dqcheckr

Automated data quality checks for recurring dataset deliveries.

For each new file arrival, dqcheckr runs a battery of quality checks, compares the file to the previous delivery, writes a self-contained HTML report, and records summary statistics in a local SQLite database so that quality trends can be tracked over time. Supports CSV and fixed-width formats. Custom organisation-specific checks can be supplied as plain R files.

This is a CLI/API package — no UI. If you’d rather configure and run checks without writing R code, see dqcheckrGUI, a Shiny front-end built on top of this package.

What it does

• Runs single-snapshot quality checks (schema, missing rates, types, patterns, bounds, and more) against an incoming file
• Compares the file to the previous delivery (row/column differences, drift)
• Writes a self-contained HTML report for each run
• Records summary statistics in a SQLite snapshot database for long-term trend tracking and historical drift comparison
• Lets you supply your own organisation-specific checks as plain R files

Installation

install.packages("dqcheckr")

# or, the development version from GitHub
devtools::install_github("mickmioduszewski/dqcheckr")

Usage

A data officer runs a single command for each arriving dataset:

library(dqcheckr)

run_dq_check("customer_accounts", config_dir = "path/to/configs")

This prints a one-line console summary, writes an HTML report, and returns list(status, report_path, snapshot_id) invisibly.

Two YAML files control every run: a global dqcheckr.yml (default thresholds shared across datasets) and a per-dataset <dataset_name>.yml (file location, expected columns, column-level rules and overrides).

Learn more

See vignette("dqcheckr") for a full walkthrough of configuration and the available checks, or the package documentation site.

License

MIT