Duplikat penuh

Anda telah diberitahu bahwa ada pembaruan pada pipeline data bike sharing agar lebih efisien, tetapi akibatnya kemungkinan duplikat menjadi lebih tinggi. Agar Anda tetap dapat menggunakan skrip yang sama untuk menjalankan analisis mingguan tentang statistik perjalanan, Anda perlu memastikan bahwa semua duplikat dalam himpunan data dihapus terlebih dahulu.

Ketika beberapa baris dari sebuah data frame memiliki nilai yang sama untuk semua kolom, baris-baris tersebut merupakan duplikat penuh satu sama lain. Menghapus duplikat seperti ini penting, karena pengulangan nilai yang sama berkali-kali dapat mengubah statistik ringkasan seperti mean dan median. Setiap perjalanan, termasuk ride_id-nya, harus unik.

dplyr sudah dimuat dan bike_share_rides tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Petunjuk latihan

Dapatkan total jumlah duplikat penuh dalam bike_share_rides.
Hapus semua duplikat penuh dari bike_share_rides dan simpan data frame baru sebagai bike_share_rides_unique.
Dapatkan total jumlah duplikat penuh dalam data frame baru bike_share_rides_unique.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Count the number of full duplicates
___

# Remove duplicates
bike_share_rides_unique <- ___

# Count the full duplicates in bike_share_rides_unique
___

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 Exercise 5: Kendala rentang Exercise 6: Kendala durasi perjalanan Exercise 7: Kembali ke masa depan Exercise 8: Kendala keunikan Exercise 9: Duplikat penuh

Latihan Saat Ini

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!