lama_get: Retrieve a translation from a lama_dictionary class object
In labelmachine: Make Labeling of R Data Sets Easy

Description Usage Arguments Value Translations Missing values lama_dictionary class objects

The functions lama_get() and lama_get_() take a lama_dictionary and extract a specific translation. The function lama_get() uses non-standard evaluation, whereas lama_get_() is the standard evaluation alternative.

lama_get(.data, translation)

## S3 method for class 'lama_dictionary'
lama_get(.data, translation)

lama_get_(.data, translation)

## S3 method for class 'lama_dictionary'
lama_get_(.data, translation)

.data

A lama_dictionary object

translation

Depending on which function was used:

lama_get: An unquoted translation name.
lama_get_: A character string holding the translation name.

The wanted translation (named character vector).

A translation is a named character vector of non zero length. This named character vector defines which labels (of type character) should be assigned to which values (can be of type character, logical or numeric) (e.g. the translation c("0" = "urban", "1" = "rural") assigns the label "urban" to the value 0 and "rural" to the value 1, for example the variable x = c(0, 0, 1) is translated to x_new = c("urban", "urban", "rural")). Therefore, a translation (named character vector) contains the following information:

The names of the character vector entries correspond to the original variable levels. Variables of types numeric or logical are turned automatically into a character vector (e.g. 0 and 1 are treated like "0" and "1").
The entries (character strings) of the character vector correspond to the new labels, which will be assigned to the original variable levels. It is also allowed to have missing labels (NAs). In this case, the original values are mapped onto missing values.

The function lama_translate() is used in order to apply a translation on a variable. The resulting vector with the assigned labels can be of the following types:

character: An unordered vector holding the new character labels.
factor with character levels: An ordered vector holding the new character labels.

The original variable can be of the following types:

character vector: This is the simplest case. The character values will replaced by the corresponding labels.
numeric or logical vector: Vectors of type numeric or logical will be turned into character vectors automatically before the translation process and then simply processed like in the character case. Therefore, it is sufficient to define the translation mapping for the character case, since it also covers the numeric and logical case.
factor vector with levels of any type: When translating factor variables one can decide whether or not to keep the original ordering. Like in the other cases the levels of the factor variable will always be turned into character strings before the translation process.

It is also possible to handle missing values with lama_translate(). Therefore, the used translation must contain a information that tells how to handle a missing value. In order to define such a translation the missing value (NA) can be escaped with the character string "NA_". This can be useful in two situations:

All missing values should be labeled (e.g. the translation c("0" = "urban", "1" = "rural", NA_ = "missing") assigns the character string "missing" to all missing values of a variable).
Map some original values to NA (e.g. the translation c("0" = "urban", "1" = "rural", "2" = "NA_", "3" = "NA_") assigns NA (the missing character) to the original values 2 and 3). Actually, in this case the translation definition does not always have to use this escape mechanism, but only when defining the translations inside of a YAML file, since the YAML parser does not recognize missing values.

Each lama_dictionary class object can contain multiple translations, each with a unique name under which the translation can be found. The function lama_translate() uses a lama_dictionary class object to translate a normal vector or to translate one or more columns in a data.frame. Sometimes it may be necessary to have different translations for the same variable, in this case it is best to have multiple translations with different names (e.g. area_short = c("0" = "urb", "1" = "rur") and area = c("0" = "urban", "1" = "rural")).