  csv <- tempfile(fileext = ".csv")
  write.csv(iris, csv, row.names = FALSE)

  count_rows <- function(p) nrow(read.csv(p))

  r0 <- genproc(count_rows, data.frame(p = csv))

  # ... time passes, the upstream file is silently rewritten ...
  write.csv(head(iris), csv, row.names = FALSE)

  r1 <- genproc(count_rows, data.frame(p = csv))
  d <- diff_inputs(r0, r1)
  # d$cases_affected lists the case_ids whose inputs drifted.

  refreshed <- rerun_affected(r0, d, f = count_rows)
  refreshed$log
#>     case_id                                    p success error_message
#> 1 case_0001 /tmp/Rtmp7l8jH3/file19667d61fec9.csv    TRUE          <NA>
#>   traceback duration_secs
#> 1      <NA>             0
# }