Aggregating partial duplicates

Another way of handling partial duplicates is to compute a summary statistic of the values that differ between partial duplicates, such as mean, median, maximum, or minimum. This can come in handy when you're not sure how your data was collected and want an average, or if based on domain knowledge, you'd rather have too high of an estimate than too low of an estimate (or vice versa).

dplyr is loaded and bike_share_rides is available.

This exercise is part of the course

Cleaning Data in R

Exercise instructions

Group bike_share_rides by ride_id and date.
Add a column called duration_min_avg that contains the mean ride duration for the row's ride_id and date.
Remove duplicates based on ride_id and date, keeping all columns of the data frame.
Remove the duration_min column.

Hands-on interactive exercise

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

bike_share_rides %>%
  # Group by ride_id and date
  ___ %>%
  # Add duration_min_avg column
  mutate(duration_min_avg = ___ ) %>%
  # Remove duplicates based on ride_id and date, keep all cols
  ___ %>%
  # Remove duration_min column
  ___(-___)

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 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

Current Exercise

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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

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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

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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!