classify: Categorise your data
In oizin/labellr: Categorise Your Data

Categorise rows of a data frame according to a set of rules. These rules specify allowable values for each particular category level.

1	classify(data, definitions, default_def = "unknown")

`data`	data.frame whose rows are to be categorised according to a provided set of rules.
`definitions`	Each row of the data frame dictionary specifies a data type for a variable found in data and rules. There should be two columns in dictionary, variable and type. The column names in rules should be exactly matched in dictionary and type should be one of num, char, or date for each variable. The first column of this data frame should be the name of the (to be created) classification variable and subsequent columns should be the names of the variables which are to be tested against a rule. Each row of the rules data.frame should contain a classification level in the first column and from there the rules to test each column against.
`default_def`	the default value to be given to all unclassified rows.

Rulesets for variable type num:

present/absent: indicate that a value must be present (i.e. cannot be NA) with "1", and that a value must be absent (i.e. must be NA) with "0".
comparison to a constant: compare the entered value to a numeric constant (scalar) X with any of "==X", ">=X", "<=X", ">X" or "<X". For example, ">=0".

Rulesets for variable type char:

takes a specified value: indicate that the value must match a particular character, for example "y".
does not take a specified value: indicate that the value must not match a particular character, for example "!y" indicates the value must not be "y".
takes a value in a set: indicate that the value must be in a specific character set. Note that the elements of the set should be separated by a comma but be a vector of length 1 e.g. ("y,n"), rather than ("y","n").

Rulesets for variable type date: