Creating a function to split data frames multiple times then recombine

Question

I'm working on a large dataset in R with 3 factors: FY (6 levels), Region (10 levels), and Service (24 levels). I need to sum my numeric vector, SumOfUnits, at all three levels, and the only way I can think to do this is to split the data frames up into first: 6 data frames, split by FY, then split those 6 into 10 data frames, split on region, then those 10 into the 24 Services, then I can finally take the sum of the numeric vector and recombine all of the data frames into one. This data frame would then have 6*10*24 (1440) rows and 4 columns. The way I'm currently doing it involves a lot of splitting, so I thought there might be a function I could write that I could use at each level of the split, but I haven't used "function" very much in R so I'm not sure what to write (if there even is something). I also imagine there is probably a more efficient way to get the formatted data set, so I welcome all suggestions.

Here are a few lines from my data frame:

    FY    Region    Service               SumOfUnits
1   2006    1     Medication                 13
2   2006    1     Medication                 1
3   2006    1     Screening & Assessment    38
4   2006    1     Screening & Assessment    13
5   2006    1     Screening & Assessment    41
6   2006    1     Screening & Assessment    67
7   2006    1     Screening & Assessment    222
8   2006    1     Residential Treatment      38
9   2006    1     Residential Treatment     1558

This is the code I've been using for my splits:

# Creating a data frame by year
X <- split(MIC, MIC$FY)

Y <- lapply(seq_along(X), function(x) as.data.frame(X[[x]])[, ]) 
#Assign the dataframes in the list Y to individual objects
A <- Y[[1]]
B <- Y[[2]]
C <- Y[[3]]
D <- Y[[4]]
E <- Y[[5]]
Q <- Y[[6]]

#Creating 10 dataframes from 2006 split by region
X <- split(A, A$Region)

Y <- lapply(seq_along(X), function(x) as.data.frame(X[[x]])[, ])

Reg1 <- Y[[1]]
Reg2 <- Y[[2]]
Reg3<- Y[[3]]
Reg4 <- Y[[4]]
Reg5<- Y[[5]]
Reg6 <- Y[[6]]
Reg7 <- Y[[7]]
Reg8 <- Y[[8]]
Reg9 <- Y[[9]]
Reg10<- Y[[10]]

#Creating 24 dataframes: for 2006, region 1
X <- split(Reg1, Reg1$Service)

Y <- lapply(seq_along(X), function(x) as.data.frame(X[[x]])[, ])

Serv1 <- Y[[1]]
Serv2 <- Y[[2]]
Serv3<- Y[[3]]
Serv4 <- Y[[4]]
Serv5<- Y[[5]]
#etc...

I would want a sample of my data to look something like this:

FY    Region    Service    SumOfUnits
2006    1      Medication    4300
2006    2      Medication    3299
2006    3      Medication    2198
2007    1      Medication    5467
2007    2      Medication    3214
2007    3      Medication    9807

Answer 1

this is quite nice function to do this:

library(plyr)
ddply(MIC, .(FY, Region, Service), summarize, sumOfUnits=sum(SumOfUnits))

it gives back exactly what you need.

For MIC =

FY        Region Service SumOfUnits
1 2006      1       A          1
2 2006      2       B          4
3 2007      1       C          3
4 2007      2       D          2
5 2007      2       E          7
6 2006      1       A          3
7 2007      1       D          3
8 2007      2       B          4
9 2007      2       B          6

returns:

FY      Region Service sumOfUnits
1 2006      1       A   4
2 2006      2       B   4
3 2007      1       C   3
4 2007      1       D   3
5 2007      2       B  10
6 2007      2       D   2
7 2007      2       E   7