Leeftijd valideren

Nu je enkele inconsistenties in de total-bedragen hebt gevonden, vermoed je dat er ook inconsistenties in de kolom acct_age zitten, en je wilt zien of deze inconsistenties met elkaar samenhangen. Met de vaardigheden uit de video-oefening ga je de leeftijd van elke account valideren en controleren of de rijen met inconsistente acct_ages dezelfde zijn als die met inconsistente totals.

dplyr en lubridate zijn geladen en accounts is beschikbaar.

Deze oefening maakt deel uit van de cursus

Data opschonen in R

Cursus bekijken

Oefeninstructies

Maak een nieuwe kolom theoretical_age met de leeftijd van elke account op basis van date_opened.
Zoek de accounts waarbij acct_age niet overeenkomt met theoretical_age.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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
  ___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Data opschonen in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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: Uniformiteit Exercise 2: Uniformiteit van datums Exercise 3: Valuta-uniformiteit Exercise 4: Validatie over velden Exercise 5: Totalen valideren Exercise 6: Leeftijd valideren

Huidige oefening

Exercise 7: Volledigheid Exercise 8: Soorten ontbrekendheid Exercise 9: Ontbrekende data visualiseren Exercise 10: Omgaan met missende 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!