Memangkas string

Pada latihan sebelumnya, Anda dapat mengidentifikasi tipe data yang benar dan mengonversi user_birth_year ke tipe yang tepat, sehingga memungkinkan Anda mengekstrak jumlah yang memberi sedikit lebih banyak wawasan tentang himpunan data.

Masalah data kotor lain yang umum adalah adanya tambahan seperti tanda persen atau titik pada angka, sehingga angka tersebut dibaca sebagai character. Agar dapat mengolah angka-angka ini, bagian tambahan perlu dihapus dan angkanya perlu dikonversi dari character menjadi numeric. Pada latihan ini, Anda perlu mengonversi kolom duration dari character menjadi numeric, tetapi sebelum itu terjadi, kata "minutes" perlu dihapus dari setiap nilai.

dplyr, assertive, dan stringr telah dimuat dan bike_share_rides tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Petunjuk latihan

Gunakan str_remove() untuk menghapus "minutes" dari kolom duration pada bike_share_rides. Tambahkan hasilnya sebagai kolom baru bernama duration_trimmed.
Konversikan kolom duration_trimmed ke tipe numerik dan tambahkan sebagai kolom baru bernama duration_mins.
Lihat sekilas bike_share_rides dan pastikan bahwa kolom duration_mins bertipe numeric.
Hitung mean dari duration_mins.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

bike_share_rides <- bike_share_rides %>%
  # Remove 'minutes' from duration: duration_trimmed
  mutate(duration_trimmed = ___,
         # Convert duration_trimmed to numeric: duration_mins
         duration_mins = ___)

# Glimpse at bike_share_rides
___

# Assert duration_mins is numeric
___

# Calculate mean duration
___

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: Kendala tipe data Exercise 2: Tipe data umum Exercise 3: Mengonversi tipe data Exercise 4: Memangkas string

Latihan Saat Ini

Exercise 5: Kendala rentang Exercise 6: Kendala durasi perjalanan Exercise 7: Kembali ke masa depan Exercise 8: Kendala keunikan Exercise 9: Duplikat penuh Exercise 10: Menghapus duplikasi parsial Exercise 11: Mengagregasi duplikat parsial

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: Checking membership Exercise 2: Members only Exercise 3: Not a member Exercise 4: Categorical data problems Exercise 5: Identifying inconsistency Exercise 6: Correcting inconsistency Exercise 7: Collapsing categories Exercise 8: Cleaning text data Exercise 9: Detecting inconsistent text data Exercise 10: Replacing and removing Exercise 11: Invalid phone numbers

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!