Sum of a variable based on two grouped variables - for the previous year

Question

I have a list of customers, revenue from them through two channels (online & offline) over the last few financial years. I would like to have a variable showing each customer's total revenue (online+offline) for the previous year.

Sample data is shown below with desired variablehighlighted in yellow. Calculation is shown in adjacent column.

I tried grouping by CustomerID & Fin Year, calculate sum of Revenue and use lag() function to get previous year's total revenue but it didn't work.

df %>% group_by(CustomerID, FinYear) %>% mutate(yearly_totalRevenue = sum(Revenue)) %>% mutate(lastyear_totalRevenue = lag(yearly_totalRevenue )) %>%  ungroup() 

Note: As the data volume is in the range of 10M, a memory efficient code (preferably using data.table functionalities) will be highly appreciated.

Thanks in advance.

Edit1: dput() of the sample data is added.

structure(list(CustomerID = c("Cust1", "Cust2", "Cust3", "Cust4", 
"Cust5", "Cust1", "Cust2", "Cust3", "Cust4", "Cust5"), `Fin Year` = 
c("2010/11", 
"2011/12", "2012/13", "2013/14", "2014/15", "2010/11", "2011/12", 
"2012/13", "2013/14", "2014/15"), Channel = c("Online", "Online", 
"Online", "Online", "Online", "Offline", "Offline", "Offline", 
"Offline", "Offline"), Revenue = c(858, 733, 248, 541, 222, 316, 
412, 167, 385, 654)), row.names = c(NA, -10L), class = c("tbl_df", 
"tbl", "data.frame"))

Answer 1

You can try:

setDT(df)[, yearly_totalRevenue := sum(Revenue), .(CustomerID, FinYear)][, 
    lastyear_totalRevenue := shift(yearly_totalRevenue), .(rowid(CustomerID))]

output:

    CustomerID FinYear Channel Revenue yearly_totalRevenue lastyear_totalRevenue
 1:      Cust1 2010/11  Online     858                1174                    NA
 2:      Cust2 2011/12  Online     733                1145                  1174
 3:      Cust3 2012/13  Online     248                 415                  1145
 4:      Cust4 2013/14  Online     541                 926                   415
 5:      Cust5 2014/15  Online     222                 876                   926
 6:      Cust1 2010/11 Offline     316                1174                    NA
 7:      Cust2 2011/12 Offline     412                1145                  1174
 8:      Cust3 2012/13 Offline     167                 415                  1145
 9:      Cust4 2013/14 Offline     385                 926                   415
10:      Cust5 2014/15 Offline     654                 876                   926