R Tutorial : Discretize a variable

Want to learn more? Take the full course at https://learn.datacamp.com/courses/introduction-to-data at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- A common way of creating a new variable from an existing variable is discretizing, that is converting a numerical variable to a categorical variable based on certain criteria. For example, suppose we are not interested in the actual reading score of students, but instead whether their reading score is below average or at or above average. First, we need to calculate the average reading score with the mean function. This will give us the mean value, 52.23. However, in order to be able to refer back to this value later on, we might want to store it as an object that we can refer to by name. So instead of just printing the result, let's save it as a new object called avg underscore read. Before we more on, a quick tip: most often you'll want to do both; see the value and also store it for later use. The approach we used here, running the mean function twice, is redundant. Instead, you can simply wrap your assignment code in parentheses so that R will not only assign the average value of reading test scores to avg read, but it will also print out its value. Next we need to determine whether each student is below or at or above average. For example, a reading score of 57 is above average, so is 68, but 44 is below. Obviously, going through each record like this would be tedious and error prone. Instead we can create this new variable with the mutate function from the dplyr package. We start with the data frame, hsb2, and pipe it into mutate, to create a new variable called read cat. Note that we are using a new variable name here in order to not overwrite the existing reading score variable. The decision criteria for this new variable is simple: if the reading score of the student is below the average reading score, label "below average", otherwise, lab