Interactive Course

Categorical Data in the Tidyverse

Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.

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4 hours
13 Videos
44 Exercises
3,065 Participants
3,600 XP

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Course Description

As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. R has a special way of representing them, called factors, and this course will help you master working with them using the tidyverse package forcats. We’ll also work with other tidyverse packages, including ggplot2, dplyr, stringr, and tidyr and use real world datasets, such as the fivethirtyeight flight dataset and Kaggle’s State of Data Science and ML Survey. Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results. Get ready to categorize!

In this chapter, you’ll learn all about factors. You’ll discover the difference between categorical and ordinal variables, how R represents them, and how to inspect them to find the number and names of the levels. Finally, you’ll find how forcats, a tidyverse package, can improve your plots by letting you quickly reorder variables by their frequency.

Having gotten a good grasp of forcats, you’ll expand out to the rest of the tidyverse, learning and reviewing functions from dplyr, tidyr, and stringr. You’ll refine graphs with ggplot2 by changing axes to percentage scales, editing the layout of the text, and more.

You’ll continue to dive into the forcats package, learning how to change the order and names of levels and even collapse them into one another.

In this final chapter, you’ll take all that you’ve learned and apply it in a case study. You’ll learn more about working with strings and summarizing data, then replicate a publication quality 538 plot.

1
Introduction to Factor Variables
Free

In this chapter, you’ll learn all about factors. You’ll discover the difference between categorical and ordinal variables, how R represents them, and how to inspect them to find the number and names of the levels. Finally, you’ll find how forcats, a tidyverse package, can improve your plots by letting you quickly reorder variables by their frequency.
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Manipulating Factor Variables

You’ll continue to dive into the forcats package, learning how to change the order and names of levels and even collapse them into one another.
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Creating Factor Variables

Having gotten a good grasp of forcats, you’ll expand out to the rest of the tidyverse, learning and reviewing functions from dplyr, tidyr, and stringr. You’ll refine graphs with ggplot2 by changing axes to percentage scales, editing the layout of the text, and more.
View Chapter Details Play Chapter Now
Case Study on Flight Etiquette

In this final chapter, you’ll take all that you’ve learned and apply it in a case study. You’ll learn more about working with strings and summarizing data, then replicate a publication quality 538 plot.
View Chapter Details Play Chapter Now

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Emily works with the growth team at DataCamp on creating a robust A/B testing system. She previously worked at Etsy as a data analyst with the search team and earned her master's degree at INSEAD in Organizational Behavior. She is a vocal supporter of diversity in Data Science and tech in general and serves as a board member of the NYC R-Ladies chapter. Follow her at @robinson_es on Twitter and on her blog, Hooked on Data

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Categorical Data in the Tidyverse

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Course Description

Introduction to Factor Variables

Introduction to qualitative variables

Recognizing factor variables

Qualitative variables in theory

Understanding your qualitative variables

Getting number of levels

Examining number of levels

Examining levels

Making better plots

Reordering a variable by its frequency

Ordering one variable by another

Creating Factor Variables

Examining common themed variables

Grouping and reshaping similar columns

Summarizing data

Creating an initial plot

Tricks of ggplot2

Editing plot text

Reordering graphs

Changing and creating variables with case_when()

case_when() with single variable

case_when() from multiple columns

Manipulating Factor Variables

Reordering factors

Changing the order of factor levels

Tricks of fct_relevel()

Renaming factor levels

Distinguishing between forcats functions

Renaming a few levels

When you have a typo

Collapsing factor levels

Manually collapsing levels

Lumping variables by proportion

Preserving the most common levels

Case Study on Flight Etiquette

Case study introduction

Changing characters to factors

Tidying data

Data preparation and regex

Cleaning up strings

Dichotomizing variables