Mengoreksi ketidakkonsistenan

Sekarang setelah Anda mengidentifikasi bahwa dest_size memiliki ketidakkonsistenan spasi kosong dan cleanliness memiliki ketidakkonsistenan kapitalisasi, Anda akan menggunakan alat baru yang tersedia untuk memperbaiki nilai yang tidak konsisten di sfo_survey alih-alih menghapus titik data sepenuhnya, yang dapat menambahkan bias ke himpunan data Anda jika lebih dari 5% titik data perlu dihapus.

dplyr dan stringr sudah dimuat dan sfo_survey tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Petunjuk latihan

Tambahkan kolom ke sfo_survey bernama dest_size_trimmed yang berisi nilai pada kolom dest_size dengan semua spasi di awal dan akhir dihapus.
Tambahkan kolom lain bernama cleanliness_lower yang berisi nilai pada kolom cleanliness yang diubah seluruhnya menjadi huruf kecil.
Hitung jumlah kemunculan tiap kategori di dest_size_trimmed.
Hitung jumlah kemunculan tiap kategori di cleanliness_lower.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Add new columns to sfo_survey
sfo_survey <- sfo_survey %>%
  # dest_size_trimmed: dest_size without whitespace
  mutate(dest_size_trimmed = ___,
         # cleanliness_lower: cleanliness converted to lowercase
         cleanliness_lower = ___)

# Count values of dest_size_trimmed
sfo_survey %>%
  ___

# Count values of cleanliness_lower
sfo_survey %>%
  ___

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 Exercise 4: Masalah data kategorikal Exercise 5: Mengidentifikasi inkonsistensi Exercise 6: Mengoreksi ketidakkonsistenan

Latihan Saat Ini

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!