Nominal data (‘categorical data’) are data that consist of attributes, and each attribute consists of various discrete values (‘types’). The different values that are distinguished in comparative linguistics are mostly open to debate, and different scholars like to make different decisions as to the definition of values. The
recode function allows for an easy and transparent way to specify a recoding of an existing dataset.
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a data frame with nominal data, attributes as columns, observations as rows.
a list of attributes to be recoded. Vectors (as elements of the list) are possible to specify combinations of attributes to be recoded as a single complex attribute.
file in which the recoding should be written.
the recoding template is by default written to a file in YAML format. When
Recoding nominal data is normally considered too complex to be performed purely within R. It is possible to do it completely within R, but it is proposed here to use an external YAML document to specify the decisions that are taken in the recoding. The typical process of recoding will be to use
write.recoding.template to prepare a skeleton that allows for quick and easy YAML-specification of a recoding. Or a YAML-recoding is written manually using various shortcuts (see below), and
read.recoding is used to turn it into a full-fledged recoding that can also be used to document the decisions made. The function
recode then combines the original data with the recoding, and produces a recoded dataframe.
recoding data structure in the YAML document basically consists of a list of recodings, each of which describes a new attribute, based on one or more attributes from the original data. Each new attribute is described by:
attribute: the new attribute name.
values: a character vector with the new value names.
link: a numeric vector with length of the original number of values. Each entry specifies the number of the new value. Zero can be used for any values that should be ignored in the new attribute.
recodingOf: the name(s) of the original attribute that forms the basis of the recoding. If there are multiple attributes listed, then the new attribute will be a combination of the original attributes.
OriginalValues: a character vector with the value names from the original attribute. These are only added to the template to make it easier to specify the recoding. In the actual recoding the listing in this file will be ignored. It is important to keep the ordering as specified, otherwise the linking will be wrong. The ordering of the values follows the result of
levels, which is determined by the current locale.
For writing recodings by hand, there are various shortcuts allowed:
values, etc. can be abbreviated. The first letter should be sufficient.
recodingOf can be the full name of the attribute in the original data, or simply a number of the column in the data frame.
the specification of
values can be left out, although the result will be uninformative names like ‘Att1’ and ‘Val1’.
it is also possible to add an item
doNotRecode with a vector of original attribute names (or column numbers). These original attributes will then be included unchanged in the recoded data table.
A minimal recoding consist thus of a specification of
link nothing will be recoded. Omitting
recodingOf will lead to an error.
There is a vignette available with detailed information about the process of recoding, check
recoding nominal data.
recode returns a data frame with the recoded attributes
write.recoding.template by default (when
yaml=TRUE) writes a YAML structure to the specified file. When
yaml=FALSE the same structure is returned inside R as a nested list.
read.recoding either reads a recoding from file, or a list structure within R, and cleans up all the shortcuts used. The output is by default a list structure to be used in
recode, though it is also possible to write the result to a YAML-file (when
file is specified). When
data is specified, the output will be embelished with all the original names from the original data, which makes for an even better documentation of the recoding.
expandValues is an internal help function to show the various value-combinations when combining attributes.
Cysouw, Michael, Jeffrey Craig Good, Mihai Albu and Hans-Jörg Bibiko. 2005. Can GOLD "cope" with WALS? Retrofitting an ontology onto the World Atlas of Language Structures. Proceedings of E-MELD Workshop 2005, http://emeld.org/workshop/2005/papers/good-paper.pdf
The World Atlas of Language Structure (WALS) contains typical data that most people would very much like to recode before using for further analysis. See Cysouw et al. 2005 for a discussion of various issues surrounding the WALS data.
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