SciencesPo (version 1.3.9)

categories: Extraction of categorical values as a preprocessing step for making dummy variables

Description

categories stores all the categorical values that are present in the factors and character vectors of a data frame. Numeric and integer vectors are ignored. It is a preprocessing step for the dummy function. This function is appropriate for settings in which the user only wants to compute dummies for the categorical values that were present in another data set. This is especially useful in predictive modeling, when the new (test) data has more or other categories than the training data.

Usage

categories(x, p = "all")

Arguments

x

data frame containing factors or character vectors that need to be transformed to dummies. Numerics, dates and integers will be ignored.

p

select the top p values in terms of frequency. Either "all" (all categories in all variables), an integer scalar (top p categories in all variables), or a vector of integers (number of top categories per variable in order of appearance.

Value

• A list containing the variable names and the categories

encoding

UTF-8

See Also

dummy

Examples

#create toy data
(traindata <- data.frame(xvar=as.factor(c("a","b","b","c")),
                         yvar=as.factor(c(1,1,2,3)),
                         var3=c("val1","val2","val3","val3"),
                         stringsAsFactors=FALSE))
(newdata <- data.frame(xvar=as.factor(c("a","b","b","c","d","d")),
                       yvar=as.factor(c(1,1,2,3,4,5)),
                       var3=c("val1","val2","val3","val3","val4","val4"),
                       stringsAsFactors=FALSE))

categories(x=traindata,p="all")
categories(x=traindata,p=2)
categories(x=traindata,p=c(2,1,3))