Memvalidasi total

Dalam pelajaran ini, Anda akan terus bekerja dengan data frame accounts, tetapi kali ini Anda memiliki sedikit lebih banyak informasi tentang tiap akun. Ada tiga dana berbeda tempat pemilik akun dapat menyimpan uang mereka. Pada latihan ini, Anda akan memvalidasi apakah nilai total pada tiap akun sama dengan jumlah nilai pada fund_A, fund_B, dan fund_C. Jika ada akun yang tidak cocok, Anda dapat meninjaunya lebih lanjut untuk melihat apa yang salah dalam pembukuan yang menyebabkan ketidakkonsistenan.

dplyr sudah dimuat dan accounts tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Petunjuk latihan

Buat kolom baru bernama theoretical_total yang berisi penjumlahan nilai pada tiap dana.
Temukan akun yang nilai total-nya tidak cocok dengan theoretical_total.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Find invalid totals
accounts %>%
  # theoretical_total: sum of the three funds
  ___ %>%
  # Find accounts where total doesn't match theoretical_total
  ___

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: 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: Keseragaman Exercise 2: Keseragaman tanggal Exercise 3: Keseragaman mata uang Exercise 4: Validasi lintas kolom Exercise 5: Memvalidasi total

Latihan Saat Ini

Exercise 6: Memvalidasi usia Exercise 7: Kelengkapan Exercise 8: Jenis-jenis missingness Exercise 9: Memvisualisasikan data hilang Exercise 10: Menangani data hilang

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!