Scrutinizing Collections of Structured Datasets • scrutr

scrutr was previously developed under the name industtry (2024-2025) and has been renamed ahead of its first CRAN release.

scrutr is an R toolkit for scrutinizing collections of structured datasets (data frames). It targets workflows where you need to apply the same inspection, profiling, or conversion procedure across many related tables.

Two guiding ideas:

“Collections first”: common tasks (inspecting, detecting schema differences, batch conversion) operate at the collection level, not one dataset at a time.
“Operational tooling”: lightweight helpers for paths, duplicates, join checks, and string replacements that recur in production data pipelines.

Installation

# install.packages("devtools")
devtools::install_github("danielrak/scrutr")

API overview

Area	Main intent	Key functions
Inspection & profiling	Profile one dataset or a whole folder; export diagnostics to Excel.	inspect(), inspect_write(), inspect_vars()
Schema detection	Detect variable presence across datasets; compare classes.	vars_detect(), vars_compclasses(), detect_chars_structure_datasets()
Batch conversion / renaming	Convert file formats or rename files at scale via Excel masks.	convert_r(), convert_all(), mask_convert_r(), mask_rename_r(), rename_r()
Data hygiene	Duplicate diagnostics, join checks, proportions.	dupl_show(), dupl_sources(), ljoin_checks(), table_prop()
Paths & filesystem	Replicate folder structures, move through paths.	folder_structure_replicate(), path_move()
Utilities	Batch string replacement.	replace_multiple()

Quick examples

Inspect a single data frame

library(scrutr)
result <- inspect(CO2)
head(result)

Inspect a folder of datasets and export to Excel

mydir <- file.path(tempdir(), "example")
dir.create(mydir, showWarnings = FALSE)

saveRDS(cars, file.path(mydir, "cars.rds"))
saveRDS(mtcars, file.path(mydir, "mtcars.rds"))

inspect_vars(
  input_path = mydir,
  output_path = mydir,
  output_label = "diagnostics",
  considered_extensions = "rds"
)

Batch conversion with `convert_all()`

convert_all(
  input_folderpath = mydir,
  considered_extensions = "rds",
  to = "csv",
  output_folderpath = file.path(mydir, "csv_output")
)

Mask-driven conversion with `convert_r()`

# 1. Generate an Excel mask template
mask_convert_r(output_path = mydir)

# 2. Fill in the mask, then run:
convert_r(
  mask_filepath = file.path(mydir, "mask_convert_r.xlsx"),
  output_path = mydir
)

scrutr