make_onehot: Categorical Data Matrix to One-Hot Binary Matrix
In dannymorris/smltools: Simple Machine Learning Tools

Description Usage Arguments Details Value Examples

Categorical Data Matrix to One-Hot Binary Matrix

1 2	make_onehot(data, minus_level = FALSE, clarify_levels = TRUE, scale = FALSE)

`data`	a categorical data matrix.
`minus_level`	logical (default FALSE); if TRUE then create binary encodings for m-1 levels of a variable with m original levels
`clarify_levels`	logical (default TRUE); if TRUE then disambiguate resulting column names
`scale`	logical, if FALSE then binary matrix is returned. If TRUE, then normalization (see details) is applied to each binary transformed variable.

The normalization technique is taken from Outlier Analysis (Aggarwal, 2017), section 8.3. For each column j in the binary transformed matrix, a normalization factor is defined as sqrt(ni \* pj \* (1-pj)), where ni is the number of distinct categories in the reference variable from the raw data set and pj is the proportion of records taking the value of 1 for the jth variable

A transformed one hot encoded matrix is returned.

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df <- data.frame(gender = sample(c("male", "female"), 25, T),
                 age = sample(c("young", "old", "unknown"), 25, T))
make_onehot(data = df)