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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 Hours13 Videos44 Exercises13,161 Learners

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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.

    Play Chapter Now
    Introduction to qualitative variables
    50 xp
    Recognizing factor variables
    100 xp
    Qualitative variables in theory
    50 xp
    Understanding your qualitative variables
    50 xp
    Getting number of levels
    100 xp
    Examining number of levels
    100 xp
    Examining levels
    100 xp
    Making better plots
    50 xp
    Reordering a variable by its frequency
    100 xp
    Ordering one variable by another
    100 xp
  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.

    Play Chapter Now
    Reordering factors
    50 xp
    Changing the order of factor levels
    100 xp
    Tricks of fct_relevel()
    100 xp
    Renaming factor levels
    50 xp
    Distinguishing between forcats functions
    50 xp
    Renaming a few levels
    100 xp
    When you have a typo
    50 xp
    Collapsing factor levels
    50 xp
    Manually collapsing levels
    100 xp
    Lumping variables by proportion
    100 xp
    Preserving the most common levels
    100 xp
  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.

    Play Chapter Now
    Examining common themed variables
    50 xp
    Grouping and reshaping similar columns
    100 xp
    Summarizing data
    100 xp
    Creating an initial plot
    100 xp
    Tricks of ggplot2
    50 xp
    Editing plot text
    100 xp
    Reordering graphs
    100 xp
    Changing and creating variables with case_when()
    50 xp
    case_when() with single variable
    100 xp
    case_when() from multiple columns
    100 xp
  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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    Case study introduction
    50 xp
    Changing characters to factors
    100 xp
    Tidying data
    100 xp
    Data preparation and regex
    50 xp
    Cleaning up strings
    100 xp
    Dichotomizing variables
    100 xp
    Summarizing data
    100 xp
    Recreating the plot
    50 xp
    Creating an initial plot
    100 xp
    Fixing labels
    100 xp
    Flipping things around
    100 xp
    Finalizing the chart
    100 xp
    End of course recap
    50 xp

In the following tracks

Tidyverse Fundamentals with R

Datasets

538 Flying Etiquette surveyKaggle multiple choice responses

Collaborators

Chester Ismay
Becca Robins

Prerequisites

Reshaping Data with tidyr
Emily Robinson HeadshotEmily 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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