Mastering Data Analysis with R

Chapter 10. Classification and Clustering

In the previous chapter, we concentrated on how to compress information found in a number of continuous variables into a smaller set of numbers, but these statistical methods are somewhat limited when we are dealing with categorized data, for example when analyzing surveys.

Although some methods try to convert discrete variables into numeric ones, such as by using a number of dummy or indicator variables, in most cases it's simply better to think about our research design goals instead of trying to forcibly use previously learned methods in the analysis.

Note

We can replace a categorical variable with a number of dummy variables by creating a new variable for each label of the original discrete variable, ...