Split-Apply-Combine

A common data science problem is to split your data frame by a grouping, apply some transformation to each group, and then recombine those pieces back into one data frame. This is such a common class of problems in R that it has been given the name split-apply-combine. In Intermediate R for Finance, you will explore a number of these problems and functions that are useful when solving them, but, for now, let's do a simple example.

Suppose, for the cash data frame, you are interested in doubling the cash_flow for company A, and tripling it for company B:

grouping <- cash$company
split_cash <- split(cash, grouping)

# We can access each list element's cash_flow column by:
split_cash$A$cash_flow
[1] 1000 4000  550

split_cash$A$cash_flow <- split_cash$A$cash_flow * 2
split_cash$B$cash_flow <- split_cash$B$cash_flow * 3

new_cash <- unsplit(split_cash, grouping)

Take a look again at how you access the cash_flow column. The first $ is to access the A element of the split_cash list. The second $ is to access the cash_flow column of the data frame in A.

The split_cash data frame is available for you. Also, the grouping that was used to split cash is available.
Print split_cash to get a look at the list.
Print the cash_flow column for company B in split_cash.
Tragically, you have learned that company A went out of business. Set the cash_flow for company A to 0.
Use grouping to unsplit() the split_cash data frame. Assign this to cash_no_A.
Finally, print cash_no_A to see the modified data frame.

Exercise

Split-Apply-Combine

Instructions