R Tutorial: Introduction to qualitative data

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. --- Hi, my name is Emily Robinson, and welcome to this course where we'll learn how to effectively wrangle and plot non-numerical, or qualitative data. We'll start by learning about how to identify and inspect these variables in a dataset. Then we'll move to use the forcats package by Hadley Wickham to manipulate the variables by renaming categories, changing their order, and collapsing multiple groups into one. In the third chapter, we'll see how we can make effective visualizations by combining forcats with other tidyverse packages like dplyr, tidyr, stringr, and ggplot2. In our final chapter, we'll recreate this visualization from the FiveThirtyEight blog using all the tools we have learned. We've accessed this data from the FiveThirtyEight R package, which provides access to the code and datasets published by FiveThirtyEight. This course focuses on two types of qualitative data: categorical, or nominal data, and ordinal data. Categorical data are data that fall into unordered groups, while ordinal data have an inherent order but not a constant distance between them. Both types of data have a fixed and known set of possible values. One example of categorical data is a person's occupation. You might have a survey that has people pick their occupation from a list of 30, such as a doctor, teacher, or engineer, with an extra category for others. We could think of ways to order this data, such as by median salary or years of education needed, but they don't have an inherent order. On the other hand, if we asked people about their annual income and offered four choices, "0-$50,000", "$50,000-150,000", "$150,000-$500,000", and "more than $500,000", this would be an ordinal variable, because these groups go from smallest to largest. However, ther