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

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

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.

Introduzione al case study

Convertire i caratteri in fattori

Mettere in ordine i dati

Preparazione dei dati e regex

Pulire le stringhe

Dichotomizing variables

Riepilogare i dati

Ricreare il grafico

Creare un grafico iniziale

Sistemare le etichette

Invertire tutto

Finalizzare il grafico

Riepilogo di fine corso

Case Study on Flight Etiquette

538 Flying Etiquette survey

Kaggle multiple choice responses

Come data scientist, ti capiterà spesso di lavorare con dati non numerici, come qualifiche professionali, risposte a sondaggi o informazioni demografiche. R ha un modo specifico per rappresentarli, chiamato fattori, e questo corso ti aiuterà a padroneggiarne l’uso con il pacchetto del tidyverse forcats. Lavoreremo anche con altri pacchetti del tidyverse, tra cui ggplot2, dplyr, stringr e tidyr, e useremo insiemi di dati reali, come il dataset sui voli di fivethirtyeight e il sondaggio State of Data Science and ML di Kaggle. Al termine del corso, saprai identificare e manipolare variabili fattoriali, visualizzare rapidamente ed efficacemente i tuoi dati e comunicare i risultati in modo chiaro. Pronto a categorizzare?

Reshaping Data with tidyr

Preparati a categorizzare! In questo corso lavorerai con dati non numerici usando Tidyverse.

Dati categoriali nel Tidyverse

Preparati a classificare! In questo corso lavorerai con dati non numerici, come titoli di lavoro o risposte a sondaggi, usando il panorama Tidyverse.

Riepilogo di fine corso

Create Your Free Account