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.LengthWith 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.
- The Data Validation Cookbook
- 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 Mathematics (Tokyo, 2020 - cancelled because of the COVID-19 situation)
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 installNote that the development version likely contain bugs (please report them!) and interfaces that may not be stable.