Memvalidasi usia

Setelah Anda menemukan beberapa ketidakkonsistenan pada nilai total, Anda curiga bahwa mungkin ada ketidakkonsistenan juga pada kolom acct_age, dan Anda ingin melihat apakah ketidakkonsistenan ini saling berkaitan. Dengan keterampilan yang Anda pelajari dari video latihan, Anda perlu memvalidasi usia setiap akun dan melihat apakah baris dengan acct_age yang tidak konsisten adalah baris yang sama yang memiliki total tidak konsisten.

dplyr dan lubridate sudah dimuat, dan accounts tersedia.

Latihan ini adalah bagian dari kursus

Membersihkan Data di R

Petunjuk latihan

Buat kolom baru bernama theoretical_age yang berisi usia setiap akun berdasarkan date_opened.
Temukan akun di mana acct_age tidak cocok dengan theoretical_age.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Find invalid acct_age
accounts %>%
  # theoretical_age: age of acct based on date_opened
  mutate(theoretical_age = ___) %>%
  # Filter for rows where acct_age is different from theoretical_age
  ___

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 Exercise 6: Memvalidasi usia

Latihan Saat Ini

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!