Convalidare l’età

Ora che hai trovato alcune incoerenze negli importi di total, sospetti che possano esserci incoerenze anche nella colonna acct_age e vuoi vedere se queste incoerenze sono correlate. Usando le competenze viste nell’esercizio del video, dovrai convalidare l’età di ciascun account e verificare se le righe con acct_age incoerenti sono le stesse che avevano total incoerenti.

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Questo esercizio fa parte del corso

Pulizia dei dati in R

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Istruzioni dell'esercizio

Crea una nuova colonna chiamata theoretical_age che contenga l’età di ciascun account in base a date_opened.
Trova gli account in cui acct_age non corrisponde a theoretical_age.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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
  ___

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Pulizia dei dati in R

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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: Uniformità Exercise 2: Uniformità delle date Exercise 3: Uniformità della valuta Exercise 4: Convalida tra campi Exercise 5: Convalidare i totali Exercise 6: Convalidare l’età

Esercizio in corso

Exercise 7: Completezza Exercise 8: Tipi di mancanza Exercise 9: Visualizzare i valori mancanti Exercise 10: Gestione dei dati mancanti

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!