R Tutorial: Exploring categorical data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/exploratory-data-analysis at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, I'm Andrew Bray. I'm an Assistant Professor of Statistics at Reed College and I'll be your instructor for this course on Exploratory Data Analysis or EDA. In this course, you'll be exploring data from a wide range of contexts. The first dataset comes from comic books. Two publishers, Marvel and DC, have created a host of superheroes that have made their way into popular culture. You're probably familiar with Batman and Spiderman, but what about Mor the Mighty? The comics dataset has information on all comic characters that have been introduced by DC and Marvel. If we type the name of the dataset at the console, we get the first few rows and columns. Here we see that each row, or case, is a different character and each column, or variable, is a different observation made on that character. At the top it tell us the dimensions of this dataset: over 23,000 cases and 11 variables. Right under the variable names, it tells us that all three of these are factors, R's preferred way to represent categorical variables. The first case is Peter Parker, alias: Spiderman. The second column shows that his personal identity is kept secret and the third column tells us that his alignment is good; that he's a superhero, not a supervillain. At the bottom, we see that there are 8 additional variables that aren't shown here, including eye color and hair color, almost all of which are also factors. We can learn the different levels of a particular factor by using the levels function. It's clear that the alignment variable can be "good" or "neutral", but what other values are possible? If we run levels on the align column, we learn that there are in fact four possible alignments, including reformed criminal. I'm glad we checked that! If we do the same for identity,