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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Clock4 HoursPlay13 VideosCode44 ExercisesGroup7,061 Learners
Database3600 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!

  1. 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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  2. 2

    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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  3. 3

    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.
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  4. 4

    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.
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In the following tracks
Data Analyst Tidyverse Fundamentals
Collaborators
Chester IsmayBecca Robins
Emily Robinson Headshot

Emily Robinson

Data Scientist at DataCamp
Emily is a senior data scientist at Warby Parker. Follow her at @robinson_es on Twitter and on her blog, Hooked on Data
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