nom2dum: Nominal Variable to Dummy Variables
In quest: Prepare Questionnaire Data for Analysis

Description Usage Arguments Details Value See Also Examples

nom2dum converts a nominal variable into a set of dummy variables. There is one dummy variable for each unique value in the nominal variable. Note, base R does this recoding internally through the model.matrix.default function, but it is used in the context of regression-like models and it is not clear how to simplify it for general use cases outside that context.

1	nom2dum(nom, yes = 1L, no = 0L, prefix = "", rtn.fct = FALSE)

`nom`	character vector (or any atomic vector, including factors, which will be then coerced to a character vector) specifying the nominal variable.
`yes`	atomic vector of length 1 specifying what unique value should represent rows when the nominal category of interest is present. For a traditional dummy variable this value would be 1.
`no`	atomic vector of length 1 specifying what unique value should represent rows when the nominal category of interest is absent. For a traditional dummy variable this value would be 0.
`prefix`	character vector of length 1 specifying the string that should be appended to the beginning of each colname in the return object.
`rtn.fct`	logical vector of length 1 specifying whether the columns of the return object should be factors where the first level is `no` and the second level is `yes`.

Note, that yes and no are assumed to be the same typeof. If they are not, then the columns in the return object will be coerced to the most complex typeof (i.e., most to least: character, double, integer, logical).

data.frame of dummy columns with colnames specified by paste0(prefix, unique(nom)) and rownames specified by names(nom) or default data.frame rownames (i.e., c("1","2","3", etc.) if names(nom) is NULL.

model.matrix.default dum2nom

nom2dum(infert$"education") # default
nom2dum(infert$"education", prefix = "edu_") # use of the `prefix` argument
nom2dum(nom = infert$"education", yes = "one", no = "zero",
   rtn.fct = TRUE) # returns factor columns