R Dynamically build "list" in data.table (or ddply) - r

Question

My aggregation needs vary among columns / data.frames. I would like to pass the "list" argument to the data.table dynamically.
As a minimal example:
require(data.table)
type <- c(rep("hello", 3), rep("bye", 3), rep("ok",3))
a <- (rep(1:3, 3))
b <- runif(9)
c <- runif(9)
df <- data.frame(cbind(type, a, b, c), stringsAsFactors=F)
DT <-data.table(df)
This call:
DT[, list(suma = sum(as.numeric(a)), meanb = mean(as.numeric(b)), minc = min(as.numeric(c))), by= type]
will have result similar to this:
type suma meanb minc
1: hello 6 0.1332210 0.4265579
2: bye 6 0.5680839 0.2993667
3: ok 6 0.5694532 0.2069026
Future data.frames will have more columns that I will want to summarize differently. But for the sake of working with this small example: Is there a way to pass the list programatically?
I naïvely tried:
# create a different list
mylist <- "list(lengtha = length(as.numeric(a)), maxb = max(as.numeric(b)), meanc = mean(as.numeric(c)))"
# new call
DT[, mylist, by=type]
With the following error:
1: hello
2: bye
3: ok
mylist
1: list(lengtha = length(as.numeric(a)), maxb = max(as.numeric(b)), meanc = mean(as.numeric(c)))
2: list(lengtha = length(as.numeric(a)), maxb = max(as.numeric(b)), meanc = mean(as.numeric(c)))
3: list(lengtha = length(as.numeric(a)), maxb = max(as.numeric(b)), meanc = mean(as.numeric(c)))
Any hints appreciated! Best regards!
PS sorry about these as.numeric(), I could not quite figure out why, but I needed them for the example to run.
Minor edit inserted columns / before data.frame in initial sentence to clarify my needs.

Answer 1

This is explained FAQ 1.6 what you are looking for is quote and eval
something like
mycall <- quote(list(lengtha = length(as.numeric(a)), maxb = max(as.numeric(b)), meanc = mean(as.numeric(c))))
DT[, eval(mycall)]
After a bit of head-banging, here is a very ugly way of constructing the call for ddply using .()
myplyrcall <- .(lengtha = length(as.numeric(a)), maxb = max(as.numeric(b)), meanc = mean(as.numeric(c)))
do.call(ddply,c(.data = quote(DF), .variables = 'type',.fun = quote(summarise),myplyrcall))
You could also use as.quoted which has an as.quoted.character method to construct using paste0
myplc <-as.quoted(c("lengtha" = "length(as.numeric(a))", "maxb" = "max(as.numeric(b))", "meanc" = "mean(as.numeric(c))"))
This can be used with data.table as well!
dtcall <- as.quoted(mylist)[[1]]
DT[,eval(dtcall), by = type]
data.table all the way.

Answer 2

Another way is to use .SDcols to group the columns for which you'd like to perform the same operations together. Let's say that you require columns a,d,e to be summed by type where as, b,g should have mean taken and c,f its median, then,
# constructing an example data.table:
set.seed(45)
dt <- data.table(type=rep(c("hello","bye","ok"), each=3), a=sample(9),
b = rnorm(9), c=runif(9), d=sample(9), e=sample(9),
f = runif(9), g=rnorm(9))
# type a b c d e f g
# 1: hello 6 -2.5566166 0.7485015 9 6 0.5661358 -2.2066521
# 2: hello 3 1.1773119 0.6559926 3 3 0.4586280 -0.8376586
# 3: hello 2 -0.1015588 0.2164430 1 7 0.9299597 1.7216593
# 4: bye 8 -0.2260640 0.3924327 8 2 0.1271187 0.4360063
# 5: bye 7 -1.0720503 0.3256450 7 8 0.5774691 0.7571990
# 6: bye 5 -0.7131021 0.4855804 6 9 0.2687791 1.5398858
# 7: ok 1 -0.4680549 0.8476840 2 4 0.5633317 1.5393945
# 8: ok 4 0.4183264 0.4402595 4 1 0.7592801 2.1829996
# 9: ok 9 -1.4817436 0.5080116 5 5 0.2357030 -0.9953758
# 1) set key
setkey(dt, "type")
# 2) group col-ids by similar operations
id1 <- which(names(dt) %in% c("a", "d", "e"))
id2 <- which(names(dt) %in% c("b","g"))
id3 <- which(names(dt) %in% c("c","f"))
# 3) now use these ids in with .SDcols parameter
dt1 <- dt[, lapply(.SD, sum), by="type", .SDcols=id1]
dt2 <- dt[, lapply(.SD, mean), by="type", .SDcols=id2]
dt3 <- dt[, lapply(.SD, median), by="type", .SDcols=id3]
# 4) merge them.
dt1[dt2[dt3]]
# type a d e b g c f
# 1: bye 20 21 19 -0.6704055 0.9110304 0.3924327 0.2687791
# 2: hello 11 13 16 -0.4936211 -0.4408838 0.6559926 0.5661358
# 3: ok 14 11 10 -0.5104907 0.9090061 0.5080116 0.5633317
If/when you have many many column, making a list like the one you've might be cumbersome.

Answer 3

Another method (supporting the use of paste or paste0 to build the expression):
expr <- parse(text=mylist)
DT[, eval( expr ), by=type]
#-------
type lengtha maxb meanc
1: hello 3 0.8265407 0.5244094
2: bye 3 0.4955301 0.6289475
3: ok 3 0.9527455 0.5600915

Answer 4

I find it worrysome that apparently eval is part of the answer. From your question it is not clear to me, if and why you really want to do what you claim to want. Thus I demonstrate here that you can also use a function:
fun <- function(a,b,c) {
list(lengtha = length(as.numeric(a)),
maxb = max(as.numeric(b)),
meanc = mean(as.numeric(c)))
}
DT[, fun(a,b,c), by=type]
type lengtha maxb meanc
1: hello 3 0.8792184 0.3745643
2: bye 3 0.8718397 0.4519999
3: ok 3 0.8900764 0.4511536