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.

Introductie tot kwalitatieve variabelen

Factorvariabelen herkennen

Kwalitatieve variabelen in theorie

Inzicht in je kwalitatieve variabelen

Aantal levels ophalen

Het aantal levels onderzoeken

Niveaus onderzoeken

Betere visualisaties maken

Een variabele herschikken op basis van frequentie

Eén variabele ordenen op basis van een andere

Introduction to 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

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

Manipulating 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

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

Creating Factor Variables

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

Changing characters to factors

Tidying data

Data preparation and regex

Cleaning up strings

Dichotomizing variables

Recreating the plot

Fixing labels

Flipping things around

Finalizing the chart

End of course recap

Case Study on Flight Etiquette

538 Flying Etiquette survey

Kaggle multiple choice responses

Als data scientist werk je vaak met niet-numerieke gegevens, zoals functietitels, enquêteresultaten of demografische informatie. R heeft een speciale manier om die weer te geven: factors. In deze cursus leer je ermee werken met het tidyverse-pakket forcats. We gebruiken ook andere tidyverse-pakketten, waaronder ggplot2, dplyr, stringr en tidyr, en echte datasets zoals de fivethirtyeight-vluchtdataset en Kaggle’s State of Data Science and ML Survey. Na deze cursus kun je factorvariabelen herkennen en bewerken, je gegevens snel en efficiënt visualiseren en je resultaten duidelijk communiceren. Klaar om te categoriseren!

Reshaping Data with tidyr

Maak je klaar om te categoriseren! In deze cursus werk je met niet-numerieke data in Tidyverse.

Categorische gegevens in de Tidyverse

Tidyverse-basisprincipes in R

Factorvariabelen herkennen

Categorische gegevens in de Tidyverse

Oefeninstructies

Praktische interactieve oefening