pointblank has sophisticated features such as executing the rules in relational databases directly. It comes up with its own API for rules. Custom code could be used as rules, but it's usually not encouraged and not so straightforward. It has a rich reporting feature. Agents are the central entity in this package. The agent is given the data as well as rules, and on interrogation, the agent will generate the report.
data.validator comes up with it's own limited API along with functions from assertr package. A data validation report is generated and it's populated with the results of the validation. It has it's own reporting functionality, but seems to have limited feature than the pointblank. Doesn't seem to support YAML.
Pointblank Tr
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Why pointblank and data.validator require custom API for rules, whereas validate doesn't need one? For example, if we need to check if a column is numeric, pointblank needs col_is_numeric method and data.validator require validate_cols, however validate just works with regular base R method is. numeric(). Are the custom API allows the packages to have more control over the validations that allow them to show more details in the report?
It's observed that conditional statements are fairly straightforward to write in validate package. A regular R if statement works as a rule. It doesn't seem to be so straightforward in pointblank and data.validator. It may be because of my limited understanding of those packages, but it's not straightforward to find examples from their documentation.
In terms of functionality, wondering whether there is any validation that validate cannot do, but pointblank or data.validator can do? It seems more likely that validate is flexible enough to support complex validation rules, compare to pointblank or data.validator, is this right understanding. 17dc91bb1f
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