R Tutorial: Understanding your qualitative variables

Want to learn more? Take the full course at https://learn.datacamp.com/courses/categorical-data-in-the-tidyverse at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this lesson, we'll introduce our dataset, look at converting and understanding our qualitative variables, and learn some new dplyr functions. In the previous exercise, we were working with a dataset called multiple_choice_responses. This is a sample of data from the Kaggle 2017 Data Science survey. Kaggle is an online platform for predictive modeling and analytics competitions. This survey was given to current and aspiring data scientists, analysts, data engineers, and others in the data science field. They got about 16,000 responses on questions ranging from demographic information to the usefulness of different learning platforms to what languages they wanted to use in the coming year. We'll be using this dataset throughout the course. Just like with numerical variables, our first step when looking at categorical variables should be to get a high-level summary. Instead of numerical summaries, like the mean and the standard deviation, we can look at the number of categories and the name of each. But as we saw when examining our dataset, currently some of the variables are characters, not factors. How can we change this? First, we need to identify which columns are characters. We can use is dot character for this. Next, we can use the function as dot factor to change columns from characters to factors. If we want to do this for all character columns, we can take advantage of dplyr's mutate_if() function. This function takes two arguments. The first needs to be a function that returns true or false. mutate_if() will check each column to see if that condition is true and if so, will change the column based on the second argument. Once our columns are factors, we want to find out more about each one. We can use two functions: nlevels() a