Mengagregasi duplikat parsial

Cara lain untuk menangani duplikat parsial adalah menghitung statistik ringkasan dari nilai-nilai yang berbeda antar duplikat parsial, seperti mean, median, maksimum, atau minimum. Ini berguna ketika Anda tidak yakin bagaimana data dikumpulkan dan menginginkan nilai rata-rata, atau jika berdasarkan pengetahuan domain, Anda lebih memilih estimasi yang terlalu tinggi daripada terlalu rendah (atau sebaliknya).

dplyr sudah dimuat dan bike_share_rides tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Petunjuk latihan

Kelompokkan bike_share_rides berdasarkan ride_id dan date.
Tambahkan kolom bernama duration_min_avg yang berisi rata-rata durasi perjalanan untuk ride_id dan date pada baris tersebut.
Hapus duplikat berdasarkan ride_id dan date, dengan tetap mempertahankan semua kolom data frame.
Hapus kolom duration_min.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

bike_share_rides %>%
  # Group by ride_id and date
  ___ %>%
  # Add duration_min_avg column
  mutate(duration_min_avg = ___ ) %>%
  # Remove duplicates based on ride_id and date, keep all cols
  ___ %>%
  # Remove duration_min column
  ___(-___)

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 Exercise 10: Menghapus duplikasi parsial Exercise 11: Mengagregasi duplikat parsial

Latihan Saat Ini

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!