Correct way of vectorizing "lookup" function - r

Question

I am looking for a fast and efficient way to compute the problem described below. Any help would be appreciated, thanks in advance!
I have a couple of very large csv files that have different information about the same object, but in my final calculation I need all of the attributes in the different table. I am trying to calculate the load of a large number of electrical substations, first I have a list of unique electrical substations;
Unique_Substations <- data.frame(Name = c("SubA", "SubB", "SubC", "SubD"))
In another list I have information about the customers behind these substations;
Customer_Information <- data.frame(
Customer = 1001:1010,
SubSt_Nm = sample(unique(Unique_Substations$Name), 10, replace = TRUE),
HouseHoldType = sample(1:2, 10, replace = TRUE)
)
And in another list I have information about the, let's say, solar panels on these customers roofs (for different years);
Solar_Panels <- data.frame(
Customer = sample(1001:1010, 10, replace = TRUE),
SolarPanelYear1 = sample(10:20, 10, replace = TRUE),
SolarPanelYear2 = sample(15:20, 10, replace = TRUE)
)
Now I want see what the load is for each substation for each year. I have a household load and a solar panel load normalised for each type of household or the solarpanel;
SolarLoad <- data.frame(Load = c(0, -10, -10, 5))
HouseHoldLoad <- data.frame(Type1 = c(1, 3, 5, 2), Type2 = c(3, 5, 6, 1))
So now I have to match up these lists;
ML_SubSt_Cust <- sapply(Unique_Substations$Name,
function(x) which(Customer_Information$SubSt_Nm %in% x == TRUE))
ML_Cust_SolarP <- sapply(Customer_Information$Customer,
function(x) which(Solar_Panels$Customer %in% x == TRUE))
(Here I use the which(xxx %in% x == TRUE) method because I need multiple matches and match() only returns one match
And now we come to my big question (but probably not my only problem with this method) at last. I want to calculate the maximum load on each substation for each year. To this end I had first written a for loop that looped through the Unique_Substations list, which is of course highly inefficient. After that I tried to speed it up using outer() but I don't think I have properly vectorized my function. My maximum function looks as follows (I only wrote it out for the solar panel part to keep it simple);
GetMax <- function(i, Yr) {
max(sum(Solar_Panels[unlist(ML_Cust_SolarP[ML_SubSt_Cust[[i]]], use.names= FALSE),Yr])*SolarLoad)
}
I'm sure this is not efficient at all but I have no clue how to do it in any other way.
To get my final results I use a outer function;
Results <- outer(1:nrow(Unique_Substations), 1:2, Vectorize(GetMax))
In my example all of these data frames are much much larger (40000 rows each or so), so I really need some good optimization of the functions involved. I tried to think of ways to vectorize the function but I couldn't work it out. Any help would be appreciated.
EDIT:
Now that I fully understand the accepted awnser I have another problem. My actual Customer_Information is 188k rows long and my actual HouseHoldLoad is 53k rows long. Needless to say this does not merge() very well. Is there another solution to this problem that does not require merge() or for loops that are too slow?

Answer 1

First: set.seed() when generating random data! I did set.seed(1000) before your code for these results.
I think a bit of merge-ing and dplyr can help here. First, we get the data into a better shape:
library(dplyr)
library(reshape2)
HouseHoldLoad <- melt(HouseHoldLoad, value.name="Load") %>%
select(HouseHoldType=variable, Load) %>%
mutate(HouseHoldType=gsub("Type", "", HouseHoldType))
Solar_Panels <- melt(Solar_Panels, id.vars="Customer",
value.name="SPYearVal") %>%
select(Customer, SolarPanelYear=variable, SPYearVal) %>%
mutate(SolarPanelYear=gsub("SolarPanelYear", "", SolarPanelYear))
dat <- merge(Customer_Information, Solar_Panels, by="Customer")
That gives us:
## Customer SubSt_Nm HouseHoldType SolarPanelYear SPYearVal
## 1 1001 SubB 1 1 16
## 2 1001 SubB 1 2 18
## 3 1001 SubB 1 2 16
## 4 1001 SubB 1 1 20
## 5 1002 SubD 2 1 16
## 6 1002 SubD 2 1 13
## 7 1002 SubD 2 2 20
## 8 1002 SubD 2 2 18
## 9 1003 SubA 1 2 15
## 10 1003 SubA 1 1 16
## 11 1005 SubC 2 2 19
## 12 1005 SubC 2 1 10
## 13 1006 SubA 1 1 15
## 14 1006 SubA 1 2 19
## 15 1007 SubC 1 1 17
## 16 1007 SubC 1 2 19
## 17 1009 SubA 1 1 10
## 18 1009 SubA 1 1 18
## 19 1009 SubA 1 2 18
## 20 1009 SubA 1 2 18
Now we just group and summarize:
dat %>% group_by(SubSt_Nm, SolarPanelYear) %>%
summarise(mx=max(sum(SPYearVal)*SolarLoad))
## SubSt_Nm SolarPanelYear mx
## 1 SubA 1 295
## 2 SubA 2 350
## 3 SubB 1 180
## 4 SubB 2 170
## 5 SubC 1 135
## 6 SubC 2 190
## 7 SubD 1 145
## 8 SubD 2 190
If you use data.table vs data frames, it should be pretty speedy even with 40K entries.
UPDATE For those who cannot install dplyr, this just uses reshape2 (hopefully that is installable)
library(reshape2)
HouseHoldLoad <- melt(HouseHoldLoad, value.name="Load")
colnames(HouseHoldLoad) <- c("HouseHoldType", "Load")
HouseHoldLoad$HouseHoldType <- gsub("Type", "", HouseHoldLoad$HouseHoldType)
Solar_Panels <- melt(Solar_Panels, id.vars="Customer", value.name="SPYearVal")
colnames(Solar_Panels) <- c("Customer", "SolarPanelYear", "SPYearVal")
Solar_Panels$SolarPanelYear <- gsub("SolarPanelYear", "", Solar_Panels$SolarPanelYear)
dat <- merge(Customer_Information, Solar_Panels, by="Customer")
rbind(by(dat, list(dat$SubSt_Nm, dat$SolarPanelYear), function(x) {
mx <- max(sum(x$SPYearVal) * SolarLoad)
}))
## 1 2
## SubA 295 350
## SubB 180 170
## SubC 135 190
## SubD 145 190
If you really can't install even reshape2, then this works with just the base stats package:
colnames(HouseHoldLoad) <- c("Load.1", "Load.2")
HouseHoldLoad <- reshape(HouseHoldLoad, varying=c("Load.1", "Load.2"), direction="long", timevar="HouseHoldType")[1:2]
colnames(Solar_Panels) <- c("Customer", "SolarPanelYear.1", "SolarPanelYear.2")
Solar_Panels <- reshape(Solar_Panels, varying=c("SolarPanelYear.1", "SolarPanelYear.2"), direction="long", timevar="SolarPanelYear")[1:2]
colnames(Solar_Panels) <- c("Customer", "SPYearVal")
Solar_Panels$SolarPanelYear <- gsub("^[0-9]+\\.", "", rownames(Solar_Panels))
dat <- merge(Customer_Information, Solar_Panels, by="Customer")
rbind(by(dat, list(dat$SubSt_Nm, dat$SolarPanelYear), function(x) {
mx <- max(sum(x$SPYearVal) * SolarLoad)
}))
## 1 2
## SubA 295 350
## SubB 180 170
## SubC 135 190
## SubD 145 190