Inconsistenties corrigeren

Nu je hebt vastgesteld dat dest_size inconsistenties in witruimte heeft en cleanliness inconsistenties in hoofdletters, ga je de nieuwe tools gebruiken om de inconsistente waarden in sfo_survey te herstellen in plaats van de datapunten volledig te verwijderen. Dat zou namelijk bias kunnen toevoegen aan je gegevensset als meer dan 5% van de datapunten moet worden weggegooid.

dplyr en stringr zijn geladen en sfo_survey is beschikbaar.

Deze oefening maakt deel uit van de cursus

Data opschonen in R

Cursus bekijken

Oefeninstructies

Voeg een kolom toe aan sfo_survey met de naam dest_size_trimmed die de waarden in de kolom dest_size bevat, met alle spaties aan het begin en einde verwijderd.
Voeg nog een kolom toe met de naam cleanliness_lower die de waarden in de kolom cleanliness bevat, omgezet naar kleine letters.
Tel het aantal voorkomens van elke categorie in dest_size_trimmed.
Tel het aantal voorkomens van elke categorie in cleanliness_lower.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Add new columns to sfo_survey
sfo_survey <- sfo_survey %>%
  # dest_size_trimmed: dest_size without whitespace
  mutate(dest_size_trimmed = ___,
         # cleanliness_lower: cleanliness converted to lowercase
         cleanliness_lower = ___)

# Count values of dest_size_trimmed
sfo_survey %>%
  ___

# Count values of cleanliness_lower
sfo_survey %>%
  ___

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: Lidmaatschap controleren Exercise 2: Alleen voor leden Exercise 3: Geen lid Exercise 4: Problemen met categorische data Exercise 5: Inconsistenties herkennen Exercise 6: Inconsistenties corrigeren

Huidige oefening

Exercise 7: Categorieën samenvoegen Exercise 8: Tekstgegevens opschonen Exercise 9: Inconsistente tekstgegevens opsporen Exercise 10: Vervangen en verwijderen Exercise 11: Ongeldige telefoonnummers

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 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!