Piping

For these exercises, we’ll use the dog breed traits data set.

1. Create a pipeline to do all of the following:

• assign pipeline to traits
• import data from https://jeffreyrstevens.github.io/dpavir2025/data/dog_breed_traits.csv
• subset only the columns Breed through Coat Length
• remove the Drooling Level column

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2. Rename the column names to "breed", "affectionate", "children", "other_dogs", "shedding", "grooming", "coat_type", "coat_length" using this vector (either at the end of the last pipeline or in a separate line).

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3. In a new pipe, use traits to do the following.

• assign to traits2
• rescale all of the ratings columns by subtracting 1 from all of the values
• 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 -
• 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” and places the new column after shedding
• calculate the mean rating for the children and other_dogs columns in a column called mean_rating and place it after other_dogs

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4. In a new pipe, use traits2 to do the following.

• assign to coat_grooming
• subset only the grooming and coat_type columns
• run a linear model (lm) using the formula grooming ~ coat_type (remember to use a placeholder for the data)
• apply the summary() function
• print the results to console

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