flatten nested list by averaging vectors

Question

Say I have a nested list of vectors.

lst1 <- list(`A`=c(a=1,b=1), `B`=c(a=1), `C`=c(b=1), `D`=c(a=1,b=1,c=1))
lst2 <- list(`A`=c(b=1), `B`=c(a=1,b=1), `C`=c(a=1,c=1), `D`=c(a=1,c=1))
lstX <- list(lst1, lst2)

As seen, each vector A,B,C,D occur twice with a,b,c present in different frequencies.

How would be the most efficient way of flatten the lists so that a,b,c is summed, or averaged over A,B,C,D across the nested lists, as seen below. The real list has several hundreds of thousands of nested lists.

#summed
  a b  c
A 1 2 NA
B 2 1 NA
C 1 1  1
D 2 1  2

#averaged
  a   b   c
A 0.5 1   NA
B 1   0.5 NA
C 0.5 0.5 0.5
D 1   0.5 1

Answer 1

Here's a simple base R solution (which will return 0 instead of NAs (not sure if good enough)

temp <- unlist(lstX)
res <- data.frame(do.call(rbind, strsplit(names(temp), "\\.")), value = temp)

Sums

xtabs(value ~ X1 + X2, res)
#    X2
# X1  a b c
# A   1 2 0
# B   2 1 0
# C   1 1 1
# D   2 1 2

Means

xtabs(value ~ X1 + X2, res) / length(lstX)
#    X2
# X1  a   b   c
# A 0.5 1.0 0.0
# B 1.0 0.5 0.0
# C 0.5 0.5 0.5
# D 1.0 0.5 1.0

---

Alternatively, more flexible data.table solution

library(data.table) #V1.9.6+
temp <- unlist(lstX)
res <- data.table(names(temp))[, tstrsplit(V1, "\\.")][, value := temp]

Sums

dcast(res, V1 ~ V2, sum, value.var = "value", fill = NA)
#    V1 a b  c
# 1:  A 1 2 NA
# 2:  B 2 1 NA
# 3:  C 1 1  1
# 4:  D 2 1  2

Means

dcast(res, V1 ~ V2, function(x) sum(x)/length(lstX), value.var = "value", fill = NA)
#    V1   a   b   c
# 1:  A 0.5 1.0  NA
# 2:  B 1.0 0.5  NA
# 3:  C 0.5 0.5 0.5
# 4:  D 1.0 0.5 1.0

In general, you can use pretty much any function with dcast