Mutating columns

For these exercises, we’ll use the dog breed traits data set, so import that from https://jeffreyrstevens.github.io/dpavir2025/data/dog_breed_traits.csv (if you don’t already have it) and assign it to traits.

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1. View traits to see what it looks like.

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2. Reassign traits with only the columns Breed through Coat Length.

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3. Reassign traits removing the Drooling Level column. That’s gross.

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4. What terrible column names! Reassign traits and change the column names to "breed", "affectionate", "children", "other_dogs", "shedding", "grooming", "coat_type", "coat_length". Note, use the colnames() function rather than select() or rename() since you already have the full vector of names.

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5. The ratings are supposed to run from 0 to 4 rather than 1 to 5. Change the affectionate column by subtracting 1 from the original numbers to rescale the values. Don’t reassign traits.

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6. Actually, all of the ratings need to be rescaled. Subtract 1 from all of the ratings columns by using across().

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7. Create a new column called coat that combines the coat_type and coat_length columns by pasting the values of those two columns separated by -.

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8. Create a new column called shed that dichotomizes shedding such that values of 3 and above are “A lot” and values below 3 are “Not much”. Do you need to account for missing data?

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9. Use rowwise() to calculate the mean rating for the children and other_dogs columns in a column called mean_rating.

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10. Create a column called coat_type2 that categorizes the coat_type values in the following way and puts it after coat_type:

• “very petable” = “Smooth”, “Silky”, “Wavy”
• “petable” = “Double”, “Curly”
• “not petable” = “Wiry”, “Hairless”, “Rough”, “Corded”

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