课程

Categorical Data in the Tidyverse

基础技能水平

更新时间 2026年1月

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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RData Manipulation4 小时13 视频44 练习3,600 经验值16,445成就声明

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课程描述

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!

先决条件

Reshaping Data with tidyr

1

Introduction to Factor Variables

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.

Introduction to qualitative variables

50 经验值

Recognizing factor variables

100 经验值

Qualitative variables in theory

50 经验值

Understanding your qualitative variables

50 经验值

Getting number of levels

100 经验值

Examining number of levels

100 经验值

Examining levels

100 经验值

Making better plots

50 经验值

Reordering a variable by its frequency

100 经验值

Ordering one variable by another

100 经验值

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.

Reordering factors

50 经验值

Changing the order of factor levels

100 经验值

Tricks of fct_relevel()

100 经验值

Renaming factor levels

50 经验值

Distinguishing between forcats functions

50 经验值

Renaming a few levels

100 经验值

When you have a typo

50 经验值

Collapsing factor levels

50 经验值

Manually collapsing levels

100 经验值

Lumping variables by proportion

100 经验值

Preserving the most common levels

100 经验值

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.

Examining common themed variables

50 经验值

Grouping and reshaping similar columns

100 经验值

Summarizing data

100 经验值

Creating an initial plot

100 经验值

Tricks of ggplot2

50 经验值

Editing plot text

100 经验值

Reordering graphs

100 经验值

Changing and creating variables with case_when()

50 经验值

case_when() with single variable

100 经验值

case_when() from multiple columns

100 经验值

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.

Case study introduction

50 经验值

Changing characters to factors

100 经验值

Tidying data

100 经验值

Data preparation and regex

50 经验值

Cleaning up strings

100 经验值

Dichotomizing variables

100 经验值

Summarizing data

100 经验值

Recreating the plot

50 经验值

Creating an initial plot

100 经验值

Fixing labels

100 经验值

Flipping things around

100 经验值

Finalizing the chart

100 经验值

End of course recap

50 经验值

Categorical Data in the Tidyverse

课程完成

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