Professional data validation for the R environment
validateR-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
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 latest release can be installed from the R command-line
The development version can be installed as follows.
bash 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.