industtry

industtry is a toolkit for industrial-style exploitation of structured datasets (primarily data frames), with an emphasis on set-level operations: applying the same procedure to many inputs as long as they share an identified structure.

A large part of the package is built around two pragmatic ideas:

1. “Collections first”: many workflows fail to scale because they are designed for a single dataset; industtry pushes common tasks to the collection level (importing, inspecting, detecting schema differences, batch conversion, etc.).
2. “Operational tooling”: lightweight helpers for day-to-day work (paths, duplicates, joins checks, string replacements, etc.) that tend to recur in production data pipelines.

Some features integrate tightly with RStudio (background jobs / interactive workflows). Those parts degrade gracefully when RStudio is not available (examples are marked accordingly).

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Installation

You can install the development version from GitHub:

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

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API map (what the package covers)

The exported surface is intentionally broad; the table below is a functional map of the main user-facing tools.

Area	Main intent	Key functions
Import collections	Load multiple datasets into the Global Environment (serialized or parallelized).	serial_import(), parallel_import()
Batch conversion / renaming	Operate through Excel masks to convert file formats or rename files at scale.	mask_convert_r(), convert_r(), mask_rename_r(), rename_r()
Inspection & profiling	Inspect one dataset or a whole folder of datasets; export diagnostics to Excel.	inspect(), inspect_write(), inspect_vars()
Schema detection / consistency	Detect variables across datasets and compare classes/structures.	vars_detect*(), vars_compclasses*(), chars_structure*(), detect_chars_structure*()
Data hygiene helpers	Duplicate diagnostics, join checks, proportions, etc.	dupl_show(), dupl_sources(), ljoin_checks(), table_prop()
Paths & filesystem	Replicate folder structures / move files.	folder_structure_replicate(), path_move()
Utilities	String replacement, global assignment, script location, etc.	replace_multiple(), assign_to_global(), current_script_location()

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Core workflow 1 — import datasets as a collection

A lot of analysis pipelines begin with import. When you have many datasets, importing them “one by one” (and keeping names consistent) becomes error-prone.

industtry provides:

• serial_import() to import a set of datasets sequentially.
• parallel_import() to import a set of datasets in parallel (RStudio only).

library(industtry)

yourdir <- system.file("permadir_examples_and_tests/importations", package = "industtry")

lfiles <- list.files(yourdir, full.names = TRUE) %>%
  purrr::keep(stringr::str_detect(., "\\.rds$"))

lfiles

Sequential import

serial_import(lfiles)

Parallel import (RStudio)

{r, eval = FALSE} # RStudio only: parallel_import(lfiles)

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Core workflow 2 — inspect datasets (single + collection)

When datasets come from heterogeneous sources (different producers, different time windows, different exports), a fast “schema + variable” diagnostic helps you converge quickly.

Inspect one data frame to Excel

# built-in dataset as a simple example
data(cars)

out_dir <- tempdir()
inspect_write(
  data_frame_name = "cars",
  output_path = out_dir,
  output_label = "cars"
)

list.files(out_dir)

Inspect a whole folder of datasets to Excel

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

saveRDS(cars, file.path(mydir, "cars1.rds"))
saveRDS(mtcars, file.path(mydir, "cars2.rds"))

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

list.files(mydir)

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Core workflow 3 — industrialized conversion (mask-driven)

convert_r() is designed for batch conversion of dataset file formats using an Excel mask: a deterministic, auditable interface to define what to convert and how to name outputs.

High-level pattern:

1. Create a mask with mask_convert_r().
2. Fill the mask columns.
3. Run convert_r(mask_filepath, output_path) (RStudio only).

mydir <- system.file("permadir_examples_and_tests/convert_r", package =
                "industtry")

mask_convert_r(output_path = mydir) 

convert_r(mask_filepath =
            file.path(mydir, "mask_convert_r.xlsx"),
          output_path = mydir)

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Core workflow 4 — industrialized file renaming (mask-driven)

Similarly, rename_r() performs batch renaming based on an Excel mask created by mask_rename_r().

mydir <- tempfile()
dir.create(mydir)

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

mask_rename_r(input_path = mydir)

list.files(mydir)

rename_r(mask_filepath = file.path(mydir, "mask_rename_r.xlsx"))