can the value.var in dcast be a list or have multiple value variables?

Question

In the help files for dcast.data.table, there is a note stating that a new feature has been implemented: \"dcast.data.table allows value.var column to be of typ

Answer 1

Update

Apparently, the fix was much easier...

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Technically, your statement that "apparently there is no such feature" isn't quite correct. There is such a feature in the recast function (which sort of hides the melting and casting process), but it seems like Hadley forgot to finish the function or something: the function returns a list of the relevant parts of your operation.

Here's a minimal example...

Some sample data:

set.seed(1)
mydf <- data.frame(x1 = rep(1:3, each = 3),
                   x2 = rep(1:3, 3),
                   salt = sample(10, 9, TRUE),
                   sugar = sample(7, 9, TRUE))

mydf
#   x1 x2 salt sugar
# 1  1  1    3     1
# 2  1  2    4     2
# 3  1  3    6     2
# 4  2  1   10     5
# 5  2  2    3     3
# 6  2  3    9     6
# 7  3  1   10     4
# 8  3  2    7     6
# 9  3  3    7     7

The effect you seem to be trying to achieve:

reshape(mydf, idvar='x1', timevar='x2', direction='wide')
#   x1 salt.1 sugar.1 salt.2 sugar.2 salt.3 sugar.3
# 1  1      3       1      4       2      6       2
# 4  2     10       5      3       3      9       6
# 7  3     10       4      7       6      7       7

recast in action. (Note that the values are all what we would expect in the dimensions we would expect it.)

library(reshape2)
out <- recast(mydf, x1 ~ x2 + variable, measure.var = c("salt", "sugar"))
### recast(mydf, x1 ~ x2 + variable, id.var = c("x1", "x2"))
out
# $data
#      [,1] [,2] [,3] [,4] [,5] [,6]
# [1,]    3    1    4    2    6    2
# [2,]   10    5    3    3    9    6
# [3,]   10    4    7    6    7    7
# 
# $labels
# $labels[[1]]
#   x1
# 1  1
# 2  2
# 3  3
# 
# $labels[[2]]
#   x2 variable
# 1  1     salt
# 2  1    sugar
# 3  2     salt
# 4  2    sugar
# 5  3     salt
# 6  3    sugar

I'm honestly not sure if this was an incomplete function, or if it is a helper function to another function.

All of the information is there to be able to put the data back together again, making it easy to write a function like this:

recast2 <- function(...) {
  inList <- recast(...)
  setNames(cbind(inList[[2]][[1]], inList[[1]]),
           c(names(inList[[2]][[1]]), 
             do.call(paste, c(rev(inList[[2]][[2]]), sep = "_"))))
}
recast2(mydf, x1 ~ x2 + variable, measure.var = c("salt", "sugar"))
#   x1 salt_1 sugar_1 salt_2 sugar_2 salt_3 sugar_3
# 1  1      3       1      4       2      6       2
# 2  2     10       5      3       3      9       6
# 3  3     10       4      7       6      7       7

Again, a possible advantage with the recast2 approach is the ability to aggregate as well as reshape in the same step.