It is easy to do this kind of matching for one row, but hard to do it for multiple rows.
Luckily, dplyr can efficiently do this for the entire table using the
inner_join() method looks for columns that are common between two data frames and then looks for rows where those columns’ values are the same. It then combines the matching rows into a single row in a new table.
We can call the
inner_join() method with two data frames like this:
joined_df <- orders %>% inner_join(customers)
This will match up all of the customer information to the orders that each customer made.
You are an analyst at Cool T-Shirts Inc. You are going to help them analyze some of their sales data.
There are two data frames defined in the file notebook.Rmd:
salescontains the monthly revenue for Cool T-Shirts Inc. It has two columns:
targetscontains the goals for monthly revenue for each month. It has two columns:
Create a new data frame
sales_vs_targets which contains the
Cool T-Shirts Inc. wants to know the months when they crushed their targets.
sales_vs_targets to only include the rows where
revenue is greater than
target. Save these rows to the variable