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.

Pengantar variabel kualitatif

Mengenali variabel faktor

Variabel kualitatif dalam teori

Memahami variabel kualitatif Anda

Mendapatkan jumlah level

Menelaah jumlah level

Menguji level

Membuat plot yang lebih baik

Mengurutkan variabel berdasarkan frekuensi

Mengurutkan satu variabel berdasarkan variabel lain

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

Sebagai data scientist, Anda sering bekerja dengan data non-numerik, seperti jabatan pekerjaan, respons survei, atau informasi demografis. R memiliki cara khusus untuk merepresentasikannya, yaitu faktor (factors), dan kursus ini akan membantu Anda menguasai cara bekerja dengannya menggunakan paket tidyverse, forcats. Kita juga akan menggunakan paket tidyverse lainnya, termasuk ggplot2, dplyr, stringr, dan tidyr serta himpunan data dunia nyata, seperti himpunan data penerbangan fivethirtyeight dan Survei State of Data Science and ML dari Kaggle. Setelah mengikuti kursus ini, Anda akan dapat mengidentifikasi dan memanipulasi variabel faktor, memvisualisasikan data dengan cepat dan efisien, serta mengomunikasikan hasil secara efektif. Siap untuk mengategorikan!

Reshaping Data with tidyr

Bersiaplah untuk mengkategorikan! Dalam kursus ini, Anda akan bekerja dengan data non-numerik menggunakan Tidyverse.

Data Kategorikal di Tidyverse

Dasar-Dasar Tidyverse di R

Mengurutkan variabel berdasarkan frekuensi

Data Kategorikal di Tidyverse

Latihan interaktif praktis