R Tutorial: Measuring distance for categorical data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/cluster-analysis-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- So far you have exclusively worked with one type distance metric, the euclidean distance. This is a commonly used metric and is a great starting point when working with data that is continuous. But what happens if the data you have isn't continuous but is categorical? Let's start with the most basic case of categorical features, those that are binary, meaning that the values can only be one of two possibilities. Here you are presented with survey data, let's call it survey a. The participants of this survey were asked whether they enjoy drinking various types of alcoholic beverages. Since they can only answer yes or no we can code this binary response as TRUE or FALSE. We would be interested to learn which participants are similar to one another based on their responses. To calculate this we will use the similarity score called the Jaccard Index. This measure of similarity captures the ratio between the intersection of A and B to the union of A and B. Or more intuitively the ratio between the number of times the features of both observations are TRUE to the number of times they are ever TRUE. So going back to the previous example. Let us calculate the Jaccard similarity for two observations one and two. They only agree in one category, beer, so for the intersection, we get the value of one. While the number of categories these observations are ever true, or the union, is four. Dividing the intersection by the union we get the Jaccard similarity value of 0-point-25. But what about the distance. Well remember that distance is 1 - similarity, so in this case, the distance is just 0-point-75. To learn how to do this in R lets start with a subset of our data containing three observations, called survey a. In order to calculate the Jaccard distan