A point-and-click Shiny interface for dqcheckr — automated data quality checks for recurring dataset deliveries.
Use this if you want to configure and run quality checks without writing any R
code. If you prefer scripting, use dqcheckr directly.
dqcheckrGUI is feature-complete. It receives bug fixes and compatibility
updates only — configuration features are developed in dqcheckr itself.
Existing deployments continue to work unchanged.
Since dqcheckr 0.3.0 the whole workflow this interface provides is available
as plain function calls, with no Shiny app to run:
# inspect a real delivery, write a fully-commented YAML config
generate_dataset_config("input_data/sales/2026-07.csv", dataset_name = "sales")
validate_config("sales") # check the config before you rely on it
run_dq_check("sales") # run the checks, render the report
list_runs("sales") # what the history screen used to show
The generated YAML documents every available option inline, so it is meant to be
read and hand-edited. See
vignette("workflow", package = "dqcheckr")
for the full walkthrough, including drift comparison and fixed-width files.
# Install dqcheckr first (required)
install.packages("dqcheckr")
# Install dqcheckrGUI from GitHub
# devtools::install_github("mickmioduszewski/dqcheckrGUI")
install.packages("dqcheckrGUI", repos = c('https://mickmioduszewski.r-universe.dev', 'https://cloud.r-project.org'))
dqcheckrGUI::run_app()
Or, if you have downloaded the source, double-click launch.command (Mac),
launch.sh (Linux), or launch.bat (Windows).
Example YAML configuration files using the Star Wars dataset are included with the package. Copy them to a local folder to use as a starting point:
file.copy(
system.file("extdata/example_config", package = "dqcheckrGUI"),
"my_config",
recursive = TRUE
)
See the dqcheckr documentation for a full description of all configuration options and quality checks.