R: Syntax to define validation or indicator rules

syntax {validate} | R Documentation

## Syntax to define validation or indicator rules

### Description

A concise overview of the `validate` syntax.

### Basic syntax

The basic rule is that an R-statement that evaluates to a `logical` is a validating statement. This is established by static code inspection when `validator` reads a (set of) user-defined validation rule(s).

### Comparisons

All basic comparisons, including `>, >=, ==, !=, <=, <`, `%in%` are validating statements. When executing a validating statement, the `%in%` operator is replaced with `%vin%`.

### Logical operations

Unary logical operators '`!`', `all()` and `any` define validating statements. Binary logical operations including `&, &&, |, ||`, are validating when `P` and `Q` in e.g. `P & Q` are validating. (note that the short-circuits `&&` and `&` onnly return the first logical value, in cases where for `P && Q`, `P` and/or `Q` are vectors. Binary logical implication P\Rightarrow Q (P implies Q) is implemented as `if ( P ) Q`. The latter is interpreted as `!(P) | Q`.

### Type checking

Any function starting with `is.` (e.g. `is.numeric`) is a validating expression.

### Text search

`grepl` is a validating expression.

### Functional dependencies

Armstrong's functional dependencies, of the form A + B \to C + D are represented using the `~`, e.g. `A + B ~ C + D`. For example `postcode ~ city` means, that when two records have the same value for `postcode`, they must have the same value for `city`.

### Reference the dataset as a whole

Metadata such as numer of rows, columns, column names and so on can be tested by referencing the whole data set with the '`.`'. For example, the rule `nrow(.) == 15` checks whether there are 15 rows in the dataset at hand.

### Uniqueness, completeness

These can be tested in principle with the 'dot' syntax. However, there are some convenience functions: `is_complete`, `all_complete` `is_unique`, `all_unique`.

### Local, transient assignment

The operator '`:=`' can be used to set up local variables (during, for example, validation) to save time (the rhs of an assignment is computed only once) or to make your validation code more maintainable. Assignments work more or less like common R assignments: they are only valid for statements coming after the assignment and they may be overwritten. The result of computing the rhs is not part of a `confront`ation with data.

### Groups

Often the same constraints/rules are valid for groups of variables. `validate` allows for compact notation. Variable groups can be used in-statement or by defining them with the `:=` operator.

`validator( var_group(a,b) > 0 )`

is equivalent to

`validator(G := var_group(a,b), G > 0)`

is equivalent to

`validator(a>0,b>0)`.

Using two groups results in the cartesian product of checks. So the statement

`validator( f=var_group(c,d), g=var_group(a,b), g > f)`

is equivalent to

`validator(a > c, b > c, a > d, b > d)`

### File parsing

Please see the vignette on how to read rules from and write rules to file:

`vignette("rule-files",package="validate")`

[Package validate version 0.9.3 Index]