Guided Project: Creating An Efficient Data Analysis Workflow, Part 2

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My Code:

sales <- sales %>% filter(! ([["user_submitted_review"]])))

total_purchased_mean <- mean(sales[["total_purchased"]], na.rm = T) 

sales_up <- sales %>% mutate(updated_total_purchased = if_else(, total_purchased_mean, total_purchased)) 

What I expected to happen: I expected this to replace the corresponding row with β€˜na’ in the total_purchased column with the mean of the total_purchased

What actually happened:

Error : x `false` must be a double vector not an integer vector 

I’m confused, should I cast the total_purchased_mean as an integer.

Hello! Hopefully I can clarify on this matter. This type of error is actually a subtle check by the if_else() function. From what I can see in the code, total_purchased is being interpreted as an integer, but total_purchased_mean ends up having a double type. if_else() tries to make sure that everything in the column it creates (ie updated_total_purchased) is the same type. R distinguishes between doubles and integers, even though they’re both numbers to us.

I would check the types of both total_purchased_mean and the total_purchased column. Then, make sure they are the same type and try it out again with if_else(). I hope this helps!

1 Like

Thanks it helped, I have been able to resolve the issue.