Membersihkan string

Dalam pelajaran ini, kita mempelajari dasar-dasar "regex," atau regular expressions, yang memungkinkan kita menangkap pola umum. Kita telah membahas dua notasi:

Ekspresi	Fungsinya
`.`	cocok dengan karakter apa pun
`*`	nol kali atau lebih

Sebagai contoh, ".*science " akan cocok dengan "data science " dalam string "data science rocks!"

Mari gunakan yang telah kita pelajari untuk mengubah response_var dalam himpunan data yang Anda buat pada pelajaran sebelumnya, gathered_data.

Latihan ini adalah bagian dari kursus

Data Kategorikal di Tidyverse

Petunjuk latihan

Gunakan str_remove untuk menghapus semua yang ada sebelum dan termasuk "rude to " (dengan spasi di akhir) pada kolom response_var.
Gunakan str_remove untuk menghapus "on a plane" dari kolom response_var.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

gathered_data %>%
    # Remove everything before and including "rude to " (with that space at the end!)
    mutate(response_var = ___(response_var, ___)) %>%
    # Remove "on a plane"
    mutate(response_var = ___(response_var, ___))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Data Kategorikal di Tidyverse

SkillTag.level.beginnerSkillTag.label

4.8+

Mulai Kursus Gratis

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.

Exercise 1: Introduction to qualitative variables Exercise 2: Recognizing factor variables Exercise 3: Qualitative variables in theory Exercise 4: Understanding your qualitative variables Exercise 5: Getting number of levels Exercise 6: Examining number of levels Exercise 7: Examining levels Exercise 8: Making better plots Exercise 9: Reordering a variable by its frequency Exercise 10: Ordering one variable by another

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.

Exercise 1: Reordering factors Exercise 2: Changing the order of factor levels Exercise 3: Tricks of fct_relevel()Exercise 4: Renaming factor levels Exercise 5: Distinguishing between forcats functions Exercise 6: Renaming a few levels Exercise 7: When you have a typo Exercise 8: Collapsing factor levels Exercise 9: Manually collapsing levels Exercise 10: Lumping variables by proportion Exercise 11: Preserving the most common levels

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.

Exercise 1: Examining common themed variables Exercise 2: Grouping and reshaping similar columns Exercise 3: Summarizing data Exercise 4: Creating an initial plot Exercise 5: Tricks of ggplot2 Exercise 6: Editing plot text Exercise 7: Reordering graphs Exercise 8: Changing and creating variables with case_when()Exercise 9: case_when() with single variable Exercise 10: case_when() from multiple columns

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.

Exercise 1: Pengantar studi kasus Exercise 2: Mengubah karakter menjadi faktor Exercise 3: Merapikan data Exercise 4: Persiapan data dan regex Exercise 5: Membersihkan string

Latihan Saat Ini

Exercise 6: Dikotomisasi variabel Exercise 7: Meringkas data Exercise 8: Membuat ulang plot Exercise 9: Membuat plot awal Exercise 10: Memperbaiki label Exercise 11: Membalik tampilan Exercise 12: Menyelesaikan bagan Exercise 13: Ringkasan akhir kursus