scrutr
Scrutinizing Collections of Structured Datasets
Provides a coherent interface for exploring and transforming multiple related data frames that share a common structure. Complements single-dataset inspection tools by operating across an entire collection at once. Also includes lightweight utilities for related file and folder management tasks.
scrutr
scrutrwas previously developed under the nameindusttry(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
)