Reputation: 14614
From example from a data.frame:
x = data.frame(c(1,1,2,2,3,3), c(1,2,1,2,1,2), c(1,1,1,2,2,2), c(12,14,22,24,34,28))
colnames(x)=c("Store","Dept","Year","Sales")
I would like to obtain:
Sales = array(NA, dim=c(2,2,2))
Sales being an array of 3 dimensions: (Store, Dept, Year) populated with all the data from x.
I am looking for a solution that scales to more dimensions, and thousands of records in the inital data frame (x).
Edit: I thought the solution below were working but it seems they are not exactly what I wanted. I think the problem is the indexing is lost in the process.
Here is a small data set:
structure(list(Store = c(35L, 35L, 35L, 35L, 35L), Dept = c(71L,
71L, 71L, 71L, 71L), Year = c(1, 2, 3, 4, 5), Sales = c(10908.04,
12279.99, 11061.82, 12288.1, 9950.55)), .Names = c("Store", "Dept",
"Year", "Sales"), row.names = c(NA, -5L), class = "data.frame")
> x
Store Dept Year Sales
1 35 71 1 10908.04
2 35 71 2 12279.99
3 35 71 3 11061.82
4 35 71 4 12288.10
5 35 71 5 9950.55
Now I would like to be able to call Sales[35,71,2] to get 10908.04.
Both solutions below get the data by calling Sales[1,1,1], which is unusable for me at this point.
Upvotes: 0
Views: 1722
Reputation: 174
You have to construct the array before with the appropriate dimension :
Sales <- array(NA, c(max(x$Store), max(x$Dept), max(x$Year)))
and then fill in the data :
for (i in 1:nrow(x))
Sales[x[i,"Store"], x[i,"Dept"], x[i,"Year"]] <- x[i, "Sales"]
Sales[35,71,1]
Upvotes: 0
Reputation: 193677
Are you, perhaps, looking for xtabs
?
xtabs(Sales ~ Store + Dept + Year, x)
# , , Year = 1
#
# Dept
# Store 1 2
# 1 12 14
# 2 22 0
# 3 0 0
#
# , , Year = 2
#
# Dept
# Store 1 2
# 1 0 0
# 2 0 24
# 3 34 28
Upvotes: 1
Reputation: 174
Something like :
tapply(X = x[["Sales"]], INDEX = x[setdiff(names(x), "Sales")], FUN = identity)
could work, but it is a bit strange to use tapply
with the identity function.
Upvotes: 1