PCA won’t be effective with categorical variables since they lack a variance structure (they are not numerical).Ĭonverting categorical variables into a sequence of binary variables with 0 and 1 values is one way to do the PCA in a data set with categorical variables. The primary explanation for this is that the PCA, which involves dissecting the variance structure of the variable, is made to function better with numerical or continuous variables.
The answer is not straightforward: although it is technically possible to run a PCA on a data frame containing categorical variables, this doesn’t appear to be the best course of action. You’ll discover how to apply Principal Component Analysis (PCA) to data frames that include categorical variables in this course.Īdditionally, you’ll discover how to use the R programming language to put these alternatives into practice.Ĭan a Data Frame with Categorical Variables be Used for PCA? However, can PCA be applied to a data set with categorical variables? PCA for Categorical Variables in R, Using Principal Component Analysis to minimize the dimensionality of your data frame may have crossed your mind (PCA). If you are interested to learn more about data science, you can find more articles here finnstats. The post PCA for Categorical Variables in R appeared first on finnstats.
