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Compare input file fingerprints between two genproc runs

Source: R/diff_inputs.R
diff_inputs.Rd

Takes two genproc_result objects produced by genproc() (the same function over the same mask, run at two different times) and reports which referenced input files have changed since the first run.

Usage

diff_inputs(r0, r1)

# S3 method for class 'genproc_input_diff'
print(x, ...)

Arguments

r0, r1

Two genproc_result objects. By convention, r0 is the earlier run and r1 the later one, but the function is symmetric with respect to changed / unchanged. The labels removed (present in r0, absent in r1) and added (the opposite) follow the asymmetric convention.

x

A genproc_input_diff object.

...

Ignored (present for S3 method consistency).

Value

An object of class genproc_input_diff (a named list) with components:

method

Character, e.g. "stat".

changed

A data.frame with columns path, size_before, size_after, mtime_before, mtime_after. One row per file whose size or mtime differs.

unchanged

Character vector of paths whose size and mtime are identical in both runs.

removed

Character vector of paths present in r0's snapshot but absent in r1's.

added

Character vector of paths present in r1's snapshot but absent in r0's.

cases_affected

A data.frame with columns case_id, path, column, change_type (one of "changed", "removed", "added"). One row per (case, input column) impacted by the diff. Pass to rerun_affected() to re-run only the impacted cases.

Details

Files are matched by canonical absolute path. The method field must agree between the two runs.

Examples

# Two runs of the same procedure, with one input file rewritten
# in between. `diff_inputs()` reports the drift.
src <- file.path(tempdir(), "diff-inputs-demo")
dir.create(src, showWarnings = FALSE, recursive = TRUE)
write.csv(head(iris), file.path(src, "a.csv"), row.names = FALSE)

mask <- data.frame(
  path = file.path(src, "a.csv"),
  stringsAsFactors = FALSE
)
read_one <- function(path) nrow(read.csv(path))

r0 <- genproc(read_one, mask)

# Rewrite the file with strictly more rows: size changes.
write.csv(iris, file.path(src, "a.csv"), row.names = FALSE)

r1 <- genproc(read_one, mask)
diff_inputs(r0, r1)
#> genproc input diff (method: stat)
#>   Changed:   1
#>   Unchanged: 0
#>   Removed:   0
#>   Added:     0
#>   Cases affected: 1
#> 
#> Changed files:
#>   /tmp/Rtmp7l8jH3/diff-inputs-demo/a.csv
#>       size:  214 B -> 3.9 KB
#>       mtime: 2026-05-21 17:21:54 -> 2026-05-21 17:21:54
#> 
#> Cases affected (use rerun_affected() to re-run):
#>   case_0001