Easy data validation for the masses.

The validate R-package makes it super-easy to check whether data lives up to expectations you have based on domain knowledge. It works by allowing you to define data validation rules independent of the code or data set. Next you can confront a dataset, or various versions thereof with the rules. Results can be summarized, plotted, and so on. Below is a simple example.

> library(validate)
> library(magrittr)
> check_that(iris, Sepal.Width < 0.5*Sepal.Length) %>% summary()
  rule items passes fails nNA error warning                       expression
1   V1   150     79    71   0 FALSE   FALSE Sepal.Width < 0.5 * Sepal.Length

To get started, please read our Introductory vignette.

With validate, data validation rules are treated as first-class citizens. This means you can import, export, annotate, investigate and manipulate data validation rules in a meaninful way. See this vignette for rule import/export.

Resources

• The validate paper, accepted for publication in JSS.
• Slides of the useR2016 talk (Stanford University, June 28 2016).
• Video of the satRdays talk (Hungarian Academy of Sciences, Sept 3 2016).
• Slides and exercises from the useR2018 tutorial.
• Materials for the uRos2018 workshop (The Hague, 2018)
• Materials for the ENBES|EESW workshop (Bilbao, 2019)
• Materials for the planned workshop at the Institute for Statistical Methemathics (Tokyo, 2020 - cancelled because of the COVID-19 situation)

Installation

The latest release can be installed from the R command-line

install.packages("validate")

The development version can be installed as follows.

git clone https://github.com/data-cleaning/validate
cd validate
make install

Note that the development version likely contain bugs (please report them!) and interfaces that may not be stable.