Bukan anggota

Sekarang setelah Anda berlatih mengidentifikasi masalah batasan keanggotaan, saatnya memperbaiki masalah tersebut pada himpunan data baru. Sepanjang bab ini, Anda akan bekerja dengan himpunan data bernama sfo_survey, yang berisi tanggapan survei dari penumpang yang terbang dari San Francisco International Airport (SFO). Peserta ditanya tentang kebersihan bandara, waktu tunggu, keamanan, dan kepuasan keseluruhan mereka.

Ada beberapa masalah selama pengumpulan data yang menyebabkan ketidakkonsistenan dalam himpunan data. Dalam latihan ini, Anda akan bekerja dengan kolom dest_size, yang mengategorikan ukuran bandara tujuan tempat penumpang terbang. Sebuah data frame bernama dest_sizes tersedia dan berisi semua kemungkinan ukuran tujuan. Misi Anda adalah menemukan baris dengan dest_size tidak valid dan menghapusnya dari data frame.

dplyr telah dimuat dan sfo_survey serta dest_sizes tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Count the number of occurrences of dest_size
sfo_survey %>%
  count(___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Converting data types Exercise 4: Trimming strings Exercise 5: Range constraints Exercise 6: Ride duration constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: Full duplicates Exercise 10: Removing partial duplicates Exercise 11: Aggregating partial duplicates

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

Exercise 1: Memeriksa keanggotaan Exercise 2: Hanya untuk anggota Exercise 3: Bukan anggota

Latihan Saat Ini

Exercise 4: Masalah data kategorikal Exercise 5: Mengidentifikasi inkonsistensi Exercise 6: Mengoreksi ketidakkonsistenan Exercise 7: Menggabungkan kategori Exercise 8: Membersihkan data teks Exercise 9: Mendeteksi data teks yang tidak konsisten Exercise 10: Mengganti dan menghapus Exercise 11: Nomor telepon tidak valid

In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.

Exercise 1: Uniformity Exercise 2: Date uniformity Exercise 3: Currency uniformity Exercise 4: Cross field validation Exercise 5: Validating totals Exercise 6: Validating age Exercise 7: Completeness Exercise 8: Types of missingness Exercise 9: Visualizing missing data Exercise 10: Treating missing data

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Calculating distance Exercise 3: Small distance, small difference Exercise 4: Fixing typos with string distance Exercise 5: Generating and comparing pairs Exercise 6: Link or join?Exercise 7: Pair blocking Exercise 8: Comparing pairs Exercise 9: Scoring and linking Exercise 10: Score then select or select then score?Exercise 11: Putting it together Exercise 12: Congratulations!