Replace value with the mean based on two classes - r

Question

I have a dataset with 2 calendar variables (Week & Hour) and 1 Amount variable:
Week Hour Amount
35 1 367
35 2 912
36 1 813
36 2 482
37 1 112
37 2 155
35 1 182
35 2 912
36 1 551
36 2 928
37 1 125
37 2 676
I wish to replace each value of Amount with the mean from each observation with the same Week/Hour pair. For instance, here there are 2 obs. for (Week=35, Hour=1), with Amount values of 367 and 182. Hence, for this example, the 2 rows with (Week=35, Hour=1) should have the Amount replaced with mean(c(367,182). The final output should be:
Week Hour Amount
35 1 274.5
35 2 912.0
36 1 682.0
36 2 705.0
37 1 118.5
37 2 415.5
35 1 274.5
35 2 912.0
36 1 682.0
36 2 705.0
37 1 118.5
37 2 415.5
I have the following code that solves this issue. However, for the complete dataset with thousands of rows, it is very slow. Is there any way to automatically reshape with with this paired means?
dataset = data.frame(Week=c(35,35,36,36,37,37,35,35,36,36,37,37),
Hour = c(1,2,1,2,1,2,1,2,1,2,1,2),
Amount = c(367,912,813,482,112,155,182,912,551,928,125,676))
means <- reshape2::dcast(dataset, Week~Hour, value.var="Value", mean)
for (i in 1:nrow(dataset)) {
print(i)
dataset$Amount[i] <- means[means$Week==dataset$Week[i],which(colnames(means)==dataset$Hour[i])]
}

Answer 1

Possible solution with dplyr:
dataset %>%
group_by(Week, Hour) %>%
summarise(mean_amount = mean(Amount))
You group by Week and Hour and calculate the mean based on this condition.
EDIT
To keep the original structure (number of rows) alter the code to
dataset %>%
group_by(Week, Hour) %>%
mutate(Amount = mean(Amount))

Answer 2

If the idea is just to get the mean Amount by Week and Hour, this would work:
aggregate(Amount ~ ., dataset, mean)
Week Hour Amount
1 35 1 274.5
2 36 1 682.0
3 37 1 118.5
4 35 2 912.0
5 36 2 705.0
6 37 2 415.5
EDIT:
If, however, the idea is to put the averages back into the dataset, then this should work:
x <- aggregate(Amount ~ ., dataset, mean)
dataset$Amount <- x$Amount[match(apply(dataset[,1:2], 1, paste0, collapse = " "),
apply(x[,1:2], 1, paste0, collapse = " "))]
dataset
Week Hour Amount
1 35 1 274.5
2 35 2 912.0
3 36 1 682.0
4 36 2 705.0
5 37 1 118.5
6 37 2 415.5
7 35 1 274.5
8 35 2 912.0
9 36 1 682.0
10 36 2 705.0
11 37 1 118.5
12 37 2 415.5
Explanation:
This pastes together into strings the rows of the first two columns in the means dataframe x and in datasetusing the function apply it uses match on these strings to assign the means values to the corresponding rows in dataset
EDIT 2:
Alternatively, you can use interaction and, respectively, %in% for this transformation:
dataset$Amount <- x$Amount[match(interaction(dataset[,1:2]), interaction(x[,1:2]))]
# or:
dataset$Amount <- x$Amount[interaction(x[,1:2]) %in% interaction(dataset[,1:2])]

Answer 3

Base R solution:
dataset$Amount <- with(dataset, ave(dataset$Amount, dataset$Week, dataset$Hour, FUN = mean))
Data:
dataset = data.frame(Week=c(35,35,36,36,37,37,35,35,36,36,37,37),
Hour = c(1,2,1,2,1,2,1,2,1,2,1,2),
Amount = c(367,912,813,482,112,155,182,912,551,928,125,676))