Trimming strings

In the previous exercise, you were able to identify the correct data type and convert user_birth_year to the correct type, allowing you to extract counts that gave you a bit more insight into the dataset.

Another common dirty data problem is having extra bits like percent signs or periods in numbers, causing them to be read in as characters. In order to be able to crunch these numbers, the extra bits need to be removed and the numbers need to be converted from character to numeric. In this exercise, you'll need to convert the duration column from character to numeric, but before this can happen, the word "minutes" needs to be removed from each value.

dplyr, assertive, and stringr are loaded and bike_share_rides is available.

This exercise is part of the course

Cleaning Data in R

Exercise instructions

Use str_remove() to remove "minutes" from the duration column of bike_share_rides. Add this as a new column called duration_trimmed.
Convert the duration_trimmed column to a numeric type and add this as a new column called duration_mins.
Glimpse at bike_share_rides and assert that the duration_mins column is numeric.
Calculate the mean of duration_mins.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

bike_share_rides <- bike_share_rides %>%
  # Remove 'minutes' from duration: duration_trimmed
  mutate(duration_trimmed = ___,
         # Convert duration_trimmed to numeric: duration_mins
         duration_mins = ___)

# Glimpse at bike_share_rides
___

# Assert duration_mins is numeric
___

# Calculate mean duration
___

Edit and Run Code

This exercise is part of the course

Cleaning Data in R

IntermediateSkill Level

4.8+

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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

Current Exercise

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 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!