Validating age

Now that you found some inconsistencies in the total amounts, you're suspicious that there may also be inconsistencies in the acct_agecolumn, and you want to see if these inconsistencies are related. Using the skills you learned from the video exercise, you'll need to validate the age of each account and see if rows with inconsistent acct_ages are the same ones that had inconsistent totals

dplyr and lubridate are loaded, and accounts is available.

Bu egzersiz

Cleaning Data in R

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Create a new column called theoretical_age that contains the age of each account based on the date_opened.
Find the accounts where the acct_age doesn't match the theoretical_age.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# 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
  ___

Kodu Düzenle ve Çalıştır

Bu egzersiz

Cleaning Data in R

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Uniformity Exercise 2: Date uniformity Exercise 3: Currency uniformity Exercise 4: Cross field validation Exercise 5: Validating totals Exercise 6: Validating age

Geçerli Egzersiz

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!