  Checking data quality against domain knowledge is a common activity that
pervades statistical analysis from raw data to output. The R package 'validate'
facilitates this task by capturing and applying expert knowledge in the form of
validation rules: logical restrictions on variables, records, or data sets that
should be satisfied before they are considered valid input for further
analysis. In the validate package, validation rules are objects of computation
that can be manipulated, investigated, and confronted with data or versions of
a data set. The results of a confrontation are then available for further
investigation, summarization or visualization. Validation rules can also be
endowed with metadata and documentation and they may be stored or retrieved
from external sources such as text files or tabular formats. This data
validation infrastructure thus allows for systematic, user-defined definition
of data quality requirements that can be reused for various versions of a data
set or by data correction algorithms that are parameterized by validation
rules.
